Metabolomics for Biomarker Discovery: Moving to the Clinic

PubMed Central

Zhang, Aihua; Sun, Hui; Yan, Guangli; Wang, Ping; Wang, Xijun

2015-01-01

To improve the clinical course of diseases, more accurate diagnostic and assessment methods are required as early as possible. In order to achieve this, metabolomics offers new opportunities for biomarker discovery in complex diseases and may provide pathological understanding of diseases beyond traditional technologies. It is the systematic analysis of low-molecular-weight metabolites in biological samples and has become an important tool in clinical research and the diagnosis of human disease and has been applied to discovery and identification of the perturbed pathways. It provides a powerful approach to discover biomarkers in biological systems and offers a holistic approach with the promise to clinically enhance diagnostics. When carried out properly, it could provide insight into the understanding of the underlying mechanisms of diseases, help to identify patients at risk of disease, and predict the response to specific treatments. Currently, metabolomics has become an important tool in clinical research and the diagnosis of human disease and becomes a hot topic. This review will highlight the importance and benefit of metabolomics for identifying biomarkers that accurately screen potential biomarkers of diseases. PMID:26090402

Improving the quality of biomarker discovery research: the right samples and enough of them.

PubMed

Pepe, Margaret S; Li, Christopher I; Feng, Ziding

2015-06-01

Biomarker discovery research has yielded few biomarkers that validate for clinical use. A contributing factor may be poor study designs. The goal in discovery research is to identify a subset of potentially useful markers from a large set of candidates assayed on case and control samples. We recommend the PRoBE design for selecting samples. We propose sample size calculations that require specifying: (i) a definition for biomarker performance; (ii) the proportion of useful markers the study should identify (Discovery Power); and (iii) the tolerable number of useless markers amongst those identified (False Leads Expected, FLE). We apply the methodology to a study of 9,000 candidate biomarkers for risk of colon cancer recurrence where a useful biomarker has positive predictive value ≥ 30%. We find that 40 patients with recurrence and 160 without recurrence suffice to filter out 98% of useless markers (2% FLE) while identifying 95% of useful biomarkers (95% Discovery Power). Alternative methods for sample size calculation required more assumptions. Biomarker discovery research should utilize quality biospecimen repositories and include sample sizes that enable markers meeting prespecified performance characteristics for well-defined clinical applications to be identified. The scientific rigor of discovery research should be improved. ©2015 American Association for Cancer Research.

Multiplexing of miniaturized planar antibody arrays for serum protein profiling--a biomarker discovery in SLE nephritis.

PubMed

Petersson, Linn; Dexlin-Mellby, Linda; Bengtsson, Anders A; Sturfelt, Gunnar; Borrebaeck, Carl A K; Wingren, Christer

2014-06-07

In the quest to decipher disease-associated biomarkers, miniaturized and multiplexed antibody arrays may play a central role in generating protein expression profiles, or protein maps, of crude serum samples. In this conceptual study, we explored a novel, 4-times larger pen design, enabling us to, in a unique manner, simultaneously print 48 different reagents (antibodies) as individual 78.5 μm(2) (10 μm in diameter) sized spots at a density of 38,000 spots cm(-2) using dip-pen nanolithography technology. The antibody array set-up was interfaced with a high-resolution fluorescent-based scanner for sensitive sensing. The performance and applicability of this novel 48-plex recombinant antibody array platform design was demonstrated in a first clinical application targeting SLE nephritis, a severe chronic autoimmune connective tissue disorder, as the model disease. To this end, crude, directly biotinylated serum samples were targeted. The results showed that the miniaturized and multiplexed array platform displayed adequate performance, and that SLE-associated serum biomarker panels reflecting the disease process could be deciphered, outlining the use of miniaturized antibody arrays for disease proteomics and biomarker discovery.

BluePen Biomarkers LLC: integrated biomarker solutions

PubMed Central

Blair, Ian A; Mesaros, Clementina; Lilley, Patrick; Nunez, Matthew

2016-01-01

BluePen Biomarkers provides a unique comprehensive multi-omics biomarker discovery and validation platform. We can quantify, integrate and analyze genomics, proteomics, metabolomics and lipidomics biomarkers, alongside clinical data, demographics and other phenotypic data. A unique bio-inspired signal processing analytic approach is used that has the proven ability to identify biomarkers in a wide variety of diseases. The resulting biomarkers can be used for diagnosis, prognosis, mechanistic studies and predicting treatment response, in contexts from core research through clinical trials. BluePen Biomarkers provides an additional groundbreaking research goal: identifying surrogate biomarkers from different modalities. This not only provides new biological insights, but enables least invasive, least-cost tests that meet or exceed the predictive quality of current tests. PMID:28031971

Application of “omics” to Prion Biomarker Discovery

PubMed Central

Huzarewich, Rhiannon L. C. H.; Siemens, Christine G.; Booth, Stephanie A.

2010-01-01

The advent of genomics and proteomics has been a catalyst for the discovery of biomarkers able to discriminate biological processes such as the pathogenesis of complex diseases. Prompt detection of prion diseases is particularly desirable given their transmissibility, which is responsible for a number of human health risks stemming from exogenous sources of prion protein. Diagnosis relies on the ability to detect the biomarker PrPSc, a pathological isoform of the host protein PrPC, which is an essential component of the infectious prion. Immunochemical detection of PrPSc is specific and sensitive enough for antemortem testing of brain tissue, however, this is not the case in accessible biological fluids or for the detection of recently identified novel prions with unique biochemical properties. A complementary approach to the detection of PrPSc itself is to identify alternative, “surrogate” gene or protein biomarkers indicative of disease. Biomarkers are also useful to track the progress of disease, especially important in the assessment of therapies, or to identify individuals “at risk”. In this review we provide perspective on current progress and pitfalls in the use of “omics” technologies to screen body fluids and tissues for biomarker discovery in prion diseases. PMID:20224650

Aptamer-Based Multiplexed Proteomic Technology for Biomarker Discovery

PubMed Central

Gold, Larry; Ayers, Deborah; Bertino, Jennifer; Bock, Christopher; Bock, Ashley; Brody, Edward N.; Carter, Jeff; Dalby, Andrew B.; Eaton, Bruce E.; Fitzwater, Tim; Flather, Dylan; Forbes, Ashley; Foreman, Trudi; Fowler, Cate; Gawande, Bharat; Goss, Meredith; Gunn, Magda; Gupta, Shashi; Halladay, Dennis; Heil, Jim; Heilig, Joe; Hicke, Brian; Husar, Gregory; Janjic, Nebojsa; Jarvis, Thale; Jennings, Susan; Katilius, Evaldas; Keeney, Tracy R.; Kim, Nancy; Koch, Tad H.; Kraemer, Stephan; Kroiss, Luke; Le, Ngan; Levine, Daniel; Lindsey, Wes; Lollo, Bridget; Mayfield, Wes; Mehan, Mike; Mehler, Robert; Nelson, Sally K.; Nelson, Michele; Nieuwlandt, Dan; Nikrad, Malti; Ochsner, Urs; Ostroff, Rachel M.; Otis, Matt; Parker, Thomas; Pietrasiewicz, Steve; Resnicow, Daniel I.; Rohloff, John; Sanders, Glenn; Sattin, Sarah; Schneider, Daniel; Singer, Britta; Stanton, Martin; Sterkel, Alana; Stewart, Alex; Stratford, Suzanne; Vaught, Jonathan D.; Vrkljan, Mike; Walker, Jeffrey J.; Watrobka, Mike; Waugh, Sheela; Weiss, Allison; Wilcox, Sheri K.; Wolfson, Alexey; Wolk, Steven K.; Zhang, Chi; Zichi, Dom

2010-01-01

Background The interrogation of proteomes (“proteomics”) in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology and medicine. Methodology/Principal Findings We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 µL of serum or plasma). Our current assay measures 813 proteins with low limits of detection (1 pM median), 7 logs of overall dynamic range (∼100 fM–1 µM), and 5% median coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding signature of DNA aptamer concentrations, which is quantified on a DNA microarray. Our assay takes advantage of the dual nature of aptamers as both folded protein-binding entities with defined shapes and unique nucleotide sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to rapidly discover unique protein signatures characteristic of various disease states. Conclusions/Significance We describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of

Aptamer-based multiplexed proteomic technology for biomarker discovery.

PubMed

Gold, Larry; Ayers, Deborah; Bertino, Jennifer; Bock, Christopher; Bock, Ashley; Brody, Edward N; Carter, Jeff; Dalby, Andrew B; Eaton, Bruce E; Fitzwater, Tim; Flather, Dylan; Forbes, Ashley; Foreman, Trudi; Fowler, Cate; Gawande, Bharat; Goss, Meredith; Gunn, Magda; Gupta, Shashi; Halladay, Dennis; Heil, Jim; Heilig, Joe; Hicke, Brian; Husar, Gregory; Janjic, Nebojsa; Jarvis, Thale; Jennings, Susan; Katilius, Evaldas; Keeney, Tracy R; Kim, Nancy; Koch, Tad H; Kraemer, Stephan; Kroiss, Luke; Le, Ngan; Levine, Daniel; Lindsey, Wes; Lollo, Bridget; Mayfield, Wes; Mehan, Mike; Mehler, Robert; Nelson, Sally K; Nelson, Michele; Nieuwlandt, Dan; Nikrad, Malti; Ochsner, Urs; Ostroff, Rachel M; Otis, Matt; Parker, Thomas; Pietrasiewicz, Steve; Resnicow, Daniel I; Rohloff, John; Sanders, Glenn; Sattin, Sarah; Schneider, Daniel; Singer, Britta; Stanton, Martin; Sterkel, Alana; Stewart, Alex; Stratford, Suzanne; Vaught, Jonathan D; Vrkljan, Mike; Walker, Jeffrey J; Watrobka, Mike; Waugh, Sheela; Weiss, Allison; Wilcox, Sheri K; Wolfson, Alexey; Wolk, Steven K; Zhang, Chi; Zichi, Dom

2010-12-07

The interrogation of proteomes ("proteomics") in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology and medicine. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 µL of serum or plasma). Our current assay measures 813 proteins with low limits of detection (1 pM median), 7 logs of overall dynamic range (~100 fM-1 µM), and 5% median coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding signature of DNA aptamer concentrations, which is quantified on a DNA microarray. Our assay takes advantage of the dual nature of aptamers as both folded protein-binding entities with defined shapes and unique nucleotide sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to rapidly discover unique protein signatures characteristic of various disease states. We describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine.

Biomarker detection for disease diagnosis using cost-effective microfluidic platforms.

PubMed

Sanjay, Sharma T; Fu, Guanglei; Dou, Maowei; Xu, Feng; Liu, Rutao; Qi, Hao; Li, XiuJun

2015-11-07

Early and timely detection of disease biomarkers can prevent the spread of infectious diseases, and drastically decrease the death rate of people suffering from different diseases such as cancer and infectious diseases. Because conventional diagnostic methods have limited application in low-resource settings due to the use of bulky and expensive instrumentation, simple and low-cost point-of-care diagnostic devices for timely and early biomarker diagnosis is the need of the hour, especially in rural areas and developing nations. The microfluidics technology possesses remarkable features for simple, low-cost, and rapid disease diagnosis. There have been significant advances in the development of microfluidic platforms for biomarker detection of diseases. This article reviews recent advances in biomarker detection using cost-effective microfluidic devices for disease diagnosis, with the emphasis on infectious disease and cancer diagnosis in low-resource settings. This review first introduces different microfluidic platforms (e.g. polymer and paper-based microfluidics) used for disease diagnosis, with a brief description of their common fabrication techniques. Then, it highlights various detection strategies for disease biomarker detection using microfluidic platforms, including colorimetric, fluorescence, chemiluminescence, electrochemiluminescence (ECL), and electrochemical detection. Finally, it discusses the current limitations of microfluidic devices for disease biomarker detection and future prospects.

Proteomic profiling of human plasma for cancer biomarker discovery.

PubMed

Huang, Zhao; Ma, Linguang; Huang, Canhua; Li, Qifu; Nice, Edouard C

2017-03-01

Over the past decades, substantial advances have been made in both the early diagnosis and accurate prognosis of many cancers because of the impressive development of novel proteomic strategies. However, it remains difficult to standardize proteomic approaches. In addition, the heterogeneity of proteins in distinct tissues results in incomplete population of the whole proteome, which inevitably limits its clinical practice. As one of the most complex proteomes in the human body, the plasma proteome contains secreted proteins originating from multiple organs and tissues, making it a favorable matrix for comprehensive biomarker discovery. Here, we will discuss the roles of plasma proteome profiling in cancer biomarker discovery and validation, and highlight both the inherent advantages and disadvantages. Although several hurdles lay ahead, further advances in this technology will greatly increase our understanding of cancer biology, reveal new biomarkers and biomarker panels, and open a new avenue for more efficient early diagnosis and surveillance of cancer, leading toward personalized medicine. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Knowledge-Integrated Network Biomarkers Discovery for Major Adverse Cardiac Events

PubMed Central

Jin, Guangxu; Zhou, Xiaobo; Wang, Honghui; Zhao, Hong; Cui, Kemi; Zhang, Xiang-Sun; Chen, Luonan; Hazen, Stanley L.; Li, King; Wong, Stephen T. C.

2010-01-01

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein–protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein–protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction. PMID:18665624

Large-scale serum protein biomarker discovery in Duchenne muscular dystrophy.

PubMed

Hathout, Yetrib; Brody, Edward; Clemens, Paula R; Cripe, Linda; DeLisle, Robert Kirk; Furlong, Pat; Gordish-Dressman, Heather; Hache, Lauren; Henricson, Erik; Hoffman, Eric P; Kobayashi, Yvonne Monique; Lorts, Angela; Mah, Jean K; McDonald, Craig; Mehler, Bob; Nelson, Sally; Nikrad, Malti; Singer, Britta; Steele, Fintan; Sterling, David; Sweeney, H Lee; Williams, Steve; Gold, Larry

2015-06-09

Serum biomarkers in Duchenne muscular dystrophy (DMD) may provide deeper insights into disease pathogenesis, suggest new therapeutic approaches, serve as acute read-outs of drug effects, and be useful as surrogate outcome measures to predict later clinical benefit. In this study a large-scale biomarker discovery was performed on serum samples from patients with DMD and age-matched healthy volunteers using a modified aptamer-based proteomics technology. Levels of 1,125 proteins were quantified in serum samples from two independent DMD cohorts: cohort 1 (The Parent Project Muscular Dystrophy-Cincinnati Children's Hospital Medical Center), 42 patients with DMD and 28 age-matched normal volunteers; and cohort 2 (The Cooperative International Neuromuscular Research Group, Duchenne Natural History Study), 51 patients with DMD and 17 age-matched normal volunteers. Forty-four proteins showed significant differences that were consistent in both cohorts when comparing DMD patients and healthy volunteers at a 1% false-discovery rate, a large number of significant protein changes for such a small study. These biomarkers can be classified by known cellular processes and by age-dependent changes in protein concentration. Our findings demonstrate both the utility of this unbiased biomarker discovery approach and suggest potential new diagnostic and therapeutic avenues for ameliorating the burden of DMD and, we hope, other rare and devastating diseases.

Discovery of novel biomarkers and phenotypes by semantic technologies

PubMed Central

2013-01-01

Background Biomarkers and target-specific phenotypes are important to targeted drug design and individualized medicine, thus constituting an important aspect of modern pharmaceutical research and development. More and more, the discovery of relevant biomarkers is aided by in silico techniques based on applying data mining and computational chemistry on large molecular databases. However, there is an even larger source of valuable information available that can potentially be tapped for such discoveries: repositories constituted by research documents. Results This paper reports on a pilot experiment to discover potential novel biomarkers and phenotypes for diabetes and obesity by self-organized text mining of about 120,000 PubMed abstracts, public clinical trial summaries, and internal Merck research documents. These documents were directly analyzed by the InfoCodex semantic engine, without prior human manipulations such as parsing. Recall and precision against established, but different benchmarks lie in ranges up to 30% and 50% respectively. Retrieval of known entities missed by other traditional approaches could be demonstrated. Finally, the InfoCodex semantic engine was shown to discover new diabetes and obesity biomarkers and phenotypes. Amongst these were many interesting candidates with a high potential, although noticeable noise (uninteresting or obvious terms) was generated. Conclusions The reported approach of employing autonomous self-organising semantic engines to aid biomarker discovery, supplemented by appropriate manual curation processes, shows promise and has potential to impact, conservatively, a faster alternative to vocabulary processes dependent on humans having to read and analyze all the texts. More optimistically, it could impact pharmaceutical research, for example to shorten time-to-market of novel drugs, or speed up early recognition of dead ends and adverse reactions. PMID:23402646

Approach to Cerebrospinal Fluid (CSF) Biomarker Discovery and Evaluation in HIV Infection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Price, Richard W.; Peterson, Julia; Fuchs, Dietmar

2013-12-13

Central nervous system (CNS) infection is a nearly universal facet of systemic HIV infection that varies in character and neurological consequences. While clinical staging and neuropsychological test performance have been helpful in evaluating patients, cerebrospinal fluid (CSF) biomarkers present a valuable and objective approach to more accurate diagnosis, assessment of treatment effects and understanding of evolving pathobiology. We review some lessons from our recent experience with CSF biomarker studies. We have used two approaches to biomarker analysis: targeted, hypothesis-driven and non-targeted exploratory discovery methods. We illustrate the first with data from a cross-sectional study of defined subject groups across themore » spectrum of systemic and CNS disease progression and the second with a longitudinal study of the CSF proteome in subjects initiating antiretroviral treatment. Both approaches can be useful and, indeed, complementary. The first is helpful in assessing known or hypothesized biomarkers while the second can identify novel biomarkers and point to broad interactions in pathogenesis. Common to both is the need for well-defined samples and subjects that span a spectrum of biological activity and biomarker concentrations. Previouslydefined guide biomarkers of CNS infection, inflammation and neural injury are useful in categorizing samples for analysis and providing critical biological context for biomarker discovery studies. CSF biomarkers represent an underutilized but valuable approach to understanding the interactions of HIV and the CNS and to more objective diagnosis and assessment of disease activity. Both hypothesis-based and discovery methods can be useful in advancing the definition and use of these biomarkers.« less

Approach to cerebrospinal fluid (CSF) biomarker discovery and evaluation in HIV infection.

PubMed

Price, Richard W; Peterson, Julia; Fuchs, Dietmar; Angel, Thomas E; Zetterberg, Henrik; Hagberg, Lars; Spudich, Serena; Smith, Richard D; Jacobs, Jon M; Brown, Joseph N; Gisslen, Magnus

2013-12-01

Central nervous system (CNS) infection is a nearly universal facet of systemic HIV infection that varies in character and neurological consequences. While clinical staging and neuropsychological test performance have been helpful in evaluating patients, cerebrospinal fluid (CSF) biomarkers present a valuable and objective approach to more accurate diagnosis, assessment of treatment effects and understanding of evolving pathobiology. We review some lessons from our recent experience with CSF biomarker studies. We have used two approaches to biomarker analysis: targeted, hypothesis-driven and non-targeted exploratory discovery methods. We illustrate the first with data from a cross-sectional study of defined subject groups across the spectrum of systemic and CNS disease progression and the second with a longitudinal study of the CSF proteome in subjects initiating antiretroviral treatment. Both approaches can be useful and, indeed, complementary. The first is helpful in assessing known or hypothesized biomarkers while the second can identify novel biomarkers and point to broad interactions in pathogenesis. Common to both is the need for well-defined samples and subjects that span a spectrum of biological activity and biomarker concentrations. Previously-defined guide biomarkers of CNS infection, inflammation and neural injury are useful in categorizing samples for analysis and providing critical biological context for biomarker discovery studies. CSF biomarkers represent an underutilized but valuable approach to understanding the interactions of HIV and the CNS and to more objective diagnosis and assessment of disease activity. Both hypothesis-based and discovery methods can be useful in advancing the definition and use of these biomarkers.

Experimental Design in Clinical 'Omics Biomarker Discovery.

PubMed

Forshed, Jenny

2017-11-03

This tutorial highlights some issues in the experimental design of clinical 'omics biomarker discovery, how to avoid bias and get as true quantities as possible from biochemical analyses, and how to select samples to improve the chance of answering the clinical question at issue. This includes the importance of defining clinical aim and end point, knowing the variability in the results, randomization of samples, sample size, statistical power, and how to avoid confounding factors by including clinical data in the sample selection, that is, how to avoid unpleasant surprises at the point of statistical analysis. The aim of this Tutorial is to help translational clinical and preclinical biomarker candidate research and to improve the validity and potential of future biomarker candidate findings.

Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

McDermott, Jason E.; Wang, Jing; Mitchell, Hugh D.

2013-01-01

The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities both for purely statistical and expert knowledge-based approaches and would benefit from improved integration of the two. Areas covered In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges thatmore » have been encountered. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. Expert opinion Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to biomarker discovery and characterization are key to future success in the biomarker field. We will describe our recommendations of possible approaches to this problem including metrics for the evaluation of biomarkers.« less

Innovative Programmable Bio-Nano-Chip Digitizes Biology Using Sensors That Learn Bridging Biomarker Discovery and Clinical Implementation

PubMed Central

Christodoulides, Nicolaos J.; McRae, Michael P.; Abram, Timothy J.; Simmons, Glennon W.; McDevitt, John T.

2017-01-01

The lack of standard tools and methodologies and the absence of a streamlined multimarker approval process have hindered the translation rate of new biomarkers into clinical practice for a variety of diseases afflicting humankind. Advanced novel technologies with superior analytical performance and reduced reagent costs, like the programmable bio-nano-chip system featured in this article, have potential to change the delivery of healthcare. This universal platform system has the capacity to digitize biology, resulting in a sensor modality with a capacity to learn. With well-planned device design, development, and distribution plans, there is an opportunity to translate benchtop discoveries in the genomics, proteomics, metabolomics, and glycomics fields by transforming the information content of key biomarkers into actionable signatures that can empower physicians and patients for a better management of healthcare. While the process is complicated and will take some time, showcased here are three application areas for this flexible platform that combines biomarker content with minimally invasive or non-invasive sampling, such as brush biopsy for oral cancer risk assessment; serum, plasma, and small volumes of blood for the assessment of cardiac risk and wellness; and oral fluid sampling for drugs of abuse testing at the point of need. PMID:28589118

Biomarker Discovery and Mechanistic Studies of Prostate Cancer Using Targeted Proteomic Approaches

DTIC Science & Technology

2010-07-01

1-0431 TITLE: Biomarker Discovery and Mechanistic Studies of Prostate Cancer Using Targeted Proteomic Approaches PRINCIPAL INVESTIGATOR...June 2010 4. TITLE AND SUBTITLE Biomarker Discovery and Mechanistic Studies of Prostate Cancer Using Targeted Proteomic 5a. CONTRACT NUMBER...1-0430; W81XWH-08-1-0431; Grant sponsor: NIH/NCRR COBRE Grant; Grant number: 1P20RR020171; Grant sponsor: NIH/NIDDK Grant; Grant number: R01DK053525

Proteomic Approaches in Biomarker Discovery: New Perspectives in Cancer Diagnostics

PubMed Central

Kocevar, Nina; Komel, Radovan

2014-01-01

Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies. PMID:24550697

Metabolomics in cancer biomarker discovery: current trends and future perspectives.

PubMed

Armitage, Emily G; Barbas, Coral

2014-01-01

Cancer is one of the most devastating human diseases that causes a vast number of mortalities worldwide each year. Cancer research is one of the largest fields in the life sciences and despite many astounding breakthroughs and contributions over the past few decades, there is still a considerable amount to unveil on the function of cancer. It is well known that cancer metabolism differs from that of normal tissue and an important hypothesis published in the 1950s by Otto Warburg proposed that cancer cells rely on anaerobic metabolism as the source for energy, even under physiological oxygen levels. Following this, cancer central carbon metabolism has been researched extensively and beyond respiration, cancer has been found to involve a wide range of metabolic processes, and many more are still to be unveiled. Studying cancer through metabolomics could reveal new biomarkers for cancer that could be useful for its future prognosis, diagnosis and therapy. Metabolomics is becoming an increasingly popular tool in the life sciences since it is a relatively fast and accurate technique that can be applied with either a particular focus or in a global manner to reveal new knowledge about biological systems. There have been many examples of its application to reveal potential biomarkers in different cancers that have employed a range of different analytical platforms. In this review, approaches in metabolomics that have been employed in cancer biomarker discovery are discussed and some of the most noteworthy research in the field is highlighted. Copyright © 2013 Elsevier B.V. All rights reserved.

Urine Metabolomics Analysis for Biomarker Discovery and Detection of Jaundice Syndrome in Patients With Liver Disease*

PubMed Central

Wang, Xijun; Zhang, Aihua; Han, Ying; Wang, Ping; Sun, Hui; Song, Gaochen; Dong, Tianwei; Yuan, Ye; Yuan, Xiaoxia; Zhang, Miao; Xie, Ning; Zhang, He; Dong, Hui; Dong, Wei

2012-01-01

Metabolomics is a powerful new technology that allows for the assessment of global metabolic profiles in easily accessible biofluids and biomarker discovery in order to distinguish between diseased and nondiseased status information. Deciphering the molecular networks that distinguish diseases may lead to the identification of critical biomarkers for disease aggressiveness. However, current diagnostic methods cannot predict typical Jaundice syndrome (JS) in patients with liver disease and little is known about the global metabolomic alterations that characterize JS progression. Emerging metabolomics provides a powerful platform for discovering novel biomarkers and biochemical pathways to improve diagnostic, prognostication, and therapy. Therefore, the aim of this study is to find the potential biomarkers from JS disease by using a nontarget metabolomics method, and test their usefulness in human JS diagnosis. Multivariate data analysis methods were utilized to identify the potential biomarkers. Interestingly, 44 marker metabolites contributing to the complete separation of JS from matched healthy controls were identified. Metabolic pathways (Impact-value≥0.10) including alanine, aspartate, and glutamate metabolism and synthesis and degradation of ketone bodies were found to be disturbed in JS patients. This study demonstrates the possibilities of metabolomics as a diagnostic tool in diseases and provides new insight into pathophysiologic mechanisms. PMID:22505723

Soluble biomarkers development in osteoarthritis: from discovery to personalized medicine

PubMed Central

Henrotin, Yves; Sanchez, Christelle; Cornet, Anne; Van de Put, Joachim; Douette, Pierre; Gharbi, Myriam

2015-01-01

Abstract Context: Specific soluble biomarkers could be a precious tool for diagnosis, prognosis and personalized management of osteoarthritic (OA) patients. Objective: To describe the path of soluble biomarker development from discovery to clinical qualification and regulatory adoption toward OA-related biomarker qualification. Methods and results: This review summarizes current guidance on the use of biomarkers in OA in clinical trials and their utility at five stages, including preclinical development and phase 1 to phase 4 trials. It also presents all the available regulatory requirements. Conclusions: The path through the adoption of a specific soluble biomarker for OA is steep but is worth the challenge due to the benefit that it can provide. PMID:26954785

Biomarker Discovery and Mechanistic Studies of Prostate Cancer Using Targeted Proteomic Approaches

DTIC Science & Technology

2012-07-01

1-0431 TITLE: Biomarker Discovery and Mechanistic Studies of Prostate Cancer Using Targeted Proteomic Approaches PRINCIPAL INVESTIGATOR...July 2012 2. REPORT TYPE Final 3. DATES COVERED (From - To) 1 July 2008 – 30 June 2012 4. TITLE AND SUBTITLE Biomarker Discovery and Mechanistic...Department of Defense Synergistic Idea Development Award W81XWH-08-1-0430 (to H.Z) and W81XWH-08-1-0431 (to N.K.), an NIH/NCRR COBRE grant 1P20RR020171 (to

Proteomics as a Tool for Biomarker Discovery

PubMed Central

Kohn, Elise C.; Azad, Nilofer; Annunziata, Christina; Dhamoon, Amit S.; Whiteley, Gordon

2007-01-01

Novel technologies are now being advanced for the purpose of identification and validation of new disease biomarkers. A reliable and useful clinical biomarker must a) come from a readily attainable source, such as blood or urine, b) have sufficient sensitivity to correctly identify affected individuals, c) have sufficient specificity to avoid incorrect labeling of unaffected persons, and d) result in a notable benefit for the patient through intervention, such as survival or life quality improvement. Despite these critical descriptors, the few available FDA-approved biomarkers for cancer do not completely fit this definition and their benefits are limited to a small number of cancers. Ovarian cancer exemplifies the need for a diagnostic biomarker of early stage disease. Symptoms are present but not specific to the disease, delaying diagnosis until an advanced and generally incurable stage in over 70% of affected women. As such, diagnostic intervention in the form of oopherectomy can be performed in the appropriate at-risk population if identified such as with a new accurate, sensitive, and specific biomarker. If early stage disease is identified, the requirement for survival and life quality improvement will be met. One of the new technologies applied to biomarker discovery is tour-de-force analysis of serum peptides and proteins. Optimization of mass spectrometry techniques coupled with advanced bioinformatics approaches has yielded informative biomarker signatures discriminating presence of cancer from unaffected in multiple studies from different groups. Validation and randomized outcome studies are needed to determine the true value of these new biomarkers in early diagnosis, and improved survival and quality of life. PMID:18057524

Analytical validation considerations of multiplex mass-spectrometry-based proteomic platforms for measuring protein biomarkers.

PubMed

Boja, Emily S; Fehniger, Thomas E; Baker, Mark S; Marko-Varga, György; Rodriguez, Henry

2014-12-05

Protein biomarker discovery and validation in current omics era are vital for healthcare professionals to improve diagnosis, detect cancers at an early stage, identify the likelihood of cancer recurrence, stratify stages with differential survival outcomes, and monitor therapeutic responses. The success of such biomarkers would have a huge impact on how we improve the diagnosis and treatment of patients and alleviate the financial burden of healthcare systems. In the past, the genomics community (mostly through large-scale, deep genomic sequencing technologies) has been steadily improving our understanding of the molecular basis of disease, with a number of biomarker panels already authorized by the U.S. Food and Drug Administration (FDA) for clinical use (e.g., MammaPrint, two recently cleared devices using next-generation sequencing platforms to detect DNA changes in the cystic fibrosis transmembrane conductance regulator (CFTR) gene). Clinical proteomics, on the other hand, albeit its ability to delineate the functional units of a cell, more likely driving the phenotypic differences of a disease (i.e., proteins and protein-protein interaction networks and signaling pathways underlying the disease), "staggers" to make a significant impact with only an average ∼ 1.5 protein biomarkers per year approved by the FDA over the past 15-20 years. This statistic itself raises the concern that major roadblocks have been impeding an efficient transition of protein marker candidates in biomarker development despite major technological advances in proteomics in recent years.

Proteomics of gliomas: Initial biomarker discovery and evolution of technology

PubMed Central

Kalinina, Juliya; Peng, Junmin; Ritchie, James C.; Van Meir, Erwin G.

2011-01-01

Gliomas are a group of aggressive brain tumors that diffusely infiltrate adjacent brain tissues, rendering them largely incurable, even with multiple treatment modalities and agents. Mostly asymptomatic at early stages, they present in several subtypes with astrocytic or oligodendrocytic features and invariably progress to malignant forms. Gliomas are difficult to classify precisely because of interobserver variability during histopathologic grading. Identifying biological signatures of each glioma subtype through protein biomarker profiling of tumor or tumor-proximal fluids is therefore of high priority. Such profiling not only may provide clues regarding tumor classification but may identify clinical biomarkers and pathologic targets for the development of personalized treatments. In the past decade, differential proteomic profiling techniques have utilized tumor, cerebrospinal fluid, and plasma from glioma patients to identify the first candidate diagnostic, prognostic, predictive, and therapeutic response markers, highlighting the potential for glioma biomarker discovery. The number of markers identified, however, has been limited, their reproducibility between studies is unclear, and none have been validated for clinical use. Recent technological advancements in methodologies for high-throughput profiling, which provide easy access, rapid screening, low sample consumption, and accurate protein identification, are anticipated to accelerate brain tumor biomarker discovery. Reliable tools for biomarker verification forecast translation of the biomarkers into clinical diagnostics in the foreseeable future. Herein we update the reader on the recent trends and directions in glioma proteomics, including key findings and established and emerging technologies for analysis, together with challenges we are still facing in identifying and verifying potential glioma biomarkers. PMID:21852429

At the cross-roads of participatory research and biomarker discovery in autism: the need for empirical data.

PubMed

Yusuf, Afiqah; Elsabbagh, Mayada

2015-12-15

Identifying biomarkers for autism can improve outcomes for those affected by autism. Engaging the diverse stakeholders in the research process using community-based participatory research (CBPR) can accelerate biomarker discovery into clinical applications. However, there are limited examples of stakeholder involvement in autism research, possibly due to conceptual and practical concerns. We evaluate the applicability of CBPR principles to biomarker discovery in autism and critically review empirical studies adopting these principles. Using a scoping review methodology, we identified and evaluated seven studies using CBPR principles in biomarker discovery. The limited number of studies in biomarker discovery adopting CBPR principles coupled with their methodological limitations suggests that such applications are feasible but challenging. These studies illustrate three CBPR themes: community assessment, setting global priorities, and collaboration in research design. We propose that further research using participatory principles would be useful in accelerating the pace of discovery and the development of clinically meaningful biomarkers. For this goal to be successful we advocate for increased attention to previously identified conceptual and methodological challenges to participatory approaches in health research, including improving scientific rigor and developing long-term partnerships among stakeholders.

Sweetening the pot: adding glycosylation to the biomarker discovery equation.

PubMed

Drake, Penelope M; Cho, Wonryeon; Li, Bensheng; Prakobphol, Akraporn; Johansen, Eric; Anderson, N Leigh; Regnier, Fred E; Gibson, Bradford W; Fisher, Susan J

2010-02-01

Cancer has profound effects on gene expression, including a cell's glycosylation machinery. Thus, tumors produce glycoproteins that carry oligosaccharides with structures that are markedly different from the same protein produced by a normal cell. A single protein can have many glycosylation sites that greatly amplify the signals they generate compared with their protein backbones. In this article, we survey clinical tests that target carbohydrate modifications for diagnosing and treating cancer. We present the biological relevance of glycosylation to disease progression by highlighting the role these structures play in adhesion, signaling, and metastasis and then address current methodological approaches to biomarker discovery that capitalize on selectively capturing tumor-associated glycoforms to enrich and identify disease-related candidate analytes. Finally, we discuss emerging technologies--multiple reaction monitoring and lectin-antibody arrays--as potential tools for biomarker validation studies in pursuit of clinically useful tests. The future of carbohydrate-based biomarker studies has arrived. At all stages, from discovery through verification and deployment into clinics, glycosylation should be considered a primary readout or a way of increasing the sensitivity and specificity of protein-based analyses.

Challenges in Biomarker Discovery: Combining Expert Insights with Statistical Analysis of Complex Omics Data

PubMed Central

McDermott, Jason E.; Wang, Jing; Mitchell, Hugh; Webb-Robertson, Bobbie-Jo; Hafen, Ryan; Ramey, John; Rodland, Karin D.

2012-01-01

Introduction The advent of high throughput technologies capable of comprehensive analysis of genes, transcripts, proteins and other significant biological molecules has provided an unprecedented opportunity for the identification of molecular markers of disease processes. However, it has simultaneously complicated the problem of extracting meaningful molecular signatures of biological processes from these complex datasets. The process of biomarker discovery and characterization provides opportunities for more sophisticated approaches to integrating purely statistical and expert knowledge-based approaches. Areas covered In this review we will present examples of current practices for biomarker discovery from complex omic datasets and the challenges that have been encountered in deriving valid and useful signatures of disease. We will then present a high-level review of data-driven (statistical) and knowledge-based methods applied to biomarker discovery, highlighting some current efforts to combine the two distinct approaches. Expert opinion Effective, reproducible and objective tools for combining data-driven and knowledge-based approaches to identify predictive signatures of disease are key to future success in the biomarker field. We will describe our recommendations for possible approaches to this problem including metrics for the evaluation of biomarkers. PMID:23335946

Front view of the Orbiter Discovery from an elevated platform ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Front view of the Orbiter Discovery from an elevated platform in the Vehicle Assembly Building at NASA's Kennedy Space Center. - Space Transportation System, Orbiter Discovery (OV-103), Lyndon B. Johnson Space Center, 2101 NASA Parkway, Houston, Harris County, TX

Towards Discovery and Targeted Peptide Biomarker Detection Using nanoESI-TIMS-TOF MS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garabedian, Alyssa; Benigni, Paolo; Ramirez, Cesar E.

Abstract. In the present work, the potential of trapped ion mobility spectrometry coupled to TOF mass spectrometry (TIMS-TOF MS) for discovery and targeted monitoring of peptide biomarkers from human-in-mouse xenograft tumor tissue was evaluated. In particular, a TIMS-MS workflow was developed for the detection and quantification of peptide biomarkers using internal heavy analogs, taking advantage of the high mobility resolution (R = 150–250) prior to mass analysis. Five peptide biomarkers were separated, identified, and quantified using offline nanoESI-TIMSCID- TOF MS; the results were in good agreement with measurements using a traditional LC-ESI-MS/MS proteomics workflow. The TIMS-TOF MS analysis permitted peptidemore » biomarker detection based on accurate mobility, mass measurements, and high sequence coverage for concentrations in the 10–200 nM range, while simultaneously achieving discovery measurements« less

SITC/iSBTc Cancer Immunotherapy Biomarkers Resource Document: Online resources and useful tools - a compass in the land of biomarker discovery

PubMed Central

2011-01-01

Recent positive clinical results in cancer immunotherapy point to the potential of immune-based strategies to provide effective treatment of a variety of cancers. In some patients, the responses to cancer immunotherapy are durable, dramatically extending survival. Extensive research efforts are being made to identify and validate biomarkers that can help identify subsets of cancer patients that will benefit most from these novel immunotherapies. In addition to the clear advantage of such predictive biomarkers, immune biomarkers are playing an important role in the development, clinical evaluation and monitoring of cancer immunotherapies. This Cancer Immunotherapy Resource Document, prepared by the Society for Immunotherapy of Cancer (SITC, formerly the International Society for Biological Therapy of Cancer, iSBTc), provides key references and online resources relevant to the discovery, evaluation and clinical application of immune biomarkers. These key resources were identified by experts in the field who are actively pursuing research in biomarker identification and validation. This organized collection of the most useful references, online resources and tools serves as a compass to guide discovery of biomarkers essential to advancing novel cancer immunotherapies. PMID:21929757

The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Hui; Shi, Tujin; Qian, Wei-Jun

2015-12-04

Mass spectrometry-based proteomics has become an indispensable tool in biomedical research with broad applications ranging from fundamental biology, systems biology, and biomarker discovery. Recent advances in LC-MS have made it become a major technology in clinical applications, especially in cancer biomarker discovery and verification. To overcome the challenges associated with the analysis of clinical samples, such as extremely wide dynamic range of protein concentrations in biofluids and the need to perform high throughput and accurate quantification, significant efforts have been devoted to improve the overall performance of LC-MS bases clinical proteomics. In this review, we summarize the recent advances inmore » LC-MS in the aspect of cancer biomarker discovery and quantification, and discuss its potentials, limitations, and future perspectives.« less

Novel ageing-biomarker discovery using data-intensive technologies.

PubMed

Griffiths, H R; Augustyniak, E M; Bennett, S J; Debacq-Chainiaux, F; Dunston, C R; Kristensen, P; Melchjorsen, C J; Navarrete, Santos A; Simm, A; Toussaint, O

2015-11-01

Ageing is accompanied by many visible characteristics. Other biological and physiological markers are also well-described e.g. loss of circulating sex hormones and increased inflammatory cytokines. Biomarkers for healthy ageing studies are presently predicated on existing knowledge of ageing traits. The increasing availability of data-intensive methods enables deep-analysis of biological samples for novel biomarkers. We have adopted two discrete approaches in MARK-AGE Work Package 7 for biomarker discovery; (1) microarray analyses and/or proteomics in cell systems e.g. endothelial progenitor cells or T cell ageing including a stress model; and (2) investigation of cellular material and plasma directly from tightly-defined proband subsets of different ages using proteomic, transcriptomic and miR array. The first approach provided longitudinal insight into endothelial progenitor and T cell ageing. This review describes the strategy and use of hypothesis-free, data-intensive approaches to explore cellular proteins, miR, mRNA and plasma proteins as healthy ageing biomarkers, using ageing models and directly within samples from adults of different ages. It considers the challenges associated with integrating multiple models and pilot studies as rational biomarkers for a large cohort study. From this approach, a number of high-throughput methods were developed to evaluate novel, putative biomarkers of ageing in the MARK-AGE cohort. Crown Copyright © 2015. Published by Elsevier Ireland Ltd. All rights reserved.

Sweetening the pot: adding glycosylation to the biomarker discovery equation

PubMed Central

Drake, Penelope M.; Cho, Wonryeon; Li, Bensheng; Prakobphol, Akraporn; Johansen, Eric; Anderson, N. Leigh; Regnier, Fred E.; Gibson, Bradford W.; Fisher, Susan J.

2010-01-01

Background Cancer has profound effects on gene expression, including a cell’s glycosylation machinery. Thus, tumors produce glycoproteins that carry oligosaccharides with structures that are markedly different from the same protein produced by a normal cell. A single protein can have many glycosylation sites that greatly amplify the signals they generate as compared to their protein backbones. Content We survey clinical tests that target carbohydrate modifications. for diagnosing and treating cancer. Next, we present the biological relevance of glycosylation to disease progression by highlighting the role these structures play in adhesion, signaling and metastasis, and then address current methodological approaches to biomarker discovery that capitalize on selectively capturing tumor-associated glycoforms to enrich and identify disease-related candidate analytes. Finally, we discuss emerging technologies—multiple reaction monitoring and lectin-antibody arrays—as potential tools for biomarker validation studies in pursuit of clinically useful tests. Summary The future of carbohydrate-based biomarker studies has arrived. At all stages, from discovery through verification and deployment into clinics, glycosylation should be considered a primary readout or a way of increasing the sensitivity and specificity of protein-based analyses. PMID:19959616

Advances in Lipidomics for Cancer Biomarkers Discovery

PubMed Central

Perrotti, Francesca; Rosa, Consuelo; Cicalini, Ilaria; Sacchetta, Paolo; Del Boccio, Piero; Genovesi, Domenico; Pieragostino, Damiana

2016-01-01

Lipids play critical functions in cellular survival, proliferation, interaction and death, since they are involved in chemical-energy storage, cellular signaling, cell membranes, and cell–cell interactions. These cellular processes are strongly related to carcinogenesis pathways, particularly to transformation, progression, and metastasis, suggesting the bioactive lipids are mediators of a number of oncogenic processes. The current review gives a synopsis of a lipidomic approach in tumor characterization; we provide an overview on potential lipid biomarkers in the oncology field and on the principal lipidomic methodologies applied. The novel lipidomic biomarkers are reviewed in an effort to underline their role in diagnosis, in prognostic characterization and in prediction of therapeutic outcomes. A lipidomic investigation through mass spectrometry highlights new insights on molecular mechanisms underlying cancer disease. This new understanding will promote clinical applications in drug discovery and personalized therapy. PMID:27916803

Towards Discovery and Targeted Peptide Biomarker Detection Using nanoESI-TIMS-TOF MS

NASA Astrophysics Data System (ADS)

Garabedian, Alyssa; Benigni, Paolo; Ramirez, Cesar E.; Baker, Erin S.; Liu, Tao; Smith, Richard D.; Fernandez-Lima, Francisco

2018-05-01

In the present work, the potential of trapped ion mobility spectrometry coupled to TOF mass spectrometry (TIMS-TOF MS) for discovery and targeted monitoring of peptide biomarkers from human-in-mouse xenograft tumor tissue was evaluated. In particular, a TIMS-MS workflow was developed for the detection and quantification of peptide biomarkers using internal heavy analogs, taking advantage of the high mobility resolution (R = 150-250) prior to mass analysis. Five peptide biomarkers were separated, identified, and quantified using offline nanoESI-TIMS-CID-TOF MS; the results were in good agreement with measurements using a traditional LC-ESI-MS/MS proteomics workflow. The TIMS-TOF MS analysis permitted peptide biomarker detection based on accurate mobility, mass measurements, and high sequence coverage for concentrations in the 10-200 nM range, while simultaneously achieving discovery measurements of not initially targeted peptides as markers from the same proteins and, eventually, other proteins. [Figure not available: see fulltext.

Development of Transcriptomics-based Biomarkers for Selected Endocrine Disrupting Chemicals in Zebrafish (Danio rerio)

EPA Science Inventory

Genome-wide transcriptional profiling by microarrays provides a powerful platform for gene expression-based biomarker discovery. After their wide acceptance in human disease diagnosis, prognosis, and drug discovery, these gene signatures are increasingly being adopted for environ...

Development of transcriptomics-based biomarkers for selected endocrine disrupting chemicals in zebrafish (Danio rerio)

EPA Science Inventory

Genome-wide transcriptional profiling by microarrays provides a powerful platform for gene expression-based biomarker discovery. After their wide acceptance in human disease diagnosis, prognosis, and drug discovery, these gene signatures are increasingly being adopted for environ...

Advances in fragment-based drug discovery platforms.

PubMed

Orita, Masaya; Warizaya, Masaichi; Amano, Yasushi; Ohno, Kazuki; Niimi, Tatsuya

2009-11-01

Fragment-based drug discovery (FBDD) has been established as a powerful alternative and complement to traditional high-throughput screening techniques for identifying drug leads. At present, this technique is widely used among academic groups as well as small biotech and large pharmaceutical companies. In recent years, > 10 new compounds developed with FBDD have entered clinical development, and more and more attention in the drug discovery field is being focused on this technique. Under the FBDD approach, a fragment library of relatively small compounds (molecular mass = 100 - 300 Da) is screened by various methods and the identified fragment hits which normally weakly bind to the target are used as starting points to generate more potent drug leads. Because FBDD is still a relatively new drug discovery technology, further developments and optimizations in screening platforms and fragment exploitation can be expected. This review summarizes recent advances in FBDD platforms and discusses the factors important for the successful application of this technique. Under the FBDD approach, both identifying the starting fragment hit to be developed and generating the drug lead from that starting fragment hit are important. Integration of various techniques, such as computational technology, X-ray crystallography, NMR, surface plasmon resonance, isothermal titration calorimetry, mass spectrometry and high-concentration screening, must be applied in a situation-appropriate manner.

Statistical Design for Biospecimen Cohort Size in Proteomics-based Biomarker Discovery and Verification Studies

PubMed Central

Skates, Steven J.; Gillette, Michael A.; LaBaer, Joshua; Carr, Steven A.; Anderson, N. Leigh; Liebler, Daniel C.; Ransohoff, David; Rifai, Nader; Kondratovich, Marina; Težak, Živana; Mansfield, Elizabeth; Oberg, Ann L.; Wright, Ian; Barnes, Grady; Gail, Mitchell; Mesri, Mehdi; Kinsinger, Christopher R.; Rodriguez, Henry; Boja, Emily S.

2014-01-01

Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC), with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance, and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step towards building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research. PMID:24063748

Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies.

PubMed

Skates, Steven J; Gillette, Michael A; LaBaer, Joshua; Carr, Steven A; Anderson, Leigh; Liebler, Daniel C; Ransohoff, David; Rifai, Nader; Kondratovich, Marina; Težak, Živana; Mansfield, Elizabeth; Oberg, Ann L; Wright, Ian; Barnes, Grady; Gail, Mitchell; Mesri, Mehdi; Kinsinger, Christopher R; Rodriguez, Henry; Boja, Emily S

2013-12-06

Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor, and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung, and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC) with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step toward building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research.

Parkinson's disease biomarkers: perspective from the NINDS Parkinson's Disease Biomarkers Program

PubMed Central

Gwinn, Katrina; David, Karen K; Swanson-Fischer, Christine; Albin, Roger; Hillaire-Clarke, Coryse St; Sieber, Beth-Anne; Lungu, Codrin; Bowman, F DuBois; Alcalay, Roy N; Babcock, Debra; Dawson, Ted M; Dewey, Richard B; Foroud, Tatiana; German, Dwight; Huang, Xuemei; Petyuk, Vlad; Potashkin, Judith A; Saunders-Pullman, Rachel; Sutherland, Margaret; Walt, David R; West, Andrew B; Zhang, Jing; Chen-Plotkin, Alice; Scherzer, Clemens R; Vaillancourt, David E; Rosenthal, Liana S

2017-01-01

Biomarkers for Parkinson's disease (PD) diagnosis, prognostication and clinical trial cohort selection are an urgent need. While many promising markers have been discovered through the National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarker Program (PDBP) and other mechanisms, no single PD marker or set of markers are ready for clinical use. Here we discuss the current state of biomarker discovery for platforms relevant to PDBP. We discuss the role of the PDBP in PD biomarker identification and present guidelines to facilitate their development. These guidelines include: harmonizing procedures for biofluid acquisition and clinical assessments, replication of the most promising biomarkers, support and encouragement of publications that report negative findings, longitudinal follow-up of current cohorts including the PDBP, testing of wearable technologies to capture readouts between study visits and development of recently diagnosed (de novo) cohorts to foster identification of the earliest markers of disease onset. PMID:28644039

Analytical considerations for mass spectrometry profiling in serum biomarker discovery.

PubMed

Whiteley, Gordon R; Colantonio, Simona; Sacconi, Andrea; Saul, Richard G

2009-03-01

The potential of using mass spectrometry profiling as a diagnostic tool has been demonstrated for a wide variety of diseases. Various cancers and cancer-related diseases have been the focus of much of this work because of both the paucity of good diagnostic markers and the knowledge that early diagnosis is the most powerful weapon in treating cancer. The implementation of mass spectrometry as a routine diagnostic tool has proved to be difficult, however, primarily because of the stringent controls that are required for the method to be reproducible. The method is evolving as a powerful guide to the discovery of biomarkers that could, in turn, be used either individually or in an array or panel of tests for early disease detection. Using proteomic patterns to guide biomarker discovery and the possibility of deployment in the clinical laboratory environment on current instrumentation or in a hybrid technology has the possibility of being the early diagnosis tool that is needed.

As if Biomarker Discovery Isn't Hard Enough: the Consequences of Poorly Characterized Reagents

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rodland, Karin D.

The advent of high throughput omic technologies over the past two decades has driven a vast expansion in the search for clinical biomarkers, as manifested by the plethora of publications on biomarker discovery (over 8,600) listed on PubMed since 2000. Unfortunately, the same time period has seen a relative dearth of clinically validated biomarkers that have received FDA approval; only 10 new cancer biomarkers have been approved by the FDA in the same time period [1].

Strategies for Utilizing Neuroimaging Biomarkers in CNS Drug Discovery and Development: CINP/JSNP Working Group Report.

PubMed

Suhara, Tetsuya; Chaki, Shigeyuki; Kimura, Haruhide; Furusawa, Makoto; Matsumoto, Mitsuyuki; Ogura, Hiroo; Negishi, Takaaki; Saijo, Takeaki; Higuchi, Makoto; Omura, Tomohiro; Watanabe, Rira; Miyoshi, Sosuke; Nakatani, Noriaki; Yamamoto, Noboru; Liou, Shyh-Yuh; Takado, Yuhei; Maeda, Jun; Okamoto, Yasumasa; Okubo, Yoshiaki; Yamada, Makiko; Ito, Hiroshi; Walton, Noah M; Yamawaki, Shigeto

2017-04-01

Despite large unmet medical needs in the field for several decades, CNS drug discovery and development has been largely unsuccessful. Biomarkers, particularly those utilizing neuroimaging, have played important roles in aiding CNS drug development, including dosing determination of investigational new drugs (INDs). A recent working group was organized jointly by CINP and Japanese Society of Neuropsychopharmacology (JSNP) to discuss the utility of biomarkers as tools to overcome issues of CNS drug development.The consensus statement from the working group aimed at creating more nuanced criteria for employing biomarkers as tools to overcome issues surrounding CNS drug development. To accomplish this, a reverse engineering approach was adopted, in which criteria for the utilization of biomarkers were created in response to current challenges in the processes of drug discovery and development for CNS disorders. Based on this analysis, we propose a new paradigm containing 5 distinct tiers to further clarify the use of biomarkers and establish new strategies for decision-making in the context of CNS drug development. Specifically, we discuss more rational ways to incorporate biomarker data to determine optimal dosing for INDs with novel mechanisms and targets, and propose additional categorization criteria to further the use of biomarkers in patient stratification and clinical efficacy prediction. Finally, we propose validation and development of new neuroimaging biomarkers through public-private partnerships to further facilitate drug discovery and development for CNS disorders. © The Author 2016. Published by Oxford University Press on behalf of CINP.

State of the Art in Tumor Antigen and Biomarker Discovery

PubMed Central

Even-Desrumeaux, Klervi; Baty, Daniel; Chames, Patrick

2011-01-01

Our knowledge of tumor immunology has resulted in multiple approaches for the treatment of cancer. However, a gap between research of new tumors markers and development of immunotherapy has been established and very few markers exist that can be used for treatment. The challenge is now to discover new targets for active and passive immunotherapy. This review aims at describing recent advances in biomarkers and tumor antigen discovery in terms of antigen nature and localization, and is highlighting the most recent approaches used for their discovery including “omics” technology. PMID:24212823

Discovery of urine biomarkers for bladder cancer via global metabolomics.

PubMed

Shi, Hangchuan; Li, Xiang; Zhang, Qingyang; Yang, Hongmei; Zhang, Xiaoping

2016-11-01

Bladder cancer (BC) is latent in its early stage and lethal in its late stage. Therefore, early diagnosis and intervention are essential for successful BC treatment. Considering the limitations of current diagnostic tools, noninvasive biomarkers that are both highly sensitive and specific are needed to improve the overall survival and quality of life of patients. With the advent of systems biology, "-omics" technologies have been developed over the past few decades. As a promising member, global metabolomics has increasingly been found to have clear potential for biomarker discovery. However, urinary metabolomics studies related to BC have lagged behind those of other urinary cancers, and major findings have not been systematically reported. The objective of this review is to comprehensively list the currently identified potential urinary metabolite biomarkers for BC.

Implementation of proteomic biomarkers: making it work

PubMed Central

Mischak, Harald; Ioannidis, John PA; Argiles, Angel; Attwood, Teresa K; Bongcam-Rudloff, Erik; Broenstrup, Mark; Charonis, Aristidis; Chrousos, George P; Delles, Christian; Dominiczak, Anna; Dylag, Tomasz; Ehrich, Jochen; Egido, Jesus; Findeisen, Peter; Jankowski, Joachim; Johnson, Robert W; Julien, Bruce A; Lankisch, Tim; Leung, Hing Y; Maahs, David; Magni, Fulvio; Manns, Michael P; Manolis, Efthymios; Mayer, Gert; Navis, Gerjan; Novak, Jan; Ortiz, Alberto; Persson, Frederik; Peter, Karlheinz; Riese, Hans H; Rossing, Peter; Sattar, Naveed; Spasovski, Goce; Thongboonkerd, Visith; Vanholder, Raymond; Schanstra, Joost P; Vlahou, Antonia

2012-01-01

While large numbers of proteomic biomarkers have been described, they are generally not implemented in medical practice. We have investigated the reasons for this shortcoming, focusing on hurdles downstream of biomarker verification, and describe major obstacles and possible solutions to ease valid biomarker implementation. Some of the problems lie in suboptimal biomarker discovery and validation, especially lack of validated platforms with well-described performance characteristics to support biomarker qualification. These issues have been acknowledged and are being addressed, raising the hope that valid biomarkers may start accumulating in the foreseeable future. However, successful biomarker discovery and qualification alone does not suffice for successful implementation. Additional challenges include, among others, limited access to appropriate specimens and insufficient funding, the need to validate new biomarker utility in interventional trials, and large communication gaps between the parties involved in implementation. To address this problem, we propose an implementation roadmap. The implementation effort needs to involve a wide variety of stakeholders (clinicians, statisticians, health economists, and representatives of patient groups, health insurance, pharmaceutical companies, biobanks, and regulatory agencies). Knowledgeable panels with adequate representation of all these stakeholders may facilitate biomarker evaluation and guide implementation for the specific context of use. This approach may avoid unwarranted delays or failure to implement potentially useful biomarkers, and may expedite meaningful contributions of the biomarker community to healthcare. PMID:22519700

Biomarkers as drug development tools: discovery, validation, qualification and use.

PubMed

Kraus, Virginia B

2018-06-01

The 21st Century Cures Act, approved in the USA in December 2016, has encouraged the establishment of the national Precision Medicine Initiative and the augmentation of efforts to address disease prevention, diagnosis and treatment on the basis of a molecular understanding of disease. The Act adopts into law the formal process, developed by the FDA, of qualification of drug development tools, including biomarkers and clinical outcome assessments, to increase the efficiency of clinical trials and encourage an era of molecular medicine. The FDA and European Medicines Agency (EMA) have developed similar processes for the qualification of biomarkers intended for use as companion diagnostics or for development and regulatory approval of a drug or therapeutic. Biomarkers that are used exclusively for the diagnosis, monitoring or stratification of patients in clinical trials are not subject to regulatory approval, although their qualification can facilitate the conduct of a trial. In this Review, the salient features of biomarker discovery, analytical validation, clinical qualification and utilization are described in order to provide an understanding of the process of biomarker development and, through this understanding, convey an appreciation of their potential advantages and limitations.

Application of multi-target phytotherapeutic concept in malaria drug discovery: a systems biology approach in biomarker identification.

PubMed

Tarkang, Protus Arrey; Appiah-Opong, Regina; Ofori, Michael F; Ayong, Lawrence S; Nyarko, Alexander K

2016-01-01

There is an urgent need for new anti-malaria drugs with broad therapeutic potential and novel mode of action, for effective treatment and to overcome emerging drug resistance. Plant-derived anti-malarials remain a significant source of bioactive molecules in this regard. The multicomponent formulation forms the basis of phytotherapy. Mechanistic reasons for the poly-pharmacological effects of plants constitute increased bioavailability, interference with cellular transport processes, activation of pro-drugs/deactivation of active compounds to inactive metabolites and action of synergistic partners at different points of the same signaling cascade. These effects are known as the multi-target concept. However, due to the intrinsic complexity of natural products-based drug discovery, there is need to rethink the approaches toward understanding their therapeutic effect. This review discusses the multi-target phytotherapeutic concept and its application in biomarker identification using the modified reverse pharmacology - systems biology approach. Considerations include the generation of a product library, high throughput screening (HTS) techniques for efficacy and interaction assessment, High Performance Liquid Chromatography (HPLC)-based anti-malarial profiling and animal pharmacology. This approach is an integrated interdisciplinary implementation of tailored technology platforms coupled to miniaturized biological assays, to track and characterize the multi-target bioactive components of botanicals as well as identify potential biomarkers. While preserving biodiversity, this will serve as a primary step towards the development of standardized phytomedicines, as well as facilitate lead discovery for chemical prioritization and downstream clinical development.

Single Domain Antibodies as New Biomarker Detectors

PubMed Central

Fischer, Katja; Leow, Chiuan Yee; Chuah, Candy; McCarthy, James

2017-01-01

Biomarkers are defined as indicators of biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention. Biomarkers have been widely used for early detection, prediction of response after treatment, and for monitoring the progression of diseases. Antibodies represent promising tools for recognition of biomarkers, and are widely deployed as analytical tools in clinical settings. For immunodiagnostics, antibodies are now exploited as binders for antigens of interest across a range of platforms. More recently, the discovery of antibody surface display and combinatorial chemistry techniques has allowed the exploration of new binders from a range of animals, for instance variable domains of new antigen receptors (VNAR) from shark and variable heavy chain domains (VHH) or nanobodies from camelids. These single domain antibodies (sdAbs) have some advantages over conventional murine immunoglobulin owing to the lack of a light chain, making them the smallest natural biomarker binders thus far identified. In this review, we will discuss several biomarkers used as a means to validate diseases progress. The potential functionality of modern singe domain antigen binders derived from phylogenetically early animals as new biomarker detectors for current diagnostic and research platforms development will be described. PMID:29039819

HEx: A heterologous expression platform for the discovery of fungal natural products

PubMed Central

Schlecht, Ulrich; Horecka, Joe; Lin, Hsiao-Ching; Naughton, Brian; Miranda, Molly; Li, Yong Fuga; Hennessy, James R.; Vandova, Gergana A.; Steinmetz, Lars M.; Sattely, Elizabeth; Khosla, Chaitan; Hillenmeyer, Maureen E.

2018-01-01

For decades, fungi have been a source of U.S. Food and Drug Administration–approved natural products such as penicillin, cyclosporine, and the statins. Recent breakthroughs in DNA sequencing suggest that millions of fungal species exist on Earth, with each genome encoding pathways capable of generating as many as dozens of natural products. However, the majority of encoded molecules are difficult or impossible to access because the organisms are uncultivable or the genes are transcriptionally silent. To overcome this bottleneck in natural product discovery, we developed the HEx (Heterologous EXpression) synthetic biology platform for rapid, scalable expression of fungal biosynthetic genes and their encoded metabolites in Saccharomyces cerevisiae. We applied this platform to 41 fungal biosynthetic gene clusters from diverse fungal species from around the world, 22 of which produced detectable compounds. These included novel compounds with unexpected biosynthetic origins, particularly from poorly studied species. This result establishes the HEx platform for rapid discovery of natural products from any fungal species, even those that are uncultivable, and opens the door to discovery of the next generation of natural products. PMID:29651464

Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer.

PubMed

Whiteaker, Jeffrey R; Zhang, Heidi; Zhao, Lei; Wang, Pei; Kelly-Spratt, Karen S; Ivey, Richard G; Piening, Brian D; Feng, Li-Chia; Kasarda, Erik; Gurley, Kay E; Eng, Jimmy K; Chodosh, Lewis A; Kemp, Christopher J; McIntosh, Martin W; Paulovich, Amanda G

2007-10-01

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.

The BioFIND study: Characteristics of a clinically typical Parkinson's disease biomarker cohort

PubMed Central

Goldman, Jennifer G.; Alcalay, Roy N.; Xie, Tao; Tuite, Paul; Henchcliffe, Claire; Hogarth, Penelope; Amara, Amy W.; Frank, Samuel; Rudolph, Alice; Casaceli, Cynthia; Andrews, Howard; Gwinn, Katrina; Sutherland, Margaret; Kopil, Catherine; Vincent, Lona; Frasier, Mark

2016-01-01

ABSTRACT Background Identifying PD‐specific biomarkers in biofluids will greatly aid in diagnosis, monitoring progression, and therapeutic interventions. PD biomarkers have been limited by poor discriminatory power, partly driven by heterogeneity of the disease, variability of collection protocols, and focus on de novo, unmedicated patients. Thus, a platform for biomarker discovery and validation in well‐characterized, clinically typical, moderate to advanced PD cohorts is critically needed. Methods BioFIND (Fox Investigation for New Discovery of Biomarkers in Parkinson's Disease) is a cross‐sectional, multicenter biomarker study that established a repository of clinical data, blood, DNA, RNA, CSF, saliva, and urine samples from 118 moderate to advanced PD and 88 healthy control subjects. Inclusion criteria were designed to maximize diagnostic specificity by selecting participants with clinically typical PD symptoms, and clinical data and biospecimen collection utilized standardized procedures to minimize variability across sites. Results We present the study methodology and data on the cohort's clinical characteristics. Motor scores and biospecimen samples including plasma are available for practically defined off and on states and thus enable testing the effects of PD medications on biomarkers. Other biospecimens are available from off state PD assessments and from controls. Conclusion Our cohort provides a valuable resource for biomarker discovery and validation in PD. Clinical data and biospecimens, available through The Michael J. Fox Foundation for Parkinson's Research and the National Institute of Neurological Disorders and Stroke, can serve as a platform for discovering biomarkers in clinically typical PD and comparisons across PD's broad and heterogeneous spectrum. © 2016 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society PMID:27113479

Emerging concepts in biomarker discovery; The US-Japan workshop on immunological molecular markers in oncology

PubMed Central

Tahara, Hideaki; Sato, Marimo; Thurin, Magdalena; Wang, Ena; Butterfield, Lisa H; Disis, Mary L; Fox, Bernard A; Lee, Peter P; Khleif, Samir N; Wigginton, Jon M; Ambs, Stefan; Akutsu, Yasunori; Chaussabel, Damien; Doki, Yuichiro; Eremin, Oleg; Fridman, Wolf Hervé; Hirohashi, Yoshihiko; Imai, Kohzoh; Jacobson, James; Jinushi, Masahisa; Kanamoto, Akira; Kashani-Sabet, Mohammed; Kato, Kazunori; Kawakami, Yutaka; Kirkwood, John M; Kleen, Thomas O; Lehmann, Paul V; Liotta, Lance; Lotze, Michael T; Maio, Michele; Malyguine, Anatoli; Masucci, Giuseppe; Matsubara, Hisahiro; Mayrand-Chung, Shawmarie; Nakamura, Kiminori; Nishikawa, Hiroyoshi; Palucka, A Karolina; Petricoin, Emanuel F; Pos, Zoltan; Ribas, Antoni; Rivoltini, Licia; Sato, Noriyuki; Shiku, Hiroshi; Slingluff, Craig L; Streicher, Howard; Stroncek, David F; Takeuchi, Hiroya; Toyota, Minoru; Wada, Hisashi; Wu, Xifeng; Wulfkuhle, Julia; Yaguchi, Tomonori; Zeskind, Benjamin; Zhao, Yingdong; Zocca, Mai-Britt; Marincola, Francesco M

2009-01-01

Supported by the Office of International Affairs, National Cancer Institute (NCI), the "US-Japan Workshop on Immunological Biomarkers in Oncology" was held in March 2009. The workshop was related to a task force launched by the International Society for the Biological Therapy of Cancer (iSBTc) and the United States Food and Drug Administration (FDA) to identify strategies for biomarker discovery and validation in the field of biotherapy. The effort will culminate on October 28th 2009 in the "iSBTc-FDA-NCI Workshop on Prognostic and Predictive Immunologic Biomarkers in Cancer", which will be held in Washington DC in association with the Annual Meeting. The purposes of the US-Japan workshop were a) to discuss novel approaches to enhance the discovery of predictive and/or prognostic markers in cancer immunotherapy; b) to define the state of the science in biomarker discovery and validation. The participation of Japanese and US scientists provided the opportunity to identify shared or discordant themes across the distinct immune genetic background and the diverse prevalence of disease between the two Nations. Converging concepts were identified: enhanced knowledge of interferon-related pathways was found to be central to the understanding of immune-mediated tissue-specific destruction (TSD) of which tumor rejection is a representative facet. Although the expression of interferon-stimulated genes (ISGs) likely mediates the inflammatory process leading to tumor rejection, it is insufficient by itself and the associated mechanisms need to be identified. It is likely that adaptive immune responses play a broader role in tumor rejection than those strictly related to their antigen-specificity; likely, their primary role is to trigger an acute and tissue-specific inflammatory response at the tumor site that leads to rejection upon recruitment of additional innate and adaptive immune mechanisms. Other candidate systemic and/or tissue-specific biomarkers were recognized that

A POCT platform for sepsis biomarkers (Conference Presentation)

NASA Astrophysics Data System (ADS)

Baldini, Francesco; Adinolfi, Barbara; Berneschi, Simone; Bernini, Romeo; Giannetti, Ambra; Grimaldi, Immacolata Angelica; Persichetti, Gianluca; Testa, Genni; Tombelli, Sara; Trono, Cosimo

2017-06-01

Infectious diseases and sepsis, as a severe and potential medical condition in which the immune system overreacts and finally turns against itself, are a worldwide problem. As a matter of fact, it is considered the main cause of mortality in intensive care. For such a pathology, a timely diagnosis is essential, since it has been shown that each hour of delay in the administration of an effective pharmacological treatment increases the mortality rate of 7%. Therefore, the advent of a POCT platform for sepsis is highly requested by physicians. Biomarkers have gained importance for the diagnosis and treatment monitoring of septic patients, since biomarkers can indicate the severity of sepsis and can differentiate bacterial from viral and fungal infection, and systemic sepsis from local infection. The present paper deals with the development of fluorescence-based bioassays for the sepsis biomarkers and their integration on a multianalyte chip. Among the different biomarker candidates, the attention was focused on procalcitonin (PCT), C-reactive protein (CRP) and interleukine-6 (IL-6) as well as on soluble urokinase plasminogen activator receptor (suPAR) recently proposed as a very effective inflammatory marker, potentially capable of acting also as a prognostic biomarker. Starting point of this new setup was an already developed fluorescence-based optical platform, which makes use of multichannel polymethylmetacrylate chips for the detection of different bioanalytes, and the serial interrogation of the microfluidic channels of the chip. The novel proposed optical setup makes use of a suitable fluorescence excitation and detection scheme, capable of performing the simultaneous interrogation of all the channels. For the excitation part of the optical setup, a diffractive optical element is used which generates a pattern of parallel lines, for the simultaneous excitation of all the channels and for the optimization of the optical power distribution. For the detection part

Evaluation of high throughput gene expression platforms using a genomic biomarker signature for prediction of skin sensitization.

PubMed

Forreryd, Andy; Johansson, Henrik; Albrekt, Ann-Sofie; Lindstedt, Malin

2014-05-16

Allergic contact dermatitis (ACD) develops upon exposure to certain chemical compounds termed skin sensitizers. To reduce the occurrence of skin sensitizers, chemicals are regularly screened for their capacity to induce sensitization. The recently developed Genomic Allergen Rapid Detection (GARD) assay is an in vitro alternative to animal testing for identification of skin sensitizers, classifying chemicals by evaluating transcriptional levels of a genomic biomarker signature. During assay development and biomarker identification, genome-wide expression analysis was applied using microarrays covering approximately 30,000 transcripts. However, the microarray platform suffers from drawbacks in terms of low sample throughput, high cost per sample and time consuming protocols and is a limiting factor for adaption of GARD into a routine assay for screening of potential sensitizers. With the purpose to simplify assay procedures, improve technical parameters and increase sample throughput, we assessed the performance of three high throughput gene expression platforms--nCounter®, BioMark HD™ and OpenArray®--and correlated their performance metrics against our previously generated microarray data. We measured the levels of 30 transcripts from the GARD biomarker signature across 48 samples. Detection sensitivity, reproducibility, correlations and overall structure of gene expression measurements were compared across platforms. Gene expression data from all of the evaluated platforms could be used to classify most of the sensitizers from non-sensitizers in the GARD assay. Results also showed high data quality and acceptable reproducibility for all platforms but only medium to poor correlations of expression measurements across platforms. In addition, evaluated platforms were superior to the microarray platform in terms of cost efficiency, simplicity of protocols and sample throughput. We evaluated the performance of three non-array based platforms using a limited set of

The discovery and development of proteomic safety biomarkers for the detection of drug-induced liver toxicity.

PubMed

Amacher, David E

2010-05-15

Biomarkers are biometric measurements that provide critical quantitative information about the biological condition of the animal or individual being tested. In drug safety studies, established toxicity biomarkers are used along with other conventional study data to determine dose-limiting organ toxicity, and to define species sensitivity for new chemical entities intended for possible use as human medicines. A continuing goal of drug safety scientists in the pharmaceutical industry is to discover and develop better trans-species biomarkers that can be used to determine target organ toxicities for preclinical species in short-term studies at dose levels that are some multiple of the intended human dose and again later in full development for monitoring clinical trials at lower therapeutic doses. Of particular value are early, predictive, noninvasive biomarkers that have in vitro, in vivo, and clinical transferability. Such translational biomarkers bridge animal testing used in preclinical science and human studies that are part of subsequent clinical testing. Although suitable for in vivo preclinical regulatory studies, conventional hepatic safety biomarkers are basically confirmatory markers because they signal organ toxicity after some pathological damage has occurred, and are therefore not well-suited for short-term, predictive screening assays early in the discovery-to-development progression of new chemical entities (NCEs) available in limited quantities. Efforts between regulatory agencies and the pharmaceutical industry are underway for the coordinated discovery, qualification, verification and validation of early predictive toxicity biomarkers. Early predictive safety biomarkers are those that are detectable and quantifiable prior to the onset of irreversible tissue injury and which are associated with a mechanism of action relevant to a specific type of potential hepatic injury. Potential drug toxicity biomarkers are typically endogenous macromolecules in

Biomarker Discovery by Modeling Behçet's Disease with Patient-Specific Human Induced Pluripotent Stem Cells.

PubMed

Son, Mi-Young; Kim, Young-Dae; Seol, Binna; Lee, Mi-Ok; Na, Hee-Jun; Yoo, Bin; Chang, Jae-Suk; Cho, Yee Sook

2017-01-15

Behçet's disease (BD) is a chronic inflammatory and multisystemic autoimmune disease of unknown etiology. Due to the lack of a specific test for BD, its diagnosis is very difficult and therapeutic options are limited. Induced pluripotent stem cell (iPSC) technology, which provides inaccessible disease-relevant cell types, opens a new era for disease treatment. In this study, we generated BD iPSCs from patient somatic cells and differentiated them into hematopoietic precursor cells (BD iPSC-HPCs) as BD model cells. Based on comparative transcriptome analysis using our BD model cells, we identified eight novel BD-specific genes, AGTR2, CA9, CD44, CXCL1, HTN3, IL-2, PTGER4, and TSLP, which were differentially expressed in BD patients compared with healthy controls or patients with other immune diseases. The use of CXCL1 as a BD biomarker was further validated at the protein level using both a BD iPSC-HPC-based assay system and BD patient serum samples. Furthermore, we show that our BD iPSC-HPC-based drug screening system is highly effective for testing CXCL1 BD biomarkers, as determined by monitoring the efficacy of existing anti-inflammatory drugs. Our results shed new light on the usefulness of patient-specific iPSC technology in the development of a benchmarking platform for disease-specific biomarkers, phenotype- or target-driven drug discovery, and patient-tailored therapies.

atBioNet--an integrated network analysis tool for genomics and biomarker discovery.

PubMed

Ding, Yijun; Chen, Minjun; Liu, Zhichao; Ding, Don; Ye, Yanbin; Zhang, Min; Kelly, Reagan; Guo, Li; Su, Zhenqiang; Harris, Stephen C; Qian, Feng; Ge, Weigong; Fang, Hong; Xu, Xiaowei; Tong, Weida

2012-07-20

Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

Open discovery: An integrated live Linux platform of Bioinformatics tools.

PubMed

Vetrivel, Umashankar; Pilla, Kalabharath

2008-01-01

Historically, live linux distributions for Bioinformatics have paved way for portability of Bioinformatics workbench in a platform independent manner. Moreover, most of the existing live Linux distributions limit their usage to sequence analysis and basic molecular visualization programs and are devoid of data persistence. Hence, open discovery - a live linux distribution has been developed with the capability to perform complex tasks like molecular modeling, docking and molecular dynamics in a swift manner. Furthermore, it is also equipped with complete sequence analysis environment and is capable of running windows executable programs in Linux environment. Open discovery portrays the advanced customizable configuration of fedora, with data persistency accessible via USB drive or DVD. The Open Discovery is distributed free under Academic Free License (AFL) and can be downloaded from http://www.OpenDiscovery.org.in.

A Comprehensive Workflow of Mass Spectrometry-Based Untargeted Metabolomics in Cancer Metabolic Biomarker Discovery Using Human Plasma and Urine

PubMed Central

Zou, Wei; She, Jianwen; Tolstikov, Vladimir V.

2013-01-01

Current available biomarkers lack sensitivity and/or specificity for early detection of cancer. To address this challenge, a robust and complete workflow for metabolic profiling and data mining is described in details. Three independent and complementary analytical techniques for metabolic profiling are applied: hydrophilic interaction liquid chromatography (HILIC–LC), reversed-phase liquid chromatography (RP–LC), and gas chromatography (GC). All three techniques are coupled to a mass spectrometer (MS) in the full scan acquisition mode, and both unsupervised and supervised methods are used for data mining. The univariate and multivariate feature selection are used to determine subsets of potentially discriminative predictors. These predictors are further identified by obtaining accurate masses and isotopic ratios using selected ion monitoring (SIM) and data-dependent MS/MS and/or accurate mass MSn ion tree scans utilizing high resolution MS. A list combining all of the identified potential biomarkers generated from different platforms and algorithms is used for pathway analysis. Such a workflow combining comprehensive metabolic profiling and advanced data mining techniques may provide a powerful approach for metabolic pathway analysis and biomarker discovery in cancer research. Two case studies with previous published data are adapted and included in the context to elucidate the application of the workflow. PMID:24958150

Tissue constructs: platforms for basic research and drug discovery.

PubMed

Elson, Elliot L; Genin, Guy M

2016-02-06

The functions, form and mechanical properties of cells are inextricably linked to their extracellular environment. Cells from solid tissues change fundamentally when, isolated from this environment, they are cultured on rigid two-dimensional substrata. These changes limit the significance of mechanical measurements on cells in two-dimensional culture and motivate the development of constructs with cells embedded in three-dimensional matrices that mimic the natural tissue. While measurements of cell mechanics are difficult in natural tissues, they have proven effective in engineered tissue constructs, especially constructs that emphasize specific cell types and their functions, e.g. engineered heart tissues. Tissue constructs developed as models of disease also have been useful as platforms for drug discovery. Underlying the use of tissue constructs as platforms for basic research and drug discovery is integration of multiscale biomaterials measurement and computational modelling to dissect the distinguishable mechanical responses separately of cells and extracellular matrix from measurements on tissue constructs and to quantify the effects of drug treatment on these responses. These methods and their application are the main subjects of this review.

Tissue constructs: platforms for basic research and drug discovery

PubMed Central

Elson, Elliot L.; Genin, Guy M.

2016-01-01

The functions, form and mechanical properties of cells are inextricably linked to their extracellular environment. Cells from solid tissues change fundamentally when, isolated from this environment, they are cultured on rigid two-dimensional substrata. These changes limit the significance of mechanical measurements on cells in two-dimensional culture and motivate the development of constructs with cells embedded in three-dimensional matrices that mimic the natural tissue. While measurements of cell mechanics are difficult in natural tissues, they have proven effective in engineered tissue constructs, especially constructs that emphasize specific cell types and their functions, e.g. engineered heart tissues. Tissue constructs developed as models of disease also have been useful as platforms for drug discovery. Underlying the use of tissue constructs as platforms for basic research and drug discovery is integration of multiscale biomaterials measurement and computational modelling to dissect the distinguishable mechanical responses separately of cells and extracellular matrix from measurements on tissue constructs and to quantify the effects of drug treatment on these responses. These methods and their application are the main subjects of this review. PMID:26855763

Open discovery: An integrated live Linux platform of Bioinformatics tools

PubMed Central

Vetrivel, Umashankar; Pilla, Kalabharath

2008-01-01

Historically, live linux distributions for Bioinformatics have paved way for portability of Bioinformatics workbench in a platform independent manner. Moreover, most of the existing live Linux distributions limit their usage to sequence analysis and basic molecular visualization programs and are devoid of data persistence. Hence, open discovery ‐ a live linux distribution has been developed with the capability to perform complex tasks like molecular modeling, docking and molecular dynamics in a swift manner. Furthermore, it is also equipped with complete sequence analysis environment and is capable of running windows executable programs in Linux environment. Open discovery portrays the advanced customizable configuration of fedora, with data persistency accessible via USB drive or DVD. Availability The Open Discovery is distributed free under Academic Free License (AFL) and can be downloaded from http://www.OpenDiscovery.org.in PMID:19238235

Candidate biomarker discovery and selection for ‘Granny Smith' superficial scald risk management and diagnosis, poster board

USDA-ARS?s Scientific Manuscript database

Discovery of candidate biomarkers for superficial scald, a peel disorder that develops during storage of susceptible apple cultivars, is part of a larger project aimed at developing biomarker-based risk-management and diagnostic tools for multiple apple postharvest disorders (http://www.tfrec.wsu.ed...

Mass spectrometry-assisted gel-based proteomics in cancer biomarker discovery: approaches and application

PubMed Central

Huang, Rongrong; Chen, Zhongsi; He, Lei; He, Nongyue; Xi, Zhijiang; Li, Zhiyang; Deng, Yan; Zeng, Xin

2017-01-01

There is a critical need for the discovery of novel biomarkers for early detection and targeted therapy of cancer, a major cause of deaths worldwide. In this respect, proteomic technologies, such as mass spectrometry (MS), enable the identification of pathologically significant proteins in various types of samples. MS is capable of high-throughput profiling of complex biological samples including blood, tissues, urine, milk, and cells. MS-assisted proteomics has contributed to the development of cancer biomarkers that may form the foundation for new clinical tests. It can also aid in elucidating the molecular mechanisms underlying cancer. In this review, we discuss MS principles and instrumentation as well as approaches in MS-based proteomics, which have been employed in the development of potential biomarkers. Furthermore, the challenges in validation of MS biomarkers for their use in clinical practice are also reviewed. PMID:28912895

Validation of Biomarker Proteins Using Reverse Capture Protein Microarrays.

PubMed

Jozwik, Catherine; Eidelman, Ofer; Starr, Joshua; Pollard, Harvey B; Srivastava, Meera

2017-01-01

Genomics has revolutionized large-scale and high-throughput sequencing and has led to the discovery of thousands of new proteins. Protein chip technology is emerging as a miniaturized and highly parallel platform that is suited to rapid, simultaneous screening of large numbers of proteins and the analysis of various protein-binding activities, enzyme substrate relationships, and posttranslational modifications. Specifically, reverse capture protein microarrays provide the most appropriate platform for identifying low-abundance, disease-specific biomarker proteins in a sea of high-abundance proteins from biological fluids such as blood, serum, plasma, saliva, urine, and cerebrospinal fluid as well as tissues and cells obtained by biopsy. Samples from hundreds of patients can be spotted in serial dilutions on many replicate glass slides. Each slide can then be probed with one specific antibody to the biomarker of interest. That antibody's titer can then be determined quantitatively for each patient, allowing for the statistical assessment and validation of the diagnostic or prognostic utility of that particular antigen. As the technology matures and the availability of validated, platform-compatible antibodies increases, the platform will move further into the desirable realm of discovery science for detecting and quantitating low-abundance signaling proteins. In this chapter, we describe methods for the successful application of the reverse capture protein microarray platform for which we have made substantial contributions to the development and application of this method, particularly in the use of body fluids other than serum/plasma.

High-throughput and targeted in-depth mass spectrometry-based approaches for biofluid profiling and biomarker discovery.

PubMed

Jimenez, Connie R; Piersma, Sander; Pham, Thang V

2007-12-01

Proteomics aims to create a link between genomic information, biological function and disease through global studies of protein expression, modification and protein-protein interactions. Recent advances in key proteomics tools, such as mass spectrometry (MS) and (bio)informatics, provide tremendous opportunities for biomarker-related clinical applications. In this review, we focus on two complementary MS-based approaches with high potential for the discovery of biomarker patterns and low-abundant candidate biomarkers in biofluids: high-throughput matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy-based methods for peptidome profiling and label-free liquid chromatography-based methods coupled to MS for in-depth profiling of biofluids with a focus on subproteomes, including the low-molecular-weight proteome, carrier-bound proteome and N-linked glycoproteome. The two approaches differ in their aims, throughput and sensitivity. We discuss recent progress and challenges in the analysis of plasma/serum and proximal fluids using these strategies and highlight the potential of liquid chromatography-MS-based proteomics of cancer cell and tumor secretomes for the discovery of candidate blood-based biomarkers. Strategies for candidate validation are also described.

Detection of biomarkers using recombinant antibodies coupled to nanostructured platforms

PubMed Central

Kierny, Michael R.; Cunningham, Thomas D.; Kay, Brian K.

2012-01-01

The utility of biomarker detection in tomorrow's personalized health care field will mean early and accurate diagnosis of many types of human physiological conditions and diseases. In the search for biomarkers, recombinant affinity reagents can be generated to candidate proteins or post-translational modifications that differ qualitatively or quantitatively between normal and diseased tissues. The use of display technologies, such as phage-display, allows for manageable selection and optimization of affinity reagents for use in biomarker detection. Here we review the use of recombinant antibody fragments, such as scFvs and Fabs, which can be affinity-selected from phage-display libraries, to bind with both high specificity and affinity to biomarkers of cancer, such as Human Epidermal growth factor Receptor 2 (HER2) and Carcinoembryonic antigen (CEA). We discuss how these recombinant antibodies can be fabricated into nanostructures, such as carbon nanotubes, nanowires, and quantum dots, for the purpose of enhancing detection of biomarkers at low concentrations (pg/mL) within complex mixtures such as serum or tissue extracts. Other sensing technologies, which take advantage of ‘Surface Enhanced Raman Scattering’ (gold nanoshells), frequency changes in piezoelectric crystals (quartz crystal microbalance), or electrical current generation and sensing during electrochemical reactions (electrochemical detection), can effectively provide multiplexed platforms for detection of cancer and injury biomarkers. Such devices may soon replace the traditional time consuming ELISAs and Western blots, and deliver rapid, point-of-care diagnostics to market. PMID:22833780

Novel potential serological prostate cancer biomarkers using CT100+ cancer antigen microarray platform in a multi-cultural South African cohort

PubMed Central

Adeola, Henry A.; Smith, Muneerah; Kaestner, Lisa; Blackburn, Jonathan M.; Zerbini, Luiz F.

2016-01-01

There is a growing need for high throughput diagnostic tools for early diagnosis and treatment monitoring of prostate cancer (PCa) in Africa. The role of cancer-testis antigens (CTAs) in PCa in men of African descent is poorly researched. Hence, we aimed to elucidate the role of 123 Tumour Associated Antigens (TAAs) using antigen microarray platform in blood samples (N = 67) from a South African PCa, Benign prostatic hyperplasia (BPH) and disease control (DC) cohort. Linear (fold-over-cutoff) and differential expression quantitation of autoantibody signal intensities were performed. Molecular signatures of candidate PCa antigen biomarkers were identified and analyzed for ethnic group variation. Potential cancer diagnostic and immunotherapeutic inferences were drawn. We identified a total of 41 potential diagnostic/therapeutic antigen biomarkers for PCa. By linear quantitation, four antigens, GAGE1, ROPN1, SPANXA1 and PRKCZ were found to have higher autoantibody titres in PCa serum as compared with BPH where MAGEB1 and PRKCZ were highly expressed. Also, p53 S15A and p53 S46A were found highly expressed in the disease control group. Statistical analysis by differential expression revealed twenty-four antigens as upregulated in PCa samples, while 11 were downregulated in comparison to BPH and DC (FDR = 0.01). FGFR2, COL6A1and CALM1 were verifiable biomarkers of PCa analysis using urinary shotgun proteomics. Functional pathway annotation of identified biomarkers revealed similar enrichment both at genomic and proteomic level and ethnic variations were observed. Cancer antigen arrays are emerging useful in potential diagnostic and immunotherapeutic antigen biomarker discovery. PMID:26885621

Morph-X-Select: Morphology-based tissue aptamer selection for ovarian cancer biomarker discovery

PubMed Central

Wang, Hongyu; Li, Xin; Volk, David E.; Lokesh, Ganesh L.-R.; Elizondo-Riojas, Miguel-Angel; Li, Li; Nick, Alpa M.; Sood, Anil K.; Rosenblatt, Kevin P.; Gorenstein, David G.

2016-01-01

High affinity aptamer-based biomarker discovery has the advantage of simultaneously discovering an aptamer affinity reagent and its target biomarker protein. Here, we demonstrate a morphology-based tissue aptamer selection method that enables us to use tissue sections from individual patients and identify high-affinity aptamers and their associated target proteins in a systematic and accurate way. We created a combinatorial DNA aptamer library that has been modified with thiophosphate substitutions of the phosphate ester backbone at selected 5′dA positions for enhanced nuclease resistance and targeting. Based on morphological assessment, we used image-directed laser microdissection (LMD) to dissect regions of interest bound with the thioaptamer (TA) library and further identified target proteins for the selected TAs. We have successfully identified and characterized the lead candidate TA, V5, as a vimentin-specific sequence that has shown specific binding to tumor vasculature of human ovarian tissue and human microvascular endothelial cells. This new Morph-X-Select method allows us to select high-affinity aptamers and their associated target proteins in a specific and accurate way, and could be used for personalized biomarker discovery to improve medical decision-making and to facilitate the development of targeted therapies to achieve more favorable outcomes. PMID:27839510

Determining conserved metabolic biomarkers from a million database queries.

PubMed

Kurczy, Michael E; Ivanisevic, Julijana; Johnson, Caroline H; Uritboonthai, Winnie; Hoang, Linh; Fang, Mingliang; Hicks, Matthew; Aldebot, Anthony; Rinehart, Duane; Mellander, Lisa J; Tautenhahn, Ralf; Patti, Gary J; Spilker, Mary E; Benton, H Paul; Siuzdak, Gary

2015-12-01

Metabolite databases provide a unique window into metabolome research allowing the most commonly searched biomarkers to be catalogued. Omic scale metabolite profiling, or metabolomics, is finding increased utility in biomarker discovery largely driven by improvements in analytical technologies and the concurrent developments in bioinformatics. However, the successful translation of biomarkers into clinical or biologically relevant indicators is limited. With the aim of improving the discovery of translatable metabolite biomarkers, we present search analytics for over one million METLIN metabolite database queries. The most common metabolites found in METLIN were cross-correlated against XCMS Online, the widely used cloud-based data processing and pathway analysis platform. Analysis of the METLIN and XCMS common metabolite data has two primary implications: these metabolites, might indicate a conserved metabolic response to stressors and, this data may be used to gauge the relative uniqueness of potential biomarkers. METLIN can be accessed by logging on to: https://metlin.scripps.edu siuzdak@scripps.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Isomer-specific profiling of N-glycans derived from human serum for potential biomarker discovery in pancreatic cancer.

PubMed

Liu, Yufei; Wang, Chang; Wang, Ran; Wu, Yike; Zhang, Liang; Liu, Bi-Feng; Cheng, Liming; Liu, Xin

2018-06-15

Glycosylation is one of the most important post-translational modifications of protein. Recently, global profiling of human serum glycomics has become a noninvasive method for cancer-related biomarker discovery and many studies have focused on compositional glycan profiling. In contrast, structure-specific glycan profiling may provide more potential biomarkers with higher specificity than compositional profiling. In this work, N-glycans released from human serum were neutralized with methylamine and reduced by ammonia-borane complex prior to profiling using nanoLC-ESI-MS with porous graphitized carbon (PGC) and relative abundances of over 280 isomers were compared between pancreatic cancer (PC) cases (n = 32) and healthy controls (n = 32). Statistical analysis identified 25 specific-isomeric biomarkers with significant differences (p-value < 0.05). ROC and PCA analysis were performed to assess the potential biomarkers which were identified as being significantly altered in cancer. The AUCs of the significantly changed specific-isomers were ranging from 0.712 to 0.949. In addition, with the combination of all potential biomarkers, a higher AUC of 0.976 with sensitivity (93.5%) and specificity (90.6%) was obtained. Overall, the proposed strategy coupled to relative quantitative analysis of isomeric glycans make it possible to discover new biomarkers for the diagnosis of PC. Pancreatic cancer (PC) has a poor prognosis with a five-year survival rate <5%. Therefore, a strategy for accurate diagnosis of PC is indeed required. In this paper, a dual-derivatized strategy for structure-specific glycan profiling has been used and according to our best knowledge, this is the first application of this strategy for PC biomarker discovery, in which the separation, identification and relative quantification of isomeric glycans can be simultaneously obtained. In addition, by in-depth analysis of isomeric glycans, the full description of the stereo- and region- diversity

Oblique view of the Orbiter Discovery from an elevated platform ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Oblique view of the Orbiter Discovery from an elevated platform in the Vehicle Assembly Building at NASA's Kennedy Space Center. Note the Forward Reaction Control System (RCS) Module from the forward section and the Orbiter Maneuvering System (OMS)/RCS pods from the aft section have been removed. Ground support equipment called Strongbacks are attached to the payload bay doors and the Flight Deck windows have been covered by ground support equipment. Also note the scale figure standing by the Orbiter. - Space Transportation System, Orbiter Discovery (OV-103), Lyndon B. Johnson Space Center, 2101 NASA Parkway, Houston, Harris County, TX

A Common Platform for Antibiotic Dereplication and Adjuvant Discovery.

PubMed

Cox, Georgina; Sieron, Arthur; King, Andrew M; De Pascale, Gianfranco; Pawlowski, Andrew C; Koteva, Kalinka; Wright, Gerard D

2017-01-19

Solving the antibiotic resistance crisis requires the discovery of new antimicrobial drugs and the preservation of existing ones. The discovery of inhibitors of antibiotic resistance, antibiotic adjuvants, is a proven example of the latter. A major difficulty in identifying new antibiotics is the frequent rediscovery of known compounds, necessitating laborious "dereplication" to identify novel chemical entities. We have developed an antibiotic resistance platform (ARP) that can be used for both the identification of antibiotic adjuvants and for antibiotic dereplication. The ARP is a cell-based array of mechanistically distinct individual resistance elements in an identical genetic background. In dereplication mode, we demonstrate the rapid identification, and thus discrimination, of common antibiotics. In adjuvant discovery mode, we show that the ARP can be harnessed in screens to identify inhibitors of resistance. The ARP is therefore a powerful tool that has broad application in confronting the resistance crisis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Novel Biomarker Discovery for Diagnostic and Therapeutic Strategies in Prostate Cancer

DTIC Science & Technology

2015-06-01

PURPOSE: to identify high affinity aptamers that distinguish between prostate cancers that are likely to remain organ- confined and those with potential to...metastasize. SCOPE: This was a pilot project to generate RNA aptamers that selectively react with a prostate cancer cell line that remains confined... Aptamer -Facilitated Biomarker Discovery (AptaBiD) technology. TASKS AND PROGRESS: (1) Non-metastatic LNCaP-Pro-5 cells, metastasis-prone LNCaP-LN3

Novel Biomarker Discovery for Diagnostic and Therapeutic Strategies in Prostate Cancer

DTIC Science & Technology

2014-03-01

aptamers that distinguish between prostate cancers that are likely to remain organ-confined and those with potential to metastasize, The scope of this...pilot is to generate DNA aptamers that selectively react with a prostate cancer cell line that remains confined to the prostate (LNCaP) vs. a...subpopulation of this cell line that has acquired the ability to metastasize aggressively, employing Cell-Selex and Aptamer -Facilitated Biomarker Discovery

Automated microfluidic platform of bead-based electrochemical immunosensor integrated with bioreactor for continual monitoring of cell secreted biomarkers

PubMed Central

Riahi, Reza; Shaegh, Seyed Ali Mousavi; Ghaderi, Masoumeh; Zhang, Yu Shrike; Shin, Su Ryon; Aleman, Julio; Massa, Solange; Kim, Duckjin; Dokmeci, Mehmet Remzi; Khademhosseini, Ali

2016-01-01

There is an increasing interest in developing microfluidic bioreactors and organs-on-a-chip platforms combined with sensing capabilities for continual monitoring of cell-secreted biomarkers. Conventional approaches such as ELISA and mass spectroscopy cannot satisfy the needs of continual monitoring as they are labor-intensive and not easily integrable with low-volume bioreactors. This paper reports on the development of an automated microfluidic bead-based electrochemical immunosensor for in-line measurement of cell-secreted biomarkers. For the operation of the multi-use immunosensor, disposable magnetic microbeads were used to immobilize biomarker-recognition molecules. Microvalves were further integrated in the microfluidic immunosensor chip to achieve programmable operations of the immunoassay including bead loading and unloading, binding, washing, and electrochemical sensing. The platform allowed convenient integration of the immunosensor with liver-on-chips to carry out continual quantification of biomarkers secreted from hepatocytes. Transferrin and albumin productions were monitored during a 5-day hepatotoxicity assessment in which human primary hepatocytes cultured in the bioreactor were treated with acetaminophen. Taken together, our unique microfluidic immunosensor provides a new platform for in-line detection of biomarkers in low volumes and long-term in vitro assessments of cellular functions in microfluidic bioreactors and organs-on-chips. PMID:27098564

Automated microfluidic platform of bead-based electrochemical immunosensor integrated with bioreactor for continual monitoring of cell secreted biomarkers

NASA Astrophysics Data System (ADS)

Riahi, Reza; Shaegh, Seyed Ali Mousavi; Ghaderi, Masoumeh; Zhang, Yu Shrike; Shin, Su Ryon; Aleman, Julio; Massa, Solange; Kim, Duckjin; Dokmeci, Mehmet Remzi; Khademhosseini, Ali

2016-04-01

There is an increasing interest in developing microfluidic bioreactors and organs-on-a-chip platforms combined with sensing capabilities for continual monitoring of cell-secreted biomarkers. Conventional approaches such as ELISA and mass spectroscopy cannot satisfy the needs of continual monitoring as they are labor-intensive and not easily integrable with low-volume bioreactors. This paper reports on the development of an automated microfluidic bead-based electrochemical immunosensor for in-line measurement of cell-secreted biomarkers. For the operation of the multi-use immunosensor, disposable magnetic microbeads were used to immobilize biomarker-recognition molecules. Microvalves were further integrated in the microfluidic immunosensor chip to achieve programmable operations of the immunoassay including bead loading and unloading, binding, washing, and electrochemical sensing. The platform allowed convenient integration of the immunosensor with liver-on-chips to carry out continual quantification of biomarkers secreted from hepatocytes. Transferrin and albumin productions were monitored during a 5-day hepatotoxicity assessment in which human primary hepatocytes cultured in the bioreactor were treated with acetaminophen. Taken together, our unique microfluidic immunosensor provides a new platform for in-line detection of biomarkers in low volumes and long-term in vitro assessments of cellular functions in microfluidic bioreactors and organs-on-chips.

Separate class true discovery rate degree of association sets for biomarker identification.

PubMed

Crager, Michael R; Ahmed, Murat

2014-01-01

In 2008, Efron showed that biological features in a high-dimensional study can be divided into classes and a separate false discovery rate (FDR) analysis can be conducted in each class using information from the entire set of features to assess the FDR within each class. We apply this separate class approach to true discovery rate degree of association (TDRDA) set analysis, which is used in clinical-genomic studies to identify sets of biomarkers having strong association with clinical outcome or state while controlling the FDR. Careful choice of classes based on prior information can increase the identification power of the separate class analysis relative to the overall analysis.

Epigenome-wide association studies for cancer biomarker discovery in circulating cell-free DNA: technical advances and challenges.

PubMed

Tanić, Miljana; Beck, Stephan

2017-02-01

Since introducing the concept of epigenome-wide association studies (EWAS) in 2011, there has been a vast increase in the number of published EWAS studies in common diseases, including in cancer. These studies have increased our understanding of epigenetic events underlying carcinogenesis and have enabled the discovery of cancer-specific methylation biomarkers. In this mini-review, we have focused on the state of the art in EWAS applied to cell-free circulating DNA for epigenetic biomarker discovery in cancer and discussed associated technical advances and challenges, and our expectations for the future of the field. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Mass spectrometry based biomarker discovery, verification, and validation--quality assurance and control of protein biomarker assays.

PubMed

Parker, Carol E; Borchers, Christoph H

2014-06-01

In its early years, mass spectrometry (MS)-based proteomics focused on the cataloging of proteins found in different species or different tissues. By 2005, proteomics was being used for protein quantitation, typically based on "proteotypic" peptides which act as surrogates for the parent proteins. Biomarker discovery is usually done by non-targeted "shotgun" proteomics, using relative quantitation methods to determine protein expression changes that correlate with disease (output given as "up-or-down regulation" or "fold-increases"). MS-based techniques can also perform "absolute" quantitation which is required for clinical applications (output given as protein concentrations). Here we describe the differences between these methods, factors that affect the precision and accuracy of the results, and some examples of recent studies using MS-based proteomics to verify cancer-related biomarkers. Copyright © 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Cardiovascular Organ-on-a-Chip Platforms for Drug Discovery and Development

PubMed Central

Ribas, João; Sadeghi, Hossein; Manbachi, Amir; Leijten, Jeroen; Brinegar, Katelyn; Zhang, Yu Shrike; Ferreira, Lino

2016-01-01

Abstract Cardiovascular diseases are prevalent worldwide and are the most frequent causes of death in the United States. Although spending in drug discovery/development has increased, the amount of drug approvals has seen a progressive decline. Particularly, adverse side effects to the heart and general vasculature have become common causes for preclinical project closures, and preclinical models do not fully recapitulate human in vivo dynamics. Recently, organs-on-a-chip technologies have been proposed to mimic the dynamic conditions of the cardiovascular system—in particular, heart and general vasculature. These systems pay particular attention to mimicking structural organization, shear stress, transmural pressure, mechanical stretching, and electrical stimulation. Heart- and vasculature-on-a-chip platforms have been successfully generated to study a variety of physiological phenomena, model diseases, and probe the effects of drugs. Here, we review and discuss recent breakthroughs in the development of cardiovascular organs-on-a-chip platforms, and their current and future applications in the area of drug discovery and development. PMID:28971113

Biomarker discovery and transcriptomic responses in Daphnia magna exposed to munitions constituents.

PubMed

Garcia-Reyero, Natalia; Poynton, Helen C; Kennedy, Alan J; Guan, Xin; Escalon, B Lynn; Chang, Bonnie; Varshavsky, Julia; Loguinov, Alex V; Vulpe, Chris D; Perkins, Edward J

2009-06-01

Ecotoxicogenomic approaches are emerging as alternative methods in environmental monitoring because they allow insight into pollutant modes of action and help assess the causal agents and potential toxicity beyond the traditional end points of death, growth, and reproduction. Gene expression analysis has shown particular promise for identifying gene expression biomarkers of chemical exposure that can be further used to monitor specific chemical exposures in the environment. We focused on the development of gene expression markers to detect and discriminate between chemical exposures. Using a custom cDNA microarray for Daphnia magna, we identified distinct expression fingerprints in response to exposure at sublethal concentrations of Cu, Zn, Pb, and munitions constituents. Using the results obtained from microarray analysis, we chose a suite of potential biomarkers for each of the specific exposures. The selected potential biomarkers were tested in independent chemical exposures for specificity using quantitative reverse transcription polymerase chain reaction. Six genes were confirmed as differentially regulated bythe selected chemical exposures. Furthermore, each exposure was identified by response of a unique combination (suite) of individual gene expression biomarkers. These results demonstrate the potential for discovery and validation of novel biomarkers of chemical exposures using gene expression analysis, which could have broad applicability in environmental monitoring.

Proteogenomic studies on cancer drug resistance: towards biomarker discovery and target identification.

PubMed

Fu, Shuyue; Liu, Xiang; Luo, Maochao; Xie, Ke; Nice, Edouard C; Zhang, Haiyuan; Huang, Canhua

2017-04-01

Chemoresistance is a major obstacle for current cancer treatment. Proteogenomics is a powerful multi-omics research field that uses customized protein sequence databases generated by genomic and transcriptomic information to identify novel genes (e.g. noncoding, mutation and fusion genes) from mass spectrometry-based proteomic data. By identifying aberrations that are differentially expressed between tumor and normal pairs, this approach can also be applied to validate protein variants in cancer, which may reveal the response to drug treatment. Areas covered: In this review, we will present recent advances in proteogenomic investigations of cancer drug resistance with an emphasis on integrative proteogenomic pipelines and the biomarker discovery which contributes to achieving the goal of using precision/personalized medicine for cancer treatment. Expert commentary: The discovery and comprehensive understanding of potential biomarkers help identify the cohort of patients who may benefit from particular treatments, and will assist real-time clinical decision-making to maximize therapeutic efficacy and minimize adverse effects. With the development of MS-based proteomics and NGS-based sequencing, a growing number of proteogenomic tools are being developed specifically to investigate cancer drug resistance.

Extending the scope of pooled analyses of individual patient biomarker data from heterogeneous laboratory platforms and cohorts using merging algorithms.

PubMed

Burke, Órlaith; Benton, Samantha; Szafranski, Pawel; von Dadelszen, Peter; Buhimschi, S Catalin; Cetin, Irene; Chappell, Lucy; Figueras, Francesc; Galindo, Alberto; Herraiz, Ignacio; Holzman, Claudia; Hubel, Carl; Knudsen, Ulla; Kronborg, Camilla; Laivuori, Hannele; Lapaire, Olav; McElrath, Thomas; Moertl, Manfred; Myers, Jenny; Ness, Roberta B; Oliveira, Leandro; Olson, Gayle; Poston, Lucilla; Ris-Stalpers, Carrie; Roberts, James M; Schalekamp-Timmermans, Sarah; Schlembach, Dietmar; Steegers, Eric; Stepan, Holger; Tsatsaris, Vassilis; van der Post, Joris A; Verlohren, Stefan; Villa, Pia M; Williams, David; Zeisler, Harald; Redman, Christopher W G; Staff, Anne Cathrine

2016-01-01

A common challenge in medicine, exemplified in the analysis of biomarker data, is that large studies are needed for sufficient statistical power. Often, this may only be achievable by aggregating multiple cohorts. However, different studies may use disparate platforms for laboratory analysis, which can hinder merging. Using circulating placental growth factor (PlGF), a potential biomarker for hypertensive disorders of pregnancy (HDP) such as preeclampsia, as an example, we investigated how such issues can be overcome by inter-platform standardization and merging algorithms. We studied 16,462 pregnancies from 22 study cohorts. PlGF measurements (gestational age ⩾20 weeks) analyzed on one of four platforms: R&D Systems, AlereTriage, RocheElecsys or AbbottArchitect, were available for 13,429 women. Two merging algorithms, using Z-Score and Multiple of Median transformations, were applied. Best reference curves (BRC), based on merged, transformed PlGF measurements in uncomplicated pregnancy across six gestational age groups, were estimated. Identification of HDP by these PlGF-BRCs was compared to that of platform-specific curves. We demonstrate the feasibility of merging PlGF concentrations from different analytical platforms. Overall BRC identification of HDP performed at least as well as platform-specific curves. Our method can be extended to any set of biomarkers obtained from different laboratory platforms in any field. Merged biomarker data from multiple studies will improve statistical power and enlarge our understanding of the pathophysiology and management of medical syndromes. Copyright © 2015 International Society for the Study of Hypertension in Pregnancy. Published by Elsevier B.V. All rights reserved.

Plasma biomarker discovery in preeclampsia using a novel differential isolation technology for circulating extracellular vesicles.

PubMed

Tan, Kok Hian; Tan, Soon Sim; Sze, Siu Kwan; Lee, Wai Kheong Ryan; Ng, Mor Jack; Lim, Sai Kiang

2014-10-01

To circumvent the complex protein milieu of plasma and discover robust predictive biomarkers for preeclampsia (PE), we investigate if phospholipid-binding ligands can reduce the milieu complexity by extracting plasma extracellular vesicles for biomarker discovery. Cholera toxin B chain (CTB) and annexin V (AV) which respectively binds GM1 ganglioside and phosphatidylserine were used to isolate extracellular vesicles from plasma of PE patients and healthy pregnant women. The proteins in the vesicles were identified using enzyme-linked immunosorbent assay, antibody array, and mass spectrometry. CTB and AV were found to bind 2 distinct groups of extracellular vesicles. Antibody array and enzyme-linked immunosorbent assay revealed that PE patients had elevated levels of CD105, interleukin-6, placental growth factor, tissue inhibitor of metallopeptidase 1, and atrial natriuretic peptide in cholera toxin B- but not AV-vesicles, and elevated levels of plasminogen activator inhibitor-1, pro-calcitonin, S100b, tumor growth factor β, vascular endothelial growth factor receptor 1, brain natriuretic peptide, and placental growth factor in both cholera toxin B- and AV-vesicles. CD9 level was elevated in cholera toxin B-vesicles but reduced in AV vesicles of PE patients. Proteome analysis revealed that in cholera toxin B-vesicles, 87 and 222 proteins were present only in PE patients and healthy pregnant women respectively while in AV-vesicles, 104 and 157 proteins were present only in PE and healthy pregnant women, respectively. This study demonstrated for the first time that CTB and AV bind unique extracellular vesicles, and their protein cargo reflects the disease state of the patient. The successful use of these 2 ligands to isolate circulating plasma extracellular vesicles for biomarker discovery in PE represents a novel technology for biomarker discovery that can be applied to other specialties. Copyright © 2014 Elsevier Inc. All rights reserved.

The EGS Data Collaboration Platform: Enabling Scientific Discovery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weers, Jonathan D; Johnston, Henry; Huggins, Jay V

Collaboration in the digital age has been stifled in recent years. Reasonable responses to legitimate security concerns have created a virtual landscape of silos and fortified castles incapable of sharing information efficiently. This trend is unfortunately opposed to the geothermal scientific community's migration toward larger, more collaborative projects. To facilitate efficient sharing of information between team members from multiple national labs, universities, and private organizations, the 'EGS Collab' team has developed a universally accessible, secure data collaboration platform and has fully integrated it with the U.S. Department of Energy's (DOE) Geothermal Data Repository (GDR) and the National Geothermal Data Systemmore » (NGDS). This paper will explore some of the challenges of collaboration in the modern digital age, highlight strategies for active data management, and discuss the integration of the EGS Collab data management platform with the GDR to enable scientific discovery through the timely dissemination of information.« less

Proteomic Workflows for Biomarker Identification Using Mass Spectrometry — Technical and Statistical Considerations during Initial Discovery

PubMed Central

Orton, Dennis J.; Doucette, Alan A.

2013-01-01

Identification of biomarkers capable of differentiating between pathophysiological states of an individual is a laudable goal in the field of proteomics. Protein biomarker discovery generally employs high throughput sample characterization by mass spectrometry (MS), being capable of identifying and quantifying thousands of proteins per sample. While MS-based technologies have rapidly matured, the identification of truly informative biomarkers remains elusive, with only a handful of clinically applicable tests stemming from proteomic workflows. This underlying lack of progress is attributed in large part to erroneous experimental design, biased sample handling, as well as improper statistical analysis of the resulting data. This review will discuss in detail the importance of experimental design and provide some insight into the overall workflow required for biomarker identification experiments. Proper balance between the degree of biological vs. technical replication is required for confident biomarker identification. PMID:28250400

Molecular Elucidation of Disease Biomarkers at the Interface of Chemistry and Biology.

PubMed

Zhang, Liqin; Wan, Shuo; Jiang, Ying; Wang, Yanyue; Fu, Ting; Liu, Qiaoling; Cao, Zhijuan; Qiu, Liping; Tan, Weihong

2017-02-22

Disease-related biomarkers are objectively measurable molecular signatures of physiological status that can serve as disease indicators or drug targets in clinical diagnosis and therapy, thus acting as a tool in support of personalized medicine. For example, the prostate-specific antigen (PSA) biomarker is now widely used to screen patients for prostate cancer. However, few such biomarkers are currently available, and the process of biomarker identification and validation is prolonged and complicated by inefficient methods of discovery and few reliable analytical platforms. Therefore, in this Perspective, we look at the advanced chemistry of aptamer molecules and their significant role as molecular probes in biomarker studies. As a special class of functional nucleic acids evolved from an iterative technology termed Systematic Evolution of Ligands by Exponential Enrichment (SELEX), these single-stranded oligonucleotides can recognize their respective targets with selectivity and affinity comparable to those of protein antibodies. Because of their fast turnaround time and exceptional chemical properties, aptamer probes can serve as novel molecular tools for biomarker investigations, particularly in assisting identification of new disease-related biomarkers. More importantly, aptamers are able to recognize biomarkers from complex biological environments such as blood serum and cell surfaces, which can provide direct evidence for further clinical applications. This Perspective highlights several major advancements of aptamer-based biomarker discovery strategies and their potential contribution to the practice of precision medicine.

Respiratory Toxicity Biomarkers

EPA Science Inventory

The advancement in high throughput genomic, proteomic and metabolomic techniques have accelerated pace of lung biomarker discovery. A recent growth in the discovery of new lung toxicity/disease biomarkers have led to significant advances in our understanding of pathological proce...

SWATHtoMRM: Development of High-Coverage Targeted Metabolomics Method Using SWATH Technology for Biomarker Discovery.

PubMed

Zha, Haihong; Cai, Yuping; Yin, Yandong; Wang, Zhuozhong; Li, Kang; Zhu, Zheng-Jiang

2018-03-20

The complexity of metabolome presents a great analytical challenge for quantitative metabolite profiling, and restricts the application of metabolomics in biomarker discovery. Targeted metabolomics using multiple-reaction monitoring (MRM) technique has excellent capability for quantitative analysis, but suffers from the limited metabolite coverage. To address this challenge, we developed a new strategy, namely, SWATHtoMRM, which utilizes the broad coverage of SWATH-MS technology to develop high-coverage targeted metabolomics method. Specifically, SWATH-MS technique was first utilized to untargeted profile one pooled biological sample and to acquire the MS 2 spectra for all metabolites. Then, SWATHtoMRM was used to extract the large-scale MRM transitions for targeted analysis with coverage as high as 1000-2000 metabolites. Then, we demonstrated the advantages of SWATHtoMRM method in quantitative analysis such as coverage, reproducibility, sensitivity, and dynamic range. Finally, we applied our SWATHtoMRM approach to discover potential metabolite biomarkers for colorectal cancer (CRC) diagnosis. A high-coverage targeted metabolomics method with 1303 metabolites in one injection was developed to profile colorectal cancer tissues from CRC patients. A total of 20 potential metabolite biomarkers were discovered and validated for CRC diagnosis. In plasma samples from CRC patients, 17 out of 20 potential biomarkers were further validated to be associated with tumor resection, which may have a great potential in assessing the prognosis of CRC patients after tumor resection. Together, the SWATHtoMRM strategy provides a new way to develop high-coverage targeted metabolomics method, and facilitates the application of targeted metabolomics in disease biomarker discovery. The SWATHtoMRM program is freely available on the Internet ( http://www.zhulab.cn/software.php ).

Implementation of a protein profiling platform developed as an academic-pharmaceutical industry collaborative effort.

PubMed

Végvári, Akos; Magnusson, Mattias; Wallman, Lars; Ekström, Simon; Bolmsjö, Gunnar; Nilsson, Johan; Miliotis, Tasso; Ostling, Jörgen; Kjellström, Sven; Ottervald, Jan; Franzén, Bo; Hultberg, Hans; Marko-Varga, György; Laurell, Thomas

2008-06-01

As much attention has devoted to the proteome research during the last few years, biomarker discovery has become an increasingly hot area, potentially enabling the development of new assays for diagnosis and prognosis of severe diseases. This is the field of research interest where efforts originating from both academic and industrial groups should jointly work on solutions. In this paper, we would like to demonstrate the fruitful combination of both research domains where the scientific crossroads sprout fresh ideas from the basic research domain and how these are refined and tethered to industrial standards. We will present an approach that is based on novel microfluidic devices, utilizing their benefits in processing small-volume samples. Our biomarker discovery strategy, built around this platform, involves optimized samples processing (based on SPE and sample enrichment) and fast MALDI-MS readout. The identification of novel biomarkers at low-abundance level has been achieved by the utilization of a miniaturized sample handling platform, which offers clean-up and enrichment of proteins in one step. Complete automation has been realized in the form of a unique robotic instrumentation that is able to extract and transfer 96 samples onto standard MALDI target plates with high throughput. The developed platform was operated with a 60 sample turnaround per hour allowing sensitivities in femtomol regions of medium- and low-abundant target proteins from clinical studies on samples of multiple sclerosis and gastroesophageal reflux disease. Several proteins have been identified as new biomarkers from cerebrospinal fluid and esophagus epithelial cells.

Proteomic profiling in MPTP monkey model for early Parkinson disease biomarker discovery

PubMed Central

Lin, Xiangmin; Shi, Min; Gunasingh Masilamoni, Jeyaraj; Dator, Romel; Movius, James; Aro, Patrick; Smith, Yoland; Zhang, Jing

2015-01-01

Identification of reliable and robust biomarkers is crucial to enable early diagnosis of Parkinson disease (PD) and monitoring disease progression. While imperfect, the slow, chronic 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced non-human primate animal model system of parkinsonism is an abundant source of pre-motor or early stage PD biomarker discovery. Here, we present a study of a MPTP rhesus monkey model of PD that utilizes complementary quantitative iTRAQ-based proteomic, glycoproteomics and phosphoproteomics approaches. We compared the glycoprotein, non-glycoprotein, and phosphoprotein profiles in the putamen of asymptomatic and symptomatic MPTP-treated monkeys as well as saline injected controls. We identified 86 glycoproteins, 163 non-glycoproteins, and 71 phosphoproteins differentially expressed in the MPTP-treated groups. Functional analysis of the data sets inferred the biological processes and pathways that link to neurodegeneration in PD and related disorders. Several potential biomarkers identified in this study have already been translated for their usefulness in PD diagnosis in human subjects and further validation investigations are currently under way. In addition to providing potential early PD biomarkers, this comprehensive quantitative proteomic study may also shed insights regarding the mechanisms underlying early PD development. This article is part of a Special Issue entitled: Neuroproteomics: Applications in neuroscience and neurology. PMID:25617661

Discovery and bio-optimization of human antibody therapeutics using the XenoMouse® transgenic mouse platform.

PubMed

Foltz, Ian N; Gunasekaran, Kannan; King, Chadwick T

2016-03-01

Since the late 1990s, the use of transgenic animal platforms has transformed the discovery of fully human therapeutic monoclonal antibodies. The first approved therapy derived from a transgenic platform--the epidermal growth factor receptor antagonist panitumumab to treat advanced colorectal cancer--was developed using XenoMouse(®) technology. Since its approval in 2006, the science of discovering and developing therapeutic monoclonal antibodies derived from the XenoMouse(®) platform has advanced considerably. The emerging array of antibody therapeutics developed using transgenic technologies is expected to include antibodies and antibody fragments with novel mechanisms of action and extreme potencies. In addition to these impressive functional properties, these antibodies will be designed to have superior biophysical properties that enable highly efficient large-scale manufacturing methods. Achieving these new heights in antibody drug discovery will ultimately bring better medicines to patients. Here, we review best practices for the discovery and bio-optimization of monoclonal antibodies that fit functional design goals and meet high manufacturing standards. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A matrix approach to guide IHC-based tissue biomarker development in oncology drug discovery.

PubMed

Smith, Neil R; Womack, Christopher

2014-01-01

Immunohistochemistry (IHC) is a core platform for the analysis of tissue samples, and there is an increasing demand for reliable and quantitative IHC-based tissue biomarkers in oncology clinical research and development (R&D) environments. Biomarker assay and drug development proceed in parallel. Furthermore, biomarker assay requirements change with each phase of drug development. We have therefore developed a matrix tool to enable researchers to evaluate whether a particular IHC biomarker assay is fit for purpose. Experience gained from the development of 130 IHC biomarkers, supporting a large number of oncology drug projects, was used to formulate a practical approach to IHC assay development. The resultant matrix grid and accompanying work flow incorporates 16 core decision points that link antibody and assay specificity and sensitivity, and assay performance in preclinical and clinical samples, with stages of drug development. The matrix provides a means to ensure that relevant information on an IHC assay in development is recorded and communicated consistently and that minimum assay validation requirements are met. Copyright © 2013 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine

PubMed Central

Stern, Andrew M.; Schurdak, Mark E.; Bahar, Ivet; Berg, Jeremy M.; Taylor, D. Lansing

2016-01-01

Drug candidates exhibiting well-defined pharmacokinetic and pharmacodynamic profiles that are otherwise safe often fail to demonstrate proof-of-concept in phase II and III trials. Innovation in drug discovery and development has been identified as a critical need for improving the efficiency of drug discovery, especially through collaborations between academia, government agencies, and industry. To address the innovation challenge, we describe a comprehensive, unbiased, integrated, and iterative quantitative systems pharmacology (QSP)–driven drug discovery and development strategy and platform that we have implemented at the University of Pittsburgh Drug Discovery Institute. Intrinsic to QSP is its integrated use of multiscale experimental and computational methods to identify mechanisms of disease progression and to test predicted therapeutic strategies likely to achieve clinical validation for appropriate subpopulations of patients. The QSP platform can address biological heterogeneity and anticipate the evolution of resistance mechanisms, which are major challenges for drug development. The implementation of this platform is dedicated to gaining an understanding of mechanism(s) of disease progression to enable the identification of novel therapeutic strategies as well as repurposing drugs. The QSP platform will help promote the paradigm shift from reactive population-based medicine to proactive personalized medicine by focusing on the patient as the starting and the end point. PMID:26962875

Patient-derived tumour xenografts for breast cancer drug discovery.

PubMed

Cassidy, John W; Batra, Ankita S; Greenwood, Wendy; Bruna, Alejandra

2016-12-01

Despite remarkable advances in our understanding of the drivers of human malignancies, new targeted therapies often fail to show sufficient efficacy in clinical trials. Indeed, the cost of bringing a new agent to market has risen substantially in the last several decades, in part fuelled by extensive reliance on preclinical models that fail to accurately reflect tumour heterogeneity. To halt unsustainable rates of attrition in the drug discovery process, we must develop a new generation of preclinical models capable of reflecting the heterogeneity of varying degrees of complexity found in human cancers. Patient-derived tumour xenograft (PDTX) models prevail as arguably the most powerful in this regard because they capture cancer's heterogeneous nature. Herein, we review current breast cancer models and their use in the drug discovery process, before discussing best practices for developing a highly annotated cohort of PDTX models. We describe the importance of extensive multidimensional molecular and functional characterisation of models and combination drug-drug screens to identify complex biomarkers of drug resistance and response. We reflect on our own experiences and propose the use of a cost-effective intermediate pharmacogenomic platform (the PDTX-PDTC platform) for breast cancer drug and biomarker discovery. We discuss the limitations and unanswered questions of PDTX models; yet, still strongly envision that their use in basic and translational research will dramatically change our understanding of breast cancer biology and how to more effectively treat it. © 2016 The authors.

The Emerging Field of Quantitative Blood Metabolomics for Biomarker Discovery in Critical Illnesses

PubMed Central

Serkova, Natalie J.; Standiford, Theodore J.

2011-01-01

Metabolomics, a science of systems biology, is the global assessment of endogenous metabolites within a biologic system and represents a “snapshot” reading of gene function, enzyme activity, and the physiological landscape. Metabolite detection, either individual or grouped as a metabolomic profile, is usually performed in cells, tissues, or biofluids by either nuclear magnetic resonance spectroscopy or mass spectrometry followed by sophisticated multivariate data analysis. Because loss of metabolic homeostasis is common in critical illness, the metabolome could have many applications, including biomarker and drug target identification. Metabolomics could also significantly advance our understanding of the complex pathophysiology of acute illnesses, such as sepsis and acute lung injury/acute respiratory distress syndrome. Despite this potential, the clinical community is largely unfamiliar with the field of metabolomics, including the methodologies involved, technical challenges, and, most importantly, clinical uses. Although there is evidence of successful preclinical applications, the clinical usefulness and application of metabolomics in critical illness is just beginning to emerge, the advancement of which hinges on linking metabolite data to known and validated clinically relevant indices. In addition, other important aspects, such as patient selection, sample collection, and processing, as well as the needed multivariate data analysis, have to be taken into consideration before this innovative approach to biomarker discovery can become a reliable tool in the intensive care unit. The purpose of this review is to begin to familiarize clinicians with the field of metabolomics and its application for biomarker discovery in critical illnesses such as sepsis. PMID:21680948

Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury

PubMed Central

2013-01-01

Introduction Acute kidney injury (AKI) can evolve quickly and clinical measures of function often fail to detect AKI at a time when interventions are likely to provide benefit. Identifying early markers of kidney damage has been difficult due to the complex nature of human AKI, in which multiple etiologies exist. The objective of this study was to identify and validate novel biomarkers of AKI. Methods We performed two multicenter observational studies in critically ill patients at risk for AKI - discovery and validation. The top two markers from discovery were validated in a second study (Sapphire) and compared to a number of previously described biomarkers. In the discovery phase, we enrolled 522 adults in three distinct cohorts including patients with sepsis, shock, major surgery, and trauma and examined over 300 markers. In the Sapphire validation study, we enrolled 744 adult subjects with critical illness and without evidence of AKI at enrollment; the final analysis cohort was a heterogeneous sample of 728 critically ill patients. The primary endpoint was moderate to severe AKI (KDIGO stage 2 to 3) within 12 hours of sample collection. Results Moderate to severe AKI occurred in 14% of Sapphire subjects. The two top biomarkers from discovery were validated. Urine insulin-like growth factor-binding protein 7 (IGFBP7) and tissue inhibitor of metalloproteinases-2 (TIMP-2), both inducers of G1 cell cycle arrest, a key mechanism implicated in AKI, together demonstrated an AUC of 0.80 (0.76 and 0.79 alone). Urine [TIMP-2]·[IGFBP7] was significantly superior to all previously described markers of AKI (P <0.002), none of which achieved an AUC >0.72. Furthermore, [TIMP-2]·[IGFBP7] significantly improved risk stratification when added to a nine-variable clinical model when analyzed using Cox proportional hazards model, generalized estimating equation, integrated discrimination improvement or net reclassification improvement. Finally, in sensitivity analyses [TIMP-2]�

A Comprehensive Tool and Analytical Pathway for Differential Molecular Profiling and Biomarker Discovery

DTIC Science & Technology

2014-10-20

three possiblities: AKR , B6, and BALB_B) and MUP Protein (containing two possibilities: Intact and Denatured), then you can view a plot of the Strain...the tags for the last two labels. Again, if the attribute Strain has three tags: AKR , B6, 74 Distribution A . Approved for public release...AFRL-RH-WP-TR-2014-0131 A COMPREHENSIVE TOOL AND ANALYTICAL PATHWAY FOR DIFFERENTIAL MOLECULAR PROFILING AND BIOMARKER DISCOVERY

Statistical interpretation of machine learning-based feature importance scores for biomarker discovery.

PubMed

Huynh-Thu, Vân Anh; Saeys, Yvan; Wehenkel, Louis; Geurts, Pierre

2012-07-01

Univariate statistical tests are widely used for biomarker discovery in bioinformatics. These procedures are simple, fast and their output is easily interpretable by biologists but they can only identify variables that provide a significant amount of information in isolation from the other variables. As biological processes are expected to involve complex interactions between variables, univariate methods thus potentially miss some informative biomarkers. Variable relevance scores provided by machine learning techniques, however, are potentially able to highlight multivariate interacting effects, but unlike the p-values returned by univariate tests, these relevance scores are usually not statistically interpretable. This lack of interpretability hampers the determination of a relevance threshold for extracting a feature subset from the rankings and also prevents the wide adoption of these methods by practicians. We evaluated several, existing and novel, procedures that extract relevant features from rankings derived from machine learning approaches. These procedures replace the relevance scores with measures that can be interpreted in a statistical way, such as p-values, false discovery rates, or family wise error rates, for which it is easier to determine a significance level. Experiments were performed on several artificial problems as well as on real microarray datasets. Although the methods differ in terms of computing times and the tradeoff, they achieve in terms of false positives and false negatives, some of them greatly help in the extraction of truly relevant biomarkers and should thus be of great practical interest for biologists and physicians. As a side conclusion, our experiments also clearly highlight that using model performance as a criterion for feature selection is often counter-productive. Python source codes of all tested methods, as well as the MATLAB scripts used for data simulation, can be found in the Supplementary Material.

A platform for the discovery of new macrolide antibiotics

NASA Astrophysics Data System (ADS)

Seiple, Ian B.; Zhang, Ziyang; Jakubec, Pavol; Langlois-Mercier, Audrey; Wright, Peter M.; Hog, Daniel T.; Yabu, Kazuo; Allu, Senkara Rao; Fukuzaki, Takehiro; Carlsen, Peter N.; Kitamura, Yoshiaki; Zhou, Xiang; Condakes, Matthew L.; Szczypiński, Filip T.; Green, William D.; Myers, Andrew G.

2016-05-01

The chemical modification of structurally complex fermentation products, a process known as semisynthesis, has been an important tool in the discovery and manufacture of antibiotics for the treatment of various infectious diseases. However, many of the therapeutics obtained in this way are no longer effective, because bacterial resistance to these compounds has developed. Here we present a practical, fully synthetic route to macrolide antibiotics by the convergent assembly of simple chemical building blocks, enabling the synthesis of diverse structures not accessible by traditional semisynthetic approaches. More than 300 new macrolide antibiotic candidates, as well as the clinical candidate solithromycin, have been synthesized using our convergent approach. Evaluation of these compounds against a panel of pathogenic bacteria revealed that the majority of these structures had antibiotic activity, some efficacious against strains resistant to macrolides in current use. The chemistry we describe here provides a platform for the discovery of new macrolide antibiotics and may also serve as the basis for their manufacture.

A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine.

PubMed

Stern, Andrew M; Schurdak, Mark E; Bahar, Ivet; Berg, Jeremy M; Taylor, D Lansing

2016-07-01

Drug candidates exhibiting well-defined pharmacokinetic and pharmacodynamic profiles that are otherwise safe often fail to demonstrate proof-of-concept in phase II and III trials. Innovation in drug discovery and development has been identified as a critical need for improving the efficiency of drug discovery, especially through collaborations between academia, government agencies, and industry. To address the innovation challenge, we describe a comprehensive, unbiased, integrated, and iterative quantitative systems pharmacology (QSP)-driven drug discovery and development strategy and platform that we have implemented at the University of Pittsburgh Drug Discovery Institute. Intrinsic to QSP is its integrated use of multiscale experimental and computational methods to identify mechanisms of disease progression and to test predicted therapeutic strategies likely to achieve clinical validation for appropriate subpopulations of patients. The QSP platform can address biological heterogeneity and anticipate the evolution of resistance mechanisms, which are major challenges for drug development. The implementation of this platform is dedicated to gaining an understanding of mechanism(s) of disease progression to enable the identification of novel therapeutic strategies as well as repurposing drugs. The QSP platform will help promote the paradigm shift from reactive population-based medicine to proactive personalized medicine by focusing on the patient as the starting and the end point. © 2016 Society for Laboratory Automation and Screening.

Characterization of the liver tissue interstitial fluid (TIF) proteome indicates potential for application in liver disease biomarker discovery.

PubMed

Sun, Wei; Ma, Jie; Wu, Songfeng; Yang, Dong; Yan, Yujuan; Liu, Kehui; Wang, Jinglan; Sun, Longqin; Chen, Ning; Wei, Handong; Zhu, Yunping; Xing, Baocai; Zhao, Xiaohang; Qian, Xiaohong; Jiang, Ying; He, Fuchu

2010-02-05

Tissue interstitial fluid (TIF) forms the interface between circulating body fluids and intracellular fluid. Pathological alterations of liver cells could be reflected in TIF, making it a promising source of liver disease biomarkers. Mouse liver TIF was extracted, separated by SDS-PAGE, analyzed by linear ion trap mass spectrometer, and 1450 proteins were identified. These proteins may be secreted, shed from membrane vesicles, or represent cellular breakdown products. They show different profiling patterns, quantities, and possibly modification/cleavage of intracellular proteins. The high solubility and even distribution of liver TIF supports its suitability for proteome analysis. Comparison of mouse liver TIF data with liver tissue and plasma proteome data identified major proteins that might be released from liver to plasma and serve as blood biomarkers of liver origin. This result was partially supported by comparison of human liver TIF data with human liver and plasma proteome data. Paired TIFs from tumor and nontumor liver tissues of a hepatocellular carcinoma patient were analyzed and the profile of subtracted differential proteins supports the potential for biomarker discovery in TIF. This study is the first analysis of the liver TIF proteome and provides a foundation for further application of TIF in liver disease biomarker discovery.

Biomarker Discovery and Verification of Esophageal Squamous Cell Carcinoma Using Integration of SWATH/MRM.

PubMed

Hou, Guixue; Lou, Xiaomin; Sun, Yulin; Xu, Shaohang; Zi, Jin; Wang, Quanhui; Zhou, Baojin; Han, Bo; Wu, Lin; Zhao, Xiaohang; Lin, Liang; Liu, Siqi

2015-09-04

We propose an efficient integration of SWATH with MRM for biomarker discovery and verification when the corresponding ion library is well established. We strictly controlled the false positive rate associated with SWATH MS signals and carefully selected the target peptides coupled with SWATH and MRM. We collected 10 samples of esophageal squamous cell carcinoma (ESCC) tissues paired with tumors and adjacent regions and quantified 1758 unique proteins with FDR 1% at protein level using SWATH, in which 467 proteins were abundance-dependent with ESCC. After carefully evaluating the SWATH MS signals of the up-regulated proteins, we selected 120 proteins for MRM verification. MRM analysis of the pooled and individual esophageal tissues resulted in 116 proteins that exhibited similar abundance response modes to ESCC that were acquired with SWATH. Because the ESCC-related proteins consisted of a high percentile of secreted proteins, we conducted the MRM assay on patient sera that were collected from pre- and postoperation. Of the 116 target proteins, 42 were identified in the ESCC sera, including 11 with lowered abundances postoperation. Coupling SWATH and MRM is thus feasible and efficient for the discovery and verification of cancer-related protein biomarkers.

Using MALDI-IMS and MRM to stablish a pipeline for discovery and validation of tumor neovasculature biomarker candidates. — EDRN Public Portal

Cancer.gov

In an effort to circumvent the limitations associated with biomarker discovery workflows involving cell lines and cell cultures, histology-directed MALDI protein profiling and imaging mass spectrometry will be used for identification of vascular endothelial biomarkers suitable for early prostate cancer detection by CEUS targeted molecular imaging

Biomarker discovery for disease status and symptom severity in children with autism.

PubMed

Oztan, Ozge; Jackson, Lisa P; Libove, Robin A; Sumiyoshi, Raena D; Phillips, Jennifer M; Garner, Joseph P; Hardan, Antonio Y; Parker, Karen J

2018-03-01

Autism spectrum disorder (ASD) is characterized by social impairments and repetitive behaviors, and affects 1 in 68 US children. Despite ASD's societal impact, its disease mechanisms remain poorly understood. Recent preclinical ASD biomarker discovery research has implicated the neuropeptides oxytocin (OXT) and arginine vasopressin (AVP), and their receptors, OXTR and AVPR1A, in animal models. Efforts to translate these findings to individuals with ASD have typically involved evaluating single neuropeptide measures as biomarkers of ASD and/or behavioral functioning. Given that ASD is a heterogeneous disorder, and unidimensional ASD biomarker studies have been challenging to reproduce, here we employed a multidimensional neuropeptide biomarker analysis to more powerfully interrogate disease status and symptom severity in a well characterized child cohort comprised of ASD patients and neurotypical controls. These blood-based neuropeptide measures, considered as a whole, correctly predicted disease status for 57 out of 68 (i.e., 84%) participants. Further analysis revealed that a composite measure of OXTR and AVPR1A gene expression was the key driver of group classification, and that children with ASD had lower neuropeptide receptor mRNA levels compared to controls. Lower neuropeptide receptor mRNA levels also predicted greater symptom severity for core ASD features (i.e., social impairments and stereotyped behaviors), but were unrelated to intellectual impairment, an associated feature of ASD. Findings from this research highlight the value of assessing multiple related biological measures, and their relative contributions, in the same study, and suggest that low blood neuropeptide receptor availability may be a promising biomarker of disease presence and symptom severity in ASD. Copyright © 2018 Elsevier Ltd. All rights reserved.

Blood Based Biomarkers in Alzheimer Disease: Current State of the Science and a Novel Collaborative Paradigm for Advancing from Discovery to Clinic

PubMed Central

O’Bryant, Sid E.; Mielke, Michelle M.; Rissman, Robert A.; Lista, Simone; Vanderstichele, Hugo; Zetterberg, Henrik; Lewczuk, Piotr; Posner, Holly; Hall, James; Johnson, Leigh; Fong, Yiu-Lian; Luthman, Johan; Jeromin, Andreas; Batrla-Utermann, Richard; Villarreal, Alcibiades; Britton, Gabrielle; Snyder, Peter J.; Henriksen, Kim; Grammas, Paula; Gupta, Veer; Martins, Ralph; Hampel, Harald

2016-01-01

The last decade has seen a substantial increase in research focused on the identification of blood-based biomarkers that have utility in Alzheimer’s disease (AD). Blood-based biomarkers have significant advantages of being time- and cost-efficient as well as reduced invasiveness and increased patient acceptance. Despite these advantages and increased research efforts, the field has been hampered by lack of reproducibility as well as an unclear path for moving basic discovery towards clinical utilization. Here we reviewed the recent literature on blood-based biomarkers in AD to provide a current state-of-the-art. Additionally, a collaborative model is proposed that leverages academic and industry strengths to facilitate the field in moving past discovery only work and towards clinical use. Key resources are provided. This new public-private partnership model is intended to circumvent the traditional hand-off model and provide a clear and useful paradigm for the advancement of biomarker science in AD and other neurodegenerative diseases. PMID:27870940

Discovery of potential protein biomarkers of lung adenocarcinoma in bronchoalveolar lavage fluid by SWATH MS data-independent acquisition and targeted data extraction.

PubMed

Ortea, I; Rodríguez-Ariza, A; Chicano-Gálvez, E; Arenas Vacas, M S; Jurado Gámez, B

2016-04-14

Lung cancer currently ranks as the neoplasia with the highest global mortality rate. Although some improvements have been introduced in recent years, new advances in diagnosis are required in order to increase survival rates. New mildly invasive endoscopy-based diagnostic techniques include the collection of bronchoalveolar lavage fluid (BALF), which is discarded after using a portion of the fluid for standard pathological procedures. BALF proteomic analysis can contribute to clinical practice with more sensitive biomarkers, and can complement cytohistological studies by aiding in the diagnosis, prognosis, and subtyping of lung cancer, as well as the monitoring of treatment response. The range of quantitative proteomics methodologies used for biomarker discovery is currently being broadened with the introduction of data-independent acquisition (DIA) analysis-related approaches that address the massive quantitation of the components of a proteome. Here we report for the first time a DIA-based quantitative proteomics study using BALF as the source for the discovery of potential lung cancer biomarkers. The results have been encouraging in terms of the number of identified and quantified proteins. A panel of candidate protein biomarkers for adenocarcinoma in BALF is reported; this points to the activation of the complement network as being strongly over-represented and suggests this pathway as a potential target for lung cancer research. In addition, the results reported for haptoglobin, complement C4-A, and glutathione S-transferase pi are consistent with previous studies, which indicates that these proteins deserve further consideration as potential lung cancer biomarkers in BALF. Our study demonstrates that the analysis of BALF proteins by liquid chromatography-tandem mass spectrometry (LC-MS/MS), combining a simple sample pre-treatment and SWATH DIA MS, is a useful method for the discovery of potential lung cancer biomarkers. Bronchoalveolar lavage fluid (BALF

Biomarker discovery study design for type 1 diabetes in The Environmental Determinants of Diabetes in the Young (TEDDY) study.

PubMed

Lee, Hye-Seung; Burkhardt, Brant R; McLeod, Wendy; Smith, Susan; Eberhard, Chris; Lynch, Kristian; Hadley, David; Rewers, Marian; Simell, Olli; She, Jin-Xiong; Hagopian, Bill; Lernmark, Ake; Akolkar, Beena; Ziegler, Anette G; Krischer, Jeffrey P

2014-07-01

The Environmental Determinants of Diabetes in the Young planned biomarker discovery studies on longitudinal samples for persistent confirmed islet cell autoantibodies and type 1 diabetes using dietary biomarkers, metabolomics, microbiome/viral metagenomics and gene expression. This article describes the details of planning The Environmental Determinants of Diabetes in the Young biomarker discovery studies using a nested case-control design that was chosen as an alternative to the full cohort analysis. In the frame of a nested case-control design, it guides the choice of matching factors, selection of controls, preparation of external quality control samples and reduction of batch effects along with proper sample allocation. Our design is to reduce potential bias and retain study power while reducing the costs by limiting the numbers of samples requiring laboratory analyses. It also covers two primary end points (the occurrence of diabetes-related autoantibodies and the diagnosis of type 1 diabetes). The resulting list of case-control matched samples for each laboratory was augmented with external quality control samples. Copyright © 2013 John Wiley & Sons, Ltd.

Quantitative structure-activity relationship: promising advances in drug discovery platforms.

PubMed

Wang, Tao; Wu, Mian-Bin; Lin, Jian-Ping; Yang, Li-Rong

2015-12-01

Quantitative structure-activity relationship (QSAR) modeling is one of the most popular computer-aided tools employed in medicinal chemistry for drug discovery and lead optimization. It is especially powerful in the absence of 3D structures of specific drug targets. QSAR methods have been shown to draw public attention since they were first introduced. In this review, the authors provide a brief discussion of the basic principles of QSAR, model development and model validation. They also highlight the current applications of QSAR in different fields, particularly in virtual screening, rational drug design and multi-target QSAR. Finally, in view of recent controversies, the authors detail the challenges faced by QSAR modeling and the relevant solutions. The aim of this review is to show how QSAR modeling can be applied in novel drug discovery, design and lead optimization. QSAR should intentionally be used as a powerful tool for fragment-based drug design platforms in the field of drug discovery and design. Although there have been an increasing number of experimentally determined protein structures in recent years, a great number of protein structures cannot be easily obtained (i.e., membrane transport proteins and G-protein coupled receptors). Fragment-based drug discovery, such as QSAR, could be applied further and have a significant role in dealing with these problems. Moreover, along with the development of computer software and hardware, it is believed that QSAR will be increasingly important.

The development of biomarkers to reduce attrition rate in drug discovery focused on oncology and central nervous system.

PubMed

Safavi, Maliheh; Sabourian, Reyhaneh; Abdollahi, Mohammad

2016-10-01

The task of discovery and development of novel therapeutic agents remains an expensive, uncertain, time-consuming, competitive, and inefficient enterprise. Due to a steady increase in the cost and time of drug development and the considerable amount of resources required, a predictive tool is needed for assessing the safety and efficacy of a new chemical entity. This study is focused on the high attrition rate in discovery and development of oncology and central nervous system (CNS) medicines, because the failure rate of these medicines is higher than others. Some approaches valuable in reducing attrition rates are proposed and the judicious use of biomarkers is discussed. Unlike the significant progress made in identifying and characterizing novel mechanisms of disease processes and targeted therapies, the process of novel drug development is associated with an unacceptably high attrition rate. The application of clinically qualified predictive biomarkers holds great promise for further development of therapeutic targets, improved survival, and ultimately personalized medicine sets for patients. Decisions such as candidate selection, development risks, dose ranging, early proof of concept/principle, and patient stratification are based on the measurements of biologically and/or clinically validated biomarkers.

Application in pesticide analysis: Liquid chromatography - A review of the state of science for biomarker discovery and identification

EPA Science Inventory

Book Chapter 18, titled Application in pesticide analysis: Liquid chromatography - A review of the state of science for biomarker discovery and identification, will be published in the book titled High Performance Liquid Chromatography in Pesticide Residue Analysis (Part of the C...

MetaCoMET: a web platform for discovery and visualization of the core microbiome

USDA-ARS?s Scientific Manuscript database

A key component of the analysis of microbiome datasets is the identification of OTUs shared between multiple experimental conditions, commonly referred to as the core microbiome. Results: We present a web platform named MetaCoMET that enables the discovery and visualization of the core microbiome an...

Sensitive Detection of Protein and miRNA Cancer Biomarkers using Silicon-Based Photonic Crystals and A Resonance Coupling Laser Scanning Platform

PubMed Central

George, Sherine; Chaudhery, Vikram; Lu, Meng; Takagi, Miki; Amro, Nabil; Pokhriyal, Anusha; Tan, Yafang; Ferreira, Placid; Cunningham, Brian T.

2013-01-01

Enhancement of the fluorescent output of surface-based fluorescence assays by performing them upon nanostructured photonic crystal (PC) surfaces has been demonstrated to increase signal intensities by >8000×. Using the multiplicative effects of optical resonant coupling to the PC in increasing the electric field intensity experienced by fluorescent labels (“enhanced excitation”) and the spatially biased funneling of fluorophore emissions through coupling to PC resonances (“enhanced extraction”), PC enhanced fluorescence (PCEF) can be adapted to reduce the limits of detection of disease biomarker assays, and to reduce the size and cost of high sensitivity detection instrumentation. In this work, we demonstrate the first silicon-based PCEF detection platform for multiplexed biomarker assay. The sensor in this platform is a silicon-based PC structure, comprised of a SiO2 grating that is overcoated with a thin film of high refractive index TiO2 and is produced in a semiconductor foundry for low cost, uniform, and reproducible manufacturing. The compact detection instrument that completes this platform was designed to efficiently couples fluorescence excitation from a semiconductor laser to the resonant optical modes of the PC, resulting in elevated electric field strength that is highly concentrated within the region <100 nm from the PC surface. This instrument utilizes a cylindrically focused line to scan a microarray in <1 minute. To demonstrate the capabilities of this sensor-detector platform, microspot fluorescent sandwich immunoassays using secondary antibodies labeled with Cy5 for two cancer biomarkers (TNF-α and IL-3) were performed. Biomarkers were detected at concentrations as low as 0.1 pM. In a fluorescent microarray for detection of a breast cancer miRNA biomarker miR-21, the miRNA was detectable at a concentration of 0.6 pM. PMID:23963502

GWATCH: a web platform for automated gene association discovery analysis.

PubMed

Svitin, Anton; Malov, Sergey; Cherkasov, Nikolay; Geerts, Paul; Rotkevich, Mikhail; Dobrynin, Pavel; Shevchenko, Andrey; Guan, Li; Troyer, Jennifer; Hendrickson, Sher; Dilks, Holli Hutcheson; Oleksyk, Taras K; Donfield, Sharyne; Gomperts, Edward; Jabs, Douglas A; Sezgin, Efe; Van Natta, Mark; Harrigan, P Richard; Brumme, Zabrina L; O'Brien, Stephen J

2014-01-01

As genome-wide sequence analyses for complex human disease determinants are expanding, it is increasingly necessary to develop strategies to promote discovery and validation of potential disease-gene associations. Here we present a dynamic web-based platform - GWATCH - that automates and facilitates four steps in genetic epidemiological discovery: 1) Rapid gene association search and discovery analysis of large genome-wide datasets; 2) Expanded visual display of gene associations for genome-wide variants (SNPs, indels, CNVs), including Manhattan plots, 2D and 3D snapshots of any gene region, and a dynamic genome browser illustrating gene association chromosomal regions; 3) Real-time validation/replication of candidate or putative genes suggested from other sources, limiting Bonferroni genome-wide association study (GWAS) penalties; 4) Open data release and sharing by eliminating privacy constraints (The National Human Genome Research Institute (NHGRI) Institutional Review Board (IRB), informed consent, The Health Insurance Portability and Accountability Act (HIPAA) of 1996 etc.) on unabridged results, which allows for open access comparative and meta-analysis. GWATCH is suitable for both GWAS and whole genome sequence association datasets. We illustrate the utility of GWATCH with three large genome-wide association studies for HIV-AIDS resistance genes screened in large multicenter cohorts; however, association datasets from any study can be uploaded and analyzed by GWATCH.

Establishment of Protocols for Global Metabolomics by LC-MS for Biomarker Discovery.

PubMed

Saigusa, Daisuke; Okamura, Yasunobu; Motoike, Ikuko N; Katoh, Yasutake; Kurosawa, Yasuhiro; Saijyo, Reina; Koshiba, Seizo; Yasuda, Jun; Motohashi, Hozumi; Sugawara, Junichi; Tanabe, Osamu; Kinoshita, Kengo; Yamamoto, Masayuki

2016-01-01

Metabolomics is a promising avenue for biomarker discovery. Although the quality of metabolomic analyses, especially global metabolomics (G-Met) using mass spectrometry (MS), largely depends on the instrumentation, potential bottlenecks still exist at several basic levels in the metabolomics workflow. Therefore, we established a precise protocol initially for the G-Met analyses of human blood plasma to overcome some these difficulties. In our protocol, samples are deproteinized in a 96-well plate using an automated liquid-handling system, and conducted either using a UHPLC-QTOF/MS system equipped with a reverse phase column or a LC-FTMS system equipped with a normal phase column. A normalization protocol of G-Met data was also developed to compensate for intra- and inter-batch differences, and the variations were significantly reduced along with our normalization, especially for the UHPLC-QTOF/MS data with a C18 reverse-phase column for positive ions. Secondly, we examined the changes in metabolomic profiles caused by the storage of EDTA-blood specimens to identify quality markers for the evaluation of the specimens' pre-analytical conditions. Forty quality markers, including lysophospholipids, dipeptides, fatty acids, succinic acid, amino acids, glucose, and uric acid were identified by G-Met for the evaluation of plasma sample quality and established the equation of calculating the quality score. We applied our quality markers to a small-scale study to evaluate the quality of clinical samples. The G-Met protocols and quality markers established here should prove useful for the discovery and development of biomarkers for a wider range of diseases.

Blood-based biomarkers in Alzheimer disease: Current state of the science and a novel collaborative paradigm for advancing from discovery to clinic.

PubMed

O'Bryant, Sid E; Mielke, Michelle M; Rissman, Robert A; Lista, Simone; Vanderstichele, Hugo; Zetterberg, Henrik; Lewczuk, Piotr; Posner, Holly; Hall, James; Johnson, Leigh; Fong, Yiu-Lian; Luthman, Johan; Jeromin, Andreas; Batrla-Utermann, Richard; Villarreal, Alcibiades; Britton, Gabrielle; Snyder, Peter J; Henriksen, Kim; Grammas, Paula; Gupta, Veer; Martins, Ralph; Hampel, Harald

2017-01-01

The last decade has seen a substantial increase in research focused on the identification of blood-based biomarkers that have utility in Alzheimer's disease (AD). Blood-based biomarkers have significant advantages of being time- and cost-efficient as well as reduced invasiveness and increased patient acceptance. Despite these advantages and increased research efforts, the field has been hampered by lack of reproducibility and an unclear path for moving basic discovery toward clinical utilization. Here we reviewed the recent literature on blood-based biomarkers in AD to provide a current state of the art. In addition, a collaborative model is proposed that leverages academic and industry strengths to facilitate the field in moving past discovery only work and toward clinical use. Key resources are provided. This new public-private partnership model is intended to circumvent the traditional handoff model and provide a clear and useful paradigm for the advancement of biomarker science in AD and other neurodegenerative diseases. Copyright © 2016 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Functionalized nanoparticles for measurement of biomarkers using a SERS nanochannel platform

NASA Astrophysics Data System (ADS)

Benford, Melodie; Wang, Miao; Kameoka, Jun; Good, Theresa; Cote, Gerard

2010-02-01

The overall goal of this research is to develop a new point-of-care system for early detection and characterization of cardiac markers to aid in diagnosis of acute coronary syndrome. The envisioned final technology platform incorporates functionalized gold colloidal nanoparticles trapped at the entrance to a nanofluidic device providing a robust means for analyte detection at trace levels using surface enhanced Raman spectroscopy (SERS). To discriminate a specific biomarker, we designed an assay format analogous to a competitive ELISA. Notably, the biomarker would be captured by an antibody and in turn displace a peptide fragment, containing the binding epitope of the antibody labeled with a Raman reporter molecule that would not interfere with blood serum proteins. To demonstrate the feasibility of this approach, we used C-reactive protein (CRP) as a surrogate biomarker. We functionalized agarose beads with anti-CRP that were placed outside the nanochannel, then added either Rhodamine-6-G (R6G) labeled-CRP and gold (as a surrogate of a sample without analyte present), or R6G labeled CRP, gold, and unlabeled CRP (as a surrogate of a sample with analyte present). Analyzing the spectra we see an increase in peak intensity in the presence of analyte at characteristic peaks for R6G specifically, 1284 and1567 cm- 1. Further, our results illustrate the reproducibility of the Raman spectra collected for R6G-labeled CRP in the nanochannel. Overall, we believe that this method will provide the advantage of sensitivity and narrow line widths characteristic of SERS as well as the specificity toward the biomarker of interest.

A multiplexable, microfluidic platform for the rapid quantitation of a biomarker panel for early ovarian cancer detection at the point-of-care

PubMed Central

Shadfan, Basil H.; Simmons, Archana R.; Simmons, Glennon W.; Ho, Andy; Wong, Jorge; Lu, Karen H.; Bast, Robert C.; McDevitt, John T.

2015-01-01

Point-of-care (POC) diagnostic platforms have the potential to enable low-cost, large-scale screening. As no single biomarker is shed by all ovarian cancers, multiplexed biomarker panels promise improved sensitivity and specificity to address the unmet need for early detection of ovarian cancer. We have configured the programmable bio-nano-chip (p-BNC) - a multiplexable, microfluidic, modular platform - to quantify a novel multimarker panel comprising CA125, HE4, MMP-7 and CA72-4. The p-BNC is a bead-based immunoanalyzer system with a credit-card-sized footprint that integrates automated sample metering, bubble and debris removal, reagent storage and waste disposal, permitting POC analysis. Multiplexed p-BNC immunoassays demonstrated high specificity, low cross-reactivity, low limits of detection suitable for early detection, and a short analysis time of 43 minutes. Day-to-day variability, a critical factor for longitudinally monitoring biomarkers, ranged between 5.4–10.5%, well below the biological variation for all four markers. Biomarker concentrations for 31 late-stage sera correlated well (R2 = 0.71 to 0.93 for various biomarkers) with values obtained on the Luminex® platform. In a 31 patient cohort encompassing early- and late-stage ovarian cancers along with benign and healthy controls, the multiplexed p-BNC panel was able to distinguish cases from controls with 68.7% sensitivity at 80% specificity. Utility for longitudinal biomarker monitoring was demonstrated with pre-diagnostic sera from 2 cases and 4 controls. Taken together, the p-BNC shows strong promise as a diagnostic tool for large-scale screening that takes advantage of faster results and lower costs while leveraging possible improvement in sensitivity and specificity from biomarker panels. PMID:25388014

Transfer Learning of Classification Rules for Biomarker Discovery and Verification from Molecular Profiling Studies

PubMed Central

Ganchev, Philip; Malehorn, David; Bigbee, William L.; Gopalakrishnan, Vanathi

2013-01-01

We present a novel framework for integrative biomarker discovery from related but separate data sets created in biomarker profiling studies. The framework takes prior knowledge in the form of interpretable, modular rules, and uses them during the learning of rules on a new data set. The framework consists of two methods of transfer of knowledge from source to target data: transfer of whole rules and transfer of rule structures. We evaluated the methods on three pairs of data sets: one genomic and two proteomic. We used standard measures of classification performance and three novel measures of amount of transfer. Preliminary evaluation shows that whole-rule transfer improves classification performance over using the target data alone, especially when there is more source data than target data. It also improves performance over using the union of the data sets. PMID:21571094

Biomarker Development for Intraductal Papillary Mucinous Neoplasms Using Multiple Reaction Monitoring Mass Spectrometry.

PubMed

Kim, Yikwon; Kang, MeeJoo; Han, Dohyun; Kim, Hyunsoo; Lee, KyoungBun; Kim, Sun-Whe; Kim, Yongkang; Park, Taesung; Jang, Jin-Young; Kim, Youngsoo

2016-01-04

Intraductal papillary mucinous neoplasm (IPMN) is a common precursor of pancreatic cancer (PC). Much clinical attention has been directed toward IPMNs due to the increase in the prevalence of PC. The diagnosis of IPMN depends primarily on a radiological examination, but the diagnostic accuracy of this tool is not satisfactory, necessitating the development of accurate diagnostic biomarkers for IPMN to prevent PC. Recently, high-throughput targeted proteomic quantification methods have accelerated the discovery of biomarkers, rendering them powerful platforms for the evolution of IPMN diagnostic biomarkers. In this study, a robust multiple reaction monitoring (MRM) pipeline was applied to discovery and verify IPMN biomarker candidates in a large cohort of plasma samples. Through highly reproducible MRM assays and a stringent statistical analysis, 11 proteins were selected as IPMN marker candidates with high confidence in 184 plasma samples, comprising a training (n = 84) and test set (n = 100). To improve the discriminatory power, we constructed a six-protein panel by combining marker candidates. The multimarker panel had high discriminatory power in distinguishing between IPMN and controls, including other benign diseases. Consequently, the diagnostic accuracy of IPMN can be improved dramatically with this novel plasma-based panel in combination with a radiological examination.

Discovery and Validation of Predictive Biomarkers of Survival for Non-small Cell Lung Cancer Patients Undergoing Radical Radiotherapy: Two Proteins With Predictive Value

PubMed Central

Walker, Michael J.; Zhou, Cong; Backen, Alison; Pernemalm, Maria; Williamson, Andrew J.K.; Priest, Lynsey J.C.; Koh, Pek; Faivre-Finn, Corinne; Blackhall, Fiona H.; Dive, Caroline; Whetton, Anthony D.

2015-01-01

Lung cancer is the most frequent cause of cancer-related death world-wide. Radiotherapy alone or in conjunction with chemotherapy is the standard treatment for locally advanced non-small cell lung cancer (NSCLC). Currently there is no predictive marker with clinical utility to guide treatment decisions in NSCLC patients undergoing radiotherapy. Identification of such markers would allow treatment options to be considered for more effective therapy. To enable the identification of appropriate protein biomarkers, plasma samples were collected from patients with non-small cell lung cancer before and during radiotherapy for longitudinal comparison following a protocol that carries sufficient power for effective discovery proteomics. Plasma samples from patients pre- and during radiotherapy who had survived > 18 mo were compared to the same time points from patients who survived < 14 mo using an 8 channel isobaric tagging tandem mass spectrometry discovery proteomics platform. Over 650 proteins were detected and relatively quantified. Proteins which showed a change during radiotherapy were selected for validation using an orthogonal antibody-based approach. Two of these proteins were verified in a separate patient cohort: values of CRP and LRG1 combined gave a highly significant indication of extended survival post one week of radiotherapy treatment. PMID:26425690

Institutional profile: the national Swedish academic drug discovery & development platform at SciLifeLab

PubMed Central

Arvidsson, Per I; Sandberg, Kristian; Sakariassen, Kjell S

2017-01-01

The Science for Life Laboratory Drug Discovery and Development Platform (SciLifeLab DDD) was established in Stockholm and Uppsala, Sweden, in 2014. It is one of ten platforms of the Swedish national SciLifeLab which support projects run by Swedish academic researchers with large-scale technologies for molecular biosciences with a focus on health and environment. SciLifeLab was created by the coordinated effort of four universities in Stockholm and Uppsala: Stockholm University, Karolinska Institutet, KTH Royal Institute of Technology and Uppsala University, and has recently expanded to other Swedish university locations. The primary goal of the SciLifeLab DDD is to support selected academic discovery and development research projects with tools and resources to discover novel lead therapeutics, either molecules or human antibodies. Intellectual property developed with the help of SciLifeLab DDD is wholly owned by the academic research group. The bulk of SciLifeLab DDD's research and service activities are funded from the Swedish state, with only consumables paid by the academic research group through individual grants. PMID:28670468

Institutional profile: the national Swedish academic drug discovery & development platform at SciLifeLab.

PubMed

Arvidsson, Per I; Sandberg, Kristian; Sakariassen, Kjell S

2017-06-01

The Science for Life Laboratory Drug Discovery and Development Platform (SciLifeLab DDD) was established in Stockholm and Uppsala, Sweden, in 2014. It is one of ten platforms of the Swedish national SciLifeLab which support projects run by Swedish academic researchers with large-scale technologies for molecular biosciences with a focus on health and environment. SciLifeLab was created by the coordinated effort of four universities in Stockholm and Uppsala: Stockholm University, Karolinska Institutet, KTH Royal Institute of Technology and Uppsala University, and has recently expanded to other Swedish university locations. The primary goal of the SciLifeLab DDD is to support selected academic discovery and development research projects with tools and resources to discover novel lead therapeutics, either molecules or human antibodies. Intellectual property developed with the help of SciLifeLab DDD is wholly owned by the academic research group. The bulk of SciLifeLab DDD's research and service activities are funded from the Swedish state, with only consumables paid by the academic research group through individual grants.

Analytical Pipeline for Discovery and Verification of Glycoproteins from Plasma-Derived Extracellular Vesicles as Breast Cancer Biomarkers.

PubMed

Chen, I-Hsuan; Aguilar, Hillary Andaluz; Paez Paez, J Sebastian; Wu, Xiaofeng; Pan, Li; Wendt, Michael K; Iliuk, Anton B; Zhang, Ying; Tao, W Andy

2018-05-15

Glycoproteins comprise more than half of current FDA-approved protein cancer markers, but the development of new glycoproteins as disease biomarkers has been stagnant. Here we present a pipeline to develop glycoproteins from extracellular vesicles (EVs) through integrating quantitative glycoproteomics with a novel reverse phase glycoprotein array and then apply it to identify novel biomarkers for breast cancer. EV glycoproteomics show promise in circumventing the problems plaguing current serum/plasma glycoproteomics and allowed us to identify hundreds of glycoproteins that have not been identified in blood. We identified 1,453 unique glycopeptides representing 556 glycoproteins in EVs, among which 20 were verified significantly higher in individual breast cancer patients. We further applied a novel glyco-specific reverse phase protein array to quantify a subset of the candidates. Together, this study demonstrates the great potential of this integrated pipeline for biomarker discovery.

Topic model-based mass spectrometric data analysis in cancer biomarker discovery studies.

PubMed

Wang, Minkun; Tsai, Tsung-Heng; Di Poto, Cristina; Ferrarini, Alessia; Yu, Guoqiang; Ressom, Habtom W

2016-08-18

A fundamental challenge in quantitation of biomolecules for cancer biomarker discovery is owing to the heterogeneous nature of human biospecimens. Although this issue has been a subject of discussion in cancer genomic studies, it has not yet been rigorously investigated in mass spectrometry based proteomic and metabolomic studies. Purification of mass spectometric data is highly desired prior to subsequent analysis, e.g., quantitative comparison of the abundance of biomolecules in biological samples. We investigated topic models to computationally analyze mass spectrometric data considering both integrated peak intensities and scan-level features, i.e., extracted ion chromatograms (EICs). Probabilistic generative models enable flexible representation in data structure and infer sample-specific pure resources. Scan-level modeling helps alleviate information loss during data preprocessing. We evaluated the capability of the proposed models in capturing mixture proportions of contaminants and cancer profiles on LC-MS based serum proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC) and liver cirrhosis as well as synthetic data we generated based on the serum proteomic data. The results we obtained by analysis of the synthetic data demonstrated that both intensity-level and scan-level purification models can accurately infer the mixture proportions and the underlying true cancerous sources with small average error ratios (<7 %) between estimation and ground truth. By applying the topic model-based purification to mass spectrometric data, we found more proteins and metabolites with significant changes between HCC cases and cirrhotic controls. Candidate biomarkers selected after purification yielded biologically meaningful pathway analysis results and improved disease discrimination power in terms of the area under ROC curve compared to the results found prior to purification. We investigated topic model

Searching for ‘omic’ biomarkers

PubMed Central

Lin, David; Hollander, Zsuzsanna; Meredith, Anna; McManus, Bruce M

2009-01-01

Cardiovascular diseases impose enormous social and economic burdens on both individual citizens and on society as a whole. Clinical indicators such as high blood pressure, blood cholesterol and obesity have had some utility in identifying those who are at increased risk of cardiovascular events. However, there remains an urgent need for sensitive and specific indicators, preferably acquired through minimally invasive means, to help stratify patients for more personalized health care. As such, there has been a steadily growing interest in searching for ‘omic’ biomarkers of cardiovascular diseases. Historically, the transition of cardiac biomarker discovery to implementation has been a lengthy and somewhat unregulated process. Recent technological advancements, as well as concurrent efforts by regulatory agencies such as the Food and Drug Administration (United States) and Health Canada to establish policies and guidelines in the ‘omic’ arena, have helped propel the discovery and validation of biomarkers forward. The present paper provides perspective on current strategies in the bio-marker development pathway, as well as the potential limitations associated with each step from discovery to clinical uptake. Canadian biomarker studies now underway illustrate the possibilities for assessment of risk, diagnosis, prognosis and response to therapy, and for the drug discovery process. PMID:19521568

Portable Biomarker Detection with Magnetic Nanotags

PubMed Central

Hall, Drew A.; Wang, Shan X.; Murmann, Boris; Gaster, Richard S.

2012-01-01

This paper presents a hand-held, portable biosensor platform for quantitative biomarker measurement. By combining magnetic nanoparticle (MNP) tags with giant magnetoresistive (GMR) spin-valve sensors, the hand-held platform achieves highly sensitive (picomolar) and specific biomarker detection in less than 20 minutes. The rapid analysis and potential low cost make this technology ideal for point-of-care (POC) diagnostics. Furthermore, this platform is able to detect multiple biomarkers simultaneously in a single assay, creating a promising diagnostic tool for a vast number of applications. PMID:22495252

Biomarkers: Delivering on the expectation of molecularly driven, quantitative health.

PubMed

Wilson, Jennifer L; Altman, Russ B

2018-02-01

Biomarkers are the pillars of precision medicine and are delivering on expectations of molecular, quantitative health. These features have made clinical decisions more precise and personalized, but require a high bar for validation. Biomarkers have improved health outcomes in a few areas such as cancer, pharmacogenetics, and safety. Burgeoning big data research infrastructure, the internet of things, and increased patient participation will accelerate discovery in the many areas that have not yet realized the full potential of biomarkers for precision health. Here we review themes of biomarker discovery, current implementations of biomarkers for precision health, and future opportunities and challenges for biomarker discovery. Impact statement Precision medicine evolved because of the understanding that human disease is molecularly driven and is highly variable across patients. This understanding has made biomarkers, a diverse class of biological measurements, more relevant for disease diagnosis, monitoring, and selection of treatment strategy. Biomarkers' impact on precision medicine can be seen in cancer, pharmacogenomics, and safety. The successes in these cases suggest many more applications for biomarkers and a greater impact for precision medicine across the spectrum of human disease. The authors assess the status of biomarker-guided medical practice by analyzing themes for biomarker discovery, reviewing the impact of these markers in the clinic, and highlight future and ongoing challenges for biomarker discovery. This work is timely and relevant, as the molecular, quantitative approach of precision medicine is spreading to many disease indications.

A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT.

PubMed

Bown, James L; Shovman, Mark; Robertson, Paul; Boiko, Andrei; Goltsov, Alexey; Mullen, Peter; Harrison, David J

2017-05-02

Targeted cancer therapy aims to disrupt aberrant cellular signalling pathways. Biomarkers are surrogates of pathway state, but there is limited success in translating candidate biomarkers to clinical practice due to the intrinsic complexity of pathway networks. Systems biology approaches afford better understanding of complex, dynamical interactions in signalling pathways targeted by anticancer drugs. However, adoption of dynamical modelling by clinicians and biologists is impeded by model inaccessibility. Drawing on computer games technology, we present a novel visualization toolkit, SiViT, that converts systems biology models of cancer cell signalling into interactive simulations that can be used without specialist computational expertise. SiViT allows clinicians and biologists to directly introduce for example loss of function mutations and specific inhibitors. SiViT animates the effects of these introductions on pathway dynamics, suggesting further experiments and assessing candidate biomarker effectiveness. In a systems biology model of Her2 signalling we experimentally validated predictions using SiViT, revealing the dynamics of biomarkers of drug resistance and highlighting the role of pathway crosstalk. No model is ever complete: the iteration of real data and simulation facilitates continued evolution of more accurate, useful models. SiViT will make accessible libraries of models to support preclinical research, combinatorial strategy design and biomarker discovery.

Preparation and Immunoaffinity Depletion of Fresh Frozen Tissue Homogenates for Mass Spectrometry-Based Proteomics in the Context of Drug Target/Biomarker Discovery.

PubMed

Prieto, DaRue A; Chan, King C; Johann, Donald J; Ye, Xiaoying; Whitely, Gordon; Blonder, Josip

2017-01-01

The discovery of novel drug targets and biomarkers via mass spectrometry (MS)-based proteomic analysis of clinical specimens has proven to be challenging. The wide dynamic range of protein concentration in clinical specimens and the high background/noise originating from highly abundant proteins in tissue homogenates and serum/plasma encompass two major analytical obstacles. Immunoaffinity depletion of highly abundant blood-derived proteins from serum/plasma is a well-established approach adopted by numerous researchers; however, the utilization of this technique for immunodepletion of tissue homogenates obtained from fresh frozen clinical specimens is lacking. We first developed immunoaffinity depletion of highly abundant blood-derived proteins from tissue homogenates, using renal cell carcinoma as a model disease, and followed this study by applying it to different tissue types. Tissue homogenate immunoaffinity depletion of highly abundant proteins may be equally important as is the recognized need for depletion of serum/plasma, enabling more sensitive MS-based discovery of novel drug targets, and/or clinical biomarkers from complex clinical samples. Provided is a detailed protocol designed to guide the researcher through the preparation and immunoaffinity depletion of fresh frozen tissue homogenates for two-dimensional liquid chromatography, tandem mass spectrometry (2D-LC-MS/MS)-based molecular profiling of tissue specimens in the context of drug target and/or biomarker discovery.

Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm.

PubMed

Martinez, Emmanuel; Alvarez, Mario Moises; Trevino, Victor

2010-08-01

Biomarker discovery is a typical application from functional genomics. Due to the large number of genes studied simultaneously in microarray data, feature selection is a key step. Swarm intelligence has emerged as a solution for the feature selection problem. However, swarm intelligence settings for feature selection fail to select small features subsets. We have proposed a swarm intelligence feature selection algorithm based on the initialization and update of only a subset of particles in the swarm. In this study, we tested our algorithm in 11 microarray datasets for brain, leukemia, lung, prostate, and others. We show that the proposed swarm intelligence algorithm successfully increase the classification accuracy and decrease the number of selected features compared to other swarm intelligence methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

Multifunctional Core–Shell Nanoparticles: Discovery of Previously Invisible Biomarkers

PubMed Central

2011-01-01

Many low-abundance biomarkers for early detection of cancer and other diseases are invisible to mass spectrometry because they exist in body fluids in very low concentrations, are masked by high-abundance proteins such as albumin and immunoglobulins, and are very labile. To overcome these barriers, we created porous, buoyant, core–shell hydrogel nanoparticles containing novel high affinity reactive chemical baits for protein and peptide harvesting, concentration, and preservation in body fluids. Poly(N-isopropylacrylamide-co-acrylic acid) nanoparticles were functionalized with amino-containing dyes via zero-length cross-linking amidation reactions. Nanoparticles functionalized in the core with 17 different (12 chemically novel) molecular baits showed preferential high affinities (KD < 10–11 M) for specific low-abundance protein analytes. A poly(N-isopropylacrylamide-co-vinylsulfonic acid) shell was added to the core particles. This shell chemistry selectively prevented unwanted entry of all size peptides derived from albumin without hindering the penetration of non-albumin small proteins and peptides. Proteins and peptides entered the core to be captured with high affinity by baits immobilized in the core. Nanoparticles effectively protected interleukin-6 from enzymatic degradation in sweat and increased the effective detection sensitivity of human growth hormone in human urine using multiple reaction monitoring analysis. Used in whole blood as a one-step, in-solution preprocessing step, the nanoparticles greatly enriched the concentration of low-molecular weight proteins and peptides while excluding albumin and other proteins above 30 kDa; this achieved a 10,000-fold effective amplification of the analyte concentration, enabling mass spectrometry (MS) discovery of candidate biomarkers that were previously undetectable. PMID:21999289

Transgenic mouse strains as platforms for the successful discovery and development of human therapeutic monoclonal antibodies.

PubMed

Green, Larry L

2014-03-01

Transgenic mice have yielded seven of the ten currently-approved human antibody drugs, making them the most successful platform for the discovery of fully human antibody therapeutics. The use of the in vivo immune system helps drive this success by taking advantage of the natural selection process that produces antibodies with desirable characteristics. Appropriately genetically-engineered mice act as robust engines for the generation of diverse repertoires of affinity- matured fully human variable regions with intrinsic properties necessary for successful antibody drug development including high potency, specificity, manufacturability, solubility and low risk of immunogenicity. A broad range of mAb drug targets are addressable in these mice, comprising both secreted and transmembrane targets, including membrane multi-spanning targets, as well as human target antigens that share high sequence identity with their mouse orthologue. Transgenic mice can routinely yield antibodies with sub-nanomolar binding affinity for their antigen, with lead candidate mAbs frequently possessing affinities for binding to their target of less than 100 picomolar, without requiring any ex vivo affinity optimization. While the originator transgenic mice platforms are no longer broadly available, a new generation of transgenic platforms is in development for discovery of the next wave of human therapeutic antibodies.

Crowdsourcing Disease Biomarker Discovery Research: The IP4IC Study.

PubMed

Chancellor, Michael B; Bartolone, Sarah N; Veerecke, Andrew; Lamb, Laura E

2018-05-01

Biomarker discovery is limited by readily assessable, cost efficient human samples available in large numbers that represent the entire heterogeneity of the disease. We developed a novel, active participation crowdsourcing method to determine BP-RS (Bladder Permeability Defect Risk Score). It is based on noninvasive urinary cytokines to discriminate patients with interstitial cystitis/bladder pain syndrome who had Hunner lesions from controls and patients with interstitial cystitis/bladder pain syndrome but without Hunner lesions. We performed a national crowdsourcing study in cooperation with the Interstitial Cystitis Association. Patients answered demographic, symptom severity and urinary frequency questionnaires on a HIPAA (Health Insurance Portability and Accountability Act) compliant website. Urine samples were collected at home, stabilized with a preservative and sent to Beaumont Hospital for analysis. The expression of 3 urinary cytokines was used in a machine learning algorithm to develop BP-RS. The IP4IC study collected a total of 448 urine samples, representing 153 patients (147 females and 6 males) with interstitial cystitis/bladder pain syndrome, of whom 54 (50 females and 4 males) had Hunner lesions. A total of 159 female and 136 male controls also participated, who were age matched. A defined BP-RS was calculated to predict interstitial cystitis/bladder pain syndrome with Hunner lesions or a bladder permeability defect etiology with 89% validity. In this novel participation crowdsourcing study we obtained a large number of urine samples from 46 states, which were collected at home, shipped and stored at room temperature. Using a machine learning algorithm we developed BP-RS to quantify the risk of interstitial cystitis/bladder pain syndrome with Hunner lesions, which is indicative of a bladder permeability defect etiology. To our knowledge BP-RS is the first validated urine biomarker assay for interstitial cystitis/bladder pain syndrome and one of the

Plenario: An Open Data Discovery and Exploration Platform for Urban Science

DOE Office of Scientific and Technical Information (OSTI.GOV)

Catlett, Charlie; Malik, Tanu; Goldstein, Brett J.

2014-12-01

The past decade has seen the widespread release of open data concerning city services, conditions, and activities by government bodies and public institutions of all sizes. Hundreds of open data portals now host thousands of datasets of many different types. These new data sources represent enormous po- tential for improved understanding of urban dynamics and processes—and, ultimately, for more livable, efficient, and prosperous communities. However, those who seek to realize this potential quickly discover that discovering and applying those data relevant to any particular question can be extraordinarily dif- ficult, due to decentralized storage, heterogeneous formats, and poor documentation. Inmore » this context, we introduce Plenario, a platform designed to automating time-consuming tasks associated with the discovery, exploration, and application of open city data—and, in so doing, reduce barriers to data use for researchers, policymakers, service providers, journalists, and members of the general public. Key innovations include a geospatial data warehouse that allows data from many sources to be registered into a common spatial and temporal frame; simple and intuitive interfaces that permit rapid discovery and exploration of data subsets pertaining to a particular area and time, regardless of type and source; easy export of such data subsets for further analysis; a user-configurable data ingest framework for automated importing and periodic updating of new datasets into the data warehouse; cloud hosting for elastic scaling and rapid creation of new Plenario instances; and an open source implementation to enable community contributions. We describe here the architecture and implementation of the Plenario platform, discuss lessons learned from its use by several communities, and outline plans for future work.« less

Unraveling the molecular repertoire of tears as a source of biomarkers: beyond ocular diseases.

PubMed

Pieragostino, Damiana; D'Alessandro, Michele; di Ioia, Maria; Di Ilio, Carmine; Sacchetta, Paolo; Del Boccio, Piero

2015-02-01

Proteomics and metabolomics investigations of body fluids present several challenges for biomarker discovery of several diseases. The search for biomarkers is actually conducted in different body fluids, even if the ideal biomarker can be found in an easily accessible biological fluid, because, if validated, the biomarker could be sought in the healthy population. In this regard, tears could be considered an optimum material obtained by noninvasive procedures. In the past years, the scientific community has become more interested in the study of tears for the research of new biomarkers not only for ocular diseases. In this review, we provide a discussion on the current state of biomarkers research in tears and their relevance for clinical practice, and report the main results of clinical proteomics studies on systemic and eye diseases. We summarize the main methods for tear samples analyses and report recent advances in "omics" platforms for tears investigations. Moreover, we want to take stock of the emerging field of metabolomics and lipidomics as a new and integrated approach to study protein-metabolites interplay for biomarkers research, where tears represent a still unexplored and attractive field. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Targeted Mass Spectrometric Approach for Biomarker Discovery and Validation with Nonglycosylated Tryptic Peptides from N-linked Glycoproteins in Human Plasma*

PubMed Central

Lee, Ju Yeon; Kim, Jin Young; Park, Gun Wook; Cheon, Mi Hee; Kwon, Kyung-Hoon; Ahn, Yeong Hee; Moon, Myeong Hee; Lee, Hyoung–Joo; Paik, Young Ki; Yoo, Jong Shin

2011-01-01

A simple mass spectrometric approach for the discovery and validation of biomarkers in human plasma was developed by targeting nonglycosylated tryptic peptides adjacent to glycosylation sites in an N-linked glycoprotein, one of the most important biomarkers for early detection, prognoses, and disease therapies. The discovery and validation of novel biomarkers requires complex sample pretreatment steps, such as depletion of highly abundant proteins, enrichment of desired proteins, or the development of new antibodies. The current study exploited the steric hindrance of glycan units in N-linked glycoproteins, which significantly affects the efficiency of proteolytic digestion if an enzymatically active amino acid is adjacent to the N-linked glycosylation site. Proteolytic digestion then results in quantitatively different peptide products in accordance with the degree of glycosylation. The effect of glycan steric hindrance on tryptic digestion was first demonstrated using alpha-1-acid glycoprotein (AGP) as a model compound versus deglycosylated alpha-1-acid glycoprotein. Second, nonglycosylated tryptic peptide biomarkers, which generally show much higher sensitivity in mass spectrometric analyses than their glycosylated counterparts, were quantified in human hepatocellular carcinoma plasma using a label-free method with no need for N-linked glycoprotein enrichment. Finally, the method was validated using a multiple reaction monitoring analysis, demonstrating that the newly discovered nonglycosylated tryptic peptide targets were present at different levels in normal and hepatocellular carcinoma plasmas. The area under the receiver operating characteristic curve generated through analyses of nonglycosylated tryptic peptide from vitronectin precursor protein was 0.978, the highest observed in a group of patients with hepatocellular carcinoma. This work provides a targeted means of discovering and validating nonglycosylated tryptic peptides as biomarkers in human plasma

Accurate inclusion mass screening: a bridge from unbiased discovery to targeted assay development for biomarker verification.

PubMed

Jaffe, Jacob D; Keshishian, Hasmik; Chang, Betty; Addona, Theresa A; Gillette, Michael A; Carr, Steven A

2008-10-01

Verification of candidate biomarker proteins in blood is typically done using multiple reaction monitoring (MRM) of peptides by LC-MS/MS on triple quadrupole MS systems. MRM assay development for each protein requires significant time and cost, much of which is likely to be of little value if the candidate biomarker is below the detection limit in blood or a false positive in the original discovery data. Here we present a new technology, accurate inclusion mass screening (AIMS), designed to provide a bridge from unbiased discovery to MS-based targeted assay development. Masses on the software inclusion list are monitored in each scan on the Orbitrap MS system, and MS/MS spectra for sequence confirmation are acquired only when a peptide from the list is detected with both the correct accurate mass and charge state. The AIMS experiment confirms that a given peptide (and thus the protein from which it is derived) is present in the plasma. Throughput of the method is sufficient to qualify up to a hundred proteins/week. The sensitivity of AIMS is similar to MRM on a triple quadrupole MS system using optimized sample preparation methods (low tens of ng/ml in plasma), and MS/MS data from the AIMS experiments on the Orbitrap can be directly used to configure MRM assays. The method was shown to be at least 4-fold more efficient at detecting peptides of interest than undirected LC-MS/MS experiments using the same instrumentation, and relative quantitation information can be obtained by AIMS in case versus control experiments. Detection by AIMS ensures that a quantitative MRM-based assay can be configured for that protein. The method has the potential to qualify large number of biomarker candidates based on their detection in plasma prior to committing to the time- and resource-intensive steps of establishing a quantitative assay.

Cancer biomarker discovery is improved by accounting for variability in general levels of drug sensitivity in pre-clinical models.

PubMed

Geeleher, Paul; Cox, Nancy J; Huang, R Stephanie

2016-09-21

We show that variability in general levels of drug sensitivity in pre-clinical cancer models confounds biomarker discovery. However, using a very large panel of cell lines, each treated with many drugs, we could estimate a general level of sensitivity to all drugs in each cell line. By conditioning on this variable, biomarkers were identified that were more likely to be effective in clinical trials than those identified using a conventional uncorrected approach. We find that differences in general levels of drug sensitivity are driven by biologically relevant processes. We developed a gene expression based method that can be used to correct for this confounder in future studies.

Mass spectrometry-driven drug discovery for development of herbal medicine.

PubMed

Zhang, Aihua; Sun, Hui; Wang, Xijun

2018-05-01

Herbal medicine (HM) has made a major contribution to the drug discovery process with regard to identifying products compounds. Currently, more attention has been focused on drug discovery from natural compounds of HM. Despite the rapid advancement of modern analytical techniques, drug discovery is still a difficult and lengthy process. Fortunately, mass spectrometry (MS) can provide us with useful structural information for drug discovery, has been recognized as a sensitive, rapid, and high-throughput technology for advancing drug discovery from HM in the post-genomic era. It is essential to develop an efficient, high-quality, high-throughput screening method integrated with an MS platform for early screening of candidate drug molecules from natural products. We have developed a new chinmedomics strategy reliant on MS that is capable of capturing the candidate molecules, facilitating their identification of novel chemical structures in the early phase; chinmedomics-guided natural product discovery based on MS may provide an effective tool that addresses challenges in early screening of effective constituents of herbs against disease. This critical review covers the use of MS with related techniques and methodologies for natural product discovery, biomarker identification, and determination of mechanisms of action. It also highlights high-throughput chinmedomics screening methods suitable for lead compound discovery illustrated by recent successes. © 2016 Wiley Periodicals, Inc.

High-throughput platform assay technology for the discovery of pre-microrna-selective small molecule probes.

PubMed

Lorenz, Daniel A; Song, James M; Garner, Amanda L

2015-01-21

MicroRNAs (miRNA) play critical roles in human development and disease. As such, the targeting of miRNAs is considered attractive as a novel therapeutic strategy. A major bottleneck toward this goal, however, has been the identification of small molecule probes that are specific for select RNAs and methods that will facilitate such discovery efforts. Using pre-microRNAs as proof-of-concept, herein we report a conceptually new and innovative approach for assaying RNA-small molecule interactions. Through this platform assay technology, which we term catalytic enzyme-linked click chemistry assay or cat-ELCCA, we have designed a method that can be implemented in high throughput, is virtually free of false readouts, and is general for all nucleic acids. Through cat-ELCCA, we envision the discovery of selective small molecule ligands for disease-relevant miRNAs to promote the field of RNA-targeted drug discovery and further our understanding of the role of miRNAs in cellular biology.

The first decade of MALDI protein profiling: a lesson in translational biomarker research.

PubMed

Albrethsen, Jakob

2011-05-16

MALDI protein profiling has identified several important challenges in omics-based biomarker research. First, research into the analytical performance of a novel omics-platform of potential diagnostic impact must be carried out in a critical manner and according to common guidelines. Evaluation studies should be performed at an early time and preferably before massive advancement into explorative biomarker research. In particular, MALDI profiling underscores the need for an adequate understanding of the causal relationship between molecular abundance and the quantitative measure in multivariate biomarker research. Secondly, MALDI profiling has raised awareness of the significant risk of false-discovery in biomarker research due to several confounding factors, including sample processing and unspecific host-response to disease. Here, the experience from MALDI profiling supports that a central challenge in unbiased molecular profiling is to pinpoint the aberrations of clinical interest among potentially massive unspecific changes that can accompany disease. The lessons from the first decade of MALDI protein profiling are relevant for future efforts in advancing omics-based biomarker research beyond the laboratory setting and into clinical verification. Copyright © 2011 Elsevier B.V. All rights reserved.

Mass spectrometry for biomarker development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Chaochao; Liu, Tao; Baker, Erin Shammel

2015-06-19

Biomarkers potentially play a crucial role in early disease diagnosis, prognosis and targeted therapy. In the past decade, mass spectrometry based proteomics has become increasingly important in biomarker development due to large advances in technology and associated methods. This chapter mainly focuses on the application of broad (e.g. shotgun) proteomics in biomarker discovery and the utility of targeted proteomics in biomarker verification and validation. A range of mass spectrometry methodologies are discussed emphasizing their efficacy in the different stages in biomarker development, with a particular emphasis on blood biomarker development.

Cross-Disciplinary Biomarkers Research: Lessons Learned by the CKD Biomarkers Consortium.

PubMed

Hsu, Chi-Yuan; Ballard, Shawn; Batlle, Daniel; Bonventre, Joseph V; Böttinger, Erwin P; Feldman, Harold I; Klein, Jon B; Coresh, Josef; Eckfeldt, John H; Inker, Lesley A; Kimmel, Paul L; Kusek, John W; Liu, Kathleen D; Mauer, Michael; Mifflin, Theodore E; Molitch, Mark E; Nelsestuen, Gary L; Rebholz, Casey M; Rovin, Brad H; Sabbisetti, Venkata S; Van Eyk, Jennifer E; Vasan, Ramachandran S; Waikar, Sushrut S; Whitehead, Krista M; Nelson, Robert G

2015-05-07

Significant advances are needed to improve the diagnosis, prognosis, and management of persons with CKD. Discovery of new biomarkers and improvements in currently available biomarkers for CKD hold great promise to achieve these necessary advances. Interest in identification and evaluation of biomarkers for CKD has increased substantially over the past decade. In 2009, the National Institute of Diabetes and Digestive and Kidney Diseases established the CKD Biomarkers Consortium (http://www.ckdbiomarkersconsortium.org/), a multidisciplinary, collaborative study group located at over a dozen academic medical centers. The main objective of the consortium was to evaluate new biomarkers for purposes related to CKD in established prospective cohorts, including those enriched for CKD. During the first 5 years of the consortium, many insights into collaborative biomarker research were gained that may be useful to other investigators involved in biomarkers research. These lessons learned are outlined in this Special Feature and include a wide range of issues related to biospecimen collection, storage, and retrieval, and the internal and external quality assessment of laboratories that performed the assays. The authors propose that investigations involving biomarker discovery and validation are greatly enhanced by establishing and following explicit quality control metrics, including the use of blind replicate and proficiency samples, by carefully considering the conditions under which specimens are collected, handled, and stored, and by conducting pilot and feasibility studies when there are concerns about the condition of the specimens or the accuracy or reproducibility of the assays. Copyright © 2015 by the American Society of Nephrology.

Integration of Antibody Array Technology into Drug Discovery and Development.

PubMed

Huang, Wei; Whittaker, Kelly; Zhang, Huihua; Wu, Jian; Zhu, Si-Wei; Huang, Ruo-Pan

Antibody arrays represent a high-throughput technique that enables the parallel detection of multiple proteins with minimal sample volume requirements. In recent years, antibody arrays have been widely used to identify new biomarkers for disease diagnosis or prognosis. Moreover, many academic research laboratories and commercial biotechnology companies are starting to apply antibody arrays in the field of drug discovery. In this review, some technical aspects of antibody array development and the various platforms currently available will be addressed; however, the main focus will be on the discussion of antibody array technologies and their applications in drug discovery. Aspects of the drug discovery process, including target identification, mechanisms of drug resistance, molecular mechanisms of drug action, drug side effects, and the application in clinical trials and in managing patient care, which have been investigated using antibody arrays in recent literature will be examined and the relevance of this technology in progressing this process will be discussed. Protein profiling with antibody array technology, in addition to other applications, has emerged as a successful, novel approach for drug discovery because of the well-known importance of proteins in cell events and disease development.

Warehousing re-annotated cancer genes for biomarker meta-analysis.

PubMed

Orsini, M; Travaglione, A; Capobianco, E

2013-07-01

Translational research in cancer genomics assigns a fundamental role to bioinformatics in support of candidate gene prioritization with regard to both biomarker discovery and target identification for drug development. Efforts in both such directions rely on the existence and constant update of large repositories of gene expression data and omics records obtained from a variety of experiments. Users who interactively interrogate such repositories may have problems in retrieving sample fields that present limited associated information, due for instance to incomplete entries or sometimes unusable files. Cancer-specific data sources present similar problems. Given that source integration usually improves data quality, one of the objectives is keeping the computational complexity sufficiently low to allow an optimal assimilation and mining of all the information. In particular, the scope of integrating intraomics data can be to improve the exploration of gene co-expression landscapes, while the scope of integrating interomics sources can be that of establishing genotype-phenotype associations. Both integrations are relevant to cancer biomarker meta-analysis, as the proposed study demonstrates. Our approach is based on re-annotating cancer-specific data available at the EBI's ArrayExpress repository and building a data warehouse aimed to biomarker discovery and validation studies. Cancer genes are organized by tissue with biomedical and clinical evidences combined to increase reproducibility and consistency of results. For better comparative evaluation, multiple queries have been designed to efficiently address all types of experiments and platforms, and allow for retrieval of sample-related information, such as cell line, disease state and clinical aspects. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Integrative analysis for the discovery of lung cancer serological markers and validation by MRM-MS

PubMed Central

An, Byung Chull; Choi, Yoo-Duk; Yang, Eun Gyeong; Na, Kook-Joo; Lee, Seung-Taek; Park, Jae-Il; Kim, Seon-Young; Lee, Cheolju

2017-01-01

Non-small-cell lung cancer (NSCLC) constitutes approximately 80% of all diagnosed lung cancers, and diagnostic markers detectable in the plasma/serum of NSCLC patients are greatly needed. In this study, we established a pipeline for the discovery of markers using 9 transcriptome datasets from publicly available databases and profiling of six lung cancer cell secretomes. Thirty-one out of 312 proteins that overlapped between two-fold differentially expressed genes and identified cell secretome proteins were detected in the pooled plasma of lung cancer patients. To quantify the candidates in the serum of NSCLC patients, multiple-reaction-monitoring mass spectrometry (MRM-MS) was performed for five candidate biomarkers. Finally, two potential biomarkers (BCHE and GPx3; AUC = 0.713 and 0.673, respectively) and one two-marker panel generated by logistic regression (BCHE/GPx3; AUC = 0.773) were identified. A validation test was performed by ELISA to evaluate the reproducibility of GPx3 and BCHE expression in an independent set of samples (BCHE and GPx3; AUC = 0.630 and 0.759, respectively, BCHE/GPx3 panel; AUC = 0.788). Collectively, these results demonstrate the feasibility of using our pipeline for marker discovery and our MRM-MS platform for verifying potential biomarkers of human diseases. PMID:28837649

Integrative analysis for the discovery of lung cancer serological markers and validation by MRM-MS.

PubMed

Shin, Jihye; Song, Sang-Yun; Ahn, Hee-Sung; An, Byung Chull; Choi, Yoo-Duk; Yang, Eun Gyeong; Na, Kook-Joo; Lee, Seung-Taek; Park, Jae-Il; Kim, Seon-Young; Lee, Cheolju; Lee, Seung-Won

2017-01-01

Non-small-cell lung cancer (NSCLC) constitutes approximately 80% of all diagnosed lung cancers, and diagnostic markers detectable in the plasma/serum of NSCLC patients are greatly needed. In this study, we established a pipeline for the discovery of markers using 9 transcriptome datasets from publicly available databases and profiling of six lung cancer cell secretomes. Thirty-one out of 312 proteins that overlapped between two-fold differentially expressed genes and identified cell secretome proteins were detected in the pooled plasma of lung cancer patients. To quantify the candidates in the serum of NSCLC patients, multiple-reaction-monitoring mass spectrometry (MRM-MS) was performed for five candidate biomarkers. Finally, two potential biomarkers (BCHE and GPx3; AUC = 0.713 and 0.673, respectively) and one two-marker panel generated by logistic regression (BCHE/GPx3; AUC = 0.773) were identified. A validation test was performed by ELISA to evaluate the reproducibility of GPx3 and BCHE expression in an independent set of samples (BCHE and GPx3; AUC = 0.630 and 0.759, respectively, BCHE/GPx3 panel; AUC = 0.788). Collectively, these results demonstrate the feasibility of using our pipeline for marker discovery and our MRM-MS platform for verifying potential biomarkers of human diseases.

Recursive feature elimination for biomarker discovery in resting-state functional connectivity.

PubMed

Ravishankar, Hariharan; Madhavan, Radhika; Mullick, Rakesh; Shetty, Teena; Marinelli, Luca; Joel, Suresh E

2016-08-01

Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate analysis suffers from the problem of multiple comparisons. Here, we adopt an alternative data-driven method for identifying population differences in functional connectivity. We propose a machine-learning approach to down-select functional connectivity features associated with symptom severity in mild traumatic brain injury (mTBI). Using this approach, we identified functional regions with altered connectivity in mTBI. including the executive control, visual and precuneus networks. We compared functional connections at multiple resolutions to determine which scale would be more sensitive to changes related to patient recovery. These modular network-level features can be used as diagnostic tools for predicting disease severity and recovery profiles.

Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers.

PubMed

Irigoyen, Antonio; Jimenez-Luna, Cristina; Benavides, Manuel; Caba, Octavio; Gallego, Javier; Ortuño, Francisco Manuel; Guillen-Ponce, Carmen; Rojas, Ignacio; Aranda, Enrique; Torres, Carolina; Prados, Jose

2018-01-01

Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses ('gained' genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease.

Association of SNCA with Parkinson: replication in the Harvard NeuroDiscovery Center Biomarker Study

PubMed Central

Ding, Hongliu; Sarokhan, Alison K.; Roderick, Sarah S.; Bakshi, Rachit; Maher, Nancy E.; Ashourian, Paymon; Kan, Caroline G.; Chang, Sunny; Santarlasci, Andrea; Swords, Kyleen E.; Ravina, Bernard M.; Hayes, Michael T.; Sohur, U. Shivraj; Wills, Anne-Marie; Flaherty, Alice W.; Unni, Vivek K.; Hung, Albert Y.; Selkoe, Dennis J.; Schwarzschild, Michael A.; Schlossmacher, Michael G.; Sudarsky, Lewis R.; Growdon, John H.; Ivinson, Adrian J.; Hyman, Bradley T.; Scherzer, Clemens R.

2011-01-01

Background Mutations in the α-synuclein gene (SNCA) cause autosomal dominant forms of Parkinson’s disease, but the substantial risk conferred by this locus to the common sporadic disease has only recently emerged from genome-wide association studies. Methods Here we genotyped a prioritized non-coding variant in SNCA intron-4 in 344 patients with Parkinson’s and 275 controls from the longitudinal Harvard NeuroDiscovery Center Biomarker Study. Results The common minor allele of rs2736990 was associated with elevated disease susceptibility (odds ratio = 1.40, P value = 0.0032). Conclusions This result increases confidence in the notion that in many clinically well-characterized patients genetic variation in SNCA contributes to “sporadic” disease. PMID:21953863

Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder.

PubMed

Gottschalk, M G; Cooper, J D; Chan, M K; Bot, M; Penninx, B W J H; Bahn, S

2015-08-01

Although social anxiety disorder (SAD) is strongly associated with the subsequent development of a depressive disorder (major depressive disorder or dysthymia), no underlying biological risk factors are known. We aimed to identify biomarkers which predict depressive episodes in SAD patients over a 2-year follow-up period. One hundred sixty-five multiplexed immunoassay analytes were investigated in blood serum of 143 SAD patients without co-morbid depressive disorders, recruited within the Netherlands Study of Depression and Anxiety (NESDA). Predictive performance of identified biomarkers, clinical variables and self-report inventories was assessed using receiver operating characteristics curves (ROC) and represented by the area under the ROC curve (AUC). Stepwise logistic regression resulted in the selection of four serum analytes (AXL receptor tyrosine kinase, vascular cell adhesion molecule 1, vitronectin, collagen IV) and four additional variables (Inventory of Depressive Symptomatology, Beck Anxiety Inventory somatic subscale, depressive disorder lifetime diagnosis, BMI) as optimal set of patient parameters. When combined, an AUC of 0.86 was achieved for the identification of SAD individuals who later developed a depressive disorder. Throughout our analyses, biomarkers yielded superior discriminative performance compared to clinical variables and self-report inventories alone. We report the discovery of a serum marker panel with good predictive performance to identify SAD individuals prone to develop subsequent depressive episodes in a naturalistic cohort design. Furthermore, we emphasise the importance to combine biological markers, clinical variables and self-report inventories for disease course predictions in psychiatry. Following replication in independent cohorts, validated biomarkers could help to identify SAD patients at risk of developing a depressive disorder, thus facilitating early intervention. Copyright © 2015 Elsevier Inc. All rights reserved.

Mass spectrometric based approaches in urine metabolomics and biomarker discovery.

PubMed

Khamis, Mona M; Adamko, Darryl J; El-Aneed, Anas

2017-03-01

Urine metabolomics has recently emerged as a prominent field for the discovery of non-invasive biomarkers that can detect subtle metabolic discrepancies in response to a specific disease or therapeutic intervention. Urine, compared to other biofluids, is characterized by its ease of collection, richness in metabolites and its ability to reflect imbalances of all biochemical pathways within the body. Following urine collection for metabolomic analysis, samples must be immediately frozen to quench any biogenic and/or non-biogenic chemical reactions. According to the aim of the experiment; sample preparation can vary from simple procedures such as filtration to more specific extraction protocols such as liquid-liquid extraction. Due to the lack of comprehensive studies on urine metabolome stability, higher storage temperatures (i.e. 4°C) and repetitive freeze-thaw cycles should be avoided. To date, among all analytical techniques, mass spectrometry (MS) provides the best sensitivity, selectivity and identification capabilities to analyze the majority of the metabolite composition in the urine. Combined with the qualitative and quantitative capabilities of MS, and due to the continuous improvements in its related technologies (i.e. ultra high-performance liquid chromatography [UPLC] and hydrophilic interaction liquid chromatography [HILIC]), liquid chromatography (LC)-MS is unequivocally the most utilized and the most informative analytical tool employed in urine metabolomics. Furthermore, differential isotope tagging techniques has provided a solution to ion suppression from urine matrix thus allowing for quantitative analysis. In addition to LC-MS, other MS-based technologies have been utilized in urine metabolomics. These include direct injection (infusion)-MS, capillary electrophoresis-MS and gas chromatography-MS. In this article, the current progresses of different MS-based techniques in exploring the urine metabolome as well as the recent findings in providing

An automated Genomes-to-Natural Products platform (GNP) for the discovery of modular natural products.

PubMed

Johnston, Chad W; Skinnider, Michael A; Wyatt, Morgan A; Li, Xiang; Ranieri, Michael R M; Yang, Lian; Zechel, David L; Ma, Bin; Magarvey, Nathan A

2015-09-28

Bacterial natural products are a diverse and valuable group of small molecules, and genome sequencing indicates that the vast majority remain undiscovered. The prediction of natural product structures from biosynthetic assembly lines can facilitate their discovery, but highly automated, accurate, and integrated systems are required to mine the broad spectrum of sequenced bacterial genomes. Here we present a genome-guided natural products discovery tool to automatically predict, combinatorialize and identify polyketides and nonribosomal peptides from biosynthetic assembly lines using LC-MS/MS data of crude extracts in a high-throughput manner. We detail the directed identification and isolation of six genetically predicted polyketides and nonribosomal peptides using our Genome-to-Natural Products platform. This highly automated, user-friendly programme provides a means of realizing the potential of genetically encoded natural products.

The future of liquid chromatography-mass spectrometry (LC-MS) in metabolic profiling and metabolomic studies for biomarker discovery

PubMed Central

Metz, Thomas O.; Zhang, Qibin; Page, Jason S.; Shen, Yufeng; Callister, Stephen J.; Jacobs, Jon M.; Smith, Richard D.

2008-01-01

SUMMARY The future utility of liquid chromatography-mass spectrometry (LC-MS) in metabolic profiling and metabolomic studies for biomarker discover will be discussed, beginning with a brief description of the evolution of metabolomics and the utilization of the three most popular analytical platforms in such studies: NMR, GC-MS, and LC-MS. Emphasis is placed on recent developments in high-efficiency LC separations, sensitive electrospray ionization approaches, and the benefits to incorporating both in LC-MS-based approaches. The advantages and disadvantages of various quantitative approaches are reviewed, followed by the current LC-MS-based tools available for candidate biomarker characterization and identification. Finally, a brief prediction on the future path of LC-MS-based methods in metabolic profiling and metabolomic studies is given. PMID:19177179

Candidate-based proteomics in the search for biomarkers of cardiovascular disease

PubMed Central

Anderson, Leigh

2005-01-01

The key concept of proteomics (looking at many proteins at once) opens new avenues in the search for clinically useful biomarkers of disease, treatment response and ageing. As the number of proteins that can be detected in plasma or serum (the primary clinical diagnostic samples) increases towards 1000, a paradoxical decline has occurred in the number of new protein markers approved for diagnostic use in clinical laboratories. This review explores the limitations of current proteomics protein discovery platforms, and proposes an alternative approach, applicable to a range of biological/physiological problems, in which quantitative mass spectrometric methods developed for analytical chemistry are employed to measure limited sets of candidate markers in large sets of clinical samples. A set of 177 candidate biomarker proteins with reported associations to cardiovascular disease and stroke are presented as a starting point for such a ‘directed proteomics’ approach. PMID:15611012

A lectin-coupled, targeted proteomic mass spectrometry (MRM MS) platform for identification of multiple liver cancer biomarkers in human plasma.

PubMed

Ahn, Yeong Hee; Shin, Park Min; Oh, Na Ree; Park, Gun Wook; Kim, Hoguen; Yoo, Jong Shin

2012-09-18

Aberrantly glycosylated proteins related to liver cancer progression were captured with specific lectin and identified from human plasma by multiple reaction monitoring (MRM) mass spectrometry as multiple biomarkers for hepatocellular carcinoma (HCC). The lectin fractionation for fucosylated protein glycoforms in human plasma was conducted with a fucose-specific aleuria aurantia lectin (AAL). Following tryptic digestion of the lectin-captured fraction, plasma samples from 30 control cases (including 10 healthy, 10 hepatitis B virus [HBV], and 10 cirrhosis cases) and 10 HCC cases were quantitatively analyzed by MRM to identify which glycoproteins are viable HCC biomarkers. A1AG1, AACT, A1AT, and CERU were found to be potent biomarkers to differentiate HCC plasma from control plasmas. The AUROC generated independently from these four biomarker candidates ranged from 0.73 to 0.92. However, the lectin-coupled MRM assay with multiple combinations of biomarker candidates is superior statistically to those generated from the individual candidates with AUROC more than 0.95, which can be an alternative to the immunoassay inevitably requiring tedious development of multiple antibodies against biomarker candidates to be verified. Eventually the lectin-coupled, targeted proteomic mass spectrometry (MRM MS) platform was found to be efficient to identify multiple biomarkers from human plasma according to cancer progression. Copyright © 2012 Elsevier B.V. All rights reserved.

MRM screening/biomarker discovery with linear ion trap MS: a library of human cancer-specific peptides.

PubMed

Yang, Xu; Lazar, Iulia M

2009-03-27

The discovery of novel protein biomarkers is essential in the clinical setting to enable early disease diagnosis and increase survivability rates. To facilitate differential expression analysis and biomarker discovery, a variety of tandem mass spectrometry (MS/MS)-based protein profiling techniques have been developed. For achieving sensitive detection and accurate quantitation, targeted MS screening approaches, such as multiple reaction monitoring (MRM), have been implemented. MCF-7 breast cancer protein cellular extracts were analyzed by 2D-strong cation exchange (SCX)/reversed phase liquid chromatography (RPLC) separations interfaced to linear ion trap MS detection. MS data were interpreted with the Sequest-based Bioworks software (Thermo Electron). In-house developed Perl-scripts were used to calculate the spectral counts and the representative fragment ions for each peptide. In this work, we report on the generation of a library of 9,677 peptides (p < 0.001), representing approximately 1,572 proteins from human breast cancer cells, that can be used for MRM/MS-based biomarker screening studies. For each protein, the library provides the number and sequence of detectable peptides, the charge state, the spectral count, the molecular weight, the parameters that characterize the quality of the tandem mass spectrum (p-value, DeltaM, Xcorr, DeltaCn, Sp, no. of matching a, b, y ions in the spectrum), the retention time, and the top 10 most intense product ions that correspond to a given peptide. Only proteins identified by at least two spectral counts are listed. The experimental distribution of protein frequencies, as a function of molecular weight, closely matched the theoretical distribution of proteins in the human proteome, as provided in the SwissProt database. The amino acid sequence coverage of the identified proteins ranged from 0.04% to 98.3%. The highest-abundance proteins in the cellular extract had a molecular weight (MW)<50,000. Preliminary experiments

MRM screening/biomarker discovery with linear ion trap MS: a library of human cancer-specific peptides

PubMed Central

2009-01-01

Background The discovery of novel protein biomarkers is essential in the clinical setting to enable early disease diagnosis and increase survivability rates. To facilitate differential expression analysis and biomarker discovery, a variety of tandem mass spectrometry (MS/MS)-based protein profiling techniques have been developed. For achieving sensitive detection and accurate quantitation, targeted MS screening approaches, such as multiple reaction monitoring (MRM), have been implemented. Methods MCF-7 breast cancer protein cellular extracts were analyzed by 2D-strong cation exchange (SCX)/reversed phase liquid chromatography (RPLC) separations interfaced to linear ion trap MS detection. MS data were interpreted with the Sequest-based Bioworks software (Thermo Electron). In-house developed Perl-scripts were used to calculate the spectral counts and the representative fragment ions for each peptide. Results In this work, we report on the generation of a library of 9,677 peptides (p < 0.001), representing ~1,572 proteins from human breast cancer cells, that can be used for MRM/MS-based biomarker screening studies. For each protein, the library provides the number and sequence of detectable peptides, the charge state, the spectral count, the molecular weight, the parameters that characterize the quality of the tandem mass spectrum (p-value, DeltaM, Xcorr, DeltaCn, Sp, no. of matching a, b, y ions in the spectrum), the retention time, and the top 10 most intense product ions that correspond to a given peptide. Only proteins identified by at least two spectral counts are listed. The experimental distribution of protein frequencies, as a function of molecular weight, closely matched the theoretical distribution of proteins in the human proteome, as provided in the SwissProt database. The amino acid sequence coverage of the identified proteins ranged from 0.04% to 98.3%. The highest-abundance proteins in the cellular extract had a molecular weight (MW)<50,000. Conclusion

Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges

PubMed Central

2013-01-01

Tremendous efforts have been made over the past few decades to discover novel cancer biomarkers for use in clinical practice. However, a striking discrepancy exists between the effort directed toward biomarker discovery and the number of markers that make it into clinical practice. One of the confounding issues in translating a novel discovery into clinical practice is that quite often the scientists working on biomarker discovery have limited knowledge of the analytical, diagnostic, and regulatory requirements for a clinical assay. This review provides an introduction to such considerations with the aim of generating more extensive discussion for study design, assay performance, and regulatory approval in the process of translating new proteomic biomarkers from discovery into cancer diagnostics. We first describe the analytical requirements for a robust clinical biomarker assay, including concepts of precision, trueness, specificity and analytical interference, and carryover. We next introduce the clinical considerations of diagnostic accuracy, receiver operating characteristic analysis, positive and negative predictive values, and clinical utility. We finish the review by describing components of the FDA approval process for protein-based biomarkers, including classification of biomarker assays as medical devices, analytical and clinical performance requirements, and the approval process workflow. While we recognize that the road from biomarker discovery, validation, and regulatory approval to the translation into the clinical setting could be long and difficult, the reward for patients, clinicians and scientists could be rather significant. PMID:24088261

A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection.

PubMed

Yasui, Yutaka; Pepe, Margaret; Thompson, Mary Lou; Adam, Bao-Ling; Wright, George L; Qu, Yinsheng; Potter, John D; Winget, Marcy; Thornquist, Mark; Feng, Ziding

2003-07-01

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of 'signature' protein profiles specific to each pathologic state (e.g. normal vs. cancer) or differential profiles between experimental conditions (e.g. treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data-analytic strategy for discovering protein biomarkers based on such high-dimensional mass spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data-analytic strategy takes properties of the SELDI mass spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After this pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary

Biomarkers to guide clinical therapeutics in rheumatology?

PubMed

Robinson, William H; Mao, Rong

2016-03-01

The use of biomarkers in rheumatology can help identify disease risk, improve diagnosis and prognosis, target therapy, assess response to treatment, and further our understanding of the underlying pathogenesis of disease. Here, we discuss the recent advances in biomarkers for rheumatic disorders, existing impediments to progress in this field, and the potential of biomarkers to enable precision medicine and thereby transform rheumatology. Although significant challenges remain, progress continues to be made in biomarker discovery and development for rheumatic diseases. The use of next-generation technologies, including large-scale sequencing, proteomic technologies, metabolomic technologies, mass cytometry, and other single-cell analysis and multianalyte analysis technologies, has yielded a slew of new candidate biomarkers. Nevertheless, these biomarkers still require rigorous validation and have yet to make their way into clinical practice and therapeutic development. This review focuses on advances in the biomarker field in the last 12 months as well as the challenges that remain. Better biomarkers, ideally mechanistic ones, are needed to guide clinical decision making in rheumatology. Although the use of next-generation techniques for biomarker discovery is making headway, it is imperative that the roadblocks in our search for new biomarkers are overcome to enable identification of biomarkers with greater diagnostic and predictive utility. Identification of biomarkers with robust diagnostic and predictive utility would enable precision medicine in rheumatology.

Knowledge-based identification of soluble biomarkers: hepatic fibrosis in NAFLD as an example.

PubMed

Page, Sandra; Birerdinc, Aybike; Estep, Michael; Stepanova, Maria; Afendy, Arian; Petricoin, Emanuel; Younossi, Zobair; Chandhoke, Vikas; Baranova, Ancha

2013-01-01

The discovery of biomarkers is often performed using high-throughput proteomics-based platforms and is limited to the molecules recognized by a given set of purified and validated antigens or antibodies. Knowledge-based, or systems biology, approaches that involve the analysis of integrated data, predominantly molecular pathways and networks may infer quantitative changes in the levels of biomolecules not included by the given assay from the levels of the analytes profiled. In this study we attempted to use a knowledge-based approach to predict biomarkers reflecting the changes in underlying protein phosphorylation events using Nonalcoholic Fatty Liver Disease (NAFLD) as a model. Two soluble biomarkers, CCL-2 and FasL, were inferred in silico as relevant to NAFLD pathogenesis. Predictive performance of these biomarkers was studied using serum samples collected from patients with histologically proven NAFLD. Serum levels of both molecules, in combination with clinical and demographic data, were predictive of hepatic fibrosis in a cohort of NAFLD patients. Our study suggests that (1) NASH-specific disruption of the kinase-driven signaling cascades in visceral adipose tissue lead to detectable changes in the levels of soluble molecules released into the bloodstream, and (2) biomarkers discovered in silico could contribute to predictive models for non-malignant chronic diseases.

Knowledge-Based Identification of Soluble Biomarkers: Hepatic Fibrosis in NAFLD as an Example

PubMed Central

Page, Sandra; Birerdinc, Aybike; Estep, Michael; Stepanova, Maria; Afendy, Arian; Petricoin, Emanuel; Younossi, Zobair; Chandhoke, Vikas; Baranova, Ancha

2013-01-01

The discovery of biomarkers is often performed using high-throughput proteomics-based platforms and is limited to the molecules recognized by a given set of purified and validated antigens or antibodies. Knowledge-based, or systems biology, approaches that involve the analysis of integrated data, predominantly molecular pathways and networks may infer quantitative changes in the levels of biomolecules not included by the given assay from the levels of the analytes profiled. In this study we attempted to use a knowledge-based approach to predict biomarkers reflecting the changes in underlying protein phosphorylation events using Nonalcoholic Fatty Liver Disease (NAFLD) as a model. Two soluble biomarkers, CCL-2 and FasL, were inferred in silico as relevant to NAFLD pathogenesis. Predictive performance of these biomarkers was studied using serum samples collected from patients with histologically proven NAFLD. Serum levels of both molecules, in combination with clinical and demographic data, were predictive of hepatic fibrosis in a cohort of NAFLD patients. Our study suggests that (1) NASH-specific disruption of the kinase-driven signaling cascades in visceral adipose tissue lead to detectable changes in the levels of soluble molecules released into the bloodstream, and (2) biomarkers discovered in silico could contribute to predictive models for non-malignant chronic diseases. PMID:23405244

Plenario: A Spatio-Temporal Platform for Discovery and Exploration of Urban Science Data

NASA Astrophysics Data System (ADS)

Engler, W. H.; Malik, T.; Catlett, C.; Foster, I.; Goldstein, B.

2015-12-01

The past decade has seen the widespread release of open data concerning city services, conditions, and activities by government bodies and public institutions of all sizes. Hundreds of open data portals now host thousands of datasets of many different types. These new data sources represent enormous potential for improved understanding of urban dynamics and processes—and, ultimately, for more livable, efficient, and prosperous communities. However, those who seek to realize this potential quickly discover that discovering and applying those data relevant to any particular question can be extraordinarily difficult, due to decentralized storage, heterogeneous formats, and poor documentation. In this context, we introduce Plenario, a platform designed to automating time-consuming tasks associated with the discovery, exploration, and application of open city data—and, in so doing, reduce barriers to data use for researchers, policymakers, service providers, journalists, and members of the general public. Key innovations include a geospatial data warehouse that allows data from many sources to be registered into a common spatial and temporal frame; simple and intuitive interfaces that permit rapid discovery and exploration of data subsets pertaining to a particular area and time, regardless of type and source; easy export of such data subsets for further analysis; a user-configurable data ingest framework for automated importing and periodic updating of new datasets into the data warehouse; cloud hosting for elastic scaling and rapid creation of new Plenario instances; and an open source implementation to enable community contributions. We describe here the architecture and implementation of the Plenario platform, discuss lessons learned from its use by several communities, and outline plans for future work.

Prioritization of biomarker targets in human umbilical cord blood: identification of proteins in infant blood serving as validated biomarkers in adults.

PubMed

Hansmeier, Nicole; Chao, Tzu-Chiao; Goldman, Lynn R; Witter, Frank R; Halden, Rolf U

2012-05-01

Early diagnosis represents one of the best lines of defense in the fight against a wide array of human diseases. Umbilical cord blood (UCB) is one of the first easily available diagnostic biofluids and can inform about the health status of newborns. However, compared with adult blood, its diagnostic potential remains largely untapped. Our goal was to accelerate biomarker research on UCB by exploring its detectable protein content and providing a priority list of potential biomarkers based on known proteins involved in disease pathways. We explored cord blood serum proteins by profiling a UCB pool of 12 neonates with different backgrounds using a combination of isoelectric focusing and liquid chromatography coupled with matrix-assisted laser desorption/ionization tandem mass spectrometry (MALDI-MS/MS) and by comparing results with information contained in metabolic and disease databases available for adult blood. A total of 1,210 UCB proteins were identified with a protein-level false discovery rate of ~ 5% as estimated by naïve target-decoy and MAYU approaches, signifying a 6-fold increase in the number of UCB proteins described to date. Identified proteins correspond to 138 different metabolic and disease pathways and provide a platform of mechanistically linked biomarker candidates for tracking disruptions in cellular processes. Moreover, among the identified proteins, 38 were found to be approved biomarkers for adult blood. The results of this study advance current knowledge of the human cord blood serum proteome. They showcase the potential of UCB as a diagnostic medium for assessing infant health by detection and identification of candidate biomarkers for known disease pathways using a global, nontargeted approach. These biomarkers may inform about mechanisms of exposure-disease relationships. Furthermore, biomarkers approved by the U.S. Food and Drug Administration for screening in adult blood were detected in UCB and represent high-priority targets for

Comprehensive Analysis of MILE Gene Expression Data Set Advances Discovery of Leukaemia Type and Subtype Biomarkers.

PubMed

Labaj, Wojciech; Papiez, Anna; Polanski, Andrzej; Polanska, Joanna

2017-03-01

Large collections of data in studies on cancer such as leukaemia provoke the necessity of applying tailored analysis algorithms to ensure supreme information extraction. In this work, a custom-fit pipeline is demonstrated for thorough investigation of the voluminous MILE gene expression data set. Three analyses are accomplished, each for gaining a deeper understanding of the processes underlying leukaemia types and subtypes. First, the main disease groups are tested for differential expression against the healthy control as in a standard case-control study. Here, the basic knowledge on molecular mechanisms is confirmed quantitatively and by literature references. Second, pairwise comparison testing is performed for juxtaposing the main leukaemia types among each other. In this case by means of the Dice coefficient similarity measure the general relations are pointed out. Moreover, lists of candidate main leukaemia group biomarkers are proposed. Finally, with this approach being successful, the third analysis provides insight into all of the studied subtypes, followed by the emergence of four leukaemia subtype biomarkers. In addition, the class enhanced DEG signature obtained on the basis of novel pipeline processing leads to significantly better classification power of multi-class data classifiers. The developed methodology consisting of batch effect adjustment, adaptive noise and feature filtration coupled with adequate statistical testing and biomarker definition proves to be an effective approach towards knowledge discovery in high-throughput molecular biology experiments.

Potential metabolomic biomarkers for reliable diagnosis of Behcet's disease using gas chromatography/ time-of-flight-mass spectrometry.

PubMed

Ahn, Joong Kyong; Kim, Jungyeon; Hwang, Jiwon; Song, Juhwan; Kim, Kyoung Heon; Cha, Hoon-Suk

2018-05-01

Although many diagnostic criteria of Behcet's disease (BD) have been developed and revised by experts, diagnosing BD is still complicated and challenging. No metabolomic studies on serum have been attempted to improve the diagnosis and to identify potential biomarkers of BD. The purposes of this study were to investigate distinctive metabolic changes in serum samples of BD patients and to identify metabolic candidate biomarkers for reliable diagnosis of BD using the metabolomics platform. Metabolomic profiling of 90 serum samples from 45 BD patients and 45 healthy controls (HCs) were performed via gas chromatography with time-of-flight mass spectrometry (GC/TOF-MS) with multivariate statistical analyses. A total of 104 metabolites were identified from samples. The serum metabolite profiles obtained from GC/TOF-MS analysis can distinguish BD patients from HC group in discovery set. The variation values of the partial least squared-discrimination analysis (PLS-DA) model are R 2 X of 0.246, R 2 Y of 0.913 and Q 2 of 0.852, respectively, indicating strong explanation and prediction capabilities of the model. A panel of five metabolic biomarkers, namely, decanoic acid, fructose, tagatose, linoleic acid and oleic acid were selected and adequately validated as putative biomarkers of BD (sensitivity 100%, specificity 97.1%, area under the curve 0.998) in the discovery set and independent set. The PLS_DA model showed clear discrimination of BD and HC groups by the five metabolic biomarkers in independent set. This is the first report on characteristic metabolic profiles and potential metabolite biomarkers in serum for reliable diagnosis of BD using GC/TOF-MS. Copyright © 2017. Published by Elsevier SAS.

Synovial fluid proteomics in the pursuit of arthritis mediators: An evolving field of novel biomarker discovery.

PubMed

Mahendran, Shalini M; Oikonomopoulou, Katerina; Diamandis, Eleftherios P; Chandran, Vinod

Synovial fluid (SF) is a protein-rich fluid produced into the joint cavity by cells of the synovial membrane. Due to its direct contact with articular cartilage, surfaces of the bone, and the synoviocytes of the inner membrane, it provides a promising reflection of the biochemical state of the joint under varying physiological and pathophysiological conditions. This property of SF has been exploited within numerous studies in search of unique biomarkers of joint pathologies with the ultimate goal of developing minimally invasive clinical assays to detect and/or monitor disease states. Several proteomic methodologies have been employed to mine the SF proteome. From elementary immunoassays to high-throughput analyses using mass spectrometry-based techniques, each has demonstrated distinct advantages and disadvantages in the identification and quantification of SF proteins. This review will explore the role of SF in the elucidation of the arthritis proteome and the extent to which high-throughput techniques have facilitated the discovery and validation of protein biomarkers from osteoarthritis (OA), rheumatoid arthritis (RA), psoriatic arthritis (PsA), and juvenile idiopathic arthritis (JIA) patients.

Translating Discovery in Zebrafish Pancreatic Development to Human Pancreatic Cancer: Biomarkers, Targets, Pathogenesis, and Therapeutics

PubMed Central

Kazi, Abid A.; Yee, Rosemary K.

2013-01-01

Abstract Experimental studies in the zebrafish have greatly facilitated understanding of genetic regulation of the early developmental events in the pancreas. Various approaches using forward and reverse genetics, chemical genetics, and transgenesis in zebrafish have demonstrated generally conserved regulatory roles of mammalian genes and discovered novel genetic pathways in exocrine pancreatic development. Accumulating evidence has supported the use of zebrafish as a model of human malignant diseases, including pancreatic cancer. Studies have shown that the genetic regulators of exocrine pancreatic development in zebrafish can be translated into potential clinical biomarkers and therapeutic targets in human pancreatic adenocarcinoma. Transgenic zebrafish expressing oncogenic K-ras and zebrafish tumor xenograft model have emerged as valuable tools for dissecting the pathogenetic mechanisms of pancreatic cancer and for drug discovery and toxicology. Future analysis of the pancreas in zebrafish will continue to advance understanding of the genetic regulation and biological mechanisms during organogenesis. Results of those studies are expected to provide new insights into how aberrant developmental pathways contribute to formation and growth of pancreatic neoplasia, and hopefully generate valid biomarkers and targets as well as effective and safe therapeutics in pancreatic cancer. PMID:23682805

Translating discovery in zebrafish pancreatic development to human pancreatic cancer: biomarkers, targets, pathogenesis, and therapeutics.

PubMed

Yee, Nelson S; Kazi, Abid A; Yee, Rosemary K

2013-06-01

Abstract Experimental studies in the zebrafish have greatly facilitated understanding of genetic regulation of the early developmental events in the pancreas. Various approaches using forward and reverse genetics, chemical genetics, and transgenesis in zebrafish have demonstrated generally conserved regulatory roles of mammalian genes and discovered novel genetic pathways in exocrine pancreatic development. Accumulating evidence has supported the use of zebrafish as a model of human malignant diseases, including pancreatic cancer. Studies have shown that the genetic regulators of exocrine pancreatic development in zebrafish can be translated into potential clinical biomarkers and therapeutic targets in human pancreatic adenocarcinoma. Transgenic zebrafish expressing oncogenic K-ras and zebrafish tumor xenograft model have emerged as valuable tools for dissecting the pathogenetic mechanisms of pancreatic cancer and for drug discovery and toxicology. Future analysis of the pancreas in zebrafish will continue to advance understanding of the genetic regulation and biological mechanisms during organogenesis. Results of those studies are expected to provide new insights into how aberrant developmental pathways contribute to formation and growth of pancreatic neoplasia, and hopefully generate valid biomarkers and targets as well as effective and safe therapeutics in pancreatic cancer.

Perspective: Proteomic approach to detect biomarkers of human growth hormone

PubMed Central

Ding, Juan; List, Edward O.; Okada, Shigeru; Kopchick, John J.

2009-01-01

Several serum biomarkers for recombinant human growth hormone (rhGH) have been established, however, none alone or in combination have generate a specific, sensitive, and reproducible ‘kit’ for the detection of rhGH abuse. Thus, the search for additional GH specific biomarkers continues. In this review, we focus on the use of proteomics in general and 2-dimensional electrophoresis (2-DE) in particular for the discovery of new GH induced serum biomarkers. Also, we review some of the protocols involved in 2DE. Finally, the possibility of tissues other than blood for biomarker discovery is discussed. PMID:19501004

Urinary metallomics as a novel biomarker discovery platform: Breast cancer as a case study.

PubMed

Burton, Casey; Dan, Yongbo; Donovan, Ariel; Liu, Kun; Shi, Honglan; Ma, Yinfa; Bosnak, Cynthia P

2016-01-15

Urinary metallomics is presented here as a new "omics" approach that aims to facilitate personalized cancer screening and prevention by improving our understanding of urinary metals in disease. Twenty-two urinary metals were examined with inductively-coupled plasma-mass spectrometry in 138 women newly diagnosed with breast cancer and benign conditions. Urinary metals from spot urine samples were adjusted to renal dilution using urine specific gravity. Two urinary metals, copper (P-value=0.036) and lead (P-value=0.003), were significantly increased in the urine of breast cancer patients. A multivariate model that comprised copper, lead, and patient age afforded encouraging discriminatory power (AUC: 0.728, P-value<0.0005), while univariate models of copper (61.7% sensitivity, 50.0% specificity) and lead (76.6% sensitivity, 51.2% specificity) at optimized cutoff thresholds compared favorably with other breast cancer diagnostic modalities such as mammography. Correlations found among various metals suggested potential geographic and dietary influences on the urine metallome that warrant further investigation. This proof-of-concept work introduces urinary metallomics as a noninvasive, potentially transformative "omics" approach to early cancer detection. Urinary copper and lead have also been preliminarily identified as potential breast cancer biomarkers. Copyright © 2015 Elsevier B.V. All rights reserved.

Advanced Visualization and Interactive Display Rapid Innovation and Discovery Evaluation Research (VISRIDER) Program Task 6: Point Cloud Visualization Techniques for Desktop and Web Platforms

DTIC Science & Technology

2017-04-01

ADVANCED VISUALIZATION AND INTERACTIVE DISPLAY RAPID INNOVATION AND DISCOVERY EVALUATION RESEARCH (VISRIDER) PROGRAM TASK 6: POINT CLOUD...To) OCT 2013 – SEP 2014 4. TITLE AND SUBTITLE ADVANCED VISUALIZATION AND INTERACTIVE DISPLAY RAPID INNOVATION AND DISCOVERY EVALUATION RESEARCH...various point cloud visualization techniques for viewing large scale LiDAR datasets. Evaluate their potential use for thick client desktop platforms

An ion channel library for drug discovery and safety screening on automated platforms.

PubMed

Wible, Barbara A; Kuryshev, Yuri A; Smith, Stephen S; Liu, Zhiqi; Brown, Arthur M

2008-12-01

Ion channels represent the third largest class of targets in drug discovery after G-protein coupled receptors and kinases. In spite of this ranking, ion channels continue to be under exploited as drug targets compared with the other two groups for several reasons. First, with 400 ion channel genes and an even greater number of functional channels due to mixing and matching of individual subunits, a systematic collection of ion channel-expressing cell lines for drug discovery and safety screening has not been available. Second, the lack of high-throughput functional assays for ion channels has limited their use as drug targets. Now that automated electrophysiology has come of age and provided the technology to assay ion channels at medium to high throughput, we have addressed the need for a library of ion channel cell lines by constructing the Ion Channel Panel (ChanTest Corp., Cleveland, OH). From 400 ion channel genes, a collection of 82 of the most relevant human ion channels for drug discovery, safety, and human disease has been assembled.Each channel has been stably overexpressed in human embryonic kidney 293 or Chinese hamster ovary cells. Cell lines have been selected and validated on automated electrophysiology systems to facilitate cost-effective screening for safe and selective compounds at earlier stages in the drug development process. The screening and validation processes as well as the relative advantages of different screening platforms are discussed.

Application of proteomics in the discovery of candidate protein biomarkers in a diabetes autoantibody standardization program sample subset.

PubMed

Metz, Thomas O; Qian, Wei-Jun; Jacobs, Jon M; Gritsenko, Marina A; Moore, Ronald J; Polpitiya, Ashoka D; Monroe, Matthew E; Camp, David G; Mueller, Patricia W; Smith, Richard D

2008-02-01

Novel biomarkers of type 1 diabetes must be identified and validated in initial, exploratory studies before they can be assessed in proficiency evaluations. Currently, untargeted "-omics" approaches are underutilized in profiling studies of clinical samples. This report describes the evaluation of capillary liquid chromatography (LC) coupled with mass spectrometry (MS) in a pilot proteomic analysis of human plasma and serum from a subset of control and type 1 diabetic individuals enrolled in the Diabetes Autoantibody Standardization Program, with the goal of identifying candidate biomarkers of type 1 diabetes. Initial high-resolution capillary LC-MS/MS experiments were performed to augment an existing plasma peptide database, while subsequent LC-FTICR studies identified quantitative differences in the abundance of plasma proteins. Analysis of LC-FTICR proteomic data identified five candidate protein biomarkers of type 1 diabetes. alpha-2-Glycoprotein 1 (zinc), corticosteroid-binding globulin, and lumican were 2-fold up-regulated in type 1 diabetic samples relative to control samples, whereas clusterin and serotransferrin were 2-fold up-regulated in control samples relative to type 1 diabetic samples. Observed perturbations in the levels of all five proteins are consistent with the metabolic aberrations found in type 1 diabetes. While the discovery of these candidate protein biomarkers of type 1 diabetes is encouraging, follow up studies are required for validation in a larger population of individuals and for determination of laboratory-defined sensitivity and specificity values using blinded samples.

Biomarker Qualification: Toward a Multiple Stakeholder Framework for Biomarker Development, Regulatory Acceptance, and Utilization.

PubMed

Amur, S; LaVange, L; Zineh, I; Buckman-Garner, S; Woodcock, J

2015-07-01

The discovery, development, and use of biomarkers for a variety of drug development purposes are areas of tremendous interest and need. Biomarkers can become accepted for use through submission of biomarker data during the drug approval process. Another emerging pathway for acceptance of biomarkers is via the biomarker qualification program developed by the Center for Drug Evaluation and Research (CDER, US Food and Drug Administration). Evidentiary standards are needed to develop and evaluate various types of biomarkers for their intended use and multiple stakeholders, including academia, industry, government, and consortia must work together to help develop this evidence. The article describes various types of biomarkers that can be useful in drug development and evidentiary considerations that are important for qualification. A path forward for coordinating efforts to identify and explore needed biomarkers is proposed for consideration. © 2015 American Society for Clinical Pharmacology and Therapeutics.

Statistical Methods for Proteomic Biomarker Discovery based on Feature Extraction or Functional Modeling Approaches.

PubMed

Morris, Jeffrey S

2012-01-01

In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational aspects of comparative proteomic studies, and summarizes contributions I along with numerous collaborators have made. First, there is an overview of comparative proteomics technologies, followed by a discussion of important experimental design and preprocessing issues that must be considered before statistical analysis can be done. Next, the two key approaches to analyzing proteomics data, feature extraction and functional modeling, are described. Feature extraction involves detection and quantification of discrete features like peaks or spots that theoretically correspond to different proteins in the sample. After an overview of the feature extraction approach, specific methods for mass spectrometry ( Cromwell ) and 2D gel electrophoresis ( Pinnacle ) are described. The functional modeling approach involves modeling the proteomic data in their entirety as functions or images. A general discussion of the approach is followed by the presentation of a specific method that can be applied, wavelet-based functional mixed models, and its extensions. All methods are illustrated by application to two example proteomic data sets, one from mass spectrometry and one from 2D gel electrophoresis. While the specific methods

Study design and data analysis considerations for the discovery of prognostic molecular biomarkers: a case study of progression free survival in advanced serous ovarian cancer.

PubMed

Qin, Li-Xuan; Levine, Douglas A

2016-06-10

Accurate discovery of molecular biomarkers that are prognostic of a clinical outcome is an important yet challenging task, partly due to the combination of the typically weak genomic signal for a clinical outcome and the frequently strong noise due to microarray handling effects. Effective strategies to resolve this challenge are in dire need. We set out to assess the use of careful study design and data normalization for the discovery of prognostic molecular biomarkers. Taking progression free survival in advanced serous ovarian cancer as an example, we conducted empirical analysis on two sets of microRNA arrays for the same set of tumor samples: arrays in one set were collected using careful study design (that is, uniform handling and randomized array-to-sample assignment) and arrays in the other set were not. We found that (1) handling effects can confound the clinical outcome under study as a result of chance even with randomization, (2) the level of confounding handling effects can be reduced by data normalization, and (3) good study design cannot be replaced by post-hoc normalization. In addition, we provided a practical approach to define positive and negative control markers for detecting handling effects and assessing the performance of a normalization method. Our work showcased the difficulty of finding prognostic biomarkers for a clinical outcome of weak genomic signals, illustrated the benefits of careful study design and data normalization, and provided a practical approach to identify handling effects and select a beneficial normalization method. Our work calls for careful study design and data analysis for the discovery of robust and translatable molecular biomarkers.

Proteomics and metabolomics for mechanistic insights and biomarker discovery in cardiovascular disease.

PubMed

Barallobre-Barreiro, Javier; Chung, Yuen-Li; Mayr, Manuel

2013-08-01

In the last decade, proteomics and metabolomics have contributed substantially to our understanding of cardiovascular diseases. The unbiased assessment of pathophysiological processes without a priori assumptions complements other molecular biology techniques that are currently used in a reductionist approach. In this review, we highlight some of the "omics" methods used to assess protein and metabolite changes in cardiovascular disease. A discrete biological function is very rarely attributed to a single molecule; more often it is the combined input of many proteins. In contrast to the reductionist approach, in which molecules are studied individually, "omics" platforms allow the study of more complex interactions in biological systems. Combining proteomics and metabolomics to quantify changes in metabolites and their corresponding enzymes will advance our understanding of pathophysiological mechanisms and aid the identification of novel biomarkers for cardiovascular disease. Copyright © 2013 Sociedad Española de Cardiología. Published by Elsevier Espana. All rights reserved.

Towards Precision Medicine in the Clinic: From Biomarker Discovery to Novel Therapeutics.

PubMed

Collins, Dearbhaile C; Sundar, Raghav; Lim, Joline S J; Yap, Timothy A

2017-01-01

Precision medicine continues to be the benchmark to which we strive in cancer research. Seeking out actionable aberrations that can be selectively targeted by drug compounds promises to optimize treatment efficacy and minimize toxicity. Utilizing these different targeted agents in combination or in sequence may further delay resistance to treatments and prolong antitumor responses. Remarkable progress in the field of immunotherapy adds another layer of complexity to the management of cancer patients. Corresponding advances in companion biomarker development, novel methods of serial tumor assessments, and innovative trial designs act synergistically to further precision medicine. Ongoing hurdles such as clonal evolution, intra- and intertumor heterogeneity, and varied mechanisms of drug resistance continue to be challenges to overcome. Large-scale data-sharing and collaborative networks using next-generation sequencing (NGS) platforms promise to take us further into the cancer 'ome' than ever before, with the goal of achieving successful precision medicine. Copyright © 2016 Elsevier Ltd. All rights reserved.

Discovery of human urinary biomarkers of aronia-citrus juice intake by HPLC-q-TOF-based metabolomic approach.

PubMed

Llorach, Rafael; Medina, Sonia; García-Viguera, Cristina; Zafrilla, Pilar; Abellán, José; Jauregui, Olga; Tomás-Barberán, Francisco A; Gil-Izquierdo, Angel; Andrés-Lacueva, Cristina

2014-06-01

Metabolomics has emerged in the field of food and nutrition sciences as a powerful tool for doing profiling approaches. In this context, HPLC-q-TOF-based metabolomics approach was applied to unveil changes in the urinary metabolome in human subjects (n = 51, 23 men and 28 women) after regular aronia-citrus juice (AC-juice) intake (250 mL/day) during 16 weeks compared to individuals given a placebo beverage. Samples were analyzed by HPLC-q-TOF followed by multivariate data analysis (orthogonal signal filtering-partial least square discriminant analysis) that discriminated relevant mass features related to AC-juice intake. The results showed that biomarkers of AC-juice intake including metabolites coming from metabolism of food components as proline betaine, ferulic acid, and two unknown mercapturate derivatives were identified. Discovery of new biomarkers of food intake will help in the building up of the food metabolome and facilitate future insights into the mechanisms of action of dietary components in population health. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

ARQiv-HTS, a versatile whole-organism screening platform enabling in vivo drug discovery at high-throughput rates

PubMed Central

White, David T; Eroglu, Arife Unal; Wang, Guohua; Zhang, Liyun; Sengupta, Sumitra; Ding, Ding; Rajpurohit, Surendra K; Walker, Steven L; Ji, Hongkai; Qian, Jiang; Mumm, Jeff S

2017-01-01

The zebrafish has emerged as an important model for whole-organism small-molecule screening. However, most zebrafish-based chemical screens have achieved only mid-throughput rates. Here we describe a versatile whole-organism drug discovery platform that can achieve true high-throughput screening (HTS) capacities. This system combines our automated reporter quantification in vivo (ARQiv) system with customized robotics, and is termed ‘ARQiv-HTS’. We detail the process of establishing and implementing ARQiv-HTS: (i) assay design and optimization, (ii) calculation of sample size and hit criteria, (iii) large-scale egg production, (iv) automated compound titration, (v) dispensing of embryos into microtiter plates, and (vi) reporter quantification. We also outline what we see as best practice strategies for leveraging the power of ARQiv-HTS for zebrafish-based drug discovery, and address technical challenges of applying zebrafish to large-scale chemical screens. Finally, we provide a detailed protocol for a recently completed inaugural ARQiv-HTS effort, which involved the identification of compounds that elevate insulin reporter activity. Compounds that increased the number of insulin-producing pancreatic beta cells represent potential new therapeutics for diabetic patients. For this effort, individual screening sessions took 1 week to conclude, and sessions were performed iteratively approximately every other day to increase throughput. At the conclusion of the screen, more than a half million drug-treated larvae had been evaluated. Beyond this initial example, however, the ARQiv-HTS platform is adaptable to almost any reporter-based assay designed to evaluate the effects of chemical compounds in living small-animal models. ARQiv-HTS thus enables large-scale whole-organism drug discovery for a variety of model species and from numerous disease-oriented perspectives. PMID:27831568

Disposable inkjet-printed electrochemical platform for detection of clinically relevant HER-2 breast cancer biomarker.

PubMed

Carvajal, Susanita; Fera, Samantha N; Jones, Abby L; Baldo, Thaisa A; Mosa, Islam M; Rusling, James F; Krause, Colleen E

2018-05-01

Rapidly fabricated, disposable sensor platforms hold tremendous promise for point-of-care detection. Here, we present an inexpensive (< $0.25) fully inkjet printed electrochemical sensor with integrated counter, reference, and working electrodes that is easily scalable for commercial fabrication. The electrochemical sensor platform featured an inkjet printed gold working 8-electrode array (WEA) and counter electrode (CE), along with an inkjet -printed silver electrode that was chlorinated with bleach to produce a Ag/AgCl quasi-reference electrode (RE). As proof of concept, the electrochemical sensor was successfully applied for detection of clinically relevant breast cancer biomarker Human Epidermal Growth Factor Receptor 2 (HER-2). Capture antibodies were bound to a chemically modified surface on the WEA and placed into a microfluidic device. A full sandwich immunoassay was constructed following a simultaneous injection of target protein, biotinylated antibody, and polymerized horseradish peroxide labels into the microfluidic device housing the WEA. With an ultra fast assay time, of only 15mins a clinically relevant limit of detection of 12pgmL -1 was achieved. Excellent reproducibility and sensitivity were observed through recovery assays preformed in human serum with recoveries ranging from 76% to 103%. These easily fabricated and scalable electrochemical sensor platforms can be readily adapted for multiplex detection following this rapid assay protocol for cancer diagnostics. Copyright © 2018 Elsevier B.V. All rights reserved.

A computational platform for MALDI-TOF mass spectrometry data: application to serum and plasma samples.

PubMed

Mantini, Dante; Petrucci, Francesca; Pieragostino, Damiana; Del Boccio, Piero; Sacchetta, Paolo; Candiano, Giovanni; Ghiggeri, Gian Marco; Lugaresi, Alessandra; Federici, Giorgio; Di Ilio, Carmine; Urbani, Andrea

2010-01-03

Mass spectrometry (MS) is becoming the gold standard for biomarker discovery. Several MS-based bioinformatics methods have been proposed for this application, but the divergence of the findings by different research groups on the same MS data suggests that the definition of a reliable method has not been achieved yet. In this work, we propose an integrated software platform, MASCAP, intended for comparative biomarker detection from MALDI-TOF MS data. MASCAP integrates denoising and feature extraction algorithms, which have already shown to provide consistent peaks across mass spectra; furthermore, it relies on statistical analysis and graphical tools to compare the results between groups. The effectiveness in mass spectrum processing is demonstrated using MALDI-TOF data, as well as SELDI-TOF data. The usefulness in detecting potential protein biomarkers is shown comparing MALDI-TOF mass spectra collected from serum and plasma samples belonging to the same clinical population. The analysis approach implemented in MASCAP may simplify biomarker detection, by assisting the recognition of proteomic expression signatures of the disease. A MATLAB implementation of the software and the data used for its validation are available at http://www.unich.it/proteomica/bioinf. (c) 2009 Elsevier B.V. All rights reserved.

NMR-based platform for fragment-based lead discovery used in screening BRD4-targeted compounds

PubMed Central

Yu, Jun-lan; Chen, Tian-tian; Zhou, Chen; Lian, Fu-lin; Tang, Xu-long; Wen, Yi; Shen, Jing-kang; Xu, Ye-chun; Xiong, Bing; Zhang, Nai-xia

2016-01-01

Aim: Fragment-based lead discovery (FBLD) is a complementary approach in drug research and development. In this study, we established an NMR-based FBLD platform that was used to screen novel scaffolds targeting human bromodomain of BRD4, and investigated the binding interactions between hit compounds and the target protein. Methods: 1D NMR techniques were primarily used to generate the fragment library and to screen compounds. The inhibitory activity of hits on the first bromodomain of BRD4 [BRD4(I)] was examined using fluorescence anisotropy binding assay. 2D NMR and X-ray crystallography were applied to characterize the binding interactions between hit compounds and the target protein. Results: An NMR-based fragment library containing 539 compounds was established, which were clustered into 56 groups (8–10 compounds in each group). Eight hits with new scaffolds were found to inhibit BRD4(I). Four out of the 8 hits (compounds 1, 2, 8 and 9) had IC50 values of 100–260 μmol/L, demonstrating their potential for further BRD4-targeted hit-to-lead optimization. Analysis of the binding interactions revealed that compounds 1 and 2 shared a common quinazolin core structure and bound to BRD4(I) in a non-acetylated lysine mimetic mode. Conclusion: An NMR-based platform for FBLD was established and used in discovery of BRD4-targeted compounds. Four potential hit-to-lead optimization candidates have been found, two of them bound to BRD4(I) in a non-acetylated lysine mimetic mode, being selective BRD4(I) inhibitors. PMID:27238211

Computational drug discovery

PubMed Central

Ou-Yang, Si-sheng; Lu, Jun-yan; Kong, Xiang-qian; Liang, Zhong-jie; Luo, Cheng; Jiang, Hualiang

2012-01-01

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field. PMID:22922346

A data-driven, knowledge-based approach to biomarker discovery: application to circulating microRNA markers of colorectal cancer prognosis.

PubMed

Vafaee, Fatemeh; Diakos, Connie; Kirschner, Michaela B; Reid, Glen; Michael, Michael Z; Horvath, Lisa G; Alinejad-Rokny, Hamid; Cheng, Zhangkai Jason; Kuncic, Zdenka; Clarke, Stephen

2018-01-01

Recent advances in high-throughput technologies have provided an unprecedented opportunity to identify molecular markers of disease processes. This plethora of complex-omics data has simultaneously complicated the problem of extracting meaningful molecular signatures and opened up new opportunities for more sophisticated integrative and holistic approaches. In this era, effective integration of data-driven and knowledge-based approaches for biomarker identification has been recognised as key to improving the identification of high-performance biomarkers, and necessary for translational applications. Here, we have evaluated the role of circulating microRNA as a means of predicting the prognosis of patients with colorectal cancer, which is the second leading cause of cancer-related death worldwide. We have developed a multi-objective optimisation method that effectively integrates a data-driven approach with the knowledge obtained from the microRNA-mediated regulatory network to identify robust plasma microRNA signatures which are reliable in terms of predictive power as well as functional relevance. The proposed multi-objective framework has the capacity to adjust for conflicting biomarker objectives and to incorporate heterogeneous information facilitating systems approaches to biomarker discovery. We have found a prognostic signature of colorectal cancer comprising 11 circulating microRNAs. The identified signature predicts the patients' survival outcome and targets pathways underlying colorectal cancer progression. The altered expression of the identified microRNAs was confirmed in an independent public data set of plasma samples of patients in early stage vs advanced colorectal cancer. Furthermore, the generality of the proposed method was demonstrated across three publicly available miRNA data sets associated with biomarker studies in other diseases.

Application of proteomics in the discovery of candidate protein biomarkers in a Diabetes Autoantibody Standardization Program (DASP) sample subset

PubMed Central

Metz, Thomas O.; Qian, Wei-Jun; Jacobs, Jon M.; Gritsenko, Marina A.; Moore, Ronald J.; Polpitiya, Ashoka D.; Monroe, Matthew E.; Camp, David G.; Mueller, Patricia W.; Smith, Richard D.

2009-01-01

Novel biomarkers of type 1 diabetes must be identified and validated in initial, exploratory studies before they can be assessed in proficiency evaluations. Currently, untargeted “-omics” approaches are under-utilized in profiling studies of clinical samples. This report describes the evaluation of capillary liquid chromatography (LC) coupled with mass spectrometry (MS) in a pilot proteomic analysis of human plasma and serum from a subset of control and type 1 diabetic individuals enrolled in the Diabetes Autoantibody Standardization Program with the goal of identifying candidate biomarkers of type 1 diabetes. Initial high-resolution capillary LC-MS/MS experiments were performed to augment an existing plasma peptide database, while subsequent LC-FTICR studies identified quantitative differences in the abundance of plasma proteins. Analysis of LC-FTICR proteomic data identified five candidate protein biomarkers of type 1 diabetes. Alpha-2-glycoprotein 1 (zinc), corticosteroid-binding globulin, and lumican were 2-fold up-regulated in type 1 diabetic samples relative to control samples, whereas clusterin and serotransferrin were 2-fold up-regulated in control samples relative to type 1 diabetic samples. Observed perturbations in the levels of all five proteins are consistent with the metabolic aberrations found in type 1 diabetes. While the discovery of these candidate protein biomarkers of type 1 diabetes is encouraging, follow up studies are required for validation in a larger population of individuals and for determination of laboratory-defined sensitivity and specificity values using blinded samples. PMID:18092746

Study Designs and Statistical Analyses for Biomarker Research

PubMed Central

Gosho, Masahiko; Nagashima, Kengo; Sato, Yasunori

2012-01-01

Biomarkers are becoming increasingly important for streamlining drug discovery and development. In addition, biomarkers are widely expected to be used as a tool for disease diagnosis, personalized medication, and surrogate endpoints in clinical research. In this paper, we highlight several important aspects related to study design and statistical analysis for clinical research incorporating biomarkers. We describe the typical and current study designs for exploring, detecting, and utilizing biomarkers. Furthermore, we introduce statistical issues such as confounding and multiplicity for statistical tests in biomarker research. PMID:23012528

Prioritization of Biomarker Targets in Human Umbilical Cord Blood: Identification of Proteins in Infant Blood Serving as Validated Biomarkers in Adults

PubMed Central

Hansmeier, Nicole; Chao, Tzu-Chiao; Goldman, Lynn R.; Witter, Frank R.

2012-01-01

Background: Early diagnosis represents one of the best lines of defense in the fight against a wide array of human diseases. Umbilical cord blood (UCB) is one of the first easily available diagnostic biofluids and can inform about the health status of newborns. However, compared with adult blood, its diagnostic potential remains largely untapped. Objectives: Our goal was to accelerate biomarker research on UCB by exploring its detectable protein content and providing a priority list of potential biomarkers based on known proteins involved in disease pathways. Methods: We explored cord blood serum proteins by profiling a UCB pool of 12 neonates with different backgrounds using a combination of isoelectric focusing and liquid chromatography coupled with matrix-assisted laser desorption/ionization tandem mass spectrometry (MALDI-MS/MS) and by comparing results with information contained in metabolic and disease databases available for adult blood. Results: A total of 1,210 UCB proteins were identified with a protein-level false discovery rate of ~ 5% as estimated by naïve target-decoy and MAYU approaches, signifying a 6-fold increase in the number of UCB proteins described to date. Identified proteins correspond to 138 different metabolic and disease pathways and provide a platform of mechanistically linked biomarker candidates for tracking disruptions in cellular processes. Moreover, among the identified proteins, 38 were found to be approved biomarkers for adult blood. Conclusions: The results of this study advance current knowledge of the human cord blood serum proteome. They showcase the potential of UCB as a diagnostic medium for assessing infant health by detection and identification of candidate biomarkers for known disease pathways using a global, nontargeted approach. These biomarkers may inform about mechanisms of exposure–disease relationships. Furthermore, biomarkers approved by the U.S. Food and Drug Administration for screening in adult blood were

Discovering biomarkers for antidepressant response: protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort.

PubMed

Lam, Raymond W; Milev, Roumen; Rotzinger, Susan; Andreazza, Ana C; Blier, Pierre; Brenner, Colleen; Daskalakis, Zafiris J; Dharsee, Moyez; Downar, Jonathan; Evans, Kenneth R; Farzan, Faranak; Foster, Jane A; Frey, Benicio N; Geraci, Joseph; Giacobbe, Peter; Feilotter, Harriet E; Hall, Geoffrey B; Harkness, Kate L; Hassel, Stefanie; Ismail, Zahinoor; Leri, Francesco; Liotti, Mario; MacQueen, Glenda M; McAndrews, Mary Pat; Minuzzi, Luciano; Müller, Daniel J; Parikh, Sagar V; Placenza, Franca M; Quilty, Lena C; Ravindran, Arun V; Salomons, Tim V; Soares, Claudio N; Strother, Stephen C; Turecki, Gustavo; Vaccarino, Anthony L; Vila-Rodriguez, Fidel; Kennedy, Sidney H

2016-04-16

Major Depressive Disorder (MDD) is among the most prevalent and disabling medical conditions worldwide. Identification of clinical and biological markers ("biomarkers") of treatment response could personalize clinical decisions and lead to better outcomes. This paper describes the aims, design, and methods of a discovery study of biomarkers in antidepressant treatment response, conducted by the Canadian Biomarker Integration Network in Depression (CAN-BIND). The CAN-BIND research program investigates and identifies biomarkers that help to predict outcomes in patients with MDD treated with antidepressant medication. The primary objective of this initial study (known as CAN-BIND-1) is to identify individual and integrated neuroimaging, electrophysiological, molecular, and clinical predictors of response to sequential antidepressant monotherapy and adjunctive therapy in MDD. CAN-BIND-1 is a multisite initiative involving 6 academic health centres working collaboratively with other universities and research centres. In the 16-week protocol, patients with MDD are treated with a first-line antidepressant (escitalopram 10-20 mg/d) that, if clinically warranted after eight weeks, is augmented with an evidence-based, add-on medication (aripiprazole 2-10 mg/d). Comprehensive datasets are obtained using clinical rating scales; behavioural, dimensional, and functioning/quality of life measures; neurocognitive testing; genomic, genetic, and proteomic profiling from blood samples; combined structural and functional magnetic resonance imaging; and electroencephalography. De-identified data from all sites are aggregated within a secure neuroinformatics platform for data integration, management, storage, and analyses. Statistical analyses will include multivariate and machine-learning techniques to identify predictors, moderators, and mediators of treatment response. From June 2013 to February 2015, a cohort of 134 participants (85 outpatients with MDD and 49 healthy participants

Innovation Incentives and Biomarkers.

PubMed

Stern, Ariel D; Alexander, Brian M; Chandra, Amitabh

2018-01-01

Previously, we have discussed the importance of economic incentives in shaping markets for precision medicines. Here we consider incentives for biomarker development, including discovery and establishment. Biomarkers can reveal valuable information regarding diagnosis and prognosis, predict treatment efficacy or toxicity, serve as markers of disease progression, and serve as auxiliary endpoints for clinical trials. Some have multiple uses, while others have a specialized role, resulting in diverse incentives across players in the healthcare system. © 2017, ASCPT.

Advances in microfluidics for drug discovery.

PubMed

Lombardi, Dario; Dittrich, Petra S

2010-11-01

Microfluidics is considered as an enabling technology for the development of unconventional and innovative methods in the drug discovery process. The concept of micrometer-sized reaction systems in the form of continuous flow reactors, microdroplets or microchambers is intriguing, and the versatility of the technology perfectly fits with the requirements of drug synthesis, drug screening and drug testing. In this review article, we introduce key microfluidic approaches to the drug discovery process, highlighting the latest and promising achievements in this field, mainly from the years 2007 - 2010. Despite high expectations of microfluidic approaches to several stages of the drug discovery process, up to now microfluidic technology has not been able to significantly replace conventional drug discovery platforms. Our aim is to identify bottlenecks that have impeded the transfer of microfluidics into routine platforms for drug discovery and show some recent solutions to overcome these hurdles. Although most microfluidic approaches are still applied only for proof-of-concept studies, thanks to creative microfluidic research in the past years unprecedented novel capabilities of microdevices could be demonstrated, and general applicable, robust and reliable microfluidic platforms seem to be within reach.

Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans

PubMed Central

Sagoo, Pervinder; Perucha, Esperanza; Sawitzki, Birgit; Tomiuk, Stefan; Stephens, David A.; Miqueu, Patrick; Chapman, Stephanie; Craciun, Ligia; Sergeant, Ruhena; Brouard, Sophie; Rovis, Flavia; Jimenez, Elvira; Ballow, Amany; Giral, Magali; Rebollo-Mesa, Irene; Le Moine, Alain; Braudeau, Cecile; Hilton, Rachel; Gerstmayer, Bernhard; Bourcier, Katarzyna; Sharif, Adnan; Krajewska, Magdalena; Lord, Graham M.; Roberts, Ian; Goldman, Michel; Wood, Kathryn J.; Newell, Kenneth; Seyfert-Margolis, Vicki; Warrens, Anthony N.; Janssen, Uwe; Volk, Hans-Dieter; Soulillou, Jean-Paul; Hernandez-Fuentes, Maria P.; Lechler, Robert I.

2010-01-01

Identifying transplant recipients in whom immunological tolerance is established or is developing would allow an individually tailored approach to their posttransplantation management. In this study, we aimed to develop reliable and reproducible in vitro assays capable of detecting tolerance in renal transplant recipients. Several biomarkers and bioassays were screened on a training set that included 11 operationally tolerant renal transplant recipients, recipient groups following different immunosuppressive regimes, recipients undergoing chronic rejection, and healthy controls. Highly predictive assays were repeated on an independent test set that included 24 tolerant renal transplant recipients. Tolerant patients displayed an expansion of peripheral blood B and NK lymphocytes, fewer activated CD4+ T cells, a lack of donor-specific antibodies, donor-specific hyporesponsiveness of CD4+ T cells, and a high ratio of forkhead box P3 to α-1,2-mannosidase gene expression. Microarray analysis further revealed in tolerant recipients a bias toward differential expression of B cell–related genes and their associated molecular pathways. By combining these indices of tolerance as a cross-platform biomarker signature, we were able to identify tolerant recipients in both the training set and the test set. This study provides an immunological profile of the tolerant state that, with further validation, should inform and shape drug-weaning protocols in renal transplant recipients. PMID:20501943

Metabolomics as a tool in the identification of dietary biomarkers.

PubMed

Gibbons, Helena; Brennan, Lorraine

2017-02-01

Current dietary assessment methods including FFQ, 24-h recalls and weighed food diaries are associated with many measurement errors. In an attempt to overcome some of these errors, dietary biomarkers have emerged as a complementary approach to these traditional methods. Metabolomics has developed as a key technology for the identification of new dietary biomarkers and to date, metabolomic-based approaches have led to the identification of a number of putative biomarkers. The three approaches generally employed when using metabolomics in dietary biomarker discovery are: (i) acute interventions where participants consume specific amounts of a test food, (ii) cohort studies where metabolic profiles are compared between consumers and non-consumers of a specific food and (iii) the analysis of dietary patterns and metabolic profiles to identify nutritypes and biomarkers. The present review critiques the current literature in terms of the approaches used for dietary biomarker discovery and gives a detailed overview of the currently proposed biomarkers, highlighting steps needed for their full validation. Furthermore, the present review also evaluates areas such as current databases and software tools, which are needed to advance the interpretation of results and therefore enhance the utility of dietary biomarkers in nutrition research.

Discovery of safety biomarkers for atorvastatin in rat urine using mass spectrometry based metabolomics combined with global and targeted approach.

PubMed

Kumar, Bhowmik Salil; Lee, Young-Joo; Yi, Hong Jae; Chung, Bong Chul; Jung, Byung Hwa

2010-02-19

In order to develop a safety biomarker for atorvastatin, this drug was orally administrated to hyperlipidemic rats, and a metabolomic study was performed. Atorvastatin was given in doses of either 70 mg kg(-1) day(-1) or 250 mg kg(-1) day(-1) for a period of 7 days (n=4 for each group). To evaluate any abnormal effects of the drug, physiological and plasma biochemical parameters were measured and histopathological tests were carried out. Safety biomarkers were derived by comparing these parameters and using both global and targeted metabolic profiling. Global metabolic profiling was performed using liquid chromatography/time of flight/mass spectrometry (LC/TOF/MS) with multivariate data analysis. Several safety biomarker candidates that included various steroids and amino acids were discovered as a result of global metabolic profiling, and they were also confirmed by targeted metabolic profiling using gas chromatography/mass spectrometry (GC/MS) and capillary electrophoresis/mass spectrometry (CE/MS). Serum biochemical and histopathological tests were used to detect abnormal drug reactions in the liver after repeating oral administration of atorvastatin. The metabolic differences between control and the drug-treated groups were compared using PLS-DA score plots. These results were compared with the physiological and plasma biochemical parameters and the results of a histopathological test. Estrone, cortisone, proline, cystine, 3-ureidopropionic acid and histidine were proposed as potential safety biomarkers related with the liver toxicity of atorvastatin. These results indicate that the combined application of global and targeted metabolic profiling could be a useful tool for the discovery of drug safety biomarkers. Copyright 2009 Elsevier B.V. All rights reserved.

Concepts and Principles of Photodynamic Therapy as an Alternative Antifungal Discovery Platform

PubMed Central

Dai, Tianhong; Fuchs, Beth B.; Coleman, Jeffrey J.; Prates, Renato A.; Astrakas, Christos; St. Denis, Tyler G.; Ribeiro, Martha S.; Mylonakis, Eleftherios; Hamblin, Michael R.; Tegos, George P.

2012-01-01

Opportunistic fungal pathogens may cause superficial or serious invasive infections, especially in immunocompromised and debilitated patients. Invasive mycoses represent an exponentially growing threat for human health due to a combination of slow diagnosis and the existence of relatively few classes of available and effective antifungal drugs. Therefore systemic fungal infections result in high attributable mortality. There is an urgent need to pursue and deploy novel and effective alternative antifungal countermeasures. Photodynamic therapy (PDT) was established as a successful modality for malignancies and age-related macular degeneration but photodynamic inactivation has only recently been intensively investigated as an alternative antimicrobial discovery and development platform. The concept of photodynamic inactivation requires microbial exposure to either exogenous or endogenous photosensitizer molecules, followed by visible light energy, typically wavelengths in the red/near infrared region that cause the excitation of the photosensitizers resulting in the production of singlet oxygen and other reactive oxygen species that react with intracellular components, and consequently produce cell inactivation and death. Antifungal PDT is an area of increasing interest, as research is advancing (i) to identify the photochemical and photophysical mechanisms involved in photoinactivation; (ii) to develop potent and clinically compatible photosensitizers; (iii) to understand how photoinactivation is affected by key microbial phenotypic elements multidrug resistance and efflux, virulence and pathogenesis determinants, and formation of biofilms; (iv) to explore novel photosensitizer delivery platforms; and (v) to identify photoinactivation applications beyond the clinical setting such as environmental disinfectants. PMID:22514547

Bioanalytical qualification of clinical biomarker assays in plasma using a novel multi-analyte Simple Plex™ platform.

PubMed

Gupta, Vinita; Davancaze, Teresa; Good, Jeremy; Kalia, Navdeep; Anderson, Michael; Wallin, Jeffrey J; Brady, Ann; Song, An; Xu, Wenfeng

2016-12-01

Immune-checkpoint inhibitors are presumed to break down the tolerogenic state of immune cells by activating T-lymphocytes that release cytokines and enhance effector cell function for elimination of tumors. Measurement of cytokines is being pursued for better understanding of the mechanism of action of immune-checkpoint inhibitors, as well as to identify potential predictive biomarkers. In this study, we show bioanalytical qualification of cytokine assays in plasma on a novel multi-analyte immunoassay platform, Simple Plex ™ . The qualified assays exhibited excellent sensitivity as evidenced by measurement of all samples within the quantifiable range. The accuracy and precision were 80-120% and 10%, respectively. The qualified assays will be useful in assessing mechanism of action cancer immunotherapies.

Glycoblotting method allows for rapid and efficient glycome profiling of human Alzheimer's disease brain, serum and cerebrospinal fluid towards potential biomarker discovery.

PubMed

Gizaw, Solomon T; Ohashi, Tetsu; Tanaka, Masakazu; Hinou, Hiroshi; Nishimura, Shin-Ichiro

2016-08-01

Understanding of the significance of posttranslational glycosylation in Alzheimer's disease (AD) is of growing importance for the investigation of the pathogenesis of AD as well as discovery research of the disease-specific serum biomarkers. We designed a standard protocol for the glycoblotting combined with MALDI-TOFMS to perform rapid and quantitative profiling of the glycan parts of glycoproteins (N-glycans) and glycosphingolipids (GSLs) using human AD's post-mortem samples such as brain tissues (dissected cerebral cortices such as frontal, parietal, occipital, and temporal domains), serum and cerebrospinal fluid (CSF). The structural profiles of the major N-glycans released from glycoproteins and the total expression levels of the glycans were found to be mostly similar between the brain tissues of the AD patients and those of the normal control group. In contrast, the expression levels of the serum and CSF protein N-glycans such as bisect-type and multiply branched glycoforms were increased significantly in AD patient group. In addition, the levels of some gangliosides such as GM1, GM2 and GM3 appeared to alter in the AD patient brain and serum samples when compared with the normal control groups. Alteration of the expression levels of major N- and GSL-glycans in human brain tissues, serum and CSF of AD patients can be monitored quantitatively by means of the glycoblotting-based standard protocols. The changes in the expression levels of the glycans derived from the human post-mortem samples uncovered by the standardized glycoblotting method provides potential serum biomarkers in central nervous system disorders and can contribute to the insight into the molecular mechanisms in the pathogenesis of neurodegenerative diseases and future drug discovery. Most importantly, the present preliminary trials using human post-mortem samples of AD patients suggest that large-scale serum glycomics cohort by means of various-types of human AD patients as well as the normal

Biomarkers in inflammatory bowel disease: current practices and recent advances.

PubMed

Iskandar, Heba N; Ciorba, Matthew A

2012-04-01

Crohn's disease and ulcerative colitis represent the two main forms of the idiopathic chronic inflammatory bowel diseases (IBD). Currently available blood and stool based biomarkers provide reproducible, quantitative tools that can complement clinical assessment to aid clinicians in IBD diagnosis and management. C-reactive protein and fecal based leukocyte markers can help the clinician distinguish IBD from noninflammatory diarrhea and assess disease activity. The ability to differentiate between forms of IBD and predict risk for disease complications is specific to serologic tests including antibodies against Saccharomyces cerevisiae and perinuclear antineutrophil cytoplasmic proteins. Advances in genomic, proteomic, and metabolomic array based technologies are facilitating the development of new biomarkers for IBD. The discovery of novel biomarkers, which can correlate with mucosal healing or predict long-term disease course has the potential to significantly improve patient care. This article reviews the uses and limitations of currently available biomarkers and highlights recent advances in IBD biomarker discovery. Copyright © 2012 Mosby, Inc. All rights reserved.

ALS Biomarkers for Therapy Development: State of the Field & Future Directions

PubMed Central

Benatar, Michael; Boylan, Kevin; Jeromin, Andreas; Rutkove, Seward B.; Berry, James; Atassi, Nazem; Bruijn, Lucie

2015-01-01

Biomarkers have become the focus of intense research in the field of amyotrophic lateral sclerosis (ALS), with the hope that they might aid therapy development efforts. Notwithstanding the discovery of many candidate biomarkers, none have yet emerged as validated tools for drug development. In this review we present a nuanced view of biomarkers based on the perspective of the FDA; highlight the distinction between discovery and validation; describe existing and emerging resources; review leading biological fluid-based, electrophysiological and neuroimaging candidates relevant to therapy development efforts; discuss lessons learned from biomarker initiatives in related neurodegenerative diseases; and outline specific steps that we, as a field, might take in order to hasten the development and validation of biomarkers that will prove useful in enhancing efforts to develop effective treatments for ALS patients. Most important among these perhaps is the proposal to establish a federated ALS Biomarker Consortium (ABC) in which all interested and willing stakeholders may participate with equal opportunity to contribute to the broader mission of biomarker development and validation. PMID:26574709

Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discovery.

PubMed

Crabtree, Nathaniel M; Moore, Jason H; Bowyer, John F; George, Nysia I

2017-01-01

A computational evolution system (CES) is a knowledge discovery engine that can identify subtle, synergistic relationships in large datasets. Pareto optimization allows CESs to balance accuracy with model complexity when evolving classifiers. Using Pareto optimization, a CES is able to identify a very small number of features while maintaining high classification accuracy. A CES can be designed for various types of data, and the user can exploit expert knowledge about the classification problem in order to improve discrimination between classes. These characteristics give CES an advantage over other classification and feature selection algorithms, particularly when the goal is to identify a small number of highly relevant, non-redundant biomarkers. Previously, CESs have been developed only for binary class datasets. In this study, we developed a multi-class CES. The multi-class CES was compared to three common feature selection and classification algorithms: support vector machine (SVM), random k-nearest neighbor (RKNN), and random forest (RF). The algorithms were evaluated on three distinct multi-class RNA sequencing datasets. The comparison criteria were run-time, classification accuracy, number of selected features, and stability of selected feature set (as measured by the Tanimoto distance). The performance of each algorithm was data-dependent. CES performed best on the dataset with the smallest sample size, indicating that CES has a unique advantage since the accuracy of most classification methods suffer when sample size is small. The multi-class extension of CES increases the appeal of its application to complex, multi-class datasets in order to identify important biomarkers and features.

Plasma Metabolomic Changes following PI3K Inhibition as Pharmacodynamic Biomarkers: Preclinical Discovery to Phase I Trial Evaluation.

PubMed

Ang, Joo Ern; Pandher, Rupinder; Ang, Joo Chew; Asad, Yasmin J; Henley, Alan T; Valenti, Melanie; Box, Gary; de Haven Brandon, Alexis; Baird, Richard D; Friedman, Lori; Derynck, Mika; Vanhaesebroeck, Bart; Eccles, Suzanne A; Kaye, Stan B; Workman, Paul; de Bono, Johann S; Raynaud, Florence I

2016-06-01

PI3K plays a key role in cellular metabolism and cancer. Using a mass spectrometry-based metabolomics platform, we discovered that plasma concentrations of 26 metabolites, including amino acids, acylcarnitines, and phosphatidylcholines, were decreased in mice bearing PTEN-deficient tumors compared with non-tumor-bearing controls and in addition were increased following dosing with class I PI3K inhibitor pictilisib (GDC-0941). These candidate metabolomics biomarkers were evaluated in a phase I dose-escalation clinical trial of pictilisib. Time- and dose-dependent effects were observed in patients for 22 plasma metabolites. The changes exceeded baseline variability, resolved after drug washout, and were recapitulated on continuous dosing. Our study provides a link between modulation of the PI3K pathway and changes in the plasma metabolome and demonstrates that plasma metabolomics is a feasible and promising strategy for biomarker evaluation. Also, our findings provide additional support for an association between insulin resistance, branched-chain amino acids, and related metabolites following PI3K inhibition. Mol Cancer Ther; 15(6); 1412-24. ©2016 AACR. ©2016 American Association for Cancer Research.

Alzheimer's disease biomarker discovery using in silico literature mining and clinical validation

PubMed Central

2012-01-01

Background Alzheimer’s Disease (AD) is the most widespread form of dementia in the elderly but despite progress made in recent years towards a mechanistic understanding, there is still an urgent need for disease modification therapy and for early diagnostic tests. Substantial international efforts are being made to discover and validate biomarkers for AD using candidate analytes and various data-driven 'omics' approaches. Cerebrospinal fluid is in many ways the tissue of choice for biomarkers of brain disease but is limited by patient and clinician acceptability, and increasing attention is being paid to the search for blood-based biomarkers. The aim of this study was to use a novel in silico approach to discover a set of candidate biomarkers for AD. Methods We used an in silico literature mining approach to identify potential biomarkers by creating a summarized set of assertional metadata derived from relevant legacy information. We then assessed the validity of this approach using direct assays of the identified biomarkers in plasma by immunodetection methods. Results Using this in silico approach, we identified 25 biomarker candidates, at least three of which have subsequently been reported to be altered in blood or CSF from AD patients. Two further candidate biomarkers, indicated from the in silico approach, were choline acetyltransferase and urokinase-type plasminogen activator receptor. Using immunodetection, we showed that, in a large sample set, these markers are either altered in disease or correlate with MRI markers of atrophy. Conclusions These data support as a proof of concept the use of data mining and in silico analyses to derive valid biomarker candidates for AD and, by extension, for other disorders. PMID:23113945

Crucial considerations for pipelines to validate circulating biomarkers for breast cancer.

PubMed

Ewaisha, Radwa; Gawryletz, Chelsea D; Anderson, Karen S

2016-01-01

Despite decades of progress in breast imaging, breast cancer remains the second most common cause of cancer mortality in women. The rapidly proliferative breast cancers that are associated with high relapse rates and mortality frequently present in younger women, in unscreened individuals, or in the intervals between screening mammography. Biomarkers exist for monitoring metastatic disease, such as CEA, CA27.29 and CA15-3, but there are no circulating biomarkers clinically available for early detection, prognosis, or monitoring for clinical relapse. There has been significant progress in the discovery of potential circulating biomarkers, including proteins, autoantibodies, nucleic acids, exosomes, and circulating tumor cells, but the vast majority of these biomarkers have not progressed beyond initial research discovery, and none have yet been approved for clinical use in early stage disease. Here, the authors review the crucial considerations of developing pipelines for the rapid evaluation of circulating biomarkers for breast cancer.

Cancer biomarker discovery for cholangiocarcinoma: the high-throughput approaches

PubMed Central

Silsirivanit, Atit; Sawanyawisuth, Kanlayanee; Riggins, Gregory J.; Wongkham, Chaisiri

2015-01-01

Cholangiocarcinoma (CCA) is difficult to diagnose at an early stage and most tumors are detected at late stage where surgery or other therapy is ineffective. Many advanced techniques are applied to diagnose CCA; however, most are expensive and have varying degrees of accuracy. A less invasive and simpler procedure such as serum markers would be of substantial clinical benefit for diagnosis, monitoring, and predicting outcome for CCA patients. Recent advances in “Omics” technologies offer remarkable opportunities for establishment of biomarker-related to diseases. In this review, the potential biomarkers obtained from proteomics and glycomic studies are evaluated. Several protein markers were discovered from patient specimen, using two dimensional-differential gel electrophoresis couple with liquid chromatography tandem mass spectrometry (2D-DIGE/LC-MS-MS), matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF-MS), surface enhanced laser desorption/ionization (SELDI)-TOF-MS and capillary electrophoresis (CE)-MS, etc. Newly reported CCA-associated glycobiomarkers were identified using lectin-assisted, monoclonal antibody-assisted or specific-target strategies. The combination between carbohydrate binding-lectin and core protein-binding mAb significantly increased the values for detection of the glyco-biomarkers for CCA. Searching for specific and sensitive molecular markers to be used for population screening is worth being evaluated. This could lead to earlier diagnosis and improve outcome. Further investigation of those biomarker functions is also of value in order to better understand the tumor biology and use them as targets for future therapeutic agents. PMID:24616382

New horizon for breast cancer biomarker discoveries: What might the liquid biopsy of nipple aspirate fluid hold?

PubMed

Mannello, Ferdinando

2017-09-01

The existence of cellular, molecular and biochemical heterogeneity of human breast cancers reveals the intricacy of biomarkers complexity, stimulating studies on new approaches (like "liquid biopsies") for the improvements in precision medicine. Breast cancer is recognized as a leading cause of morbidity and mortality worldwide with tumors significantly diverse and containing many types of cells showing different genetic and epigenetic profiles. In this field, the technology of liquid biopsy (applied to a fluid produced by breast gland, named nipple aspirate fluids, NAF) highlights the power of combining basic and clinical research. NAF is the mirror of the entire ductal/alveolar breast tree providing almost complete proteomic profile and a valuable source for biomarker discovery, in non-invasive manner than tissue biopsies. The liquid biopsy technology using NAF may represent the outstanding breakthrough of proteomic cancer research revealing novel diagnostic and prognostic applications. In conjunction to metabolomic and degradome profiling, the use of NAF as liquid biopsy approach will improve the detection of changes in the cellular microenvironment of the breast tumors, understanding molecular and biochemical mechanisms which drive breast tumor initiation, maintenance and progression, and finally enhancing the development of novel drug targets and new treatment strategies. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Interferometric biosensing platform for multiplexed digital detection of viral pathogens and biomarkers

NASA Astrophysics Data System (ADS)

Daaboul, George

Label-free optical biosensors have been established as proven tools for monitoring specific biomolecular interactions. However, compact and robust embodiments of such instruments have yet to be introduced in order to provide sensitive, quantitative, and high-throughput biosensing for low-cost research and clinical applications. Here we present the interferometric reflectance-imaging sensor (IRIS). IRIS allows sensitive label free analysis using an inexpensive and durable multi-color LED illumination source on a silicon based surface. IRIS monitors biomolecular interaction through measurement of biomass addition to the sensor's surface. We demonstrate the capability of this system to dynamically monitor antigen---antibody interactions with a noise floor of 5.2 pg/mm 2 and DNA single mismatch detection under isothermal melting conditions in an array format. Ensemble detection of binding events using IRIS did not provide the sensitivity needed for detection of infectious disease and biomarkers at clinically relevant concentrations. Therefore, a new approach was adapted to the IRIS platform that allowed the detection and identification of individual nanoparticles on the sensor's surface. The new detection method was termed single-particle IRIS (SP-IRIS). We developed two detection modalities for SP-IRIS. The first modality is when the target is a nanoparticle such as a virus. We verified that SP-IRIS can accurately detect and size individual viral particles. Then we demonstrated that single nanoparticle counting and sizing methodology on SP-IRIS leads to a specific and sensitive virus sensor that can be multiplexed. Finally, we developed an assay for the detection of Ebola and Marburg. A detection limit of 3 x 103 PFU/ml was demonstrated for vesicular stomatitis virus (VSV) pseudotyped with Ebola or Marburg virus glycoprotein. We have demonstrated that virus detection can be done in human whole blood directly without the need for sample preparation. The second modality

Immunohistochemistry for predictive biomarkers in non-small cell lung cancer.

PubMed

Mino-Kenudson, Mari

2017-10-01

In the era of targeted therapy, predictive biomarker testing has become increasingly important for non-small cell lung cancer. Of multiple predictive biomarker testing methods, immunohistochemistry (IHC) is widely available and technically less challenging, can provide clinically meaningful results with a rapid turn-around-time and is more cost efficient than molecular platforms. In fact, several IHC assays for predictive biomarkers have already been implemented in routine pathology practice. In this review, we will discuss: (I) the details of anaplastic lymphoma kinase (ALK) and proto-oncogene tyrosine-protein kinase ROS (ROS1) IHC assays including the performance of multiple antibody clones, pros and cons of IHC platforms and various scoring systems to design an optimal algorithm for predictive biomarker testing; (II) issues associated with programmed death-ligand 1 (PD-L1) IHC assays; (III) appropriate pre-analytical tissue handling and selection of optimal tissue samples for predictive biomarker IHC.

Immunohistochemistry for predictive biomarkers in non-small cell lung cancer

PubMed Central

2017-01-01

In the era of targeted therapy, predictive biomarker testing has become increasingly important for non-small cell lung cancer. Of multiple predictive biomarker testing methods, immunohistochemistry (IHC) is widely available and technically less challenging, can provide clinically meaningful results with a rapid turn-around-time and is more cost efficient than molecular platforms. In fact, several IHC assays for predictive biomarkers have already been implemented in routine pathology practice. In this review, we will discuss: (I) the details of anaplastic lymphoma kinase (ALK) and proto-oncogene tyrosine-protein kinase ROS (ROS1) IHC assays including the performance of multiple antibody clones, pros and cons of IHC platforms and various scoring systems to design an optimal algorithm for predictive biomarker testing; (II) issues associated with programmed death-ligand 1 (PD-L1) IHC assays; (III) appropriate pre-analytical tissue handling and selection of optimal tissue samples for predictive biomarker IHC. PMID:29114473

Academic College of Emergency Experts in India's INDO-US Joint Working Group and OPUS12 Foundation Consensus Statement on Creating A Coordinated, Multi-Disciplinary, Patient-Centered, Global Point-of-Care Biomarker Discovery Network.

PubMed

Stawicki, Stanislaw P; Stoltzfus, Jill C; Aggarwal, Praveen; Bhoi, Sanjeev; Bhatt, Shashi; Kalra, O P; Bhalla, Ashish; Hoey, Brian A; Galwankar, Sagar C; Paladino, Lorenzo; Papadimos, Thomas J

2014-07-01

Biomarker science brings great promise to clinical medicine. This is especially true in the era of technology miniaturization, rapid dissemination of knowledge, and point-of-care (POC) implementation of novel diagnostics. Despite this tremendous progress, the journey from a candidate biomarker to a scientifically validated biomarker continues to be an arduous one. In addition to substantial financial resources, biomarker research requires considerable expertise and a multidisciplinary approach. Investigational designs must also be taken into account, with the randomized controlled trial remaining the "gold standard". The authors present a condensed overview of biomarker science and associated investigational methods, followed by specific examples from clinical areas where biomarker development and/or implementation resulted in tangible enhancements in patient care. This manuscript also serves as a call to arms for the establishment of a truly global, well-coordinated infrastructure dedicated to biomarker research and development, with focus on delivery of the latest discoveries directly to the patient via point-of-care technology.

Omics-based biomarkers: current status and potential use in the clinic.

PubMed

Quezada, Héctor; Guzmán-Ortiz, Ana Laura; Díaz-Sánchez, Hugo; Valle-Rios, Ricardo; Aguirre-Hernández, Jesús

In recent years, the use of high-throughput omics technologies has led to the rapid discovery of many candidate biomarkers. However, few of them have made the transition to the clinic. In this review, the promise of omics technologies to contribute to the process of biomarker development is described. An overview of the current state in this area is presented with examples of genomics, proteomics, transcriptomics, metabolomics and microbiomics biomarkers in the field of oncology, along with some proposed strategies to accelerate their validation and translation to improve the care of patients with neoplasms. The inherent complexity underlying neoplasms combined with the requirement of developing well-designed biomarker discovery processes based on omics technologies present a challenge for the effective development of biomarkers that may be useful in guiding therapies, addressing disease risks, and predicting clinical outcomes. Copyright © 2017 Hospital Infantil de México Federico Gómez. Publicado por Masson Doyma México S.A. All rights reserved.

Current and Prospective Protein Biomarkers of Lung Cancer

PubMed Central

Zamay, Tatiana N.; Zamay, Galina S.; Kolovskaya, Olga S.; Zukov, Ruslan A.; Petrova, Marina M.; Gargaun, Ana; Berezovski, Maxim V.

2017-01-01

Lung cancer is a malignant lung tumor with various histological variants that arise from different cell types, such as bronchial epithelium, bronchioles, alveoli, or bronchial mucous glands. The clinical course and treatment efficacy of lung cancer depends on the histological variant of the tumor. Therefore, accurate identification of the histological type of cancer and respective protein biomarkers is crucial for adequate therapy. Due to the great diversity in the molecular-biological features of lung cancer histological types, detection is impossible without knowledge of the nature and origin of malignant cells, which release certain protein biomarkers into the bloodstream. To date, different panels of biomarkers are used for screening. Unfortunately, a uniform serum biomarker composition capable of distinguishing lung cancer types is yet to be discovered. As such, histological analyses of tumor biopsies and immunohistochemistry are the most frequently used methods for establishing correct diagnoses. Here, we discuss the recent advances in conventional and prospective aptamer based strategies for biomarker discovery. Aptamers like artificial antibodies can serve as molecular recognition elements for isolation detection and search of novel tumor-associated markers. Here we will describe how these small synthetic single stranded oligonucleotides can be used for lung cancer biomarker discovery and utilized for accurate diagnosis and targeted therapy. Furthermore, we describe the most frequently used in-clinic and novel lung cancer biomarkers, which suggest to have the ability of differentiating between histological types of lung cancer and defining metastasis rate. PMID:29137182

Evaluation of qPCR curve analysis methods for reliable biomarker discovery: bias, resolution, precision, and implications.

PubMed

Ruijter, Jan M; Pfaffl, Michael W; Zhao, Sheng; Spiess, Andrej N; Boggy, Gregory; Blom, Jochen; Rutledge, Robert G; Sisti, Davide; Lievens, Antoon; De Preter, Katleen; Derveaux, Stefaan; Hellemans, Jan; Vandesompele, Jo

2013-01-01

RNA transcripts such as mRNA or microRNA are frequently used as biomarkers to determine disease state or response to therapy. Reverse transcription (RT) in combination with quantitative PCR (qPCR) has become the method of choice to quantify small amounts of such RNA molecules. In parallel with the democratization of RT-qPCR and its increasing use in biomedical research or biomarker discovery, we witnessed a growth in the number of gene expression data analysis methods. Most of these methods are based on the principle that the position of the amplification curve with respect to the cycle-axis is a measure for the initial target quantity: the later the curve, the lower the target quantity. However, most methods differ in the mathematical algorithms used to determine this position, as well as in the way the efficiency of the PCR reaction (the fold increase of product per cycle) is determined and applied in the calculations. Moreover, there is dispute about whether the PCR efficiency is constant or continuously decreasing. Together this has lead to the development of different methods to analyze amplification curves. In published comparisons of these methods, available algorithms were typically applied in a restricted or outdated way, which does not do them justice. Therefore, we aimed at development of a framework for robust and unbiased assessment of curve analysis performance whereby various publicly available curve analysis methods were thoroughly compared using a previously published large clinical data set (Vermeulen et al., 2009) [11]. The original developers of these methods applied their algorithms and are co-author on this study. We assessed the curve analysis methods' impact on transcriptional biomarker identification in terms of expression level, statistical significance, and patient-classification accuracy. The concentration series per gene, together with data sets from unpublished technical performance experiments, were analyzed in order to assess the

MicroRNA Biomarkers of Toxicity in Biological Matrices ...

EPA Pesticide Factsheets

Biomarker measurements that reliably correlate with tissue injury and can be measured from sampling accessible biofluids offer enormous benefits in terms of cost, time, and convenience when assessing environmental and drug-induced toxicity in model systems or human cohorts. MicroRNAs (miRNAs) have emerged in recent years as a promising new type of biomarker for monitoring toxicity. Recent enthusiasm for miRNA biomarker research has been fueled by discoveries that certain miRNA species are cell-type specific and released during injury, thus raising the possibility of using biofluid-based miRNAs as a “liquid biopsy” that may be obtained by sampling extracellular fluids. As biomarkers, miRNAs demonstrate improved stability as compared to many protein markers and sequences are largely conserved across species, simplifying analytical techniques. Recent efforts have sought to identify miRNAs that are released into accessible biofluids following xenobiotic exposure, using compounds that target specific organs. While still early in the discovery phase, miRNA biomarkers will have an increasingly important role in the assessment of adverse effects of both environmental chemicals and pharmaceutical drugs. Here, we review the current findings of biofluid-based miRNAs, as well as highlight technical challenges in assessing toxicologic pathology using these biomarkers. MicroRNAs (miRNAs) are small, non-coding RNA species that selectively bind mRNA molecules and alter thei

EFFECT OF MECHANICAL VIBRATION GENERATED IN OSCILLATING/VIBRATORY PLATFORM ON THE CONCENTRATION OF PLASMA BIOMARKERS AND ON THE WEIGHT IN RATS.

PubMed

Frederico, Éric Heleno Freire Ferreira; de Sá-Caputo, Danúbia da Cunha; Moreira-Marconi, Eloá; Guimarães, Carlos Alberto Sampaio; Cardoso, André Luiz Bandeira Dionísio; Dionello, Carla da Fontoura; Morel, Danielle Soares; Sousa-Gonçalves, Cintia Renata; Paineiras-Domingos, Laisa Liana; Cavalcanti, Rebeca Graça Costa; Asad, Nasser Ribeiro; Marin, Pedro Jesus; Bernardo-Filho, Mario

2017-01-01

Whole body vibration (WBV) exercise has been used in health sciences. Authors have reported that changes on the concentration of plasma biomarkers could be associated with the WBV effects. The aim of this investigation is to assess the consequences of exposition of 25 Hz mechanical vibration generated in oscillating/vibratory platform (OVP) on the concentration of some plasma biomarkers and on the weight of rats. Wistar rats were divided into two groups. The animals of the Experimental Group (EG) were submitted to vibration (25 Hz) generated in an OVP with four bouts of 30 seconds with rest time of 60 seconds between the bouts. This procedure was performed daily for 12 days. The animals of the control group (CG) were not exposed to vibration. Our findings show that the WBV exercise at 25 Hz was not capable to alter significantly ( p <0.05) the weight of the rats. A significant alteration in the concentrations of amylase was found. Our results indicate a modulation of the WBV exercise with vibration of 25 Hz of frequency (i) in the pathways related to the weight and (ii) in the concentration of some biomarkers, such as amylase.

Protein biomarkers of alcohol abuse

PubMed Central

Torrente, Mariana P; Freeman, Willard M; Vrana, Kent E

2012-01-01

Alcohol abuse can lead to a number of health and social issues. Our current inability to accurately assess long-term drinking behaviors is an important obstacle to its diagnosis and treatment. Biomarkers for chronic alcohol consumption have made a number of important advances but have yet to become highly accurate and as accepted as objective tests for other diseases. Thus, there is a crucial need for the development of more sensitive and specific markers of alcohol abuse. Recent advancements in proteomic technologies have greatly increased the potential for alcohol abuse biomarker discovery. Here, the authors review established and novel protein biomarkers for long-term alcohol consumption and the proteomic technologies that have been used in their study. PMID:22967079

Integrated analysis of numerous heterogeneous gene expression profiles for detecting robust disease-specific biomarkers and proposing drug targets.

PubMed

Amar, David; Hait, Tom; Izraeli, Shai; Shamir, Ron

2015-09-18

Genome-wide expression profiling has revolutionized biomedical research; vast amounts of expression data from numerous studies of many diseases are now available. Making the best use of this resource in order to better understand disease processes and treatment remains an open challenge. In particular, disease biomarkers detected in case-control studies suffer from low reliability and are only weakly reproducible. Here, we present a systematic integrative analysis methodology to overcome these shortcomings. We assembled and manually curated more than 14,000 expression profiles spanning 48 diseases and 18 expression platforms. We show that when studying a particular disease, judicious utilization of profiles from other diseases and information on disease hierarchy improves classification quality, avoids overoptimistic evaluation of that quality, and enhances disease-specific biomarker discovery. This approach yielded specific biomarkers for 24 of the analyzed diseases. We demonstrate how to combine these biomarkers with large-scale interaction, mutation and drug target data, forming a highly valuable disease summary that suggests novel directions in disease understanding and drug repurposing. Our analysis also estimates the number of samples required to reach a desired level of biomarker stability. This methodology can greatly improve the exploitation of the mountain of expression profiles for better disease analysis. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

A microfluidic biochip platform for electrical quantification of proteins.

PubMed

Valera, Enrique; Berger, Jacob; Hassan, Umer; Ghonge, Tanmay; Liu, Julia; Rappleye, Michael; Winter, Jackson; Abboud, Daniel; Haidry, Zeeshan; Healey, Ryan; Hung, Na-Teng; Leung, Nathaniel; Mansury, Naif; Hasnain, Alexander; Lannon, Christine; Price, Zachary; White, Karen; Bashir, Rashid

2018-05-15

Sepsis, an adverse auto-immune response to an infection often causing life-threatening complications, results in the highest mortality and treatment cost of any illness in US hospitals. Several immune biomarker levels, including Interleukin 6 (IL-6), have shown a high correlation to the onset and progression of sepsis. Currently, no technology diagnoses and stratifies sepsis progression using biomarker levels. This paper reports a microfluidic biochip platform to detect proteins in undiluted human plasma samples. The device uses a differential enumeration platform that integrates Coulter counting principles, antigen specific capture chambers, and micro size bead based immunodetection to quantify cytokines. This microfluidic biochip was validated as a potential point of care technology by quantifying IL-6 from plasma samples (n = 29) with good correlation (R2 = 0.81) and agreement (Bland-Altman) compared to controls. In combination with previous applications, this point of care platform can potentially detect cell and protein biomarkers simultaneously for sepsis stratification.

A platform for discovery: The University of Pennsylvania Integrated Neurodegenerative Disease Biobank

PubMed Central

Toledo, Jon B.; Van Deerlin, Vivianna M.; Lee, Edward B.; Suh, EunRan; Baek, Young; Robinson, John L.; Xie, Sharon X.; McBride, Jennifer; Wood, Elisabeth M.; Schuck, Theresa; Irwin, David J.; Gross, Rachel G.; Hurtig, Howard; McCluskey, Leo; Elman, Lauren; Karlawish, Jason; Schellenberg, Gerard; Chen-Plotkin, Alice; Wolk, David; Grossman, Murray; Arnold, Steven E.; Shaw, Leslie M.; Lee, Virginia M.-Y.; Trojanowski, John Q.

2014-01-01

Neurodegenerative diseases (NDs) are defined by the accumulation of abnormal protein deposits in the central nervous system (CNS), and only neuropathological examination enables a definitive diagnosis. Brain banks and their associated scientific programs have shaped the actual knowledge of NDs, identifying and characterizing the CNS deposits that define new diseases, formulating staging schemes, and establishing correlations between neuropathological changes and clinical features. However, brain banks have evolved to accommodate the banking of biofluids as well as DNA and RNA samples. Moreover, the value of biobanks is greatly enhanced if they link all the multidimensional clinical and laboratory information of each case, which is accomplished, optimally, using systematic and standardized operating procedures, and in the framework of multidisciplinary teams with the support of a flexible and user-friendly database system that facilitates the sharing of information of all the teams in the network. We describe a biobanking system that is a platform for discovery research at the Center for Neurodegenerative Disease Research at the University of Pennsylvania. PMID:23978324

Improving low-level plasma protein mass spectrometry-based detection for candidate biomarker discovery and validation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Page, Jason S.; Kelly, Ryan T.; Camp, David G.

2008-09-01

Methods. To improve the detection of low abundance protein candidate biomarker discovery and validation, particularly in complex biological fluids such as blood plasma, increased sensitivity is desired using mass spectrometry (MS)-based instrumentation. A key current limitation on the sensitivity of electrospray ionization (ESI) MS is due to the fact that many sample molecules in solution are never ionized, and the vast majority of the ions that are created are lost during transmission from atmospheric pressure to the low pressure region of the mass analyzer. Two key technologies, multi-nanoelectrospray emitters and the electrodynamic ion funnel have recently been developed and refinedmore » at Pacific Northwest National Laboratory (PNNL) to greatly improve the ionization and transmission efficiency of ESI MS based analyses. Multi-emitter based ESI enables the flow from a single source (typically a liquid chromatography [LC] column) to be divided among an array of emitters (Figure 1). The flow rate delivered to each emitter is thus reduced, allowing the well-documented benefits of nanoelectrospray 1 for both sensitivity and quantitation to be realized for higher flow rate separations. To complement the increased ionization efficiency afforded by multi-ESI, tandem electrodynamic ion funnels have also been developed at PNNL, and shown to greatly improve ion transmission efficiency in the ion source interface.2, 3 These technologies have been integrated into a triple quadrupole mass spectrometer for multiple reaction monitoring (MRM) of probable biomarker candidates in blood plasma and show promise for the identification of new species even at low level concentrations.« less

Quantitative imaging biomarker ontology (QIBO) for knowledge representation of biomedical imaging biomarkers.

PubMed

Buckler, Andrew J; Liu, Tiffany Ting; Savig, Erica; Suzek, Baris E; Ouellette, M; Danagoulian, J; Wernsing, G; Rubin, Daniel L; Paik, David

2013-08-01

A widening array of novel imaging biomarkers is being developed using ever more powerful clinical and preclinical imaging modalities. These biomarkers have demonstrated effectiveness in quantifying biological processes as they occur in vivo and in the early prediction of therapeutic outcomes. However, quantitative imaging biomarker data and knowledge are not standardized, representing a critical barrier to accumulating medical knowledge based on quantitative imaging data. We use an ontology to represent, integrate, and harmonize heterogeneous knowledge across the domain of imaging biomarkers. This advances the goal of developing applications to (1) improve precision and recall of storage and retrieval of quantitative imaging-related data using standardized terminology; (2) streamline the discovery and development of novel imaging biomarkers by normalizing knowledge across heterogeneous resources; (3) effectively annotate imaging experiments thus aiding comprehension, re-use, and reproducibility; and (4) provide validation frameworks through rigorous specification as a basis for testable hypotheses and compliance tests. We have developed the Quantitative Imaging Biomarker Ontology (QIBO), which currently consists of 488 terms spanning the following upper classes: experimental subject, biological intervention, imaging agent, imaging instrument, image post-processing algorithm, biological target, indicated biology, and biomarker application. We have demonstrated that QIBO can be used to annotate imaging experiments with standardized terms in the ontology and to generate hypotheses for novel imaging biomarker-disease associations. Our results established the utility of QIBO in enabling integrated analysis of quantitative imaging data.

Cell and small animal models for phenotypic drug discovery.

PubMed

Szabo, Mihaly; Svensson Akusjärvi, Sara; Saxena, Ankur; Liu, Jianping; Chandrasekar, Gayathri; Kitambi, Satish S

2017-01-01

The phenotype-based drug discovery (PDD) approach is re-emerging as an alternative platform for drug discovery. This review provides an overview of the various model systems and technical advances in imaging and image analyses that strengthen the PDD platform. In PDD screens, compounds of therapeutic value are identified based on the phenotypic perturbations produced irrespective of target(s) or mechanism of action. In this article, examples of phenotypic changes that can be detected and quantified with relative ease in a cell-based setup are discussed. In addition, a higher order of PDD screening setup using small animal models is also explored. As PDD screens integrate physiology and multiple signaling mechanisms during the screening process, the identified hits have higher biomedical applicability. Taken together, this review highlights the advantages gained by adopting a PDD approach in drug discovery. Such a PDD platform can complement target-based systems that are currently in practice to accelerate drug discovery.

Epigenetic alterations as cancer diagnostic, prognostic, and predictive biomarkers.

PubMed

Deng, Dajun; Liu, Zhaojun; Du, Yantao

2010-01-01

Alterations of DNA methylation and transcription of microRNAs (miRNAs) are very stable phenomena in tissues and body fluids and suitable for sensitive detection. These advantages enable us to translate some important discoveries on epigenetic oncology into biomarkers for control of cancer. A few promising epigenetic biomarkers are emerging. Clinical trials using methylated CpG islands of p16, Septin9, and MGMT as biomarkers are carried out for predication of cancer development, diagnosis, and chemosensitivity. Circulating miRNAs are promising biomarkers, too. Breakthroughs in the past decade imply that epigenetic biomarkers may be useful in reducing the burden of cancer. Copyright © 2010 Elsevier Inc. All rights reserved.

Centrifugal Microfluidic Platform for Rapid, Multiplexed Detection of TB and HIV Biomarkers in Whole Blood Samples

DOE Office of Scientific and Technical Information (OSTI.GOV)

Litvinov, Julia; Moen, Scott T.; Berry, Gregory J.

Infection with Mycobacterium Tuberculosis represents a significant threat to people with immune disorders, such as HIV-positive individuals, and can result in significant health complications or death if not diagnosed and treated early. We present a centrifugal microfluidic platform for multiplexed detection of tuberculosis and HIV biomarkers in human whole blood with minimal sample preparation and a sample-to-answer time of 30 minutes. This multiplexed assay was developed for the detection of two M.tuberculosis secreted proteins, whose secretion represents an active and ongoing infection, as well as detection of HIV p24 protein and human anti-p24 antibodies. The limit of detection for thismore » multiplex assay is in the pg/mL range for both HIV and M.tuberculosis proteins, making this assay potentially useful in the clinical diagnosis of both HIV and Tuberculosis proteins indicative of active infection. Antigen detection for the HIV assay sensitivity was 89%, the specificity 85%. Serological detection had 100% sensitivity and specificity for the limited sample pool. The centrifugal microfluidic platform presented here offers the potential for a portable, fast and inexpensive multiplexed diagnostic device that can be used in resource-limited settings for diagnosis of TB and HIV.« less

Centrifugal Microfluidic Platform for Rapid, Multiplexed Detection of TB and HIV Biomarkers in Whole Blood Samples

DOE PAGES

Litvinov, Julia; Moen, Scott T.; Berry, Gregory J.; ...

2017-05-30

Infection with Mycobacterium Tuberculosis represents a significant threat to people with immune disorders, such as HIV-positive individuals, and can result in significant health complications or death if not diagnosed and treated early. We present a centrifugal microfluidic platform for multiplexed detection of tuberculosis and HIV biomarkers in human whole blood with minimal sample preparation and a sample-to-answer time of 30 minutes. This multiplexed assay was developed for the detection of two M.tuberculosis secreted proteins, whose secretion represents an active and ongoing infection, as well as detection of HIV p24 protein and human anti-p24 antibodies. The limit of detection for thismore » multiplex assay is in the pg/mL range for both HIV and M.tuberculosis proteins, making this assay potentially useful in the clinical diagnosis of both HIV and Tuberculosis proteins indicative of active infection. Antigen detection for the HIV assay sensitivity was 89%, the specificity 85%. Serological detection had 100% sensitivity and specificity for the limited sample pool. The centrifugal microfluidic platform presented here offers the potential for a portable, fast and inexpensive multiplexed diagnostic device that can be used in resource-limited settings for diagnosis of TB and HIV.« less

Miocene climate variations in the Moesian Platform sediments based on sedimentology and biomarkers

NASA Astrophysics Data System (ADS)

Butiseaca, Geanina; Vasiliev, Iuliana; Rabagia, Traian; Dinu, Corneliu; Mulch, Andreas

2017-04-01

During the Miocene the Moesian Platform (southern Romania and northern Bulgaria) had a complicated flexural behavior due to the mobility of the nearby orogens. The different behavior induced varying sediment charges, sediment distribution and sediment types. The northern part of the study area (on which the Dacian Basin is overlaid) is characterized by siliciclastic units with dominantly deep facieses, while the southern part is characterized by carbonate production in shallower basin waters. Since the Miocene, the Dacian and Black Sea basins have been highly sensitive to fluctuations in the hydrological cycle. To establish the dynamic evolution of the basin and the climate variations during the Miocene, we have sampled both northern and southern margins of the basin. To discriminate between the tectonic imprint and the eustatic influence over the sedimentation rate we have chosen a multidisciplinary approach including sedimentology, tectonics and organic geochemistry based reconstructions. The sedimentary succession is interrupted by few unconformities correspondent with the main phases of orogeny (in the Carpathian Foredeep) while the southern part seems to have been exposed more often expressed in the geological record by a higher number of unconformities and paleo-soils levels. The n-alkanes distribution recovered from the lipids extracted from the sedimentary rocks indicates a mixture of terrestrial and marine input in the northern, Romanian, closer to Carpathians, part of the Dacian Basin. Surprisingly, the southern, Bulgarian side, showed a more predominant terrestrial input (with higher contribution of the long chain n-alkanes) at least for the Sarmatian (arround 10 Ma). The estimated paleotemperatures based on branched GDGT's indicate much warmer conditions than present day, up to a value of 20 C mean annual temperatures. We will further investigate the paleoenvironmental changes during the latest Miocene of the Dacian basin, using the biomarker approach

Development of a universal metabolome-standard method for long-term LC-MS metabolome profiling and its application for bladder cancer urine-metabolite-biomarker discovery.

PubMed

Peng, Jun; Chen, Yi-Ting; Chen, Chien-Lun; Li, Liang

2014-07-01

Large-scale metabolomics study requires a quantitative method to generate metabolome data over an extended period with high technical reproducibility. We report a universal metabolome-standard (UMS) method, in conjunction with chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS), to provide long-term analytical reproducibility and facilitate metabolome comparison among different data sets. In this method, UMS of a specific type of sample labeled by an isotope reagent is prepared a priori. The UMS is spiked into any individual samples labeled by another form of the isotope reagent in a metabolomics study. The resultant mixture is analyzed by LC-MS to provide relative quantification of the individual sample metabolome to UMS. UMS is independent of a study undertaking as well as the time of analysis and useful for profiling the same type of samples in multiple studies. In this work, the UMS method was developed and applied for a urine metabolomics study of bladder cancer. UMS of human urine was prepared by (13)C2-dansyl labeling of a pooled sample from 20 healthy individuals. This method was first used to profile the discovery samples to generate a list of putative biomarkers potentially useful for bladder cancer detection and then used to analyze the verification samples about one year later. Within the discovery sample set, three-month technical reproducibility was examined using a quality control sample and found a mean CV of 13.9% and median CV of 9.4% for all the quantified metabolites. Statistical analysis of the urine metabolome data showed a clear separation between the bladder cancer group and the control group from the discovery samples, which was confirmed by the verification samples. Receiver operating characteristic (ROC) test showed that the area under the curve (AUC) was 0.956 in the discovery data set and 0.935 in the verification data set. These results demonstrated the utility of the UMS method for long-term metabolomics and

A Sol-gel Integrated Dual-readout Microarray Platform for Quantification and Identification of Prostate-specific Antigen.

PubMed

Lee, SangWook; Lee, Jong Hyun; Kwon, Hyuck Gi; Laurell, Thomas; Jeong, Ok Chan; Kim, Soyoun

2018-01-01

Here, we report a sol-gel integrated affinity microarray for on-chip matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) that enables capture and identification of prostate?specific antigen (PSA) in samples. An anti-PSA antibody (H117) was mixed with a sol?gel, and the mixture was spotted onto a porous silicon (pSi) surface without additional surface modifications. The antibody easily penetrates the sol-gel macropore fluidic network structure, making possible high affinities. To assess the capture affinity of the platform, we performed a direct assay using fluorescein isothiocyanate-labeled PSA. Pure PSA was subjected to on-chip MALDI-TOF-MS analysis, yielding three clear mass peptide peaks (m/z = 1272, 1407, and 1872). The sol-gel microarray platform enables dual readout of PSA both fluorometric and MALDI-TOF MS analysis in biological samples. Here we report a useful method for a means for discovery of biomarkers in complex body fluids.

Bioinformatics in translational drug discovery.

PubMed

Wooller, Sarah K; Benstead-Hume, Graeme; Chen, Xiangrong; Ali, Yusuf; Pearl, Frances M G

2017-08-31

Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications. © 2017 The Author(s).

Emerging proteomics biomarkers and prostate cancer burden in Africa

PubMed Central

Adeola, Henry A.; Blackburn, Jonathan M.; Rebbeck, Timothy R.; Zerbini, Luiz F.

2017-01-01

Various biomarkers have emerged via high throughput omics-based approaches for use in diagnosis, treatment, and monitoring of prostate cancer. Many of these have yet to be demonstrated as having value in routine clinical practice. Moreover, there is a dearth of information on validation of these emerging prostate biomarkers within African cohorts, despite the huge burden and aggressiveness of prostate cancer in men of African descent. This review focusses of the global landmark achievements in prostate cancer proteomics biomarker discovery and the potential for clinical implementation of these biomarkers in Africa. Biomarker validation processes at the preclinical, translational and clinical research level are discussed here, as are the challenges and prospects for the evaluation and use of novel proteomic prostate cancer biomarkers. PMID:28388542

Emerging proteomics biomarkers and prostate cancer burden in Africa.

PubMed

Adeola, Henry A; Blackburn, Jonathan M; Rebbeck, Timothy R; Zerbini, Luiz F

2017-06-06

Various biomarkers have emerged via high throughput omics-based approaches for use in diagnosis, treatment, and monitoring of prostate cancer. Many of these have yet to be demonstrated as having value in routine clinical practice. Moreover, there is a dearth of information on validation of these emerging prostate biomarkers within African cohorts, despite the huge burden and aggressiveness of prostate cancer in men of African descent. This review focusses of the global landmark achievements in prostate cancer proteomics biomarker discovery and the potential for clinical implementation of these biomarkers in Africa. Biomarker validation processes at the preclinical, translational and clinical research level are discussed here, as are the challenges and prospects for the evaluation and use of novel proteomic prostate cancer biomarkers.

EFFECT OF MECHANICAL VIBRATION GENERATED IN OSCILLATING/VIBRATORY PLATFORM ON THE CONCENTRATION OF PLASMA BIOMARKERS AND ON THE WEIGHT IN RATS

PubMed Central

Frederico, Éric Heleno Freire Ferreira; de Sá-Caputo, Danúbia da Cunha; Moreira-Marconi, Eloá; Guimarães, Carlos Alberto Sampaio; Cardoso, André Luiz Bandeira Dionísio; Dionello, Carla da Fontoura; Morel, Danielle Soares; Sousa-Gonçalves, Cintia Renata; Paineiras-Domingos, Laisa Liana; Cavalcanti, Rebeca Graça Costa; Asad, Nasser Ribeiro; Marin, Pedro Jesus; Bernardo-Filho, Mario

2017-01-01

Background: Whole body vibration (WBV) exercise has been used in health sciences. Authors have reported that changes on the concentration of plasma biomarkers could be associated with the WBV effects. The aim of this investigation is to assess the consequences of exposition of 25 Hz mechanical vibration generated in oscillating/vibratory platform (OVP) on the concentration of some plasma biomarkers and on the weight of rats. Materials and Methods: Wistar rats were divided into two groups. The animals of the Experimental Group (EG) were submitted to vibration (25 Hz) generated in an OVP with four bouts of 30 seconds with rest time of 60 seconds between the bouts. This procedure was performed daily for 12 days. The animals of the control group (CG) were not exposed to vibration. Results: Our findings show that the WBV exercise at 25 Hz was not capable to alter significantly (p<0.05) the weight of the rats. A significant alteration in the concentrations of amylase was found. Conclusion: Our results indicate a modulation of the WBV exercise with vibration of 25 Hz of frequency (i) in the pathways related to the weight and (ii) in the concentration of some biomarkers, such as amylase. PMID:28740944

Bladder cancer biomarker discovery using global metabolomic profiling of urine.

PubMed

Wittmann, Bryan M; Stirdivant, Steven M; Mitchell, Matthew W; Wulff, Jacob E; McDunn, Jonathan E; Li, Zhen; Dennis-Barrie, Aphrihl; Neri, Bruce P; Milburn, Michael V; Lotan, Yair; Wolfert, Robert L

2014-01-01

Bladder cancer (BCa) is a common malignancy worldwide and has a high probability of recurrence after initial diagnosis and treatment. As a result, recurrent surveillance, primarily involving repeated cystoscopies, is a critical component of post diagnosis patient management. Since cystoscopy is invasive, expensive and a possible deterrent to patient compliance with regular follow-up screening, new non-invasive technologies to aid in the detection of recurrent and/or primary bladder cancer are strongly needed. In this study, mass spectrometry based metabolomics was employed to identify biochemical signatures in human urine that differentiate bladder cancer from non-cancer controls. Over 1000 distinct compounds were measured including 587 named compounds of known chemical identity. Initial biomarker identification was conducted using a 332 subject sample set of retrospective urine samples (cohort 1), which included 66 BCa positive samples. A set of 25 candidate biomarkers was selected based on statistical significance, fold difference and metabolic pathway coverage. The 25 candidate biomarkers were tested against an independent urine sample set (cohort 2) using random forest analysis, with palmitoyl sphingomyelin, lactate, adenosine and succinate providing the strongest predictive power for differentiating cohort 2 cancer from non-cancer urines. Cohort 2 metabolite profiling revealed additional metabolites, including arachidonate, that were higher in cohort 2 cancer vs. non-cancer controls, but were below quantitation limits in the cohort 1 profiling. Metabolites related to lipid metabolism may be especially interesting biomarkers. The results suggest that urine metabolites may provide a much needed non-invasive adjunct diagnostic to cystoscopy for detection of bladder cancer and recurrent disease management.

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

PubMed Central

Liu, Jiangang; Jolly, Robert A.; Smith, Aaron T.; Searfoss, George H.; Goldstein, Keith M.; Uversky, Vladimir N.; Dunker, Keith; Li, Shuyu; Thomas, Craig E.; Wei, Tao

2011-01-01

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses. PMID:21935387

Searching for the noninvasive biomarker Holy Grail: Are urine proteomics the answer?

USDA-ARS?s Scientific Manuscript database

Recently, biobehavioral nursing scientists have focused their attention on the search for biomarkers or biological signatures to identify patients at risk for various health problems and poor disease outcomes. In response to the national impetus for biomarker discovery, the measurement of biological...

Biomarkers in psoriasis and psoriatic arthritis.

PubMed

Villanova, Federica; Di Meglio, Paola; Nestle, Frank O

2013-04-01

Psoriasis is a common immune-mediated disease of the skin, which associates in 20-30% of patients with psoriatic arthritis (PsA). The immunopathogenesis of both conditions is not fully understood as it is the result of a complex interaction between genetic, environmental and immunological factors. At present there is no cure for psoriasis and there are no specific markers that can accurately predict disease progression and therapeutic response. Therefore, biomarkers for disease prognosis and response to treatment are urgently needed to help clinicians with objective indications to improve patient management and outcomes. Although many efforts have been made to identify psoriasis/PsA biomarkers none of them has yet been translated into routine clinical practice. In this review we summarise the different classes of possible biomarkers explored in psoriasis and PsA so far and discuss novel strategies for biomarker discovery.

Ensuring Sample Quality for Biomarker Discovery Studies - Use of ICT Tools to Trace Biosample Life-cycle.

PubMed

Riondino, Silvia; Ferroni, Patrizia; Spila, Antonella; Alessandroni, Jhessica; D'Alessandro, Roberta; Formica, Vincenzo; Della-Morte, David; Palmirotta, Raffaele; Nanni, Umberto; Roselli, Mario; Guadagni, Fiorella

2015-01-01

The growing demand of personalized medicine marked the transition from an empirical medicine to a molecular one, aimed at predicting safer and more effective medical treatment for every patient, while minimizing adverse effects. This passage has emphasized the importance of biomarker discovery studies, and has led sample availability to assume a crucial role in biomedical research. Accordingly, a great interest in Biological Bank science has grown concomitantly. In biobanks, biological material and its accompanying data are collected, handled and stored in accordance with standard operating procedures (SOPs) and existing legislation. Sample quality is ensured by adherence to SOPs and sample whole life-cycle can be recorded by innovative tracking systems employing information technology (IT) tools for monitoring storage conditions and characterization of vast amount of data. All the above will ensure proper sample exchangeability among research facilities and will represent the starting point of all future personalized medicine-based clinical trials. Copyright© 2015, International Institute of Anticancer Research (Dr. John G. Delinasios), All rights reserved.

Cellphone-based detection platform for rbST biomarker analysis in milk extracts using a microsphere fluorescence immunoassay.

PubMed

Ludwig, Susann K J; Zhu, Hongying; Phillips, Stephen; Shiledar, Ashutosh; Feng, Steve; Tseng, Derek; van Ginkel, Leendert A; Nielen, Michel W F; Ozcan, Aydogan

2014-11-01

Current contaminant and residue monitoring throughout the food chain is based on sampling, transport, administration, and analysis in specialized control laboratories. This is a highly inefficient and costly process since typically more than 99% of the samples are found to be compliant. On-site simplified prescreening may provide a scenario in which only samples that are suspect are transported and further processed. Such a prescreening can be performed using a small attachment on a cellphone. To this end, a cellphone-based imaging platform for a microsphere fluorescence immunoassay that detects the presence of anti-recombinant bovine somatotropin (rbST) antibodies in milk extracts was developed. RbST administration to cows increases their milk production, but is illegal in the EU and a public health concern in the USA. The cellphone monitors the presence of anti-rbST antibodies (rbST biomarker), which are endogenously produced upon administration of rbST and excreted in milk. The rbST biomarker present in milk extracts was captured by rbST covalently coupled to paramagnetic microspheres and labeled by quantum dot (QD)-coupled detection antibodies. The emitted fluorescence light from these captured QDs was then imaged using the cellphone camera. Additionally, a dark-field image was taken in which all microspheres present were visible. The fluorescence and dark-field microimages were analyzed using a custom-developed Android application running on the same cellphone. With this setup, the microsphere fluorescence immunoassay and cellphone-based detection were successfully applied to milk sample extracts from rbST-treated and untreated cows. An 80% true-positive rate and 95% true-negative rate were achieved using this setup. Next, the cellphone-based detection platform was benchmarked against a newly developed planar imaging array alternative and found to be equally performing versus the much more sophisticated alternative. Using cellphone-based on-site analysis in

Biomarkers: an overview for oncology nurses.

PubMed

Richmond, Ellen S; Dunn, Debra

2012-05-01

To provide an overview of the basic principles of biomarker use in clinical oncology practice and discuss the range of biomarker forms (from genes to constitutional characteristics), biomarker functions (both disease- and drug-related), modalities (protein expression patterns to patient history), the criteria for biomarker validation, and the integral role of bioinformatics. Published nursing and medical literature. The premise of nursing assessment is the same as that of biomarker use - biological variables that appear at one level of biological organization (eg, molecule, organelle, cell, tissue, organ, and organism) correspond to processes or events occurring at other levels of biologic organization. The advent of genomic technologies has logarithmically increased the volume of biomarkers, which are expected to provide new insights that improve patient care. Nurses and patients will benefit greatly from the incorporation of molecular biomarkers into patient care. Nurses will be able to better assess (and anticipate) patient needs with the new insights that are available in the post-genomic, personalized medicine era of health care. Although the rapid rate of technological changes and new discoveries will require continuing concerted educational efforts, the improved quality of patient care will be rewarded by better outcomes. Published by Elsevier Inc.

Enrichment of low-molecular-weight proteins from biofluids for biomarker discovery.

PubMed

Chertov, Oleg; Simpson, John T; Biragyn, Arya; Conrads, Thomas P; Veenstra, Timothy D; Fisher, Robert J

2005-01-01

The dramatic progress in mass spectrometry-based methods of protein identification has triggered a new quest for disease-associated biomarkers. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and its variant surface-enhanced laser desorption/ionization mass spectrometry, provide effective means to explore the less studied information slice of the human serum proteome -- low-molecular-weight proteins and peptides. These low-molecular-weight proteins and peptides are promising for the detection of important biomarkers. Due to the significant experimental problems imposed by high-abundance and high-molecular-weight proteins, it is important to effectively remove these species prior to mass spectrometry analysis of the low-molecular-weight serum and plasma proteomes. In this review, the advantages afforded by recently introduced methods for prefractionation of serum, as they pertain to the detection and identification of biomarkers, will be discussed.

Cancer biomarker discovery: the entropic hallmark.

PubMed

Berretta, Regina; Moscato, Pablo

2010-08-18

It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles

Complex biomarker discovery in neuroimaging data: Finding a needle in a haystack☆

PubMed Central

Atluri, Gowtham; Padmanabhan, Kanchana; Fang, Gang; Steinbach, Michael; Petrella, Jeffrey R.; Lim, Kelvin; MacDonald, Angus; Samatova, Nagiza F.; Doraiswamy, P. Murali; Kumar, Vipin

2013-01-01

Neuropsychiatric disorders such as schizophrenia, bipolar disorder and Alzheimer's disease are major public health problems. However, despite decades of research, we currently have no validated prognostic or diagnostic tests that can be applied at an individual patient level. Many neuropsychiatric diseases are due to a combination of alterations that occur in a human brain rather than the result of localized lesions. While there is hope that newer imaging technologies such as functional and anatomic connectivity MRI or molecular imaging may offer breakthroughs, the single biomarkers that are discovered using these datasets are limited by their inability to capture the heterogeneity and complexity of most multifactorial brain disorders. Recently, complex biomarkers have been explored to address this limitation using neuroimaging data. In this manuscript we consider the nature of complex biomarkers being investigated in the recent literature and present techniques to find such biomarkers that have been developed in related areas of data mining, statistics, machine learning and bioinformatics. PMID:24179856

A new approach to epigenome-wide discovery of non-invasive methylation biomarkers for colorectal cancer screening in circulating cell-free DNA using pooled samples.

PubMed

Gallardo-Gómez, María; Moran, Sebastian; Páez de la Cadena, María; Martínez-Zorzano, Vicenta Soledad; Rodríguez-Berrocal, Francisco Javier; Rodríguez-Girondo, Mar; Esteller, Manel; Cubiella, Joaquín; Bujanda, Luis; Castells, Antoni; Balaguer, Francesc; Jover, Rodrigo; De Chiara, Loretta

2018-01-01

Colorectal cancer is the fourth cause of cancer-related deaths worldwide, though detection at early stages associates with good prognosis. Thus, there is a clear demand for novel non-invasive tests for the early detection of colorectal cancer and premalignant advanced adenomas, to be used in population-wide screening programs. Aberrant DNA methylation detected in liquid biopsies, such as serum circulating cell-free DNA (cfDNA), is a promising source of non-invasive biomarkers. This study aimed to assess the feasibility of using cfDNA pooled samples to identify potential serum methylation biomarkers for the detection of advanced colorectal neoplasia (colorectal cancer or advanced adenomas) using microarray-based technology. cfDNA was extracted from serum samples from 20 individuals with no colorectal findings, 20 patients with advanced adenomas, and 20 patients with colorectal cancer (stages I and II). Two pooled samples were prepared for each pathological group using equal amounts of cfDNA from 10 individuals, sex-, age-, and recruitment hospital-matched. We measured the methylation levels of 866,836 CpG positions across the genome using the MethylationEPIC array. Pooled serum cfDNA methylation data meets the quality requirements. The proportion of detected CpG in all pools (> 99% with detection p value < 0.01) exceeded Illumina Infinium methylation data quality metrics of the number of sites detected. The differential methylation analysis revealed 1384 CpG sites (5% false discovery rate) with at least 10% difference in the methylation level between no colorectal findings controls and advanced neoplasia, the majority of which were hypomethylated. Unsupervised clustering showed that cfDNA methylation patterns can distinguish advanced neoplasia from healthy controls, as well as separate tumor tissue from healthy mucosa in an independent dataset. We also observed that advanced adenomas and stage I/II colorectal cancer methylation profiles, grouped as advanced

Biomarkers for prostate cancer detection and progression: Beyond prostate-specific antigen.

PubMed

Berney, Daniel M

2010-04-01

Prostate cancer is a major health problem with an incompletely understood pathogenesis and etiology. The advent of the prostate-specific antigen (PSA) test in the 1980s caused a revolution in how the disease was detected, but the evidence for PSA as a screening test is deficient. Biomarkers have been investigated, both for detection and discrimination of indolent from aggressive cancers. Refinements to the PSA test have been proposed but for practical and evidence-based reasons none have translated through to widespread clinical use. Of the novel biomarkers, the most promising is the prostate cancer antigen 3 (PCA3) test. New biomarkers to predict aggressive disease are even more contentious. The pathological grade of tumor remains the most powerful biomarker of prognosis. Other proven variables include tumor extent on biopsy and serum PSA. Tissue biomarkers have proven unhelpful due to a variable and biased literature with multiple methodological flaws, but Ki-67 probably shows more promise than any other current tissue biomarker. The recent discovery of a family of fusion genes in the prostate has led to considerable discussion on their prognostic role. Dissection of the genetic basis of the disease may lead to discoveries that will enhance our understanding and aid the search for prognostically valuable biomarkers. Copyright 2010 Prous Science, S.A.U. or its licensors. All rights reserved.

DNA Methylation Biomarkers: Cancer and Beyond

PubMed Central

Mikeska, Thomas; Craig, Jeffrey M.

2014-01-01

Biomarkers are naturally-occurring characteristics by which a particular pathological process or disease can be identified or monitored. They can reflect past environmental exposures, predict disease onset or course, or determine a patient’s response to therapy. Epigenetic changes are such characteristics, with most epigenetic biomarkers discovered to date based on the epigenetic mark of DNA methylation. Many tissue types are suitable for the discovery of DNA methylation biomarkers including cell-based samples such as blood and tumor material and cell-free DNA samples such as plasma. DNA methylation biomarkers with diagnostic, prognostic and predictive power are already in clinical trials or in a clinical setting for cancer. Outside cancer, strong evidence that complex disease originates in early life is opening up exciting new avenues for the detection of DNA methylation biomarkers for adverse early life environment and for estimation of future disease risk. However, there are a number of limitations to overcome before such biomarkers reach the clinic. Nevertheless, DNA methylation biomarkers have great potential to contribute to personalized medicine throughout life. We review the current state of play for DNA methylation biomarkers, discuss the barriers that must be crossed on the way to implementation in a clinical setting, and predict their future use for human disease. PMID:25229548

Protein mass spectra data analysis for clinical biomarker discovery: a global review.

PubMed

Roy, Pascal; Truntzer, Caroline; Maucort-Boulch, Delphine; Jouve, Thomas; Molinari, Nicolas

2011-03-01

The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. In recent years there has been a growing interest in using high throughput technologies for the detection of such biomarkers. In particular, mass spectrometry appears as an exciting tool with great potential. However, to extract any benefit from the massive potential of clinical proteomic studies, appropriate methods, improvement and validation are required. To better understand the key statistical points involved with such studies, this review presents the main data analysis steps of protein mass spectra data analysis, from the pre-processing of the data to the identification and validation of biomarkers.

Discovery and characterization of potential prognostic biomarkers for dengue hemorrhagic fever.

PubMed

Poole-Smith, B Katherine; Gilbert, Alexa; Gonzalez, Andrea L; Beltran, Manuela; Tomashek, Kay M; Ward, Brian J; Hunsperger, Elizabeth A; Ndao, Momar

2014-12-01

Half a million patients are hospitalized with severe dengue every year, many of whom would die without timely, appropriate clinical intervention. The majority of dengue cases are uncomplicated; however, 2-5% progress to severe dengue. Severe dengue cases have been reported with increasing frequency over the last 30 years. To discover biomarkers for severe dengue, we used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to analyze dengue virus positive serum samples from the acute phase of infection. Using this method, 16 proteins were identified as candidate biomarkers for severe dengue. From these 16 biomarkers, three candidates were selected for confirmation by enzyme-linked immunosorbent assay and Western blot: vitronectin (Vtn, 55.1 kDa), hemopexin (Hx, 52.4 kDa), and serotransferrin (Tf, 79.2 kDa). Vitronectin, Hx, and Tf best differentiated between dengue and severe dengue. © The American Society of Tropical Medicine and Hygiene.

Discovery and Characterization of Potential Prognostic Biomarkers for Dengue Hemorrhagic Fever

PubMed Central

Poole-Smith, B. Katherine; Gilbert, Alexa; Gonzalez, Andrea L.; Beltran, Manuela; Tomashek, Kay M.; Ward, Brian J.; Hunsperger, Elizabeth A.; Ndao, Momar

2014-01-01

Half a million patients are hospitalized with severe dengue every year, many of whom would die without timely, appropriate clinical intervention. The majority of dengue cases are uncomplicated; however, 2–5% progress to severe dengue. Severe dengue cases have been reported with increasing frequency over the last 30 years. To discover biomarkers for severe dengue, we used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to analyze dengue virus positive serum samples from the acute phase of infection. Using this method, 16 proteins were identified as candidate biomarkers for severe dengue. From these 16 biomarkers, three candidates were selected for confirmation by enzyme-linked immunosorbent assay and Western blot: vitronectin (Vtn, 55.1 kDa), hemopexin (Hx, 52.4 kDa), and serotransferrin (Tf, 79.2 kDa). Vitronectin, Hx, and Tf best differentiated between dengue and severe dengue. PMID:25349378

Biomarkers of tolerance: searching for the hidden phenotype.

PubMed

Perucha, Esperanza; Rebollo-Mesa, Irene; Sagoo, Pervinder; Hernandez-Fuentes, Maria P

2011-08-01

Induction of transplantation tolerance remains the ideal long-term clinical and logistic solution to the current challenges facing the management of renal allograft recipients. In this review, we describe the recent studies and advances made in identifying biomarkers of renal transplant tolerance, from study inceptions, to the lessons learned and their implications for current and future studies with the same goal. With the age of biomarker discovery entering a new dimension of high-throughput technologies, here we also review the current approaches, developments, and pitfalls faced in the subsequent statistical analysis required to identify valid biomarker candidates.

Organs-on-a-chip for drug discovery.

PubMed

Selimović, Seila; Dokmeci, Mehmet R; Khademhosseini, Ali

2013-10-01

The current drug discovery process is arduous and costly, and a majority of the drug candidates entering clinical trials fail to make it to the marketplace. The standard static well culture approaches, although useful, do not fully capture the intricate in vivo environment. By merging the advances in microfluidics with microfabrication technologies, novel platforms are being introduced that lead to the creation of organ functions on a single chip. Within these platforms, microengineering enables precise control over the cellular microenvironment, whereas microfluidics provides an ability to perfuse the constructs on a chip and to connect individual sections with each other. This approach results in microsystems that may better represent the in vivo environment. These organ-on-a-chip platforms can be utilized for developing disease models as well as for conducting drug testing studies. In this article, we highlight several key developments in these microscale platforms for drug discovery applications. Copyright © 2013 Elsevier Ltd. All rights reserved.

Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery.

PubMed

Hickey, John M; Chiurugwi, Tinashe; Mackay, Ian; Powell, Wayne

2017-08-30

The rate of annual yield increases for major staple crops must more than double relative to current levels in order to feed a predicted global population of 9 billion by 2050. Controlled hybridization and selective breeding have been used for centuries to adapt plant and animal species for human use. However, achieving higher, sustainable rates of improvement in yields in various species will require renewed genetic interventions and dramatic improvement of agricultural practices. Genomic prediction of breeding values has the potential to improve selection, reduce costs and provide a platform that unifies breeding approaches, biological discovery, and tools and methods. Here we compare and contrast some animal and plant breeding approaches to make a case for bringing the two together through the application of genomic selection. We propose a strategy for the use of genomic selection as a unifying approach to deliver innovative 'step changes' in the rate of genetic gain at scale.

Quantitative proteomic analysis of microdissected oral epithelium for cancer biomarker discovery.

PubMed

Xiao, Hua; Langerman, Alexander; Zhang, Yan; Khalid, Omar; Hu, Shen; Cao, Cheng-Xi; Lingen, Mark W; Wong, David T W

2015-11-01

Specific biomarkers are urgently needed for the detection and progression of oral cancer. The objective of this study was to discover cancer biomarkers from oral epithelium through utilizing high throughput quantitative proteomics approaches. Morphologically malignant, epithelial dysplasia, and adjacent normal epithelial tissues were laser capture microdissected (LCM) from 19 patients and used for proteomics analysis. Total proteins from each group were extracted, digested and then labelled with corresponding isobaric tags for relative and absolute quantitation (iTRAQ). Labelled peptides from each sample were combined and analyzed by liquid chromatography-mass spectrometry (LC-MS/MS) for protein identification and quantification. In total, 500 proteins were identified and 425 of them were quantified. When compared with adjacent normal oral epithelium, 17 and 15 proteins were consistently up-regulated or down-regulated in malignant and epithelial dysplasia, respectively. Half of these candidate biomarkers were discovered for oral cancer for the first time. Cornulin was initially confirmed in tissue protein extracts and was further validated in tissue microarray. Its presence in the saliva of oral cancer patients was also explored. Myoglobin and S100A8 were pre-validated by tissue microarray. These data demonstrated that the proteomic biomarkers discovered through this strategy are potential targets for oral cancer detection and salivary diagnostics. Copyright © 2015 Elsevier Ltd. All rights reserved.

The NINDS Parkinson's disease biomarkers program: The Ninds Parkinson's Disease Biomarkers Program

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rosenthal, Liana S.; Drake, Daniel; Alcalay, Roy N.

Background: Neuroprotection for Parkinson Disease (PD) remains elusive. Biomarkers hold the promise of removing roadblocks to therapy development. The National Institute of Neurological Disorders and Stroke (NINDS) has therefore established the Parkinson’s Disease Biomarkers Program (PDBP) to promote discovery of biomarkers for use in phase II-III clinical trials in PD. Methods: The PDBP facilitates biomarker development to improve neuroprotective clinical trial design, essential for advancing therapeutics for PD. To date, eleven consortium projects in the PDBP are focused on the development of clinical and laboratory-based PD biomarkers for diagnosis, progression tracking, and/or the prediction of prognosis. Seven of these projectsmore » also provide detailed longitudinal data and biospecimens from PD patients and controls, as a resource for all PD researchers. Standardized operating procedures and pooled reference samples have been created in order to allow cross-project comparisons and assessment of batch effects. A web-based Data Management Resource facilitates rapid sharing of data and biosamples across the entire PD research community for additional biomarker projects. Results: Here we describe the PDBP, highlight standard operating procedures for the collection of biospecimens and data, and provide an interim report with quality control analysis on the first 1082 participants and 1033 samples with quality control analysis collected as of October 2014. Conclusions: By making samples and data available to academics and industry, encouraging the adoption of existing standards, and providing a resource which complements existing programs, the PDBP will accelerate the pace of PD biomarker research, with the goal of improving diagnostic methods and treatment.« less

Continuous measurement of breast tumor hormone receptor expression: a comparison of two computational pathology platforms

PubMed Central

Ahern, Thomas P.; Beck, Andrew H.; Rosner, Bernard A.; Glass, Ben; Frieling, Gretchen; Collins, Laura C.; Tamimi, Rulla M.

2017-01-01

Background Computational pathology platforms incorporate digital microscopy with sophisticated image analysis to permit rapid, continuous measurement of protein expression. We compared two computational pathology platforms on their measurement of breast tumor estrogen receptor (ER) and progesterone receptor (PR) expression. Methods Breast tumor microarrays from the Nurses’ Health Study were stained for ER (n=592) and PR (n=187). One expert pathologist scored cases as positive if ≥1% of tumor nuclei exhibited stain. ER and PR were then measured with the Definiens Tissue Studio (automated) and Aperio Digital Pathology (user-supervised) platforms. Platform-specific measurements were compared using boxplots, scatter plots, and correlation statistics. Classification of ER and PR positivity by platform-specific measurements was evaluated with areas under receiver operating characteristic curves (AUC) from univariable logistic regression models, using expert pathologist classification as the standard. Results Both platforms showed considerable overlap in continuous measurements of ER and PR between positive and negative groups classified by expert pathologist. Platform-specific measurements were strongly and positively correlated with one another (rho≥0.77). The user-supervised Aperio workflow performed slightly better than the automated Definiens workflow at classifying ER positivity (AUCAperio=0.97; AUCDefiniens=0.90; difference=0.07, 95% CI: 0.05, 0.09) and PR positivity (AUCAperio=0.94; AUCDefiniens=0.87; difference=0.07, 95% CI: 0.03, 0.12). Conclusion Paired hormone receptor expression measurements from two different computational pathology platforms agreed well with one another. The user-supervised workflow yielded better classification accuracy than the automated workflow. Appropriately validated computational pathology algorithms enrich molecular epidemiology studies with continuous protein expression data and may accelerate tumor biomarker discovery. PMID

Potential serum biomarkers from a metabolomics study of autism

PubMed Central

Wang, Han; Liang, Shuang; Wang, Maoqing; Gao, Jingquan; Sun, Caihong; Wang, Jia; Xia, Wei; Wu, Shiying; Sumner, Susan J.; Zhang, Fengyu; Sun, Changhao; Wu, Lijie

2016-01-01

Background Early detection and diagnosis are very important for autism. Current diagnosis of autism relies mainly on some observational questionnaires and interview tools that may involve a great variability. We performed a metabolomics analysis of serum to identify potential biomarkers for the early diagnosis and clinical evaluation of autism. Methods We analyzed a discovery cohort of patients with autism and participants without autism in the Chinese Han population using ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF MS/MS) to detect metabolic changes in serum associated with autism. The potential metabolite candidates for biomarkers were individually validated in an additional independent cohort of cases and controls. We built a multiple logistic regression model to evaluate the validated biomarkers. Results We included 73 patients and 63 controls in the discovery cohort and 100 cases and 100 controls in the validation cohort. Metabolomic analysis of serum in the discovery stage identified 17 metabolites, 11 of which were validated in an independent cohort. A multiple logistic regression model built on the 11 validated metabolites fit well in both cohorts. The model consistently showed that autism was associated with 2 particular metabolites: sphingosine 1-phosphate and docosahexaenoic acid. Limitations While autism is diagnosed predominantly in boys, we were unable to perform the analysis by sex owing to difficulty recruiting enough female patients. Other limitations include the need to perform test–retest assessment within the same individual and the relatively small sample size. Conclusion Two metabolites have potential as biomarkers for the clinical diagnosis and evaluation of autism. PMID:26395811

Biomarkers: refining diagnosis and expediting drug development - reality, aspiration and the role of open innovation.

PubMed

Salter, H; Holland, R

2014-09-01

In the last decade, there have been intensive efforts to invent, qualify and use novel biomarkers as a means to improve success rates in drug discovery and development. The biomarkers field is maturing and this article considers whether these research efforts have brought about the expected benefits. The characteristics of a clinically useful biomarker are described and the impact this area of research has had is evaluated by reviewing a few, key examples of emerging biomarkers. There is evidence that the impact has been genuine and is increasing in both the drug and the diagnostic discovery and development processes. Beneficial impact on patient health outcomes seems relatively limited thus far, with the greatest impact in oncology (again, both in terms of novel drugs and in terms of more refined diagnoses and therefore more individualized treatment). However, the momentum of research would indicate that patient benefits are likely to increase substantially and to broaden across multiple therapeutic areas. Even though this research was originally driven by a desire to improve the drug discovery and development process, and was therefore funded with this aim in mind, it seems likely that the largest impact may actually come from more refined diagnosis. Refined diagnosis will facilitate both better allocation of healthcare resources and the use of treatment regimens which are optimized for the individual patient. This article also briefly reviews emerging technological approaches and how they relate to the challenges inherent in biomarker discovery and validation, and discusses the role of public/private partnerships in innovative biomarker research. © 2014 The Association for the Publication of the Journal of Internal Medicine.

Multiomics Data Triangulation for Asthma Candidate Biomarkers and Precision Medicine.

PubMed

Pecak, Matija; Korošec, Peter; Kunej, Tanja

2018-06-01

Asthma is a common complex disorder and has been subject to intensive omics research for disease susceptibility and therapeutic innovation. Candidate biomarkers of asthma and its precision treatment demand that they stand the test of multiomics data triangulation before they can be prioritized for clinical applications. We classified the biomarkers of asthma after a search of the literature and based on whether or not a given biomarker candidate is reported in multiple omics platforms and methodologies, using PubMed and Web of Science, we identified omics studies of asthma conducted on diverse platforms using keywords, such as asthma, genomics, metabolomics, and epigenomics. We extracted data about asthma candidate biomarkers from 73 articles and developed a catalog of 190 potential asthma biomarkers (167 human, 23 animal data), comprising DNA loci, transcripts, proteins, metabolites, epimutations, and noncoding RNAs. The data were sorted according to 13 omics types: genomics, epigenomics, transcriptomics, proteomics, interactomics, metabolomics, ncRNAomics, glycomics, lipidomics, environmental omics, pharmacogenomics, phenomics, and integrative omics. Importantly, we found that 10 candidate biomarkers were apparent in at least two or more omics levels, thus promising potential for further biomarker research and development and precision medicine applications. This multiomics catalog reported herein for the first time contributes to future decision-making on prioritization of biomarkers and validation efforts for precision medicine in asthma. The findings may also facilitate meta-analyses and integrative omics studies in the future.

Genomics and transcriptomics in drug discovery.

PubMed

Dopazo, Joaquin

2014-02-01

The popularization of genomic high-throughput technologies is causing a revolution in biomedical research and, particularly, is transforming the field of drug discovery. Systems biology offers a framework to understand the extensive human genetic heterogeneity revealed by genomic sequencing in the context of the network of functional, regulatory and physical protein-drug interactions. Thus, approaches to find biomarkers and therapeutic targets will have to take into account the complex system nature of the relationships of the proteins with the disease. Pharmaceutical companies will have to reorient their drug discovery strategies considering the human genetic heterogeneity. Consequently, modeling and computational data analysis will have an increasingly important role in drug discovery. Copyright © 2013 Elsevier Ltd. All rights reserved.

Biomarkers in cancer screening: a public health perspective.

PubMed

Srivastava, Sudhir; Gopal-Srivastava, Rashmi

2002-08-01

definitive technology, such as CT scan. The National Cancer Institute's Early Detection Research Network (EDRN) has begun an innovative, investigator-initiated project to improve methods for detecting the biomarkers of cancer cells. The EDRN is a consortium of more than 32 institutions to link discovery of biomarkers to the next steps in the process of developing early detection tests. These discoveries will lead to early clinical validation of tests with improved accuracy and reliability.

MRM validation of targeted nonglycosylated peptides from N-glycoprotein biomarkers using direct trypsin digestion of undepleted human plasma.

PubMed

Lee, Ju Yeon; Kim, Jin Young; Cheon, Mi Hee; Park, Gun Wook; Ahn, Yeong Hee; Moon, Myeong Hee; Yoo, Jong Shin

2014-02-26

nonglycosylated tryptic peptides adjacent to N-linked glycosylation sites in N-linked glycoprotein biomarkers, which could be detected in human plasma samples without depleting highly abundant proteins. Second, human plasma proteins were digested with trypsin without reduction and alkylation procedures to minimize sample preparation. Third, trypsin digestion times were shortened so as to obtain reproducible results with maximization of the steric hindrance effect of the glycans during enzyme digestion. Finally, this rapid and simple sample preparation method was applied to validate targeted nonglycosylated tryptic peptides as liver cancer biomarker candidates for diagnosis in 40 normal and 41 hepatocellular carcinoma (HCC) human plasma samples. This strategy provided the necessary throughput required to monitor protein biomarkers, as well as quantitative accuracy in human plasma analysis. From biomarker discovery to clinical implementation, our method will provide a biomarker study platform that is suitable for clinical deployment, and can be applied to high-throughput approaches. Copyright © 2014 Elsevier B.V. All rights reserved.

Discovery of Colorectal Cancer Biomarker Candidates by Membrane Proteomic Analysis and Subsequent Verification using Selected Reaction Monitoring (SRM) and Tissue Microarray (TMA) Analysis*

PubMed Central

Kume, Hideaki; Muraoka, Satoshi; Kuga, Takahisa; Adachi, Jun; Narumi, Ryohei; Watanabe, Shio; Kuwano, Masayoshi; Kodera, Yoshio; Matsushita, Kazuyuki; Fukuoka, Junya; Masuda, Takeshi; Ishihama, Yasushi; Matsubara, Hisahiro; Nomura, Fumio; Tomonaga, Takeshi

2014-01-01

Recent advances in quantitative proteomic technology have enabled the large-scale validation of biomarkers. We here performed a quantitative proteomic analysis of membrane fractions from colorectal cancer tissue to discover biomarker candidates, and then extensively validated the candidate proteins identified. A total of 5566 proteins were identified in six tissue samples, each of which was obtained from polyps and cancer with and without metastasis. GO cellular component analysis predicted that 3087 of these proteins were membrane proteins, whereas TMHMM algorithm predicted that 1567 proteins had a transmembrane domain. Differences were observed in the expression of 159 membrane proteins and 55 extracellular proteins between polyps and cancer without metastasis, while the expression of 32 membrane proteins and 17 extracellular proteins differed between cancer with and without metastasis. A total of 105 of these biomarker candidates were quantitated using selected (or multiple) reaction monitoring (SRM/MRM) with stable synthetic isotope-labeled peptides as an internal control. The results obtained revealed differences in the expression of 69 of these proteins, and this was subsequently verified in an independent set of patient samples (polyps (n = 10), cancer without metastasis (n = 10), cancer with metastasis (n = 10)). Significant differences were observed in the expression of 44 of these proteins, including ITGA5, GPRC5A, PDGFRB, and TFRC, which have already been shown to be overexpressed in colorectal cancer, as well as proteins with unknown function, such as C8orf55. The expression of C8orf55 was also shown to be high not only in colorectal cancer, but also in several cancer tissues using a multicancer tissue microarray, which included 1150 cores from 14 cancer tissues. This is the largest verification study of biomarker candidate membrane proteins to date; our methods for biomarker discovery and subsequent validation using SRM/MRM will contribute to the

Discovery of colorectal cancer biomarker candidates by membrane proteomic analysis and subsequent verification using selected reaction monitoring (SRM) and tissue microarray (TMA) analysis.

PubMed

Kume, Hideaki; Muraoka, Satoshi; Kuga, Takahisa; Adachi, Jun; Narumi, Ryohei; Watanabe, Shio; Kuwano, Masayoshi; Kodera, Yoshio; Matsushita, Kazuyuki; Fukuoka, Junya; Masuda, Takeshi; Ishihama, Yasushi; Matsubara, Hisahiro; Nomura, Fumio; Tomonaga, Takeshi

2014-06-01

Recent advances in quantitative proteomic technology have enabled the large-scale validation of biomarkers. We here performed a quantitative proteomic analysis of membrane fractions from colorectal cancer tissue to discover biomarker candidates, and then extensively validated the candidate proteins identified. A total of 5566 proteins were identified in six tissue samples, each of which was obtained from polyps and cancer with and without metastasis. GO cellular component analysis predicted that 3087 of these proteins were membrane proteins, whereas TMHMM algorithm predicted that 1567 proteins had a transmembrane domain. Differences were observed in the expression of 159 membrane proteins and 55 extracellular proteins between polyps and cancer without metastasis, while the expression of 32 membrane proteins and 17 extracellular proteins differed between cancer with and without metastasis. A total of 105 of these biomarker candidates were quantitated using selected (or multiple) reaction monitoring (SRM/MRM) with stable synthetic isotope-labeled peptides as an internal control. The results obtained revealed differences in the expression of 69 of these proteins, and this was subsequently verified in an independent set of patient samples (polyps (n = 10), cancer without metastasis (n = 10), cancer with metastasis (n = 10)). Significant differences were observed in the expression of 44 of these proteins, including ITGA5, GPRC5A, PDGFRB, and TFRC, which have already been shown to be overexpressed in colorectal cancer, as well as proteins with unknown function, such as C8orf55. The expression of C8orf55 was also shown to be high not only in colorectal cancer, but also in several cancer tissues using a multicancer tissue microarray, which included 1150 cores from 14 cancer tissues. This is the largest verification study of biomarker candidate membrane proteins to date; our methods for biomarker discovery and subsequent validation using SRM/MRM will contribute to the

GenomeVIP: a cloud platform for genomic variant discovery and interpretation

PubMed Central

Mashl, R. Jay; Scott, Adam D.; Huang, Kuan-lin; Wyczalkowski, Matthew A.; Yoon, Christopher J.; Niu, Beifang; DeNardo, Erin; Yellapantula, Venkata D.; Handsaker, Robert E.; Chen, Ken; Koboldt, Daniel C.; Ye, Kai; Fenyö, David; Raphael, Benjamin J.; Wendl, Michael C.; Ding, Li

2017-01-01

Identifying genomic variants is a fundamental first step toward the understanding of the role of inherited and acquired variation in disease. The accelerating growth in the corpus of sequencing data that underpins such analysis is making the data-download bottleneck more evident, placing substantial burdens on the research community to keep pace. As a result, the search for alternative approaches to the traditional “download and analyze” paradigm on local computing resources has led to a rapidly growing demand for cloud-computing solutions for genomics analysis. Here, we introduce the Genome Variant Investigation Platform (GenomeVIP), an open-source framework for performing genomics variant discovery and annotation using cloud- or local high-performance computing infrastructure. GenomeVIP orchestrates the analysis of whole-genome and exome sequence data using a set of robust and popular task-specific tools, including VarScan, GATK, Pindel, BreakDancer, Strelka, and Genome STRiP, through a web interface. GenomeVIP has been used for genomic analysis in large-data projects such as the TCGA PanCanAtlas and in other projects, such as the ICGC Pilots, CPTAC, ICGC-TCGA DREAM Challenges, and the 1000 Genomes SV Project. Here, we demonstrate GenomeVIP's ability to provide high-confidence annotated somatic, germline, and de novo variants of potential biological significance using publicly available data sets. PMID:28522612

Theoretical modeling of masking DNA application in aptamer-facilitated biomarker discovery.

PubMed

Cherney, Leonid T; Obrecht, Natalia M; Krylov, Sergey N

2013-04-16

In aptamer-facilitated biomarker discovery (AptaBiD), aptamers are selected from a library of random DNA (or RNA) sequences for their ability to specifically bind cell-surface biomarkers. The library is incubated with intact cells, and cell-bound DNA molecules are separated from those unbound and amplified by the polymerase chain reaction (PCR). The partitioning/amplification cycle is repeated multiple times while alternating target cells and control cells. Efficient aptamer selection in AptaBiD relies on the inclusion of masking DNA within the cell and library mixture. Masking DNA lacks primer regions for PCR amplification and is typically taken in excess to the library. The role of masking DNA within the selection mixture is to outcompete any nonspecific binding sequences within the initial library, thus allowing specific DNA sequences (i.e., aptamers) to be selected more efficiently. Efficient AptaBiD requires an optimum ratio of masking DNA to library DNA, at which aptamers still bind specific binding sites but nonaptamers within the library do not bind nonspecific binding sites. Here, we have developed a mathematical model that describes the binding processes taking place within the equilibrium mixture of masking DNA, library DNA, and target cells. An obtained mathematical solution allows one to estimate the concentration of masking DNA that is required to outcompete the library DNA at a desirable ratio of bound masking DNA to bound library DNA. The required concentration depends on concentrations of the library and cells as well as on unknown cell characteristics. These characteristics include the concentration of total binding sites on the cell surface, N, and equilibrium dissociation constants, K(nsL) and K(nsM), for nonspecific binding of the library DNA and masking DNA, respectively. We developed a theory that allows the determination of N, K(nsL), and K(nsM) based on measurements of EC50 values for cells mixed separately with the library and masking DNA

BioTextQuest(+): a knowledge integration platform for literature mining and concept discovery.

PubMed

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Pafilis, Evangelos; Theodosiou, Theodosios; Schneider, Reinhard; Satagopam, Venkata P; Ouzounis, Christos A; Eliopoulos, Aristides G; Promponas, Vasilis J; Iliopoulos, Ioannis

2014-11-15

The iterative process of finding relevant information in biomedical literature and performing bioinformatics analyses might result in an endless loop for an inexperienced user, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related biological databases. Herein, we describe BioTextQuest(+), a web-based interactive knowledge exploration platform with significant advances to its predecessor (BioTextQuest), aiming to bridge processes such as bioentity recognition, functional annotation, document clustering and data integration towards literature mining and concept discovery. BioTextQuest(+) enables PubMed and OMIM querying, retrieval of abstracts related to a targeted request and optimal detection of genes, proteins, molecular functions, pathways and biological processes within the retrieved documents. The front-end interface facilitates the browsing of document clustering per subject, the analysis of term co-occurrence, the generation of tag clouds containing highly represented terms per cluster and at-a-glance popup windows with information about relevant genes and proteins. Moreover, to support experimental research, BioTextQuest(+) addresses integration of its primary functionality with biological repositories and software tools able to deliver further bioinformatics services. The Google-like interface extends beyond simple use by offering a range of advanced parameterization for expert users. We demonstrate the functionality of BioTextQuest(+) through several exemplary research scenarios including author disambiguation, functional term enrichment, knowledge acquisition and concept discovery linking major human diseases, such as obesity and ageing. The service is accessible at http://bioinformatics.med.uoc.gr/biotextquest. g.pavlopoulos@gmail.com or georgios.pavlopoulos@esat.kuleuven.be Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University

IMMUNOASSAYS FOR BIOMARKERS AND NEUTRACEUTICALS/PHARMACEUTICALS

EPA Science Inventory

Product is an abstract for an invited oral platform presentation to be given at the Pittsburgh Conference to be held February 25 - March 2, 2007 in Chicago, Ilinois. The presentation will describe methods research for the development of bioanalytical methods to measure biomarker...

Cancer cell redirection biomarker discovery using a mutual information approach.

PubMed

Roche, Kimberly; Feltus, F Alex; Park, Jang Pyo; Coissieux, Marie-May; Chang, Chenyan; Chan, Vera B S; Bentires-Alj, Mohamed; Booth, Brian W

2017-01-01

Introducing tumor-derived cells into normal mammary stem cell niches at a sufficiently high ratio of normal to tumorous cells causes those tumor cells to undergo a change to normal mammary phenotype and yield normal mammary progeny. This phenomenon has been termed cancer cell redirection. We have developed an in vitro model that mimics in vivo redirection of cancer cells by the normal mammary microenvironment. Using the RNA profiling data from this cellular model, we examined high-level characteristics of the normal, redirected, and tumor transcriptomes and found the global expression profiles clearly distinguish the three expression states. To identify potential redirection biomarkers that cause the redirected state to shift toward the normal expression pattern, we used mutual information relationships between normal, redirected, and tumor cell groups. Mutual information relationship analysis reduced a dataset of over 35,000 gene expression measurements spread over 13,000 curated gene sets to a set of 20 significant molecular signatures totaling 906 unique loci. Several of these molecular signatures are hallmark drivers of the tumor state. Using differential expression as a guide, we further refined the gene set to 120 core redirection biomarker genes. The expression levels of these core biomarkers are sufficient to make the normal and redirected gene expression states indistinguishable from each other but radically different from the tumor state.

Cancer cell redirection biomarker discovery using a mutual information approach

PubMed Central

Roche, Kimberly; Feltus, F. Alex; Park, Jang Pyo; Coissieux, Marie-May; Chang, Chenyan; Chan, Vera B. S.; Bentires-Alj, Mohamed

2017-01-01

Introducing tumor-derived cells into normal mammary stem cell niches at a sufficiently high ratio of normal to tumorous cells causes those tumor cells to undergo a change to normal mammary phenotype and yield normal mammary progeny. This phenomenon has been termed cancer cell redirection. We have developed an in vitro model that mimics in vivo redirection of cancer cells by the normal mammary microenvironment. Using the RNA profiling data from this cellular model, we examined high-level characteristics of the normal, redirected, and tumor transcriptomes and found the global expression profiles clearly distinguish the three expression states. To identify potential redirection biomarkers that cause the redirected state to shift toward the normal expression pattern, we used mutual information relationships between normal, redirected, and tumor cell groups. Mutual information relationship analysis reduced a dataset of over 35,000 gene expression measurements spread over 13,000 curated gene sets to a set of 20 significant molecular signatures totaling 906 unique loci. Several of these molecular signatures are hallmark drivers of the tumor state. Using differential expression as a guide, we further refined the gene set to 120 core redirection biomarker genes. The expression levels of these core biomarkers are sufficient to make the normal and redirected gene expression states indistinguishable from each other but radically different from the tumor state. PMID:28594912

BioGraph: unsupervised biomedical knowledge discovery via automated hypothesis generation

PubMed Central

2011-01-01

We present BioGraph, a data integration and data mining platform for the exploration and discovery of biomedical information. The platform offers prioritizations of putative disease genes, supported by functional hypotheses. We show that BioGraph can retrospectively confirm recently discovered disease genes and identify potential susceptibility genes, outperforming existing technologies, without requiring prior domain knowledge. Additionally, BioGraph allows for generic biomedical applications beyond gene discovery. BioGraph is accessible at http://www.biograph.be. PMID:21696594

AACR-FDA-NCI Cancer Biomarkers Collaborative consensus report: advancing the use of biomarkers in cancer drug development.

PubMed

Khleif, Samir N; Doroshow, James H; Hait, William N

2010-07-01

Recent discoveries in cancer biology have greatly increased our understanding of cancer at the molecular and cellular level, but translating this knowledge into safe and effective therapies for cancer patients has proved to be challenging. There is a growing imperative to modernize the drug development process by incorporating new techniques that can predict the safety and effectiveness of new drugs faster, with more certainty, and at lower cost. Biomarkers are central to accelerating the identification and adoption of new therapies, but currently, many barriers impede their use in drug development and clinical practice. In 2007, the AACR-FDA-NCI Cancer Biomarkers Collaborative stepped into the national effort to bring together disparate stakeholders to clearly delineate these barriers, to develop recommendations for integrating biomarkers into the cancer drug development enterprise, and to set in motion the necessary action plans and collaborations to see the promise of biomarkers come to fruition, efficiently delivering quality cancer care to patients.

Chemical screening platforms for autophagy drug discovery to identify therapeutic candidates for Huntington's disease and other neurodegenerative disorders.

PubMed

Sarkar, Sovan

2013-01-01

Autophagy is a cellular degradation process involved in the clearance of aggregate-prone proteins associated with neurodegenerative diseases. While the mTOR pathway has been known to be the major regulator of autophagy, recent advancements into the regulation of autophagy have identified mTOR-independent autophagy pathways that are amenable to chemical perturbations. Several chemical and genetic screens have been undertaken to identify small molecule and genetic regulators of autophagy, respectively. The small molecule autophagy enhancers offer great potential as therapeutic candidates not only for neurodegenerative diseases, but also for diverse human diseases where autophagy acts as a protective pathway. This review highlights the various chemical screening platforms for autophagy drug discovery pertinent for the treatment of neurodegenerative diseases.

A Practical Platform for Blood Biomarker Study by Using Global Gene Expression Profiling of Peripheral Whole Blood

PubMed Central

Schmid, Patrick; Yao, Hui; Galdzicki, Michal; Berger, Bonnie; Wu, Erxi; Kohane, Isaac S.

2009-01-01

Background Although microarray technology has become the most common method for studying global gene expression, a plethora of technical factors across the experiment contribute to the variable of genome gene expression profiling using peripheral whole blood. A practical platform needs to be established in order to obtain reliable and reproducible data to meet clinical requirements for biomarker study. Methods and Findings We applied peripheral whole blood samples with globin reduction and performed genome-wide transcriptome analysis using Illumina BeadChips. Real-time PCR was subsequently used to evaluate the quality of array data and elucidate the mode in which hemoglobin interferes in gene expression profiling. We demonstrated that, when applied in the context of standard microarray processing procedures, globin reduction results in a consistent and significant increase in the quality of beadarray data. When compared to their pre-globin reduction counterparts, post-globin reduction samples show improved detection statistics, lowered variance and increased sensitivity. More importantly, gender gene separation is remarkably clearer in post-globin reduction samples than in pre-globin reduction samples. Our study suggests that the poor data obtained from pre-globin reduction samples is the result of the high concentration of hemoglobin derived from red blood cells either interfering with target mRNA binding or giving the pseudo binding background signal. Conclusion We therefore recommend the combination of performing globin mRNA reduction in peripheral whole blood samples and hybridizing on Illumina BeadChips as the practical approach for biomarker study. PMID:19381341

Cancer Immunotherapy and Personalized Medicine: Emerging Technologies and Biomarker-Based Approaches.

PubMed

Maciejko, Laura; Smalley, Munisha; Goldman, Aaron

2017-09-01

The vision and strategy for the 21st century treatment of cancer calls for a personalized approach in which therapy selection is designed for each individual patient. While genomics has led the field of personalized cancer medicine over the past several decades by connecting patient-specific DNA mutations with kinase-targeted drugs, the recent discovery that tumors evade immune surveillance has created unique challenges to personalize cancer immunotherapy. In this mini-review we will discuss how personalized medicine has evolved recently to accommodate the emerging era of cancer immunotherapy. Moreover, we will discuss novel platform technologies that have been engineered to address some of the persisting limitations. Beginning with early evidence in personalized medicine, we discuss how biomarker-driven approaches to predict clinical success have evolved to account for the heterogeneous tumor ecosystem. In the emerging field of cancer immunotherapy, this challenge requires the use of a novel set of tools, distinct from the classic approach of next-generation genomic sequencing-based strategies. We will introduce new techniques that seek to tailor immunotherapy by re-programming patient-autologous T-cells, and new technologies that are emerging to predict clinical efficacy by mapping infiltration of lymphocytes, and harnessing fully humanized platforms that reconstruct and interrogate immune checkpoint blockade, ex-vivo . While cancer immunotherapy is now leading to durable outcomes in difficult-to-treat cancers, success is highly variable. Developing novel approaches to study cancer immunotherapy, personalize treatment to each patient, and achieve greater outcomes is penultimate to developing sustainable cures in the future. Numerous techniques are now emerging to help guide treatment decisions, which go beyond simple biomarker-driven strategies, and are now we are seeking to interrogate the entirety of the dynamic tumor ecosystem.

Continuous measurement of breast tumour hormone receptor expression: a comparison of two computational pathology platforms.

PubMed

Ahern, Thomas P; Beck, Andrew H; Rosner, Bernard A; Glass, Ben; Frieling, Gretchen; Collins, Laura C; Tamimi, Rulla M

2017-05-01

Computational pathology platforms incorporate digital microscopy with sophisticated image analysis to permit rapid, continuous measurement of protein expression. We compared two computational pathology platforms on their measurement of breast tumour oestrogen receptor (ER) and progesterone receptor (PR) expression. Breast tumour microarrays from the Nurses' Health Study were stained for ER (n=592) and PR (n=187). One expert pathologist scored cases as positive if ≥1% of tumour nuclei exhibited stain. ER and PR were then measured with the Definiens Tissue Studio (automated) and Aperio Digital Pathology (user-supervised) platforms. Platform-specific measurements were compared using boxplots, scatter plots and correlation statistics. Classification of ER and PR positivity by platform-specific measurements was evaluated with areas under receiver operating characteristic curves (AUC) from univariable logistic regression models, using expert pathologist classification as the standard. Both platforms showed considerable overlap in continuous measurements of ER and PR between positive and negative groups classified by expert pathologist. Platform-specific measurements were strongly and positively correlated with one another (r≥0.77). The user-supervised Aperio workflow performed slightly better than the automated Definiens workflow at classifying ER positivity (AUC Aperio =0.97; AUC Definiens =0.90; difference=0.07, 95% CI 0.05 to 0.09) and PR positivity (AUC Aperio =0.94; AUC Definiens =0.87; difference=0.07, 95% CI 0.03 to 0.12). Paired hormone receptor expression measurements from two different computational pathology platforms agreed well with one another. The user-supervised workflow yielded better classification accuracy than the automated workflow. Appropriately validated computational pathology algorithms enrich molecular epidemiology studies with continuous protein expression data and may accelerate tumour biomarker discovery. Published by the BMJ Publishing

Identification of biomarkers for lung cancer in never smokers — EDRN Public Portal

Cancer.gov

The overall goal of this project is to identify, verify and apply biomarkers for the early diagnosis or risk assessment of lung cancer in never smokers. The first year will be regarded as a year of discovery. After successful demonstration of the feasibility of the approach for novel marker discovery, funding will be applied for to perform confirmation and preclinical studies on the biomarkers and validation studies (specific aims 2 and 3, to be performed in years two and three). Year two can be regarded as the year of confirmation and year three as the year of validation.

Opportunities and challenges of disease biomarkers: a new section in the Journal of Translational Medicine.

PubMed

Wang, Xiangdong; Ward, Peter A

2012-12-05

Disease biomarkers are defined to diagnose various phases of diseases, monitor severities of diseases and responses to therapies, or predict prognosis of patients. Disease-specific biomarkers should benefit drug discovery and development, integrate multidisciplinary sciences, be validated by molecular imaging. The opportunities and challenges in biomarker development are emphasized and considered. The Journal of Translational Medicine opens a new Section of Disease Biomarkers to bridge identification and validation of gene or protein-based biomarkers, network biomarkers, dynamic network biomarkers in human diseases, patient phenotypes, and clinical applications. Disease biomarkers are also important for determining drug effects, target specificities and binding, dynamic metabolism and pharmacological kinetics, or toxicity profiles.

Can Functional Magnetic Resonance Imaging Improve Success Rates in CNS Drug Discovery?

PubMed Central

Borsook, David; Hargreaves, Richard; Becerra, Lino

2011-01-01

Introduction The bar for developing new treatments for CNS disease is getting progressively higher and fewer novel mechanisms are being discovered, validated and developed. The high costs of drug discovery necessitate early decisions to ensure the best molecules and hypotheses are tested in expensive late stage clinical trials. The discovery of brain imaging biomarkers that can bridge preclinical to clinical CNS drug discovery and provide a ‘language of translation’ affords the opportunity to improve the objectivity of decision-making. Areas Covered This review discusses the benefits, challenges and potential issues of using a science based biomarker strategy to change the paradigm of CNS drug development and increase success rates in the discovery of new medicines. The authors have summarized PubMed and Google Scholar based publication searches to identify recent advances in functional, structural and chemical brain imaging and have discussed how these techniques may be useful in defining CNS disease state and drug effects during drug development. Expert opinion The use of novel brain imaging biomarkers holds the bold promise of making neuroscience drug discovery smarter by increasing the objectivity of decision making thereby improving the probability of success of identifying useful drugs to treat CNS diseases. Functional imaging holds the promise to: (1) define pharmacodynamic markers as an index of target engagement (2) improve translational medicine paradigms to predict efficacy; (3) evaluate CNS efficacy and safety based on brain activation; (4) determine brain activity drug dose-response relationships and (5) provide an objective evaluation of symptom response and disease modification. PMID:21765857

Discovery and identification of potential biomarkers of pediatric Acute Lymphoblastic Leukemia

PubMed Central

Shi, Linan; Zhang, Jun; Wu, Peng; Feng, Kai; Li, Jing; Xie, Zhensheng; Xue, Peng; Cai, Tanxi; Cui, Ziyou; Chen, Xiulan; Hou, Junjie; Zhang, Jianzhong; Yang, Fuquan

2009-01-01

Background Acute lymphoblastic leukemia (ALL) is a common form of cancer in children. Currently, bone marrow biopsy is used for diagnosis. Noninvasive biomarkers for the early diagnosis of pediatric ALL are urgently needed. The aim of this study was to discover potential protein biomarkers for pediatric ALL. Methods Ninety-four pediatric ALL patients and 84 controls were randomly divided into a "training" set (45 ALL patients, 34 healthy controls) and a test set (49 ALL patients, 30 healthy controls and 30 pediatric acute myeloid leukemia (AML) patients). Serum proteomic profiles were measured using surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy (SELDI-TOF-MS). A classification model was established by Biomarker Pattern Software (BPS). Candidate protein biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays. Results A total of 7 protein peaks (9290 m/z, 7769 m/z, 15110 m/z, 7564 m/z, 4469 m/z, 8937 m/z, 8137 m/z) were found with differential expression levels in the sera of pediatric ALL patients and controls using SELDI-TOF-MS and then analyzed by BPS to construct a classification model in the "training" set. The sensitivity and specificity of the model were found to be 91.8%, and 90.0%, respectively, in the test set. Two candidate protein peaks (7769 and 9290 m/z) were found to be down-regulated in ALL patients, where these were identified as platelet factor 4 (PF4) and pro-platelet basic protein precursor (PBP). Two other candidate protein peaks (8137 and 8937 m/z) were found up-regulated in the sera of ALL patients, and these were identified as fragments of the complement component 3a (C3a). Conclusion Platelet factor (PF4), connective tissue activating peptide III (CTAP-III) and two fragments of C3a may be potential protein biomarkers of pediatric ALL and used to distinguish pediatric ALL patients from healthy controls and pediatric AML patients. Further studies with additional

Human cervicovaginal fluid biomarkers to predict term and preterm labor

PubMed Central

Heng, Yujing J.; Liong, Stella; Permezel, Michael; Rice, Gregory E.; Di Quinzio, Megan K. W.; Georgiou, Harry M.

2015-01-01

Preterm birth (PTB; birth before 37 completed weeks of gestation) remains the major cause of neonatal morbidity and mortality. The current generation of biomarkers predictive of PTB have limited utility. In pregnancy, the human cervicovaginal fluid (CVF) proteome is a reflection of the local biochemical milieu and is influenced by the physical changes occurring in the vagina, cervix and adjacent overlying fetal membranes. Term and preterm labor (PTL) share common pathways of cervical ripening, myometrial activation and fetal membranes rupture leading to birth. We therefore hypothesize that CVF biomarkers predictive of labor may be similar in both the term and preterm labor setting. In this review, we summarize some of the existing published literature as well as our team's breadth of work utilizing the CVF for the discovery and validation of putative CVF biomarkers predictive of human labor. Our team established an efficient method for collecting serial CVF samples for optimal 2-dimensional gel electrophoresis resolution and analysis. We first embarked on CVF biomarker discovery for the prediction of spontaneous onset of term labor using 2D-electrophoresis and solution array multiple analyte profiling. 2D-electrophoretic analyses were subsequently performed on CVF samples associated with PTB. Several proteins have been successfully validated and demonstrate that these biomarkers are associated with term and PTL and may be predictive of both term and PTL. In addition, the measurement of these putative biomarkers was found to be robust to the influences of vaginal microflora and/or semen. The future development of a multiple biomarker bed-side test would help improve the prediction of PTB and the clinical management of patients. PMID:26029118

NETL's Energy Data Exchange (EDX) - a coordination, collaboration, and data resource discovery platform for energy science

NASA Astrophysics Data System (ADS)

Rose, K.; Rowan, C.; Rager, D.; Dehlin, M.; Baker, D. V.; McIntyre, D.

2015-12-01

Multi-organizational research teams working jointly on projects often encounter problems with discovery, access to relevant existing resources, and data sharing due to large file sizes, inappropriate file formats, or other inefficient options that make collaboration difficult. The Energy Data eXchange (EDX) from Department of Energy's (DOE) National Energy Technology Laboratory (NETL) is an evolving online research environment designed to overcome these challenges in support of DOE's fossil energy goals while offering improved access to data driven products of fossil energy R&D such as datasets, tools, and web applications. In 2011, development of NETL's Energy Data eXchange (EDX) was initiated and offers i) a means for better preserving of NETL's research and development products for future access and re-use, ii) efficient, discoverable access to authoritative, relevant, external resources, and iii) an improved approach and tools to support secure, private collaboration and coordination between multi-organizational teams to meet DOE mission and goals. EDX presently supports fossil energy and SubTER Crosscut research activities, with an ever-growing user base. EDX is built on a heavily customized instance of the open source platform, Comprehensive Knowledge Archive Network (CKAN). EDX connects users to externally relevant data and tools through connecting to external data repositories built on different platforms and other CKAN platforms (e.g. Data.gov). EDX does not download and repost data or tools that already have an online presence. This leads to redundancy and even error. If a relevant resource already has an online instance, is hosted by another online entity, EDX will point users to that external host either using web services, inventorying URLs and other methods. EDX offers users the ability to leverage private-secure capabilities custom built into the system. The team is presently working on version 3 of EDX which will incorporate big data analytical

Scientific workflows as productivity tools for drug discovery.

PubMed

Shon, John; Ohkawa, Hitomi; Hammer, Juergen

2008-05-01

Large pharmaceutical companies annually invest tens to hundreds of millions of US dollars in research informatics to support their early drug discovery processes. Traditionally, most of these investments are designed to increase the efficiency of drug discovery. The introduction of do-it-yourself scientific workflow platforms has enabled research informatics organizations to shift their efforts toward scientific innovation, ultimately resulting in a possible increase in return on their investments. Unlike the handling of most scientific data and application integration approaches, researchers apply scientific workflows to in silico experimentation and exploration, leading to scientific discoveries that lie beyond automation and integration. This review highlights some key requirements for scientific workflow environments in the pharmaceutical industry that are necessary for increasing research productivity. Examples of the application of scientific workflows in research and a summary of recent platform advances are also provided.

Orbiter processing facility service platform failure and redesign

NASA Technical Reports Server (NTRS)

Harris, Jesse L.

1988-01-01

In a high bay of the Orbiter Processing Facility (OPF) at the Kennedy Space Center, technicians were preparing the space shuttle orbiter Discovery for rollout to the Vehicle Assembly Building (VAB). A service platform, commonly referred to as an OPF Bucket, was being retracted when it suddenly fell, striking a technician and impacting Discovery's payload bay door. A critical component in the OPF Bucket hoist system had failed, allowing the platform to fall. The incident was thoroughly investigated by both NASA and Lockheed, revealing many design deficiencies within the system. The deficiencies and the design changes made to correct them are reviewed.

Metabolomics-based promising candidate biomarkers and pathways in Alzheimer's disease.

PubMed

Kang, Jian; Lu, Jingli; Zhang, Xiaojian

2015-05-01

Pathologically, loss of synapses and neurons, extracellular senile plaques and intracellular neurofibrillary tangles (NFTs) are observed in the brains of patients with Alzheimer's disease (AD). These features are associated with changes Aβ (amyloid β) 40, Aβ42, total tau and phosphorylated tau (p-tau), which are as definitely biomarkers for severe AD state. However, biomarkers for effectively diagnosing AD in the pre-clinical state for directing therapeutic strategies are lacking. Metabolic profiling as a powerful tool to identify new biomarkers is receiving increasing attention in AD. This review will focus on metabolomics-based detection of promising candidate biomarkers and pathways in AD to facilitate the discovery of new medicines and disease pathways.

Emerging field of metabolomics: big promise for cancer biomarker identification and drug discovery.

PubMed

Patel, Seema; Ahmed, Shadab

2015-03-25

Most cancers are lethal and metabolic alterations are considered a hallmark of this deadly disease. Genomics and proteomics have contributed vastly to understand cancer biology. Still there are missing links as downstream to them molecular divergence occurs. Metabolomics, the omic science that furnishes a dynamic portrait of metabolic profile is expected to bridge these gaps and boost cancer research. Metabolites being the end products are more stable than mRNAs or proteins. Previous studies have shown the efficacy of metabolomics in identifying biomarkers associated with diagnosis, prognosis and treatment of cancer. Metabolites are highly informative about the functional status of the biological system, owing to their proximity to organismal phenotypes. Scores of publications have reported about high-throughput data generation by cutting-edge analytic platforms (mass spectrometry and nuclear magnetic resonance). Further sophisticated statistical softwares (chemometrics) have enabled meaningful information extraction from the metabolomic data. Metabolomics studies have demonstrated the perturbation in glycolysis, tricarboxylic acid cycle, choline and fatty acid metabolism as traits of cancer cells. This review discusses the latest progress in this field, the future trends and the deficiencies to be surmounted for optimally implementation in oncology. The authors scoured through the most recent, high-impact papers archived in Pubmed, ScienceDirect, Wiley and Springer databases to compile this review to pique the interest of researchers towards cancer metabolomics. Copyright © 2014 Elsevier B.V. All rights reserved.

Current state of the art for enhancing urine biomarker discovery

PubMed Central

Harpole, Michael; Davis, Justin; Espina, Virginia

2016-01-01

Urine is a highly desirable biospecimen for biomarker analysis because it can be collected recurrently by non-invasive techniques, in relatively large volumes. Urine contains cellular elements, biochemicals, and proteins derived from glomerular filtration of plasma, renal tubule excretion, and urogenital tract secretions that reflect, at a given time point, an individual's metabolic and pathophysiologic state. High-resolution mass spectrometry, coupled with state of the art fractionation systems are revealing the plethora of diagnostic/prognostic proteomic information existing within urinary exosomes, glycoproteins, and proteins. Affinity capture pre-processing techniques such as combinatorial peptide ligand libraries and biomarker harvesting hydrogel nanoparticles are enabling measurement/identification of previously undetectable urinary proteins. Future challenges in the urinary proteomics field include a) defining either single or multiple, universally applicable data normalization methods for comparing results within and between individual patients/data sets, and b) defining expected urinary protein levels in healthy individuals. PMID:27232439

Discovery of prognostic biomarkers for predicting lung cancer metastasis using microarray and survival data.

PubMed

Huang, Hui-Ling; Wu, Yu-Chung; Su, Li-Jen; Huang, Yun-Ju; Charoenkwan, Phasit; Chen, Wen-Liang; Lee, Hua-Chin; Chu, William Cheng-Chung; Ho, Shinn-Ying

2015-02-21

Few studies have investigated prognostic biomarkers of distant metastases of lung cancer. One of the central difficulties in identifying biomarkers from microarray data is the availability of only a small number of samples, which results overtraining. Recently obtained evidence reveals that epithelial-mesenchymal transition (EMT) of tumor cells causes metastasis, which is detrimental to patients' survival. This work proposes a novel optimization approach to discovering EMT-related prognostic biomarkers to predict the distant metastasis of lung cancer using both microarray and survival data. This weighted objective function maximizes both the accuracy of prediction of distant metastasis and the area between the disease-free survival curves of the non-distant and distant metastases. Seventy-eight patients with lung cancer and a follow-up time of 120 months are used to identify a set of gene markers and an independent cohort of 26 patients is used to evaluate the identified biomarkers. The medical records of the 78 patients show a significant difference between the disease-free survival times of the 37 non-distant- and the 41 distant-metastasis patients. The experimental results thus obtained are as follows. 1) The use of disease-free survival curves can compensate for the shortcoming of insufficient samples and greatly increase the test accuracy by 11.10%; and 2) the support vector machine with a set of 17 transcripts, such as CCL16 and CDKN2AIP, can yield a leave-one-out cross-validation accuracy of 93.59%, a test accuracy of 76.92%, a large disease-free survival area of 74.81%, and a mean survival prediction error of 3.99 months. The identified putative biomarkers are examined using related studies and signaling pathways to reveal the potential effectiveness of the biomarkers in prospective confirmatory studies. The proposed new optimization approach to identifying prognostic biomarkers by combining multiple sources of data (microarray and survival) can

Systems-based biological concordance and predictive reproducibility of gene set discovery methods in cardiovascular disease.

PubMed

Azuaje, Francisco; Zheng, Huiru; Camargo, Anyela; Wang, Haiying

2011-08-01

The discovery of novel disease biomarkers is a crucial challenge for translational bioinformatics. Demonstration of both their classification power and reproducibility across independent datasets are essential requirements to assess their potential clinical relevance. Small datasets and multiplicity of putative biomarker sets may explain lack of predictive reproducibility. Studies based on pathway-driven discovery approaches have suggested that, despite such discrepancies, the resulting putative biomarkers tend to be implicated in common biological processes. Investigations of this problem have been mainly focused on datasets derived from cancer research. We investigated the predictive and functional concordance of five methods for discovering putative biomarkers in four independently-generated datasets from the cardiovascular disease domain. A diversity of biosignatures was identified by the different methods. However, we found strong biological process concordance between them, especially in the case of methods based on gene set analysis. With a few exceptions, we observed lack of classification reproducibility using independent datasets. Partial overlaps between our putative sets of biomarkers and the primary studies exist. Despite the observed limitations, pathway-driven or gene set analysis can predict potentially novel biomarkers and can jointly point to biomedically-relevant underlying molecular mechanisms. Copyright © 2011 Elsevier Inc. All rights reserved.

Surface-Enhanced Raman Scattering-Based Immunoassay Technologies for Detection of Disease Biomarkers

PubMed Central

Smolsky, Joseph; Kaur, Sukhwinder; Hayashi, Chihiro; Batra, Surinder K.; Krasnoslobodtsev, Alexey V.

2017-01-01

Detection of biomarkers is of vital importance in disease detection, management, and monitoring of therapeutic efficacy. Extensive efforts have been devoted to the development of novel diagnostic methods that detect and quantify biomarkers with higher sensitivity and reliability, contributing to better disease diagnosis and prognosis. When it comes to such devastating diseases as cancer, these novel powerful methods allow for disease staging as well as detection of cancer at very early stages. Over the past decade, there have been some advances in the development of platforms for biomarker detection of diseases. The main focus has recently shifted to the development of simple and reliable diagnostic tests that are inexpensive, accurate, and can follow a patient’s disease progression and therapy response. The individualized approach in biomarker detection has been also emphasized with detection of multiple biomarkers in body fluids such as blood and urine. This review article covers the developments in Surface-Enhanced Raman Scattering (SERS) and related technologies with the primary focus on immunoassays. Limitations and advantages of the SERS-based immunoassay platform are discussed. The article thoroughly describes all components of the SERS immunoassay and highlights the superior capabilities of SERS readout strategy such as high sensitivity and simultaneous detection of a multitude of biomarkers. Finally, it introduces recently developed strategies for in vivo biomarker detection using SERS. PMID:28085088

Evaluating biomarkers for prognostic enrichment of clinical trials.

PubMed

Kerr, Kathleen F; Roth, Jeremy; Zhu, Kehao; Thiessen-Philbrook, Heather; Meisner, Allison; Wilson, Francis Perry; Coca, Steven; Parikh, Chirag R

2017-12-01

A potential use of biomarkers is to assist in prognostic enrichment of clinical trials, where only patients at relatively higher risk for an outcome of interest are eligible for the trial. We investigated methods for evaluating biomarkers for prognostic enrichment. We identified five key considerations when considering a biomarker and a screening threshold for prognostic enrichment: (1) clinical trial sample size, (2) calendar time to enroll the trial, (3) total patient screening costs and the total per-patient trial costs, (4) generalizability of trial results, and (5) ethical evaluation of trial eligibility criteria. Items (1)-(3) are amenable to quantitative analysis. We developed the Biomarker Prognostic Enrichment Tool for evaluating biomarkers for prognostic enrichment at varying levels of screening stringency. We demonstrate that both modestly prognostic and strongly prognostic biomarkers can improve trial metrics using Biomarker Prognostic Enrichment Tool. Biomarker Prognostic Enrichment Tool is available as a webtool at http://prognosticenrichment.com and as a package for the R statistical computing platform. In some clinical settings, even biomarkers with modest prognostic performance can be useful for prognostic enrichment. In addition to the quantitative analysis provided by Biomarker Prognostic Enrichment Tool, investigators must consider the generalizability of trial results and evaluate the ethics of trial eligibility criteria.

High-Throughput Screening Platform for the Discovery of New Immunomodulator Molecules from Natural Product Extract Libraries.

PubMed

Pérez Del Palacio, José; Díaz, Caridad; de la Cruz, Mercedes; Annang, Frederick; Martín, Jesús; Pérez-Victoria, Ignacio; González-Menéndez, Víctor; de Pedro, Nuria; Tormo, José R; Algieri, Francesca; Rodriguez-Nogales, Alba; Rodríguez-Cabezas, M Elena; Reyes, Fernando; Genilloud, Olga; Vicente, Francisca; Gálvez, Julio

2016-07-01

It is widely accepted that central nervous system inflammation and systemic inflammation play a significant role in the progression of chronic neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease, neurotropic viral infections, stroke, paraneoplastic disorders, traumatic brain injury, and multiple sclerosis. Therefore, it seems reasonable to propose that the use of anti-inflammatory drugs might diminish the cumulative effects of inflammation. Indeed, some epidemiological studies suggest that sustained use of anti-inflammatory drugs may prevent or slow down the progression of neurodegenerative diseases. However, the anti-inflammatory drugs and biologics used clinically have the disadvantage of causing side effects and a high cost of treatment. Alternatively, natural products offer great potential for the identification and development of bioactive lead compounds into drugs for treating inflammatory diseases with an improved safety profile. In this work, we present a validated high-throughput screening approach in 96-well plate format for the discovery of new molecules with anti-inflammatory/immunomodulatory activity. The in vitro models are based on the quantitation of nitrite levels in RAW264.7 murine macrophages and interleukin-8 in Caco-2 cells. We have used this platform in a pilot project to screen a subset of 5976 noncytotoxic crude microbial extracts from the MEDINA microbial natural product collection. To our knowledge, this is the first report on an high-throughput screening of microbial natural product extracts for the discovery of immunomodulators. © 2016 Society for Laboratory Automation and Screening.

Determination of burn patient outcome by large-scale quantitative discovery proteomics

PubMed Central

Finnerty, Celeste C.; Jeschke, Marc G.; Qian, Wei-Jun; Kaushal, Amit; Xiao, Wenzhong; Liu, Tao; Gritsenko, Marina A.; Moore, Ronald J.; Camp, David G.; Moldawer, Lyle L.; Elson, Constance; Schoenfeld, David; Gamelli, Richard; Gibran, Nicole; Klein, Matthew; Arnoldo, Brett; Remick, Daniel; Smith, Richard D.; Davis, Ronald; Tompkins, Ronald G.; Herndon, David N.

2013-01-01

Objective Emerging proteomics techniques can be used to establish proteomic outcome signatures and to identify candidate biomarkers for survival following traumatic injury. We applied high-resolution liquid chromatography-mass spectrometry (LC-MS) and multiplex cytokine analysis to profile the plasma proteome of survivors and non-survivors of massive burn injury to determine the proteomic survival signature following a major burn injury. Design Proteomic discovery study. Setting Five burn hospitals across the U.S. Patients Thirty-two burn patients (16 non-survivors and 16 survivors), 19–89 years of age, were admitted within 96 h of injury to the participating hospitals with burns covering >20% of the total body surface area and required at least one surgical intervention. Interventions None. Measurements and Main Results We found differences in circulating levels of 43 proteins involved in the acute phase response, hepatic signaling, the complement cascade, inflammation, and insulin resistance. Thirty-two of the proteins identified were not previously known to play a role in the response to burn. IL-4, IL-8, GM-CSF, MCP-1, and β2-microglobulin correlated well with survival and may serve as clinical biomarkers. Conclusions These results demonstrate the utility of these techniques for establishing proteomic survival signatures and for use as a discovery tool to identify candidate biomarkers for survival. This is the first clinical application of a high-throughput, large-scale LC-MS-based quantitative plasma proteomic approach for biomarker discovery for the prediction of patient outcome following burn, trauma or critical illness. PMID:23507713

Biosignature Discovery for Substance Use Disorders Using Statistical Learning.

PubMed

Baurley, James W; McMahan, Christopher S; Ervin, Carolyn M; Pardamean, Bens; Bergen, Andrew W

2018-02-01

There are limited biomarkers for substance use disorders (SUDs). Traditional statistical approaches are identifying simple biomarkers in large samples, but clinical use cases are still being established. High-throughput clinical, imaging, and 'omic' technologies are generating data from SUD studies and may lead to more sophisticated and clinically useful models. However, analytic strategies suited for high-dimensional data are not regularly used. We review strategies for identifying biomarkers and biosignatures from high-dimensional data types. Focusing on penalized regression and Bayesian approaches, we address how to leverage evidence from existing studies and knowledge bases, using nicotine metabolism as an example. We posit that big data and machine learning approaches will considerably advance SUD biomarker discovery. However, translation to clinical practice, will require integrated scientific efforts. Copyright © 2017 Elsevier Ltd. All rights reserved.

Biomarker Discovery Using New Metabolomics Software for Automated Processing of High Resolution LC-MS Data

PubMed Central

Hnatyshyn, S.; Reily, M.; Shipkova, P.; McClure, T.; Sanders, M.; Peake, D.

2011-01-01

Robust biomarkers of target engagement and efficacy are required in different stages of drug discovery. Liquid chromatography coupled to high resolution mass spectrometry provides sensitivity, accuracy and wide dynamic range required for identification of endogenous metabolites in biological matrices. LCMS is widely-used tool for biomarker identification and validation. Typical high resolution LCMS profiles from biological samples may contain greater than a million mass spectral peaks corresponding to several thousand endogenous metabolites. Reduction of the total number of peaks, component identification and statistical comparison across sample groups remains to be a difficult and time consuming challenge. Blood samples from four groups of rats (male vs. female, fully satiated and food deprived) were analyzed using high resolution accurate mass (HRAM) LCMS. All samples were separated using a 15 minute reversed-phase C18 LC gradient and analyzed in both positive and negative ion modes. Data was acquired using 15K resolution and 5ppm mass measurement accuracy. The entire data set was analyzed using software developed in collaboration between Bristol Meyers Squibb and Thermo Fisher Scientific to determine the metabolic effects of food deprivation on rats. Metabolomic LC-MS data files are extraordinarily complex and appropriate reduction of the number of spectral peaks via identification of related peaks and background removal is essential. A single component such as hippuric acid generates more than 20 related peaks including isotopic clusters, adducts and dimers. Plasma and urine may contain 500-1500 unique quantifiable metabolites. Noise filtering approaches including blank subtraction were used to reduce the number of irrelevant peaks. By grouping related signals such as isotopic peaks and alkali adducts, data processing was greatly simplified by reducing the total number of components by 10-fold. The software processes 48 samples in under 60minutes. Principle

Urine biomarkers in the early stages of diseases: current status and perspective.

PubMed

Jing, Jian; Gao, Youhe

2018-02-01

As a noninvasive and easily available biological fluid, the urine is becoming an important source for disease biomarker study. Change is essential for the usefulness of a biomarker. Without homeostasis mechanisms, urine can accommodate more changes, especially in the early stages of diseases. In this review, we summarize current status and discuss perspectives on the discovery of urine biomarkers in the early stages of diseases. We emphasize the advantages of urine biomarkers compared to plasma biomarkers for the diagnosis of diseases at early stages, propose a urine biomarker research roadmap, and highlight a novel membrane storage technique that enables large-scale urine sample collection and storage efficiently and economically. It is anticipated that urine biomarker studies will greatly promote early diagnosis, prevention, treatment, and prognosis of a variety of diseases, and provide strong support for translational and precision medicine.

A Proteomic Analysis of Eccrine Sweat: Implications for the Discovery of Schizophrenia Biomarker Proteins

PubMed Central

Raiszadeh, Michelle M.; Ross, Mark M.; Russo, Paul S.; Schaepper, Mary Ann H.; Zhou, Weidong; Deng, Jianghong; Ng, Daniel; Dickson, April; Dickson, Cindy; Strom, Monica; Osorio, Carolina; Soeprono, Thomas; Wulfkuhle, Julia D.; Kabbani, Nadine; Petricoin, Emanuel F.; Liotta, Lance A.; Kirsch, Wolff M.

2012-01-01

Liquid chromatography tandem mass spectrometry (LC-MS/MS) and multiple reaction monitoring mass spectrometry (MRM-MS) proteomics analyses were performed on eccrine sweat of healthy controls, and the results were compared with those from individuals diagnosed with schizophrenia (SZ). This is the first large scale study of the sweat proteome. First, we performed LC-MS/MS on pooled SZ samples and pooled control samples for global proteomics analysis. Results revealed a high abundance of diverse proteins and peptides in eccrine sweat. Most of the proteins identified from sweat samples were found to be different than the most abundant proteins from serum, which indicates that eccrine sweat is not simply a plasma transudate, and may thereby be a source of unique disease-associated biomolecules. A second independent set of patient and control sweat samples were analyzed by LC-MS/MS and spectral counting to determine qualitative protein differential abundances between the control and disease groups. Differential abundances of selected proteins, initially determined by spectral counting, were verified by MRM-MS analyses. Seventeen proteins showed a differential abundance of approximately two-fold or greater between the SZ pooled sample and the control pooled sample. This study demonstrates the utility of LC-MS/MS and MRM-MS as a viable strategy for the discovery and verification of potential sweat protein disease biomarkers. PMID:22256890

[Biomarkers: "Found in translation"].

PubMed

Lockhart, Brian P; Walther, Bernard

2009-04-01

Despite continued increase in global Pharma R & D expenditure, the number of innovative drugs obtaining market approval has declined since 1994. The pharmaceutical industry is now entering a crucial juncture where increasing rates of attrition in clinical drug development as well as increasing development timelines are impacted by external factors such as intense regulatory pricing and safety pressures, increasing sales erosion due to generics, as well as exponential increases in the costs of bringing a drug to market. Despite these difficulties, numerous opportunities exist such as multiple unmet medical needs, the increasing incidence of certain diseases such as Alzheimer's disease, cancer, diabetes and obesity due to demographic changes, as well as the emergence of evolving markets such as China, India, and Eastern Europe. Consequently, Pharma is now responding to this challenge by improving both the productivity and the innovation in its drug discovery and development pipelines. In this regard, the advent of new technologies and expertise such as genomics, proteomics, structural biology, and molecular informatics in an integrated systems biology approach also provides a powerful opportunity for Pharma to address some of these difficulties. The key features behind this new strategy imply a discovery process based on an improved understanding of the molecular mechanism of diseases and drugs, translational research that places the patient at the center of the research process, and the application of biomarkers throughout the discovery and development phases. Moreover, new paradigms are required to improve target validation and develop more predictive cellular and animal models of human pathologies, a greater capacity in informatics-based analysis, and, consequently, a greater access to the vast sources of accumulating biological data and its integrated analysis. In the present review, we will address some of these issues and in particular emphasize how the

Top-K Interesting Subgraph Discovery in Information Networks

DTIC Science & Technology

2014-03-03

Integrative Biomarker Discovery for Breast Cancer Metastasis from Gene Expression and Protein Interaction Data Using Error-tolerant Pattern Mining” at...Jiawei Han¶ ∗Microsoft, India . Email: gmanish@microsoft.com †State University of New York at Buffalo. Email: jing@buffalo.edu ‡University of California

3D cell culture systems--towards primary drug discovery platforms: an interview with Heinz Ruffner (Novartis) and Jan Lichtenberg (InSphero).

PubMed

Ruffner, Heinz; Lichtenberg, Jan

2012-07-01

Advanced cell culture systems for regenerative medicine, drug efficacy and toxicity testing, enabling technologies to create and analyze 3D cell culture systems were the topics of the 3D cell culture meeting taking place in March 14-16, 2012 at the Technopark in Zurich, Switzerland. At this meeting Biotechnology Journal had the pleasure to talk to Dr. Heinz Ruffner, Novartis AG, and Dr. Jan Lichtenberg, co-founder and CEO of InSphero AG, about challenges and perspectives in using 3D cell culture systems as primary drug discovery platforms. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Candidate Proteins, Metabolites and Transcripts in the Biomarkers for Spinal Muscular Atrophy (BforSMA) Clinical Study

PubMed Central

Finkel, Richard S.; Crawford, Thomas O.; Swoboda, Kathryn J.; Kaufmann, Petra; Juhasz, Peter; Li, Xiaohong; Guo, Yu; Li, Rebecca H.; Trachtenberg, Felicia; Forrest, Suzanne J.; Kobayashi, Dione T.; Chen, Karen S.; Joyce, Cynthia L.; Plasterer, Thomas

2012-01-01

Background Spinal Muscular Atrophy (SMA) is a neurodegenerative motor neuron disorder resulting from a homozygous mutation of the survival of motor neuron 1 (SMN1) gene. The gene product, SMN protein, functions in RNA biosynthesis in all tissues. In humans, a nearly identical gene, SMN2, rescues an otherwise lethal phenotype by producing a small amount of full-length SMN protein. SMN2 copy number inversely correlates with disease severity. Identifying other novel biomarkers could inform clinical trial design and identify novel therapeutic targets. Objective: To identify novel candidate biomarkers associated with disease severity in SMA using unbiased proteomic, metabolomic and transcriptomic approaches. Materials and Methods: A cross-sectional single evaluation was performed in 108 children with genetically confirmed SMA, aged 2–12 years, manifesting a broad range of disease severity and selected to distinguish factors associated with SMA type and present functional ability independent of age. Blood and urine specimens from these and 22 age-matched healthy controls were interrogated using proteomic, metabolomic and transcriptomic discovery platforms. Analyte associations were evaluated against a primary measure of disease severity, the Modified Hammersmith Functional Motor Scale (MHFMS) and to a number of secondary clinical measures. Results A total of 200 candidate biomarkers correlate with MHFMS scores: 97 plasma proteins, 59 plasma metabolites (9 amino acids, 10 free fatty acids, 12 lipids and 28 GC/MS metabolites) and 44 urine metabolites. No transcripts correlated with MHFMS. Discussion In this cross-sectional study, “BforSMA” (Biomarkers for SMA), candidate protein and metabolite markers were identified. No transcript biomarker candidates were identified. Additional mining of this rich dataset may yield important insights into relevant SMA-related pathophysiology and biological network associations. Additional prospective studies are needed to confirm

Synthetic biology platform technologies for antimicrobial applications.

PubMed

Braff, Dana; Shis, David; Collins, James J

2016-10-01

The growing prevalence of antibiotic resistance calls for new approaches in the development of antimicrobial therapeutics. Likewise, improved diagnostic measures are essential in guiding the application of targeted therapies and preventing the evolution of therapeutic resistance. Discovery platforms are also needed to form new treatment strategies and identify novel antimicrobial agents. By applying engineering principles to molecular biology, synthetic biologists have developed platforms that improve upon, supplement, and will perhaps supplant traditional broad-spectrum antibiotics. Efforts in engineering bacteriophages and synthetic probiotics demonstrate targeted antimicrobial approaches that can be fine-tuned using synthetic biology-derived principles. Further, the development of paper-based, cell-free expression systems holds promise in promoting the clinical translation of molecular biology tools for diagnostic purposes. In this review, we highlight emerging synthetic biology platform technologies that are geared toward the generation of new antimicrobial therapies, diagnostics, and discovery channels. Copyright © 2016 Elsevier B.V. All rights reserved.

Biology and Biomarkers for Wound Healing.

PubMed

Lindley, Linsey E; Stojadinovic, Olivera; Pastar, Irena; Tomic-Canic, Marjana

2016-09-01

As the population grows older, the incidence and prevalence of conditions that lead to a predisposition for poor wound healing also increase. Ultimately, this increase in nonhealing wounds has led to significant morbidity and mortality with subsequent huge economic ramifications. Therefore, understanding specific molecular mechanisms underlying aberrant wound healing is of great importance. It has and will continue to be the leading pathway to the discovery of therapeutic targets, as well as diagnostic molecular biomarkers. Biomarkers may help identify and stratify subsets of nonhealing patients for whom biomarker-guided approaches may aid in healing. A series of literature searches were performed using Medline, PubMed, Cochrane Library, and Internet searches. Currently, biomarkers are being identified using biomaterials sourced locally from human wounds and/or systemically using high-throughput "omics" modalities (genomic, proteomic, lipidomic, and metabolomic analysis). In this review, we highlight the current status of clinically applicable biomarkers and propose multiple steps in validation and implementation spectrum, including those measured in tissue specimens, for example, β-catenin and c-myc, wound fluid, matrix metalloproteinases and interleukins, swabs, wound microbiota, and serum, for example, procalcitonin and matrix metalloproteinases. Identification of numerous potential biomarkers using different avenues of sample collection and molecular approaches is currently underway. A focus on simplicity and consistent implementation of these biomarkers, as well as an emphasis on efficacious follow-up therapeutics, is necessary for transition of this technology to clinically feasible point-of-care applications.

Multi-platform 'Omics Analysis of Human Ebola Virus Disease Pathogenesis.

PubMed

Eisfeld, Amie J; Halfmann, Peter J; Wendler, Jason P; Kyle, Jennifer E; Burnum-Johnson, Kristin E; Peralta, Zuleyma; Maemura, Tadashi; Walters, Kevin B; Watanabe, Tokiko; Fukuyama, Satoshi; Yamashita, Makoto; Jacobs, Jon M; Kim, Young-Mo; Casey, Cameron P; Stratton, Kelly G; Webb-Robertson, Bobbie-Jo M; Gritsenko, Marina A; Monroe, Matthew E; Weitz, Karl K; Shukla, Anil K; Tian, Mingyuan; Neumann, Gabriele; Reed, Jennifer L; van Bakel, Harm; Metz, Thomas O; Smith, Richard D; Waters, Katrina M; N'jai, Alhaji; Sahr, Foday; Kawaoka, Yoshihiro

2017-12-13

The pathogenesis of human Ebola virus disease (EVD) is complex. EVD is characterized by high levels of virus replication and dissemination, dysregulated immune responses, extensive virus- and host-mediated tissue damage, and disordered coagulation. To clarify how host responses contribute to EVD pathophysiology, we performed multi-platform 'omics analysis of peripheral blood mononuclear cells and plasma from EVD patients. Our results indicate that EVD molecular signatures overlap with those of sepsis, imply that pancreatic enzymes contribute to tissue damage in fatal EVD, and suggest that Ebola virus infection may induce aberrant neutrophils whose activity could explain hallmarks of fatal EVD. Moreover, integrated biomarker prediction identified putative biomarkers from different data platforms that differentiated survivors and fatalities early after infection. This work reveals insight into EVD pathogenesis, suggests an effective approach for biomarker identification, and provides an important community resource for further analysis of human EVD severity. Copyright © 2017 Elsevier Inc. All rights reserved.

Effects of freezer storage time on levels of complement biomarkers.

PubMed

Morgan, Angharad R; O'Hagan, Caroline; Touchard, Samuel; Lovestone, Simon; Morgan, B Paul

2017-11-06

There is uncertainty regarding how stable complement analytes are during long-term storage at - 80 °C. As part of our work program we have measured 17 complement biomarkers (C1q, C1 inhibitor, C3, C3a, iC3b, C4, C5, C9, FB, FD, FH, FI, TCC, Bb, sCR1, sCR2, Clusterin) and the benchmark inflammatory marker C-reactive protein (CRP) in a large set of plasma samples (n = 720) that had been collected, processed and subsequently stored at - 80 °C over a period of 6.6-10.6 years, prior to laboratory analysis. The biomarkers were measured using solid-phase enzyme immunoassays with a combination of multiplex assays using the MesoScale Discovery Platform and single-plex enzyme-linked immunosorbent assays (ELISAs). As part of a post hoc analysis of extrinsic factors (co-variables) affecting the analyses we investigated the impact of freezer storage time on the values obtained for each complement analyte. With the exception of five analytes (C4, C9, sCR2, clusterin and CRP), storage time was significantly correlated with measured plasma concentrations. For ten analytes: C3, FI, FB, FD, C5, sCR1, C3a, iC3b, Bb and TCC, storage time was positively correlated with concentration and for three analytes: FH, C1q, and C1 inhibitor, storage time was negatively correlated with concentration. The results suggest that information on storage time should be regarded as an important co-variable and taken into consideration when analysing data to look for associations of complement biomarker levels and disease or other outcomes.

Discovery of biochemical biomarkers for aggression: A role for metabolomics in psychiatry.

PubMed

Hagenbeek, Fiona A; Kluft, Cornelis; Hankemeier, Thomas; Bartels, Meike; Draisma, Harmen H M; Middeldorp, Christel M; Berger, Ruud; Noto, Antonio; Lussu, Milena; Pool, René; Fanos, Vassilios; Boomsma, Dorret I

2016-07-01

Human aggression encompasses a wide range of behaviors and is related to many psychiatric disorders. We introduce the different classification systems of aggression and related disorders as a basis for discussing biochemical biomarkers and then present an overview of studies in humans (published between 1990 and 2015) that reported statistically significant associations of biochemical biomarkers with aggression, DSM-IV disorders involving aggression, and their subtypes. The markers are of different types, including inflammation markers, neurotransmitters, lipoproteins, and hormones from various classes. Most studies focused on only a limited portfolio of biomarkers, frequently a specific class only. When integrating the data, it is clear that compounds from several biological pathways have been found to be associated with aggressive behavior, indicating complexity and the need for a broad approach. In the second part of the paper, using examples from the aggression literature and psychiatric metabolomics studies, we argue that a better understanding of aggression would benefit from a more holistic approach such as provided by metabolomics. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Biomarkers for personalized oncology: recent advances and future challenges.

PubMed

Kalia, Madhu

2015-03-01

Cancer is a group of diseases characterized by the uncontrolled growth and spread of abnormal cells and oncology is a branch of medicine that deals with tumors. The last decade has seen significant advances in the development of biomarkers in oncology that play a critical role in understanding molecular and cellular mechanisms which drive tumor initiation, maintenance and progression. Clinical molecular diagnostics and biomarker discoveries in oncology are advancing rapidly as we begin to understand the complex mechanisms that transform a normal cell into an abnormal one. These discoveries have fueled the development of novel drug targets and new treatment strategies. The standard of care for patients with advanced-stage cancers has shifted away from an empirical treatment strategy based on the clinical-pathological profile to one where a biomarker driven treatment algorithm based on the molecular profile of the tumor is used. Recent advances in multiplex genotyping technologies and high-throughput genomic profiling by next-generation sequencing make possible the rapid and comprehensive analysis of the cancer genome of individual patients even from very little tumor biopsy material. Predictive (diagnostic) biomarkers are helpful in matching targeted therapies with patients and in preventing toxicity of standard (systemic) therapies. Prognostic biomarkers identify somatic germ line mutations, changes in DNA methylation, elevated levels of microRNA (miRNA) and circulating tumor cells (CTC) in blood. Predictive biomarkers using molecular diagnostics are currently in use in clinical practice of personalized oncotherapy for the treatment of five diseases: chronic myeloid leukemia, colon, breast, lung cancer and melanoma and these biomarkers are being used successfully to evaluate benefits that can be achieved through targeted therapy. Examples of these molecularly targeted biomarker therapies are: tyrosine kinase inhibitors in chronic myeloid leukemia and

Towards microfluidic technology-based MALDI-MS platforms for drug discovery: a review.

PubMed

Winkle, Richard F; Nagy, Judit M; Cass, Anthony Eg; Sharma, Sanjiv

2008-11-01

Microfluidic methods have found applications in various disciplines. It has been predicted that the microfluidic technology would be useful in performing routine steps in drug discovery ranging from target identification to lead optimisation in which the number of compounds evaluated in this regard determines the success of combinatorial screening. The sheer size of the parameter space that can be explored often poses an enormous challenge. We set out to find how close we are towards the use of integrated matrix-assisted laser desorption/ionisation mass spectrometry (MALDI-MS) microfluidic systems for drug discovery. In this article we review the latest applications of microfluidic technology in the area of MALDI-MS and drug discovery. Our literature survey revealed microfluidic technologies-based approaches for various stages of drug discovery; however, they are in still in developmental stages. Furthermore, we speculate on how these technologies could be used in the future.

Perinatal biomarkers in prematurity: Early identification of neurologic injury

PubMed Central

Andrikopoulou, Maria; Almalki, Ahmad; Farzin, Azadeh; Cordeiro, Christina N.; Johnston, Michael V.; Burd, Irina

2014-01-01

Over the past few decades, biomarkers have become increasingly utilized as non-invasive tools in the early diagnosis and management of various clinical conditions. In perinatal medicine, the improved survival of extremely premature infants who are at high risk for adverse neurologic outcomes has increased the demand for the discovery of biomarkers in detecting and predicting the prognosis of infants with neonatal brain injury. By enabling the clinician to recognize potential brain damage early, biomarkers could allow clinicians to intervene at the early stages of disease, and to monitor the efficacy of those interventions. This review will first examine the potential perinatal biomarkers for neurologic complications of prematurity, specifically, intraventricular hemorrhage (IVH), periventricular leukomalacia (PVL) and posthemorrhagic hydrocephalus (PHH). It will also evaluate knowledge gained from animal models regarding the pathogenesis of perinatal brain injury in prematurity. PMID:24768951

Enhancement of MS Signal Processing For Improved Cancer Biomarker Discovery

NASA Astrophysics Data System (ADS)

Si, Qian

Technological advances in proteomics have shown great potential in detecting cancer at the earliest stages. One way is to use the time of flight mass spectroscopy to identify biomarkers, or early disease indicators related to the cancer. Pattern analysis of time of flight mass spectra data from blood and tissue samples gives great hope for the identification of potential biomarkers among the complex mixture of biological and chemical samples for the early cancer detection. One of the keys issues is the pre-processing of raw mass spectra data. A lot of challenges need to be addressed: unknown noise character associated with the large volume of data, high variability in the mass spectroscopy measurements, and poorly understood signal background and so on. This dissertation focuses on developing statistical algorithms and creating data mining tools for computationally improved signal processing for mass spectrometry data. I have introduced an advanced accurate estimate of the noise model and a half-supervised method of mass spectrum data processing which requires little knowledge about the data.

Clinical proteomics: Applications for prostate cancer biomarker discovery and detection.

PubMed

Petricoin, Emanuel F; Ornstein, David K; Liotta, Lance A

2004-01-01

The science of proteomics comprises much more than simply generating lists of proteins that change in expression as a cause of or consequence of pathophysiology. The goal of proteomics should be to characterize the information flow through the intercellular protein circuitry that communicates with the extracellular microenvironment and then ultimately to the serum/plasma macroenvironment. Serum proteomic pattern diagnostics is a new type of proteomic concept in which patterns of ion signatures generated from high dimensional mass spectrometry data are used as diagnostic classifiers. This recent approach has exciting potential for clinical utility of diagnostic patterns because low molecular weight metabolites, peptides, and protein fragments may have higher accuracy than traditional biomarkers of cancer detection. Intriguingly, we now have discovered that this diagnostic information exists in a bound state, complexed with circulating highly abundant carrier proteins. These diagnostic fragments may one day be harvested by circulating nanoparticles, designed to absorb, enrich, and amplify the repertoire of diagnostic biomarkers generated-even at the critical, initial stages of carcinogenesis. Copyright 2004 Elsevier Inc.

Multiplex detection of pancreatic cancer biomarkers using a SERS-based immunoassay

NASA Astrophysics Data System (ADS)

Banaei, Nariman; Foley, Anne; Houghton, Jean Marie; Sun, Yubing; Kim, Byung

2017-11-01

Early diagnosis of pancreatic cancer (PC) is critical to reduce the mortality rate of this disease. Current biological analysis approaches cannot robustly detect several low abundance PC biomarkers in sera, limiting the clinical application of these biomarkers. Enzyme linked immunosorbent assay and radioimmunoassay are two common platforms for detection of biomarkers; however, they suffer from some limitation. This study demonstrates a novel system for multiplex detection of pancreatic biomarkers CA19-9, MMP7 and MUC4 in sera samples with high sensitivity using surface enhanced Raman spectroscopy. Measuring the levels of these biomarkers in PC patients, pancreatitis patients, and healthy individuals reveals the unique expression pattern of these markers in PC patients, suggesting the great potential of using this approach for early diagnostics of PCs.

Biomarkers of (osteo)arthritis

PubMed Central

Mobasheri, Ali; Henrotin, Yves

2015-01-01

Abstract Arthritic diseases are a major cause of disability and morbidity, and cause an enormous burden for health and social care systems globally. Osteoarthritis (OA) is the most common form of arthritis. The key risk factors for the development of OA are age, obesity, joint trauma or instability. Metabolic and endocrine diseases can also contribute to the pathogenesis of OA. There is accumulating evidence to suggest that OA is a whole-organ disease that is influenced by systemic mediators, inflammaging, innate immunity and the low-grade inflammation induced by metabolic syndrome. Although all joint tissues are implicated in disease progression in OA, articular cartilage has received the most attention in the context of aging, injury and disease. There is increasing emphasis on the early detection of OA as it has the capacity to target and treat the disease more effectively. Indeed it has been suggested that this is the era of “personalized prevention” for OA. However, the development of strategies for the prevention of OA require new and sensitive biomarker tools that can detect the disease in its molecular and pre-radiographic stage, before structural and functional alterations in cartilage integrity have occurred. There is also evidence to support a role for biomarkers in OA drug discovery, specifically the development of disease modifying osteoarthritis drugs. This Special Issue of Biomarkers is dedicated to recent progress in the field of OA biomarkers. The papers in this Special Issue review the current state-of-the-art and discuss the utility of OA biomarkers as diagnostic and prognostic tools. PMID:26954784

Modulators of the microRNA biogenesis pathway via arrayed lentiviral enabled RNAi screening for drug and biomarker discovery

PubMed Central

Shum, David; Bhinder, Bhavneet; Djaballah, Hakim

2013-01-01

MicroRNAs (miRNAs) are small endogenous and conserved non-coding RNA molecules that regulate gene expression. Although the first miRNA was discovered well over sixteen years ago, little is known about their biogenesis and it is only recently that we have begun to understand their scope and diversity. For this purpose, we performed an RNAi screen aimed at identifying genes involved in their biogenesis pathway with a potential use as biomarkers. Using a previously developed miRNA 21 (miR-21) EGFP-based biosensor cell based assay monitoring green fluorescence enhancements, we performed an arrayed short hairpin RNA (shRNA) screen against a lentiviral particle ready TRC1 library covering 16,039 genes in 384-well plate format, and interrogating the genome one gene at a time building a panoramic view of endogenous miRNA activity. Using the BDA method for RNAi data analysis, we nominate 497 gene candidates the knockdown of which increased the EGFP fluorescence and yielding an initial hit rate of 3.09%; of which only 22, with reported validated clones, are deemed high-confidence gene candidates. An unexpected and surprising result was that only DROSHA was identified as a hit out of the seven core essential miRNA biogenesis genes; suggesting that perhaps intracellular shRNA processing into the correct duplex may be cell dependent and with differential outcome. Biological classification revealed several major control junctions among them genes involved in transport and vesicular trafficking. In summary, we report on 22 high confidence gene candidate regulators of miRNA biogenesis with potential use in drug and biomarker discovery. PMID:23977983

Impact of biomarker development on drug safety assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marrer, Estelle, E-mail: estelle.marrer@novartis.co; Dieterle, Frank

2010-03-01

Drug safety has always been a key aspect of drug development. Recently, the Vioxx case and several cases of serious adverse events being linked to high-profile products have increased the importance of drug safety, especially in the eyes of drug development companies and global regulatory agencies. Safety biomarkers are increasingly being seen as helping to provide the clarity, predictability, and certainty needed to gain confidence in decision making: early-stage projects can be stopped quicker, late-stage projects become less risky. Public and private organizations are investing heavily in terms of time, money and manpower on safety biomarker development. An illustrative andmore » 'door opening' safety biomarker success story is the recent recognition of kidney safety biomarkers for pre-clinical and limited translational contexts by FDA and EMEA. This milestone achieved for kidney biomarkers and the 'know how' acquired is being transferred to other organ toxicities, namely liver, heart, vascular system. New technologies and molecular-based approaches, i.e., molecular pathology as a complement to the classical toolbox, allow promising discoveries in the safety biomarker field. This review will focus on the utility and use of safety biomarkers all along drug development, highlighting the present gaps and opportunities identified in organ toxicity monitoring. A last part will be dedicated to safety biomarker development in general, from identification to diagnostic tests, using the kidney safety biomarkers success as an illustrative example.« less

Biology and Biomarkers for Wound Healing

PubMed Central

Lindley, Linsey E.; Stojadinovic, Olivera; Pastar, Irena; Tomic-Canic, Marjana

2016-01-01

Background As the population grows older, the incidence and prevalence of conditions which lead to a predisposition for poor wound healing also increases. Ultimately, this increase in non-healing wounds has led to significant morbidity and mortality with subsequent huge economic ramifications. Therefore, understanding specific molecular mechanisms underlying aberrant wound healing is of great importance. It has, and will continue to be the leading pathway to the discovery of therapeutic targets as well as diagnostic molecular biomarkers. Biomarkers may help identify and stratify subsets of non-healing patients for whom biomarker-guided approaches may aid in healing. Methods A series of literature searches were performed using Medline, PubMed, Cochrane Library, and Internet searches. Results Currently, biomarkers are being identified using biomaterials sourced locally, from human wounds and/or systemically using systematic high-throughput “omics” modalities (genomic, proteomic, lipidomic, metabolomic analysis). In this review we highlight the current status of clinically applicable biomarkers and propose multiple steps in validation and implementation spectrum including those measured in tissue specimens e.g. β-catenin and c-myc, wound fluid e.g. MMP’s and interleukins, swabs e.g. wound microbiota and serum e.g. procalcitonin and MMP’s. Conclusions Identification of numerous potential biomarkers utilizing different avenues of sample collection and molecular approaches is currently underway. A focus on simplicity, and consistent implementation of these biomarkers as well as an emphasis on efficacious follow-up therapeutics is necessary for transition of this technology to clinically feasible point-of-care applications. PMID:27556760

Classification of Genes and Putative Biomarker Identification Using Distribution Metrics on Expression Profiles

PubMed Central

Huang, Hung-Chung; Jupiter, Daniel; VanBuren, Vincent

2010-01-01

Background Identification of genes with switch-like properties will facilitate discovery of regulatory mechanisms that underlie these properties, and will provide knowledge for the appropriate application of Boolean networks in gene regulatory models. As switch-like behavior is likely associated with tissue-specific expression, these gene products are expected to be plausible candidates as tissue-specific biomarkers. Methodology/Principal Findings In a systematic classification of genes and search for biomarkers, gene expression profiles (GEPs) of more than 16,000 genes from 2,145 mouse array samples were analyzed. Four distribution metrics (mean, standard deviation, kurtosis and skewness) were used to classify GEPs into four categories: predominantly-off, predominantly-on, graded (rheostatic), and switch-like genes. The arrays under study were also grouped and examined by tissue type. For example, arrays were categorized as ‘brain group’ and ‘non-brain group’; the Kolmogorov-Smirnov distance and Pearson correlation coefficient were then used to compare GEPs between brain and non-brain for each gene. We were thus able to identify tissue-specific biomarker candidate genes. Conclusions/Significance The methodology employed here may be used to facilitate disease-specific biomarker discovery. PMID:20140228

Protein biomarker discovery and fast monitoring for the identification and detection of Anisakids by parallel reaction monitoring (PRM) mass spectrometry.

PubMed

Carrera, Mónica; Gallardo, José M; Pascual, Santiago; González, Ángel F; Medina, Isabel

2016-06-16

Anisakids are fish-borne parasites that are responsible for a large number of human infections and allergic reactions around the world. World health organizations and food safety authorities aim to control and prevent this emerging health problem. In the present work, a new method for the fast monitoring of these parasites is described. The strategy is divided in three steps: (i) purification of thermostable proteins from fish-borne parasites (Anisakids), (ii) in-solution HIFU trypsin digestion and (iii) monitoring of several peptide markers by parallel reaction monitoring (PRM) mass spectrometry. This methodology allows the fast detection of Anisakids in <2h. An affordable assay utilizing this methodology will facilitate testing for regulatory and safety applications. The work describes for the first time, the Protein Biomarker Discovery and the Fast Monitoring for the identification and detection of Anisakids in fishery products. The strategy is based on the purification of thermostable proteins, the use of accelerated in-solution trypsin digestions under an ultrasonic field provided by High-Intensity Focused Ultrasound (HIFU) and the monitoring of several peptide biomarkers by Parallel Reaction Monitoring (PRM) Mass Spectrometry in a linear ion trap mass spectrometer. The workflow allows the unequivocal detection of Anisakids, in <2h. The present strategy constitutes the fastest method for Anisakids detection, whose application in the food quality control area, could provide to the authorities an effective and rapid method to guarantee the safety to the consumers. Copyright © 2016 Elsevier B.V. All rights reserved.

Quantitative multiplex detection of biomarkers on a waveguide-based biosensor using quantum dots

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xie, Hongzhi; Mukundan, Harshini; Martinez, Jennifer S

2009-01-01

The quantitative, simultaneous detection of multiple biomarkers with high sensitivity and specificity is critical for biomedical diagnostics, drug discovery and biomarker characterization [Wilson 2006, Tok 2006, Straub 2005, Joos 2002, Jani 2000]. Detection systems relying on optical signal transduction are, in general, advantageous because they are fast, portable, inexpensive, sensitive, and have the potential for multiplex detection of analytes of interest. However, conventional immunoassays for the detection of biomarkers, such as the Enzyme Linked Immunosorbant Assays (ELISAs) are semi-quantitative, time consuming and insensitive. ELISA assays are also limited by high non-specific binding, especially when used with complex biological samples suchmore » as serum and urine (REF). Organic fluorophores that are commonly used in such applications lack photostability and possess a narrow Stoke's shift that makes simultaneous detection of multiple fluorophores with a single excitation source difficult, thereby restricting their use in multiplex assays. The above limitations with traditional assay platforms have resulted in the increased use of nanotechnology-based tools and techniques in the fields of medical imaging [ref], targeted drug delivery [Caruthers 2007, Liu 2007], and sensing [ref]. One such area of increasing interest is the use of semiconductor quantum dots (QDs) for biomedical research and diagnostics [Gao and Cui 2004, Voura 2004, Michalet 2005, Chan 2002, Jaiswal 2004, Gao 2005, Medintz 2005, So 2006 2006, Wu 2003]. Compared to organic dyes, QDs provide several advantages for use in immunoassay platforms, including broad absorption bands with high extinction coefficients, narrow and symmetric emission bands with high quantum yields, high photostablility, and a large Stokes shift [Michalet 2005, Gu 2002]. These features prompted the use of QDs as probes in biodetection [Michalet 2005, Medintz 2005]. For example, Jaiswal et al. reported long term

Multi-platform ’Omics Analysis of Human Ebola Virus Disease Pathogenesis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eisfeld, Amie J.; Halfmann, Peter J.; Wendler, Jason P.

The pathogenesis of human Ebola virus disease (EVD) is complex. EVD is characterized by high levels of virus replication and dissemination, dysregulated immune responses, extensive virus- and host-mediated tissue damage, and disordered coagulation. To clarify how host responses contribute to EVD pathophysiology, we performed multi-platform ’omics analysis of peripheral blood mononuclear cells and plasma from EVD patients. Our results indicate that EVD molecular signatures overlap with those of sepsis, imply that pancreatic enzymes contribute to tissue damage in fatal EVD, and suggest that Ebola virus infection may induce aberrant neutrophils whose activity could explain hallmarks of fatal EVD. Moreover, integratedmore » biomarker prediction identified putative biomarkers from different data platforms that differentiated survivors and fatalities early after infection. This work reveals insight into EVD pathogenesis, suggests an effective approach for biomarker identification, and provides an important community resource for further analysis of human EVD severity.« less

Biomarker microRNAs for prostate cancer metastasis: screened with a network vulnerability analysis model.

PubMed

Lin, Yuxin; Chen, Feifei; Shen, Li; Tang, Xiaoyu; Du, Cui; Sun, Zhandong; Ding, Huijie; Chen, Jiajia; Shen, Bairong

2018-05-21

Prostate cancer (PCa) is a fatal malignant tumor among males in the world and the metastasis is a leading cause for PCa death. Biomarkers are therefore urgently needed to detect PCa metastatic signature at the early time. MicroRNAs are small non-coding RNAs with the potential to be biomarkers for disease prediction. In addition, computer-aided biomarker discovery is now becoming an attractive paradigm for precision diagnosis and prognosis of complex diseases. In this study, we identified key microRNAs as biomarkers for predicting PCa metastasis based on network vulnerability analysis. We first extracted microRNAs and mRNAs that were differentially expressed between primary PCa and metastatic PCa (MPCa) samples. Then we constructed the MPCa-specific microRNA-mRNA network and screened microRNA biomarkers by a novel bioinformatics model. The model emphasized the characterization of systems stability changes and the network vulnerability with three measurements, i.e. the structurally single-line regulation, the functional importance of microRNA targets and the percentage of transcription factor genes in microRNA unique targets. With this model, we identified five microRNAs as putative biomarkers for PCa metastasis. Among them, miR-101-3p and miR-145-5p have been previously reported as biomarkers for PCa metastasis and the remaining three, i.e. miR-204-5p, miR-198 and miR-152, were screened as novel biomarkers for PCa metastasis. The results were further confirmed by the assessment of their predictive power and biological function analysis. Five microRNAs were identified as candidate biomarkers for predicting PCa metastasis based on our network vulnerability analysis model. The prediction performance, literature exploration and functional enrichment analysis convinced our findings. This novel bioinformatics model could be applied to biomarker discovery for other complex diseases.

Standardization of protein biomarker measurements: is it feasible?

PubMed

Schimmel, Heinz; Zegers, Ingrid; Emons, Hendrik

2010-01-01

The standardisation of measurements of protein biomarkers, which are potentially heterogeneous in terms of fragmentation, modification, substitution, primary, secondary, tertiary and quaternary structure, is a demanding task. However, they are a prime target for standardisation efforts due to the importance of protein biomarkers in diagnostics and health care and the typically observed significant discrepancies in measurement results obtained with non-standardized platforms. Based on the experience gathered during successfully completed projects for the production of reference materials, pragmatic approaches are described how standardisation could become feasible despite the fuzziness of the target analytes.

Accounting for control mislabeling in case-control biomarker studies.

PubMed

Rantalainen, Mattias; Holmes, Chris C

2011-12-02

In biomarker discovery studies, uncertainty associated with case and control labels is often overlooked. By omitting to take into account label uncertainty, model parameters and the predictive risk can become biased, sometimes severely. The most common situation is when the control set contains an unknown number of undiagnosed, or future, cases. This has a marked impact in situations where the model needs to be well-calibrated, e.g., when the prediction performance of a biomarker panel is evaluated. Failing to account for class label uncertainty may lead to underestimation of classification performance and bias in parameter estimates. This can further impact on meta-analysis for combining evidence from multiple studies. Using a simulation study, we outline how conventional statistical models can be modified to address class label uncertainty leading to well-calibrated prediction performance estimates and reduced bias in meta-analysis. We focus on the problem of mislabeled control subjects in case-control studies, i.e., when some of the control subjects are undiagnosed cases, although the procedures we report are generic. The uncertainty in control status is a particular situation common in biomarker discovery studies in the context of genomic and molecular epidemiology, where control subjects are commonly sampled from the general population with an established expected disease incidence rate.

Pilot Study on the Investigation of Tear Fluid Biomarkers as an Indicator of Ocular, Neurological, and Immunological Health in Astronauts

NASA Technical Reports Server (NTRS)

Morton, Stephen; Crucian, Brian; Hagan, Suzanne; Satyamitra, Merriline; Daily, Anna

2018-01-01

The purpose of this pilot study is to investigate the collection, preparation, and analysis of tear biomarkers as a means of assessing ocular, neurological, and immunological health. At present, no published data exists on the cytokine profiles of tears from astronauts exposed to long periods of microgravity and space irradiations. In addition, no published data exist on cytokine (biomarker) profiles of tears that have been collected from irradiated non-human biological systems (primates and other animal models). A goal for the proposed pilot study is to discover novel tear biomarkers which can help inform researchers, clinicians, epidemiologist and healthcare providers about the health status of a living biological system, as well as informing them when a disease state is triggered. This would be done via analysis of the onset of expression of pro-inflammatory cytokines, leading up to the full progression of a disease (i.e. cancer, loss of vision, radiation-induced oxidative stress, cardiovascular disorders, fibrosis in major organs, bone loss). Another goal of this pilot study is to investigate the state of disease against proposed medical countermeasures, in order to determine whether the countermeasures are efficacious in preventing or mitigating these injuries. An example of an up and coming tear biomarker technology, Ascendant Dx, a clinical stage diagnostic company, is developing a screening test to detect breast cancer using proteins from tears. The team utilized Liquid Chromatography -Mass Spectrometry with Mass analysis (LC MS/MS) as a discovery platform followed by validation with ELISA to come up with a panel of protein biomarkers that can differentiate breast cancer samples from control ("cancer free") samples with results far surpassing the results of imaging techniques in use today. Continued research into additional proteins is underway to increase the sensitivity and specificity of the test and development efforts are on the way to transfer the

Clinical Adoption of Prognostic Biomarkers The Case for Heart Failure

PubMed Central

Kalogeropoulos, Andreas P.; Georgiopoulou, Vasiliki V.; Butler, Javed

2013-01-01

The recent explosion of scientific knowledge and technological progress has led to the discovery of a large array of circulating molecules commonly referred to as biomarkers. Biomarkers in heart failure research have been used to provide pathophysiological insights, aid in establishing the diagnosis, refine prognosis, guide management, and target treatment. However, beyond diagnostic applications of natriuretic peptides, there are currently few widely recognized applications for biomarkers in heart failure. This represents a remarkable discordance considering the number of molecules that have been shown to correlate with outcomes, refine risk prediction, or track disease severity in heart failure in the past decade. In this article, we use a broad framework proposed for cardiovascular risk markers to summarize the current state of biomarker development for heart failure patients. We utilize this framework to identify the challenges of biomarker adoption for risk prediction, disease management, and treatment selection for heart failure and suggest considerations for future research. PMID:22824105

Emerging infection and sepsis biomarkers: will they change current therapies?

PubMed Central

Jacobs, Lauren

2016-01-01

Introduction Sepsis is a heterogeneous syndrome characterized by both immune hyperactivity and relative immune suppression. Biomarkers have the potential to improve recognition and management of sepsis through three main applications: diagnosis, monitoring response to treatment, and stratifying patients based on prognosis or underlying biological response. Areas Covered This review focuses on specific examples of well-studied, evidence-supported biomarkers, and discusses their role in clinical practice with special attention to antibiotic stewardship and cost-effectiveness. Biomarkers were selected based on availability of robust prospective trials and meta-analyses which supported their role as emerging tools to improve the clinical management of sepsis. Expert Commentary Great strides have been made in candidate sepsis biomarker discovery and testing, with the biomarkers in this review showing promise. Yet sepsis remains a dynamic illness with a great degree of biological heterogeneity – heterogeneity which may be further resolved by recently discovered gene expression-based endotypes in septic shock. PMID:27533847

A study of a self diagnostic platform for the detection of A2 biomarker for Leishmania donovani

NASA Astrophysics Data System (ADS)

Roche, Philip J. R.; Cheung, Maurice C.; Najih, Mohamed; McCall, Laura-Isobel; Fakih, Ibrahim; Chodavarapu, Vamsy P.; Ward, Brian; Ndao, Momar; Kirk, Andrew G.

2012-03-01

Visceral leishmaniasis (L.donovani) is a protozoan infection that attacks mononuclear phagocytes and causes the liver and spleen damage that can cause death. The investigation presented is a proof of concept development applying a plasmonic diagnostic platform with simple microfluidic sample delivery and optical readout. An immune-assay method is applied to the quantification of A2 protein, a highly immunogenic biomarker for the pathogen. Quantification of A2 was performed in the ng/ml range, analysis by ELISA suggested that a limit of 0.1ng/ml of A2 is approximate to 1 pathogen per ml and the sensing system shows the potential to deliver a similar level of quantification. Significant reduction in assay complexity as further enzyme linked enhancement is not required when applying a plasmonic methodology to an immunoassay. The basic instrumentation required for a portable device and potential dual optical readout where both plasmonic and photoluminescent response are assessed and investigated including consideration of the application of the device to testing where non-literate communication of results is considered and issues of performance are addressed.

Quantification of Protein Biomarker Using SERS Nano-Stress Sensing with Peak Intensity Ratiometry

NASA Astrophysics Data System (ADS)

Goh, Douglas; Kong, Kien Voon; Jayakumar, Perumal; Gong, Tianxun; Dinish, U. S.; Olivo, Malini

We report a surface enhanced Raman spectroscopy (SERS) ratiometry method based on peak intensity coupled in a nano-stress sensing platform to detect and quantify biological molecules. Herein, we employed an antibody-conjugated p-aminothiophenol (ATP) functionalized on a bimetallic-film-over-nanosphere (BMFON) substrate as a sensitive SERS platform to detect human haptoglobin (Hp) protein, which is an acute phase protein and a biomarker for various cancers. Correlation between change in the ATP spectral characteristics and concentration of Hp protein was established by examining the peak intensity ratio at 1572cm-1 and 1592cm-1 that reflects the degree of stress experienced by the aromatic ring of ATP during Hp protein-antibody interaction. Development of this platform shows the potential in developing a low-cost and sensitive SERS sensor for the pre-screening of various biomarkers.

Real-Time Discovery Services over Large, Heterogeneous and Complex Healthcare Datasets Using Schema-Less, Column-Oriented Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Begoli, Edmon; Dunning, Ted; Charlie, Frasure

We present a service platform for schema-leess exploration of data and discovery of patient-related statistics from healthcare data sets. The architecture of this platform is motivated by the need for fast, schema-less, and flexible approaches to SQL-based exploration and discovery of information embedded in the common, heterogeneously structured healthcare data sets and supporting components (electronic health records, practice management systems, etc.) The motivating use cases described in the paper are clinical trials candidate discovery, and a treatment effectiveness analysis. Following the use cases, we discuss the key features and software architecture of the platform, the underlying core components (Apache Parquet,more » Drill, the web services server), and the runtime profiles and performance characteristics of the platform. We conclude by showing dramatic speedup with some approaches, and the performance tradeoffs and limitations of others.« less

Organic Biomarker Preservation in Silica-Rich Hydrothermal Systems with Implications to Mars

NASA Astrophysics Data System (ADS)

Jahnke, L. L.; Parenteau, M. N.; Farmer, J. D.

2016-05-01

Microbial community structure and preservation of organic matter in siliceous hydrothermal environments is a critical issue given the discovery of hydrothermal vents and silica on Mars. Here we discuss preservation of cyanobacterial biomarker lipid.

Computational systems biology analysis of biomarkers in lung cancer; unravelling genomic regions which frequently encode biomarkers, enriched pathways, and new candidates.

PubMed

Alanazi, Ibrahim O; AlYahya, Sami A; Ebrahimie, Esmaeil; Mohammadi-Dehcheshmeh, Manijeh

2018-06-15

Exponentially growing scientific knowledge in scientific publications has resulted in the emergence of a new interdisciplinary science of literature mining. In text mining, the machine reads the published literature and transfers the discovered knowledge to mathematical-like formulas. In an integrative approach in this study, we used text mining in combination with network discovery, pathway analysis, and enrichment analysis of genomic regions for better understanding of biomarkers in lung cancer. Particular attention was paid to non-coding biomarkers. In total, 60 MicroRNA biomarkers were reported for lung cancer, including some prognostic biomarkers. MIR21, MIR155, MALAT1, and MIR31 were the top non-coding RNA biomarkers of lung cancer. Text mining identified 447 proteins which have been studied as biomarkers in lung cancer. EGFR (receptor), TP53 (transcription factor), KRAS, CDKN2A, ENO2, KRT19, RASSF1, GRP (ligand), SHOX2 (transcription factor), and ERBB2 (receptor) were the most studied proteins. Within small molecules, thymosin-a1, oestrogen, and 8-OHdG have received more attention. We found some chromosomal bands, such as 7q32.2, 18q12.1, 6p12, 11p15.5, and 3p21.3 that are highly involved in deriving lung cancer biomarkers. Copyright © 2018 Elsevier B.V. All rights reserved.

Sepsis biomarkers.

PubMed

Prucha, Miroslav; Bellingan, Geoff; Zazula, Roman

2015-02-02

Sepsis is the most frequent cause of death in non-coronary intensive care units (ICUs). In the past 10 years, progress has been made in the early identification of septic patients and in their treatment and these improvements in support and therapy mean that the mortality is gradually decreasing but it still remains unacceptably high. Leaving clinical diagnosis aside, the laboratory diagnostics represent a complex range of investigations that can place significant demands on the system given the speed of response required. There are hundreds of biomarkers which could be potentially used for diagnosis and prognosis in septic patients. The main attributes of successful markers would be high sensitivity, specificity, possibility of bed-side monitoring, and financial accessibility. Only a fraction is used in routine clinical practice because many lack sufficient sensitivity or specificity. The following review gives a short overview of the current epidemiology of sepsis, its pathogenesis and state-of-the-art knowledge on the use of specific biochemical, hematological and immunological parameters in its diagnostics. Prospective approaches towards discovery of new diagnostic biomarkers have been shortly mentioned. Copyright © 2014 Elsevier B.V. All rights reserved.

Keratin 17 in premalignant and malignant squamous lesions of the cervix: proteomic discovery and immunohistochemical validation as a diagnostic and prognostic biomarker

PubMed Central

Escobar-Hoyos, Luisa F; Yang, Jie; Zhu, Jiawen; Cavallo, Julie-Ann; Zhai, Haiyan; Burke, Stephanie; Koller, Antonius; Chen, Emily I; Shroyer, Kenneth R

2014-01-01

Most previously described immunohistochemical markers of cervical high-grade squamous intraepithelial lesion (HSIL) and squamous cell carcinoma may help to improve diagnostic accuracy but have a minimal prognostic value. The goals of the current study were to identify and validate novel candidate biomarkers that could potentially improve diagnostic and prognostic accuracy for cervical HSIL and squamous cell carcinoma. Microdissected tissue sections from formalin-fixed paraffin-embedded normal ectocervical squamous mucosa, low-grade squamous intraepithelial lesion (LSIL), HSIL and squamous cell carcinoma sections were analyzed by mass spectrometry-based shotgun proteomics for biomarker discovery. The diagnostic specificity of candidate biomarkers was subsequently evaluated by immunohistochemical analysis of tissue microarrays. Among 1750 proteins identified by proteomic analyses, keratin 4 (KRT4) and keratin 17 (KRT17) showed reciprocal patterns of expression in the spectrum of cases ranging from normal ectocervical squamous mucosa to squamous cell carcinoma. Immunohistochemical studies confirmed that KRT4 expression was significantly decreased in squamous cell carcinoma compared with the other diagnostic categories. By contrast, KRT17 expression was significantly increased in HSIL and squamous cell carcinoma compared with normal ectocervical squamous mucosa and LSIL. KRT17 was also highly expressed in immature squamous metaplasia and in endocervical reserve cells but was generally not detected in mature squamous metaplasia. Furthermore, high levels of KRT17 expression were significantly associated with poor survival of squamous cell carcinoma patients (Hazard ratio = 14.76, P = 0.01). In summary, both KRT4 and KRT17 expressions are related to the histopathology of the cervical squamous mucosa; KRT17 is highly overexpressed in immature squamous metaplasia, in HSIL, and in squamous cell carcinoma and the level of KRT17 in squamous cell carcinoma may help to identify

Lab-on-a-chip platform for high throughput drug discovery with DNA-encoded chemical libraries

NASA Astrophysics Data System (ADS)

Grünzner, S.; Reddavide, F. V.; Steinfelder, C.; Cui, M.; Busek, M.; Klotzbach, U.; Zhang, Y.; Sonntag, F.

2017-02-01

The fast development of DNA-encoded chemical libraries (DECL) in the past 10 years has received great attention from pharmaceutical industries. It applies the selection approach for small molecular drug discovery. Because of the limited choices of DNA-compatible chemical reactions, most DNA-encoded chemical libraries have a narrow structural diversity and low synthetic yield. There is also a poor correlation between the ranking of compounds resulted from analyzing the sequencing data and the affinity measured through biochemical assays. By combining DECL with dynamical chemical library, the resulting DNA-encoded dynamic library (EDCCL) explores the thermodynamic equilibrium of reversible reactions as well as the advantages of DNA encoded compounds for manipulation/detection, thus leads to enhanced signal-to-noise ratio of the selection process and higher library quality. However, the library dynamics are caused by the weak interactions between the DNA strands, which also result in relatively low affinity of the bidentate interaction, as compared to a stable DNA duplex. To take advantage of both stably assembled dual-pharmacophore libraries and EDCCLs, we extended the concept of EDCCLs to heat-induced EDCCLs (hi-EDCCLs), in which the heat-induced recombination process of stable DNA duplexes and affinity capture are carried out separately. To replace the extremely laborious and repetitive manual process, a fully automated device will facilitate the use of DECL in drug discovery. Herein we describe a novel lab-on-a-chip platform for high throughput drug discovery with hi-EDCCL. A microfluidic system with integrated actuation was designed which is able to provide a continuous sample circulation by reducing the volume to a minimum. It consists of a cooled and a heated chamber for constant circulation. The system is capable to generate stable temperatures above 75 °C in the heated chamber to melt the double strands of the DNA and less than 15 °C in the cooled chamber

NYU Lung Cancer Biomarker Center — EDRN Public Portal

Cancer.gov

A. SPECIFIC AIMS 1. To develop and prospectively follow a large cohort at high-risk for lung cancer. Individuals are recruited to one of two different study groups: The Screening Cohort includes people with and without increased risk for lung cancer. The Rule-Out Lung Cancer Patient Group is recruited from patients referred for evaluation of suspicious nodules. All individuals answer a questionnaire, obtain PFTs, chest CT scan, sputum induction and phlebotomy. For patients undergoing lung resections or biopsies, tissue samples are collected and banked. Individuals are recruited for research bronchoscopy. All participants are then followed prospectively. The specimens obtained are banked and used for biomarker discovery and validation studies. 2. To identify and validate biomarkers for the early detection of lung cancer, and to describe preneoplastic cellular changes and lesions. Biomarker studies include DNA adducts, DNA methylation, protein markers, and other collaborations. Preneoplasia studies include: fluorescence and Superdimension bronchoscopies to obtain biopsies of preneoplastic lesions and biomarker studies in individuals with preneoplasias.

The Present and Future of Prostate Cancer Urine Biomarkers

PubMed Central

Rigau, Marina; Olivan, Mireia; Garcia, Marta; Sequeiros, Tamara; Montes, Melania; Colás, Eva; Llauradó, Marta; Planas, Jacques; de Torres, Inés; Morote, Juan; Cooper, Colin; Reventós, Jaume; Clark, Jeremy; Doll, Andreas

2013-01-01

In order to successfully cure patients with prostate cancer (PCa), it is important to detect the disease at an early stage. The existing clinical biomarkers for PCa are not ideal, since they cannot specifically differentiate between those patients who should be treated immediately and those who should avoid over-treatment. Current screening techniques lack specificity, and a decisive diagnosis of PCa is based on prostate biopsy. Although PCa screening is widely utilized nowadays, two thirds of the biopsies performed are still unnecessary. Thus the discovery of non-invasive PCa biomarkers remains urgent. In recent years, the utilization of urine has emerged as an attractive option for the non-invasive detection of PCa. Moreover, a great improvement in high-throughput “omic” techniques has presented considerable opportunities for the identification of new biomarkers. Herein, we will review the most significant urine biomarkers described in recent years, as well as some future prospects in that field. PMID:23774836

Metabolomics for Biomarker Discovery in Gastroenterological Cancer

PubMed Central

Nishiumi, Shin; Suzuki, Makoto; Kobayashi, Takashi; Matsubara, Atsuki; Azuma, Takeshi; Yoshida, Masaru

2014-01-01

The study of the omics cascade, which involves comprehensive investigations based on genomics, transcriptomics, proteomics, metabolomics, etc., has developed rapidly and now plays an important role in life science research. Among such analyses, metabolome analysis, in which the concentrations of low molecular weight metabolites are comprehensively analyzed, has rapidly developed along with improvements in analytical technology, and hence, has been applied to a variety of research fields including the clinical, cell biology, and plant/food science fields. The metabolome represents the endpoint of the omics cascade and is also the closest point in the cascade to the phenotype. Moreover, it is affected by variations in not only the expression but also the enzymatic activity of several proteins. Therefore, metabolome analysis can be a useful approach for finding effective diagnostic markers and examining unknown pathological conditions. The number of studies involving metabolome analysis has recently been increasing year-on-year. Here, we describe the findings of studies that used metabolome analysis to attempt to discover biomarker candidates for gastroenterological cancer and discuss metabolome analysis-based disease diagnosis. PMID:25003943

Multiplexed Electrochemical Immunosensors for Clinical Biomarkers

PubMed Central

Yáñez-Sedeño, Paloma; Campuzano, Susana; Pingarrón, José M.

2017-01-01

Management and prognosis of disease requires the accurate determination of specific biomarkers indicative of normal or disease-related biological processes or responses to therapy. Moreover since multiple determinations of biomarkers have demonstrated to provide more accurate information than individual determinations to assist the clinician in prognosis and diagnosis, the detection of several clinical biomarkers by using the same analytical device hold enormous potential for early detection and personalized therapy and will simplify the diagnosis providing more information in less time. In this field, electrochemical immunosensors have demonstrated to offer interesting alternatives against conventional strategies due to their simplicity, fast response, low cost, high sensitivity and compatibility with multiplexed determination, microfabrication technology and decentralized determinations, features which made them very attractive for integration in point-of-care (POC) devices. Therefore, in this review, the relevance and current challenges of multiplexed determination of clinical biomarkers are briefly introduced, and an overview of the electrochemical immunosensing platforms developed so far for this purpose is given in order to demonstrate the great potential of these methodologies. After highlighting the main features of the selected examples, the unsolved challenges and future directions in this field are also briefly discussed. PMID:28448466

Biomarkers in Breast Cancer – An Update

PubMed Central

Schmidt, M.; Fasching, P. A.; Beckmann, M. W.; Kölbl, H.

2012-01-01

The therapy of choice for breast cancer patients requiring adjuvant chemo- or radiotherapy is increasingly guided by the principle of weighing the individual effectiveness of the therapy against the associated side effects. This has only been made possible by the discovery and validation of modern biomarkers. In the last decades and in the last few years some biomarkers have been integrated in clinical practice and a number have been included in modern study concepts. The importance of biomarkers lies not merely in their prognostic value indicating the future course of disease but also in their use to predict patient response to therapy. Due to the many subgroups, mathematical models and computer-assisted analysis are increasingly being used to assess the prognostic information obtained from established clinical and histopathological factors. In addition to describing some recent computer programmes this overview will focus on established molecular markers which have already been extensively validated in clinical practice and on new molecular markers identified by genome-wide studies. PMID:26640290

Discovery of error-tolerant biclusters from noisy gene expression data.

PubMed

Gupta, Rohit; Rao, Navneet; Kumar, Vipin

2011-11-24

An important analysis performed on microarray gene-expression data is to discover biclusters, which denote groups of genes that are coherently expressed for a subset of conditions. Various biclustering algorithms have been proposed to find different types of biclusters from these real-valued gene-expression data sets. However, these algorithms suffer from several limitations such as inability to explicitly handle errors/noise in the data; difficulty in discovering small bicliusters due to their top-down approach; inability of some of the approaches to find overlapping biclusters, which is crucial as many genes participate in multiple biological processes. Association pattern mining also produce biclusters as their result and can naturally address some of these limitations. However, traditional association mining only finds exact biclusters, which limits its applicability in real-life data sets where the biclusters may be fragmented due to random noise/errors. Moreover, as they only work with binary or boolean attributes, their application on gene-expression data require transforming real-valued attributes to binary attributes, which often results in loss of information. Many past approaches have tried to address the issue of noise and handling real-valued attributes independently but there is no systematic approach that addresses both of these issues together. In this paper, we first propose a novel error-tolerant biclustering model, 'ET-bicluster', and then propose a bottom-up heuristic-based mining algorithm to sequentially discover error-tolerant biclusters directly from real-valued gene-expression data. The efficacy of our proposed approach is illustrated by comparing it with a recent approach RAP in the context of two biological problems: discovery of functional modules and discovery of biomarkers. For the first problem, two real-valued S.Cerevisiae microarray gene-expression data sets are used to demonstrate that the biclusters obtained from ET

The Eye As a Biomarker for Alzheimer's Disease

PubMed Central

Lim, Jeremiah K. H.; Li, Qiao-Xin; He, Zheng; Vingrys, Algis J.; Wong, Vickie H. Y.; Currier, Nicolas; Mullen, Jamie; Bui, Bang V.; Nguyen, Christine T. O.

2016-01-01

Alzheimer's disease (AD) is a progressive neurodegenerative disorder resulting in dementia and eventual death. It is the leading cause of dementia and the number of cases are projected to rise in the next few decades. Pathological hallmarks of AD include the presence of hyperphosphorylated tau and amyloid protein deposition. Currently, these pathological biomarkers are detected either through cerebrospinal fluid analysis, brain imaging or post-mortem. Though effective, these methods are not widely available due to issues such as the difficulty in acquiring samples, lack of infrastructure or high cost. Given that the eye possesses clear optics and shares many neural and vascular similarities to the brain, it offers a direct window to cerebral pathology. These unique characteristics lend itself to being a relatively inexpensive biomarker for AD which carries the potential for wide implementation. The development of ocular biomarkers can have far implications in the discovery of treatments which can improve the quality of lives of patients. In this review, we consider the current evidence for ocular biomarkers in AD and explore potential future avenues of research in this area. PMID:27909396

Next-generation sequencing in clinical virology: Discovery of new viruses.

PubMed

Datta, Sibnarayan; Budhauliya, Raghvendra; Das, Bidisha; Chatterjee, Soumya; Vanlalhmuaka; Veer, Vijay

2015-08-12

Viruses are a cause of significant health problem worldwide, especially in the developing nations. Due to different anthropological activities, human populations are exposed to different viral pathogens, many of which emerge as outbreaks. In such situations, discovery of novel viruses is utmost important for deciding prevention and treatment strategies. Since last century, a number of different virus discovery methods, based on cell culture inoculation, sequence-independent PCR have been used for identification of a variety of viruses. However, the recent emergence and commercial availability of next-generation sequencers (NGS) has entirely changed the field of virus discovery. These massively parallel sequencing platforms can sequence a mixture of genetic materials from a very heterogeneous mix, with high sensitivity. Moreover, these platforms work in a sequence-independent manner, making them ideal tools for virus discovery. However, for their application in clinics, sample preparation or enrichment is necessary to detect low abundance virus populations. A number of techniques have also been developed for enrichment or viral nucleic acids. In this manuscript, we review the evolution of sequencing; NGS technologies available today as well as widely used virus enrichment technologies. We also discuss the challenges associated with their applications in the clinical virus discovery.

CBD: a biomarker database for colorectal cancer.

PubMed

Zhang, Xueli; Sun, Xiao-Feng; Cao, Yang; Ye, Benchen; Peng, Qiliang; Liu, Xingyun; Shen, Bairong; Zhang, Hong

2018-01-01

Colorectal cancer (CRC) biomarker database (CBD) was established based on 870 identified CRC biomarkers and their relevant information from 1115 original articles in PubMed published from 1986 to 2017. In this version of the CBD, CRC biomarker data were collected, sorted, displayed and analysed. The CBD with the credible contents as a powerful and time-saving tool provide more comprehensive and accurate information for further CRC biomarker research. The CBD was constructed under MySQL server. HTML, PHP and JavaScript languages have been used to implement the web interface. The Apache was selected as HTTP server. All of these web operations were implemented under the Windows system. The CBD could provide to users the multiple individual biomarker information and categorized into the biological category, source and application of biomarkers; the experiment methods, results, authors and publication resources; the research region, the average age of cohort, gender, race, the number of tumours, tumour location and stage. We only collect data from the articles with clear and credible results to prove the biomarkers are useful in the diagnosis, treatment or prognosis of CRC. The CBD can also provide a professional platform to researchers who are interested in CRC research to communicate, exchange their research ideas and further design high-quality research in CRC. They can submit their new findings to our database via the submission page and communicate with us in the CBD.Database URL: http://sysbio.suda.edu.cn/CBD/.

CBD: a biomarker database for colorectal cancer

PubMed Central

Zhang, Xueli; Sun, Xiao-Feng; Ye, Benchen; Peng, Qiliang; Liu, Xingyun; Shen, Bairong; Zhang, Hong

2018-01-01

Abstract Colorectal cancer (CRC) biomarker database (CBD) was established based on 870 identified CRC biomarkers and their relevant information from 1115 original articles in PubMed published from 1986 to 2017. In this version of the CBD, CRC biomarker data were collected, sorted, displayed and analysed. The CBD with the credible contents as a powerful and time-saving tool provide more comprehensive and accurate information for further CRC biomarker research. The CBD was constructed under MySQL server. HTML, PHP and JavaScript languages have been used to implement the web interface. The Apache was selected as HTTP server. All of these web operations were implemented under the Windows system. The CBD could provide to users the multiple individual biomarker information and categorized into the biological category, source and application of biomarkers; the experiment methods, results, authors and publication resources; the research region, the average age of cohort, gender, race, the number of tumours, tumour location and stage. We only collect data from the articles with clear and credible results to prove the biomarkers are useful in the diagnosis, treatment or prognosis of CRC. The CBD can also provide a professional platform to researchers who are interested in CRC research to communicate, exchange their research ideas and further design high-quality research in CRC. They can submit their new findings to our database via the submission page and communicate with us in the CBD. Database URL: http://sysbio.suda.edu.cn/CBD/ PMID:29846545

Lessons for tumor biomarker trials: vicious cycles, scientific method & developing guidelines.

PubMed

Hayes, Daniel; Raison, Claire

2015-02-01

Interview with Daniel Hayes, by Claire Raison (Commissioning Editor) Daniel F Hayes, M.D. is the Stuart A Padnos Professor of Breast Cancer Research and co-Director of the Breast Oncology Program at the University of Michigan Comprehensive Cancer Center (Ann Arbor, MI, USA). Dr Hayes has extensive experience in clinical and translational breast cancer biomarker research, and in drug development and clinical trials. Around 30 years ago, he led the discovery of the circulating breast tumor biomarker, CA15-3, which started his career into further tumor biomarker work. The main thrust of his work since then has been in clinical trials, tumor biomarkers and trying to integrate the two. Dr Hayes is Chair of the Correlative Sciences Committee of the North American Breast Cancer Group (now called the Breast Cancer Steering Committee), and co-chairs the Expert Panel for Tumor Biomarker Practice Guidelines for the American Society of Clinical Oncology.

Inter-individual variation in expression: a missing link in biomarker biology?

PubMed

Little, Peter F R; Williams, Rohan B H; Wilkins, Marc R

2009-01-01

The past decade has seen an explosion of variation data demonstrating that diversity of both protein-coding sequences and of regulatory elements of protein-coding genes is common and of functional importance. In this article, we argue that genetic diversity can no longer be ignored in studies of human biology, even research projects without explicit genetic experimental design, and that this knowledge can, and must, inform research. By way of illustration, we focus on the potential role of genetic data in case-control studies to identify and validate cancer protein biomarkers. We argue that a consideration of genetics, in conjunction with proteomic biomarker discovery projects, should improve the proportion of biomarkers that can accurately classify patients.

A comparative analysis of high-throughput platforms for validation of a circulating microRNA signature in diabetic retinopathy.

PubMed

Farr, Ryan J; Januszewski, Andrzej S; Joglekar, Mugdha V; Liang, Helena; McAulley, Annie K; Hewitt, Alex W; Thomas, Helen E; Loudovaris, Tom; Kay, Thomas W H; Jenkins, Alicia; Hardikar, Anandwardhan A

2015-06-02

MicroRNAs are now increasingly recognized as biomarkers of disease progression. Several quantitative real-time PCR (qPCR) platforms have been developed to determine the relative levels of microRNAs in biological fluids. We systematically compared the detection of cellular and circulating microRNA using a standard 96-well platform, a high-content microfluidics platform and two ultra-high content platforms. We used extensive analytical tools to compute inter- and intra-run variability and concordance measured using fidelity scoring, coefficient of variation and cluster analysis. We carried out unprejudiced next generation sequencing to identify a microRNA signature for Diabetic Retinopathy (DR) and systematically assessed the validation of this signature on clinical samples using each of the above four qPCR platforms. The results indicate that sensitivity to measure low copy number microRNAs is inversely related to qPCR reaction volume and that the choice of platform for microRNA biomarker validation should be made based on the abundance of miRNAs of interest.

Potentials of single-cell biology in identification and validation of disease biomarkers.

PubMed

Niu, Furong; Wang, Diane C; Lu, Jiapei; Wu, Wei; Wang, Xiangdong

2016-09-01

Single-cell biology is considered a new approach to identify and validate disease-specific biomarkers. However, the concern raised by clinicians is how to apply single-cell measurements for clinical practice, translate the message of single-cell systems biology into clinical phenotype or explain alterations of single-cell gene sequencing and function in patient response to therapies. This study is to address the importance and necessity of single-cell gene sequencing in the identification and development of disease-specific biomarkers, the definition and significance of single-cell biology and single-cell systems biology in the understanding of single-cell full picture, the development and establishment of whole-cell models in the validation of targeted biological function and the figure and meaning of single-molecule imaging in single cell to trace intra-single-cell molecule expression, signal, interaction and location. We headline the important role of single-cell biology in the discovery and development of disease-specific biomarkers with a special emphasis on understanding single-cell biological functions, e.g. mechanical phenotypes, single-cell biology, heterogeneity and organization of genome function. We have reason to believe that such multi-dimensional, multi-layer, multi-crossing and stereoscopic single-cell biology definitely benefits the discovery and development of disease-specific biomarkers. © 2016 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.

Update on Biomarkers for the Detection of Endometriosis

PubMed Central

Fassbender, Amelie; Burney, Richard O.; O, Dorien F.; D'Hooghe, Thomas; Giudice, Linda

2015-01-01

Endometriosis is histologically characterized by the displacement of endometrial tissue to extrauterine locations including the pelvic peritoneum, ovaries, and bowel. An important cause of infertility and pelvic pain, the individual and global socioeconomic burden of endometriosis is significant. Laparoscopy remains the gold standard for the diagnosis of the condition. However, the invasive nature of surgery, coupled with the lack of a laboratory biomarker for the disease, results in a mean latency of 7–11 years from onset of symptoms to definitive diagnosis. Unfortunately, the delay in diagnosis may have significant consequences in terms of disease progression. The discovery of a sufficiently sensitive and specific biomarker for the nonsurgical detection of endometriosis promises earlier diagnosis and prevention of deleterious sequelae and represents a clear research priority. In this review, we describe and discuss the current status of biomarkers of endometriosis in plasma, urine, and endometrium. PMID:26240814

[Structural Study in the Platform for Drug Discovery, Informatics, and Structural Life Science].

PubMed

Senda, Toshiya

2016-01-01

The Platform for Drug Discovery, Informatics, and Structural Life Science (PDIS), which has been launched since FY2012, is a national project in the field of structural biology. The PDIS consists of three cores - structural analysis, control, and informatics - and aims to support life science researchers who are not familiar with structural biology. The PDIS project is able to provide full-scale support for structural biology research. The support provided by the PDIS project includes protein purification with various expression systems, large scale protein crystallization, crystal structure determination, small angle scattering (SAXS), NMR, electron microscopy, bioinformatics, etc. In order to utilize these methods of support, PDIS users need to submit an application form to the one-stop service office. Submitted applications will be reviewed by three referees. It is strongly encouraged that PDIS users have sufficient discussion with researchers in the PDIS project before submitting the application. This discussion is very useful in the process of project design, particularly for beginners in structural biology. In addition to this user support, the PDIS project has conducted R&D, which includes the development of synchrotron beamlines. In the PDIS project, PF and SPring-8 have developed beamlines for micro-crystallography, high-throughput data collection, supramolecular assembly, and native single anomalous dispersion (SAD) phasing. The newly developed beamlines have been open to all users, and have accelerated structural biology research. Beamlines for SAXS have also been developed, which has dramatically increased bio-SAXS users.

Biomarkers for the early diagnosis of hepatocellular carcinoma

PubMed Central

Tsuchiya, Nobuhiro; Sawada, Yu; Endo, Itaru; Saito, Keigo; Uemura, Yasushi; Nakatsura, Tetsuya

2015-01-01

Hepatocellular carcinoma (HCC) is the fifth most common cancer and the second leading cause of cancer-related deaths worldwide. Although the prognosis of patients with HCC is generally poor, the 5-year survival rate is > 70% if patients are diagnosed at an early stage. However, early diagnosis of HCC is complicated by the coexistence of inflammation and cirrhosis. Thus, novel biomarkers for the early diagnosis of HCC are required. Currently, the diagnosis of HCC without pathological correlation is achieved by analyzing serum α-fetoprotein levels combined with imaging techniques. Advances in genomics and proteomics platforms and biomarker assay techniques over the last decade have resulted in the identification of numerous novel biomarkers and have improved the diagnosis of HCC. The most promising biomarkers, such as glypican-3, osteopontin, Golgi protein-73 and nucleic acids including microRNAs, are most likely to become clinically validated in the near future. These biomarkers are not only useful for early diagnosis of HCC, but also provide insight into the mechanisms driving oncogenesis. In addition, such molecular insight creates the basis for the development of potentially more effective treatment strategies. In this article, we provide an overview of the biomarkers that are currently used for the early diagnosis of HCC. PMID:26457017

Discovery of FDA-Approved Drugs that Promote Retinal Cell Survival or Regeneration

DTIC Science & Technology

2015-10-01

1 AD______________ AWARD NUMBER: W81XWH-14-1-0407 TITLE:Discovery of FDA-Approved Drugs that Promote Retinal Cell Survival or Regeneration...SUBTITLE 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-14-1-0407Discovery of FDA-Approved Drugs that Promote Retinal Cell Survival or Regeneration 5c...vivo drug discovery platform named Automated Reporter Quantification in vivo (ARQiv). ARQiv quantifies reporter activity in transgenic zebrafish at

Tissue- and Serum-Associated Biomarkers of Hepatocellular Carcinoma

PubMed Central

Chauhan, Ranjit; Lahiri, Nivedita

2016-01-01

Hepatocellular carcinoma (HCC), one of the leading causes of cancer deaths in the world, is offering a challenge to human beings, with the current modes of treatment being a palliative approach. Lack of proper curative or preventive treatment methods encouraged extensive research around the world with an aim to detect a vaccine or therapeutic target biomolecule that could lead to development of a drug or vaccine against HCC. Biomarkers or biological disease markers have emerged as a potential tool as drug/vaccine targets, as they can accurately diagnose, predict, and even prevent the diseases. Biomarker expression in tissue, serum, plasma, or urine can detect tumor in very early stages of its development and monitor the cancer progression and also the effect of therapeutic interventions. Biomarker discoveries are driven by advanced techniques, such as proteomics, transcriptomics, whole genome sequencing, micro- and micro-RNA arrays, and translational clinics. In this review, an overview of the potential of tissue- and serum-associated HCC biomarkers as diagnostic, prognostic, and therapeutic targets for drug development is presented. In addition, we highlight recently developed micro-RNA, long noncoding RNA biomarkers, and single-nucleotide changes, which may be used independently or as complementary biomarkers. These active investigations going on around the world aimed at conquering HCC might show a bright light in the near future. PMID:27398029

Glial Biomarkers in Human Central Nervous System Disease

PubMed Central

Garden, Gwenn A.; Campbell, Brian M.

2017-01-01

There is a growing understanding that aberrant GLIA function is an underlying factor in psychiatric and neurological disorders. As drug discovery efforts begin to focus on glia-related targets, a key gap in knowledge includes the availability of validated biomarkers to help determine which patients suffer from dysfunction of glial cells or who may best respond by targeting glia-related drug mechanisms. Biomarkers are biological variables with a significant relationship to parameters of disease states and can be used as surrogate markers of disease pathology, progression, and/or responses to drug treatment. For example, imaging studies of the CNS enable localization and characterization of anatomical lesions without the need to isolate tissue for biopsy. Many biomarkers of disease pathology in the CNS involve assays of glial cell function and/or response to injury. Each major glia subtype (oligodendroglia, astroglia and microglia) are connected to a number of important and useful biomarkers. Here, we describe current and emerging glial based biomarker approaches for acute CNS injury and the major categories of chronic nervous system dysfunction including neurodegenerative, neuropsychiatric, neoplastic, and autoimmune disorders of the CNS. These descriptions are highlighted in the context of how biomarkers are employed to better understand the role of glia in human CNS disease and in the development of novel therapeutic treatments. PMID:27228454

Port side view of the Orbiter Discovery from an elevated ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Port side view of the Orbiter Discovery from an elevated platform in the Vehicle Assembly Building at NASA's Kennedy Space Center. Note the ground support hardware known as Strongbacks attached to the payload bay doors, the crew access hatch below the name Discovery on the forward section of the Orbiter and the removed Orbiter Maneuvering System/Reaction Control System pod from the aft (tai) section. - Space Transportation System, Orbiter Discovery (OV-103), Lyndon B. Johnson Space Center, 2101 NASA Parkway, Houston, Harris County, TX

Sea Level Rise Data Discovery

NASA Astrophysics Data System (ADS)

Quach, N.; Huang, T.; Boening, C.; Gill, K. M.

2016-12-01

Research related to sea level rise crosses multiple disciplines from sea ice to land hydrology. The NASA Sea Level Change Portal (SLCP) is a one-stop source for current sea level change information and data, including interactive tools for accessing and viewing regional data, a virtual dashboard of sea level indicators, and ongoing updates through a suite of editorial products that include content articles, graphics, videos, and animations. The architecture behind the SLCP makes it possible to integrate web content and data relevant to sea level change that are archived across various data centers as well as new data generated by sea level change principal investigators. The Extensible Data Gateway Environment (EDGE) is incorporated into the SLCP architecture to provide a unified platform for web content and science data discovery. EDGE is a data integration platform designed to facilitate high-performance geospatial data discovery and access with the ability to support multi-metadata standard specifications. EDGE has the capability to retrieve data from one or more sources and package the resulting sets into a single response to the requestor. With this unified endpoint, the Data Analysis Tool that is available on the SLCP can retrieve dataset and granule level metadata as well as perform geospatial search on the data. This talk focuses on the architecture that makes it possible to seamlessly integrate and enable discovery of disparate data relevant to sea level rise.

Radiation Metabolomics. 4. UPLC-ESI-QTOFMS-Based Metabolomics for Urinary Biomarker Discovery in Gamma-Irradiated Rats

PubMed Central

Johnson, Caroline H.; Patterson, Andrew D.; Krausz, Kristopher W.; Lanz, Christian; Kang, Dong Wook; Luecke, Hans; Gonzalez, Frank J.; Idle, Jeffrey R.

2011-01-01

Radiation metabolomics has aided in the identification of a number of biomarkers in cells and mice by ultra-performance liquid chromatography-coupled time-of-flight mass spectrometry (UPLC-ESI-QTOFMS) and in rats by gas chromatography-coupled mass spectrometry (GCMS). These markers have been shown to be both dose- and time-dependent. Here UPLC-ESI-QTOFMS was used to analyze rat urine samples taken from 12 rats over 7 days; they were either sham-irradiated or γ-irradiated with 3 Gy after 4 days of metabolic cage acclimatization. Using multivariate data analysis, nine urinary biomarkers of γ radiation in rats were identified, including a novel mammalian metabolite, N-acetyltaurine. These upregulated urinary biomarkers were confirmed through tandem mass spectrometry and comparisons with authentic standards. They include thymidine, 2′-deoxyuridine, 2′deoxyxanthosine, N1-acetylspermidine, N-acetylglucosamine/galactosamine-6-sulfate, N-acetyltaurine, N-hexanoylglycine, taurine and, tentatively, isethionic acid. Of these metabolites, 2′-deoxyuridine and thymidine were previously identified in the rat by GCMS (observed as uridine and thymine) and in the mouse by UPLC-ESI-QTOFMS. 2′Deoxyxanthosine, taurine and N-hexanoylglycine were also seen in the mouse by UPLC-ESI-QTOFMS. These are now unequivocal cross-species biomarkers for ionizing radiation exposure. Downregulated biomarkers were shown to be related to food deprivation and starvation mechanisms. The UPLC-ESI-QTOFMS approach has aided in the advance for finding common biomarkers of ionizing radiation exposure. PMID:21309707

The current status of clinical proteomics and the use of MRM and MRM(3) for biomarker validation.

PubMed

Lemoine, Jérôme; Fortin, Tanguy; Salvador, Arnaud; Jaffuel, Aurore; Charrier, Jean-Philippe; Choquet-Kastylevsky, Geneviève

2012-05-01

The transfer of biomarkers from the discovery field to clinical use is still, despite progress, on a road filled with pitfalls. Since the emergence of proteomics, thousands of putative biomarkers have been published, often with overlapping diagnostic capacities. The strengthening of the robustness of discovery technologies, particularly in mass spectrometry, has been followed by intense discussions on establishing well-defined evaluation procedures for the identified targets to ultimately allow the clinical validation and then the clinical use of some of these biomarkers. Some of the obstacles to the evaluation process have been the lack of the availability of quick and easy-to-develop, easy-to-use, robust, specific and sensitive alternative quantitative methods when immunoaffinity-based tests are unavailable. Multiple reaction monitoring (MRM; also called selected reaction monitoring) is currently proving its capabilities as a complementary or alternative technique to ELISA for large biomarker panel evaluation. Here, we present how MRM(3) can overcome the lack of specificity and sensitivity often encountered by MRM when tracking minor proteins diluted by complex biological matrices.

Multiple reaction monitoring (MRM) of plasma proteins in cardiovascular proteomics.

PubMed

Dardé, Verónica M; Barderas, Maria G; Vivanco, Fernando

2013-01-01

Different methodologies have been used through years to discover new potential biomarkers related with cardiovascular risk. The conventional proteomic strategy involves a discovery phase that requires the use of mass spectrometry (MS) and a validation phase, usually on an alternative platform such as immunoassays that can be further implemented in clinical practice. This approach is suitable for a single biomarker, but when large panels of biomarkers must be validated, the process becomes inefficient and costly. Therefore, it is essential to find an alternative methodology to perform the biomarker discovery, validation, and -quantification. The skills provided by quantitative MS turn it into an extremely attractive alternative to antibody-based technologies. Although it has been traditionally used for quantification of small molecules in clinical chemistry, MRM is now emerging as an alternative to traditional immunoassays for candidate protein biomarker validation.

Targeted in-gel MRM: a hypothesis driven approach for colorectal cancer biomarker discovery in human feces.

PubMed

Ang, Ching-Seng; Nice, Edouard C

2010-09-03

Colorectal cancer (CRC) is the second most common cause of cancer-related deaths in both men and women. The fecal occult blood test is currently the first line method for CRC screening but has an unacceptably low sensitivity and specificity. Improved screening tests are therefore urgently required for early stage CRC screening. We have described a hypothesis-driven approach for a rapid biomarker discovery process whereby selected proteins previously implicated as colorectal cancer-associated proteins (CCAP), which can potentially be shed into the feces from a colorectal tumor, are targeted for excision from 1D-SDS-PAGE based on their predicted molecular weight followed by directed identification and relative quantification using multiple reaction monitoring (MRM). This approach can significantly reduce the time for clinical assay development with the added advantage that many proteins will have been validated by previous in vitro and/or in vivo studies. Sixty potential CCAPs were selected from the literature and appropriate MRM conditions were established for measurement of proteotypic peptides. Nineteen of these proteins were detected in the feces from a patient with colorectal cancer. Relative quantitation of these 19 CCAP across 5 CRC patients and 5 healthy volunteers were carried out, revealing hemoglobin, myeloperoxidase, S100A9, filamin A and l-plastin to be present only in the feces of CRC patients.

Applications of Nutritional Biomarkers in Global Health Settings.

PubMed

Prentice, Andrew M

2016-01-01

In global health settings, there are three generic areas that require reliable biomarkers of nutritional status and function. Population surveillance needs to identify key nutrient deficiencies (or excesses) to monitor progress towards elimination of nutritional imbalances and to stratify populations into groups especially 'at risk' to whom public health resources can be focused. Clinical interventions need biomarkers to help identify disease pathways, to assist in targeting nutrient prescriptions, and to avoid potential harm (e.g. in the case of iron). Discovery science requires biomarkers in many domains, but especially in the study of nutrient-gene interactions and regarding the effects of nutritional status on the epigenome. Each of these applications imposes different constraints on the methodology though in all cases the optimum biomarker would have high sensitivity and specificity, would capture variation of functional significance, and would be cheap and easy to apply. These attributes are hard to achieve, and recent progress towards next-generation biomarkers, though holding much promise, has not yet delivered significant breakthroughs in the global health setting. Recent efforts to overcome these problems by two initiatives (BOND and INSPIRE) are highlighted as exemplars of a route map to progress. © 2016 Nestec Ltd., Vevey/S. Karger AG, Basel.

MRM for the verification of cancer biomarker proteins: recent applications to human plasma and serum.

PubMed

Chambers, Andrew G; Percy, Andrew J; Simon, Romain; Borchers, Christoph H

2014-04-01

Accurate cancer biomarkers are needed for early detection, disease classification, prediction of therapeutic response and monitoring treatment. While there appears to be no shortage of candidate biomarker proteins, a major bottleneck in the biomarker pipeline continues to be their verification by enzyme linked immunosorbent assays. Multiple reaction monitoring (MRM), also known as selected reaction monitoring, is a targeted mass spectrometry approach to protein quantitation and is emerging to bridge the gap between biomarker discovery and clinical validation. Highly multiplexed MRM assays are readily configured and enable simultaneous verification of large numbers of candidates facilitating the development of biomarker panels which can increase specificity. This review focuses on recent applications of MRM to the analysis of plasma and serum from cancer patients for biomarker verification. The current status of this approach is discussed along with future directions for targeted mass spectrometry in clinical biomarker validation.

Biomarkers and a tailored approach for immune monitoring in kidney transplantation

PubMed Central

Salcido-Ochoa, Francisco; Allen, John Carson

2017-01-01

A literature review on immune monitoring in kidney transplantation produced dozens of research articles and a multitude of promising biomarkers, all in the quest for the much sought after - but perennially elusive - “holy grail” of kidney biomarkers able to unequivocally predict acute transplant rejection vs non-rejection. Detection methodologies and study designs were many and varied. Hence the motivation for this editorial, which espouses the notion that in today’s kidney transplantation milieu, the judicious use of disease classifiers tailored to specific patient immune risks may be more achievable and productive in the long run and confer a greater advantage for patient treatment than the pursuit of a single “omniscient” biomarker. In addition, we desire to direct attention toward greater scrutiny of biomarker publications and decisions to implement biomarkers in practice, standardization of methods in the development of biomarkers and consideration for adoption of “biomarker-driven” biopsies. We propose “biomarker-driven” biopsies as an adjunctive to and/or alternative to random surveillance (protocol) biopsies or belated indication biopsies. The discovery of a single kidney transplantation biomarker would represent a major breakthrough in kidney transplantation practice, but until that occurs - if ever it does occur, other approaches offer substantial potential for unlocking prognostic, diagnostic and therapeutic options. We conclude our editorial with suggestions and recommendations for productively incorporating current biomarkers into diagnostic algorithms and for testing future biomarkers of acute rejection in kidney transplantation. PMID:29312857

Discovery touches down after successful mission STS-95

NASA Technical Reports Server (NTRS)

1998-01-01

Orbiter Discovery smokes its tires as it touches down on runway 33 at the Shuttle Landing Facility. Discovery returns to Earth with its crew of seven after a successful mission STS-95 lasting nearly nine days and 3.6 million miles. The mission included research payloads such as the Spartan solar-observing deployable spacecraft, the Hubble Space Telescope Orbital Systems Test Platform, the International Extreme Ultraviolet Hitchhiker, as well as the SPACEHAB single module with experiments on space flight and the aging process.

Next generation sequencing applications for microRNA biomarker discovery in toxicological studies

EPA Science Inventory

Next Generation Sequencing (NGS) technology will be reviewed for its base pair resolution, wide dynamic range, and insights into the genome and transcriptome, with special focus upon the biomarker potential of microRNAs (miRNAs). The first part of this presentation reviews commo...

Biomarkers in Sporadic and Familial Alzheimer's Disease.

PubMed

Lista, Simone; O'Bryant, Sid E; Blennow, Kaj; Dubois, Bruno; Hugon, Jacques; Zetterberg, Henrik; Hampel, Harald

2015-01-01

Most forms of Alzheimer's disease (AD) are sporadic (sAD) or inherited in a non-Mendelian fashion, and less than 1% of cases are autosomal-dominant. Forms of sAD do not exhibit familial aggregation and are characterized by complex genetic and environmental interactions. Recently, the expansion of genomic methodologies, in association with substantially larger combined cohorts, has resulted in various genome-wide association studies that have identified several novel genetic associations of AD. Currently, the most effective methods for establishing the diagnosis of AD are defined by multi-modal pathways, starting with clinical and neuropsychological assessment, cerebrospinal fluid (CSF) analysis, and brain-imaging procedures, all of which have significant cost- and access-to-care barriers. Consequently, research efforts have focused on the development and validation of non-invasive and generalizable blood-based biomarkers. Among the modalities conceptualized by the systems biology paradigm and utilized in the "exploratory biomarker discovery arena", proteome analysis has received the most attention. However, metabolomics, lipidomics, transcriptomics, and epigenomics have recently become key modalities in the search for AD biomarkers. Interestingly, biomarker changes for familial AD (fAD), in many but not all cases, seem similar to those for sAD. The integration of neurogenetics with systems biology/physiology-based strategies and high-throughput technologies for molecular profiling is expected to help identify the causes, mechanisms, and biomarkers associated with the various forms of AD. Moreover, in order to hypothesize the dynamic trajectories of biomarkers through disease stages and elucidate the mechanisms of biomarker alterations, updated and more sophisticated theoretical models have been proposed for both sAD and fAD.

Toward Precision Medicine: A Cancer Molecular Subtyping Nano-Strategy for RNA Biomarkers in Tumor and Urine.

PubMed

Koo, Kevin M; Wee, Eugene J H; Mainwaring, Paul N; Wang, Yuling; Trau, Matt

2016-12-01

Cancer is a heterogeneous disease which manifests as different molecular subtypes due to the complex nature of tumor initiation, progression, and metastasis. The concept of precision medicine aims to exploit this cancer heterogeneity by incorporating diagnostic technology to characterize each cancer patient's molecular subtype for tailored treatments. To characterize cancer molecular subtypes accurately, a suite of multiplexed bioassays have currently been developed to detect multiple oncogenic biomarkers. Despite the reliability of current multiplexed detection techniques, novel strategies are still needed to resolve limitations such as long assay time, complex protocols, and difficulty in interpreting broad overlapping spectral peaks of conventional fluorescence readouts. Herein a rapid (80 min) multiplexed platform strategy for subtyping prostate cancer tumor and urine samples based on their RNA biomarker profiles is presented. This is achieved by combining rapid multiplexed isothermal reverse transcription-recombinase polymerase amplification (RT-RPA) of target RNA biomarkers with surface-enhanced Raman spectroscopy (SERS) nanotags for "one-pot" readout. This is the first translational application of a RT-RPA/SERS-based platform for multiplexed cancer biomarker detection to address a clinical need. With excellent sensitivity of 200 zmol (100 copies) and specificity, we believed that this platform methodology could be a useful tool for rapid multiplexed subtyping of cancers. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Biomarkers in Sports and Exercise: Tracking Health, Performance, and Recovery in Athletes.

PubMed

Lee, Elaine C; Fragala, Maren S; Kavouras, Stavros A; Queen, Robin M; Pryor, John Luke; Casa, Douglas J

2017-10-01

Biomarker discovery and validation is a critical aim of the medical and scientific community. Research into exercise and diet-related biomarkers aims to improve health, performance, and recovery in military personnel, athletes, and lay persons. Exercise physiology research has identified individual biomarkers for assessing health, performance, and recovery during exercise training. However, there are few recommendations for biomarker panels for tracking changes in individuals participating in physical activity and exercise training programs. Our approach was to review the current literature and recommend a collection of validated biomarkers in key categories of health, performance, and recovery that could be used for this purpose. We determined that a comprehensive performance set of biomarkers should include key markers of (a) nutrition and metabolic health, (b) hydration status, (c) muscle status, (d) endurance performance, (e) injury status and risk, and (f) inflammation. Our review will help coaches, clinical sport professionals, researchers, and athletes better understand how to comprehensively monitor physiologic changes, as they design training cycles that elicit maximal improvements in performance while minimizing overtraining and injury risk.

"Seeing is believing": perspectives of applying imaging technology in discovery toxicology.

PubMed

Xu, Jinghai James; Dunn, Margaret Condon; Smith, Arthur Russell

2009-11-01

Efficiency and accuracy in addressing drug safety issues proactively are critical in minimizing late-stage drug attritions. Discovery toxicology has become a specialty subdivision of toxicology seeking to effectively provide early predictions and safety assessment in the drug discovery process. Among the many technologies utilized to select safer compounds for further development, in vitro imaging technology is one of the best characterized and validated to provide translatable biomarkers towards clinically-relevant outcomes of drug safety. By carefully applying imaging technologies in genetic, hepatic, and cardiac toxicology, and integrating them with the rest of the drug discovery processes, it was possible to demonstrate significant impact of imaging technology on drug research and development and substantial returns on investment.

OPEN DATA FOR DISCOVERY SCIENCE.

PubMed

Payne, Philip R O; Huang, Kun; Shah, Nigam H; Tenenbaum, Jessica

2017-01-01

The modern healthcare and life sciences ecosystem is moving towards an increasingly open and data-centric approach to discovery science. This evolving paradigm is predicated on a complex set of information needs related to our collective ability to share, discover, reuse, integrate, and analyze open biological, clinical, and population level data resources of varying composition, granularity, and syntactic or semantic consistency. Such an evolution is further impacted by a concomitant growth in the size of data sets that can and should be employed for both hypothesis discovery and testing. When such open data can be accessed and employed for discovery purposes, a broad spectrum of high impact end-points is made possible. These span the spectrum from identification of de novo biomarker complexes that can inform precision medicine, to the repositioning or repurposing of extant agents for new and cost-effective therapies, to the assessment of population level influences on disease and wellness. Of note, these types of uses of open data can be either primary, wherein open data is the substantive basis for inquiry, or secondary, wherein open data is used to augment or enrich project-specific or proprietary data that is not open in and of itself. This workshop is concerned with the key challenges, opportunities, and methodological best practices whereby open data can be used to drive the advancement of discovery science in all of the aforementioned capacities.

Targeted proteomics guided by label-free global proteome analysis in saliva reveal transition signatures from health to periodontal disease.

PubMed

Bostanci, Nagihan; Selevsek, Nathalie; Wolski, Witold; Grossmann, Jonas; Bao, Kai; Wahlander, Asa; Trachsel, Christian; Schlapbach, Ralph; Özturk, Veli Özgen; Afacan, Beral; Emingil, Gulnur; Belibasakis, Georgios N

2018-04-02

Periodontal diseases are among the most prevalent worldwide, but largely silent, chronic diseases. They affect the tooth-supporting tissues with multiple ramifications on life quality. Their early diagnosis is still challenging, due to lack of appropriate molecular diagnostic methods. Saliva offers a non-invasively collectable reservoir of clinically relevant biomarkers, which, if utilized efficiently, could facilitate early diagnosis and monitoring of ongoing disease. Despite several novel protein markers being recently enlisted by discovery proteomics, their routine diagnostic application is hampered by the lack of validation platforms that allow for rapid, accurate and simultaneous quantification of multiple proteins in large cohorts. We carried out a pipeline of two proteomic platforms; firstly, we applied open ended label-free quantitative (LFQ) proteomics for discovery in saliva (n=67, health, gingivitis, and periodontitis), followed by selected-reaction monitoring (SRM)-targeted proteomics for validation in an independent cohort (n=82). The LFQ platform led to the discovery of 119 proteins with at least two-fold significant difference between health and disease. The 65 proteins chosen for the subsequent SRM platform included 50 related proteins derived from the significantly enriched processes of the LFQ data, 11 from literature-mining, and four house-keeping ones. Among those, 60 were reproducibly quantifiable proteins (92% success rate), represented by a total of 143 peptides. Machine-learning modeling led to a narrowed-down panel of five proteins of high predictive value for periodontal diseases (higher in disease: Matrix metalloproteinase-9, Ras-related protein-1, Actin-related protein 2/3 complex subunit 5; lower in disease: Clusterin, Deleted in Malignant Brain Tumors 1), with maximum area under the receiver operating curve >0.97. This panel enriches the pool of credible clinical biomarker candidates for diagnostic assay development. Yet, the quantum

Highly sensitive dual mode electrochemical platform for microRNA detection

NASA Astrophysics Data System (ADS)

Jolly, Pawan; Batistuti, Marina R.; Miodek, Anna; Zhurauski, Pavel; Mulato, Marcelo; Lindsay, Mark A.; Estrela, Pedro

2016-11-01

MicroRNAs (miRNAs) play crucial regulatory roles in various human diseases including cancer, making them promising biomarkers. However, given the low levels of miRNAs present in blood, their use as cancer biomarkers requires the development of simple and effective analytical methods. Herein, we report the development of a highly sensitive dual mode electrochemical platform for the detection of microRNAs. The platform was developed using peptide nucleic acids as probes on gold electrode surfaces to capture target miRNAs. A simple amplification strategy using gold nanoparticles has been employed exploiting the inherent charges of the nucleic acids. Electrochemical impedance spectroscopy was used to monitor the changes in capacitance upon any binding event, without the need for any redox markers. By using thiolated ferrocene, a complementary detection mode on the same sensor was developed where the increasing peaks of ferrocene were recorded using square wave voltammetry with increasing miRNA concentration. This dual-mode approach allows detection of miRNA with a limit of detection of 0.37 fM and a wide dynamic range from 1 fM to 100 nM along with clear distinction from mismatched target miRNA sequences. The electrochemical platform developed can be easily expanded to other miRNA/DNA detection along with the development of microarray platforms.

Protocol for Standardizing High-to-Moderate Abundance Protein Biomarker Assessments Through an MRM-with-Standard-Peptides Quantitative Approach.

PubMed

Percy, Andrew J; Yang, Juncong; Chambers, Andrew G; Mohammed, Yassene; Miliotis, Tasso; Borchers, Christoph H

2016-01-01

Quantitative mass spectrometry (MS)-based approaches are emerging as a core technology for addressing health-related queries in systems biology and in the biomedical and clinical fields. In several 'omics disciplines (proteomics included), an approach centered on selected or multiple reaction monitoring (SRM or MRM)-MS with stable isotope-labeled standards (SIS), at the protein or peptide level, has emerged as the most precise technique for quantifying and screening putative analytes in biological samples. To enable the widespread use of MRM-based protein quantitation for disease biomarker assessment studies and its ultimate acceptance for clinical analysis, the technique must be standardized to facilitate precise and accurate protein quantitation. To that end, we have developed a number of kits for assessing method/platform performance, as well as for screening proposed candidate protein biomarkers in various human biofluids. Collectively, these kits utilize a bottom-up LC-MS methodology with SIS peptides as internal standards and quantify proteins using regression analysis of standard curves. This chapter details the methodology used to quantify 192 plasma proteins of high-to-moderate abundance (covers a 6 order of magnitude range from 31 mg/mL for albumin to 18 ng/mL for peroxidredoxin-2), and a 21-protein subset thereof. We also describe the application of this method to patient samples for biomarker discovery and verification studies. Additionally, we introduce our recently developed Qualis-SIS software, which is used to expedite the analysis and assessment of protein quantitation data in control and patient samples.

The Emory Chemical Biology Discovery Center: leveraging academic innovation to advance novel targets through HTS and beyond.

PubMed

Johns, Margaret A; Meyerkord-Belton, Cheryl L; Du, Yuhong; Fu, Haian

2014-03-01

The Emory Chemical Biology Discovery Center (ECBDC) aims to accelerate high throughput biology and translation of biomedical research discoveries into therapeutic targets and future medicines by providing high throughput research platforms to scientific collaborators worldwide. ECBDC research is focused at the interface of chemistry and biology, seeking to fundamentally advance understanding of disease-related biology with its HTS/HCS platforms and chemical tools, ultimately supporting drug discovery. Established HTS/HCS capabilities, university setting, and expertise in diverse assay formats, including protein-protein interaction interrogation, have enabled the ECBDC to contribute to national chemical biology efforts, empower translational research, and serve as a training ground for young scientists. With these resources, the ECBDC is poised to leverage academic innovation to advance biology and therapeutic discovery.

The search for biomarkers of hepatocellular carcinoma and the impact on patient outcome.

PubMed

Black, Alyson P; Mehta, Anand S

2018-05-14

Hepatocellular carcinoma (HCC) is the 5th most common cancer, but the 3rd leading cause of cancer death globally with approximately 700,000 fatalities annually. The severity of this cancer arises from its difficulty to detect and treat. The major etiologies of HCC are liver fibrosis or cirrhosis from chronic viral infections, as well as metabolic conditions. Since most cases arise from prior pathologies, biomarker surveillance in high-risk individuals is an essential approach for early detection and improved patient outcome. While many molecular biomarkers have been associated with HCC, there are few that have made clinical impact for this disease. Here we review some major approaches used for HCC biomarker discovery-proteomics and glycomics-and describe new methodologies being tested for biomarker development. Copyright © 2018 Elsevier Ltd. All rights reserved.

Capillary nano-immunoassays: advancing quantitative proteomics analysis, biomarker assessment, and molecular diagnostics.

PubMed

Chen, Jin-Qiu; Wakefield, Lalage M; Goldstein, David J

2015-06-06

There is an emerging demand for the use of molecular profiling to facilitate biomarker identification and development, and to stratify patients for more efficient treatment decisions with reduced adverse effects. In the past decade, great strides have been made to advance genomic, transcriptomic and proteomic approaches to address these demands. While there has been much progress with these large scale approaches, profiling at the protein level still faces challenges due to limitations in clinical sample size, poor reproducibility, unreliable quantitation, and lack of assay robustness. A novel automated capillary nano-immunoassay (CNIA) technology has been developed. This technology offers precise and accurate measurement of proteins and their post-translational modifications using either charge-based or size-based separation formats. The system not only uses ultralow nanogram levels of protein but also allows multi-analyte analysis using a parallel single-analyte format for increased sensitivity and specificity. The high sensitivity and excellent reproducibility of this technology make it particularly powerful for analysis of clinical samples. Furthermore, the system can distinguish and detect specific protein post-translational modifications that conventional Western blot and other immunoassays cannot easily capture. This review will summarize and evaluate the latest progress to optimize the CNIA system for comprehensive, quantitative protein and signaling event characterization. It will also discuss how the technology has been successfully applied in both discovery research and clinical studies, for signaling pathway dissection, proteomic biomarker assessment, targeted treatment evaluation and quantitative proteomic analysis. Lastly, a comparison of this novel system with other conventional immuno-assay platforms is performed.

Fully electronic urine dipstick probe for combinatorial detection of inflammatory biomarkers

PubMed Central

Kamakoti, Vikramshankar; Kinnamon, David; Choi, Kang Hyeok; Jagannath, Badrinath; Prasad, Shalini

2018-01-01

Aim: An electrochemical urine dipstick probe biosensor has been demonstrated using molybdenum electrodes on nanoporous polyamide substrate for the quantitative detection of two inflammatory protein biomarkers, CRP and IL-6. Materials & methods: The electrode interface was characterized using ζ-potential and Fourier transform infrared spectroscopy. Detection of biomarkers was demonstrated by measuring impedance changes associated with the dose concentrations of the two biomarkers. A proof of feasibility of point-of-care implementation of the biosensor was demonstrated using a portable electronics platform. Results & conclusion: Limit of detection of 1 pg/ml was achieved for CRP and IL-6 in human urine and synthetic urine buffers. The developed portable hardware demonstrated close correlation with benchtop equipment results. PMID:29796304

Discovery touches down after successful mission STS-95

NASA Technical Reports Server (NTRS)

1998-01-01

Orbiter Discovery startles a great white egret next to runway 33 as it touches down at the Shuttle Landing Facility. Discovery returns to Earth with its crew of seven after a successful mission STS-95 lasting nearly nine days and 3.6 million miles. The mission included research payloads such as the Spartan solar- observing deployable spacecraft, the Hubble Space Telescope Orbital Systems Test Platform, the International Extreme Ultraviolet Hitchhiker, as well as the SPACEHAB single module with experiments on space flight and the aging process.

An overview of a recent court challenge to the protection of biomarkers as intellectual property.

PubMed

Hall, Stephen C; Tromp, Justin M; Jortani, Saeed A

2011-05-12

We present an intellectual property case in the United States to demonstrate the recent developments concerning patenting novel biomarker discoveries. A court struck down several patents owned by Myriad Genetics, which were related to breast cancer (BRCA1 and BRCA2). This decision can affect patent eligibility for inventions related to biomarkers, particularly genetic biomarkers. The court proceedings for the Myriad Genetics case were reviewed by two patent attorneys (SCH and JMT). Relevant discussions applicable to the scientist involved with biomarker discovery were also prepared. In this case, the Plaintiff had argued that the analysis and comparison of various gene mutations merely involved natural phenomena, and, therefore, could not be eligible for patent protection. The patent holder (Myriad) argued that the claimed gene compositions did not exist in nature, and that the claimed methods provided practical utility for science and medicine. The Court held that the patent claims did not meet patent eligibility requirements under United States patent law. It held that the patent claims at issue were merely abstract mental processes of analyzing and comparing gene sequences, and that such abstract mental processes are not patentable. On June 22, 2010, Myriad appealed the ruling. This case provides guidance to inventors in the biomarker field who may be interested in obtaining intellectual property protection for their inventive work, as well as their patent counsel. However, the case also presented unique factors that may not be present in all situations involving biomarker patents. Copyright © 2011 Elsevier B.V. All rights reserved.

Integrating multiple ‘omics’ analyses identifies serological protein biomarkers for preeclampsia

PubMed Central

2013-01-01

Background Preeclampsia (PE) is a pregnancy-related vascular disorder which is the leading cause of maternal morbidity and mortality. We sought to identify novel serological protein markers to diagnose PE with a multi-’omics’ based discovery approach. Methods Seven previous placental expression studies were combined for a multiplex analysis, and in parallel, two-dimensional gel electrophoresis was performed to compare serum proteomes in PE and control subjects. The combined biomarker candidates were validated with available ELISA assays using gestational age-matched PE (n=32) and control (n=32) samples. With the validated biomarkers, a genetic algorithm was then used to construct and optimize biomarker panels in PE assessment. Results In addition to the previously identified biomarkers, the angiogenic and antiangiogenic factors (soluble fms-like tyrosine kinase (sFlt-1) and placental growth factor (PIGF)), we found 3 up-regulated and 6 down-regulated biomakers in PE sera. Two optimal biomarker panels were developed for early and late onset PE assessment, respectively. Conclusions Both early and late onset PE diagnostic panels, constructed with our PE biomarkers, were superior over sFlt-1/PIGF ratio in PE discrimination. The functional significance of these PE biomarkers and their associated pathways were analyzed which may provide new insights into the pathogenesis of PE. PMID:24195779

Discovery of putative salivary biomarkers for Sjögren's syndrome using high resolution mass spectrometry and bioinformatics.

PubMed

Zoukhri, Driss; Rawe, Ian; Singh, Mabi; Brown, Ashley; Kublin, Claire L; Dawson, Kevin; Haddon, William F; White, Earl L; Hanley, Kathleen M; Tusé, Daniel; Malyj, Wasyl; Papas, Athena

2012-03-01

The purpose of the current study was to determine if saliva contains biomarkers that can be used as diagnostic tools for Sjögren's syndrome (SjS). Twenty seven SjS patients and 27 age-matched healthy controls were recruited for these studies. Unstimulated glandular saliva was collected from the Wharton's duct using a suction device. Two µl of salvia were processed for mass spectrometry analyses on a prOTOF 2000 matrix-assisted laser desorption/ionization orthogonal time of flight (MALDI O-TOF) mass spectrometer. Raw data were analyzed using bioinformatic tools to identify biomarkers. MALDI O-TOF MS analyses of saliva samples were highly reproducible and the mass spectra generated were very rich in peptides and peptide fragments in the 750-7,500 Da range. Data analysis using bioinformatic tools resulted in several classification models being built and several biomarkers identified. One model based on 7 putative biomarkers yielded a sensitivity of 97.5%, specificity of 97.8% and an accuracy of 97.6%. One biomarker was present only in SjS samples and was identified as a proteolytic peptide originating from human basic salivary proline-rich protein 3 precursor. We conclude that salivary biomarkers detected by high-resolution mass spectrometry coupled with powerful bioinformatic tools offer the potential to serve as diagnostic/prognostic tools for SjS.

Evaluation of external RNA controls for the standardisation of gene expression biomarker measurements.

PubMed

Devonshire, Alison S; Elaswarapu, Ramnath; Foy, Carole A

2010-11-24

Gene expression profiling is an important approach for detecting diagnostic and prognostic biomarkers, and predicting drug safety. The development of a wide range of technologies and platforms for measuring mRNA expression makes the evaluation and standardization of transcriptomic data problematic due to differences in protocols, data processing and analysis methods. Thus, universal RNA standards, such as those developed by the External RNA Controls Consortium (ERCC), are proposed to aid validation of research findings from diverse platforms such as microarrays and RT-qPCR, and play a role in quality control (QC) processes as transcriptomic profiling becomes more commonplace in the clinical setting. Panels of ERCC RNA standards were constructed in order to test the utility of these reference materials (RMs) for performance characterization of two selected gene expression platforms, and for discrimination of biomarker profiles between groups. The linear range, limits of detection and reproducibility of microarray and RT-qPCR measurements were evaluated using panels of RNA standards. Transcripts of low abundance (≤ 10 copies/ng total RNA) showed more than double the technical variability compared to higher copy number transcripts on both platforms. Microarray profiling of two simulated 'normal' and 'disease' panels, each consisting of eight different RNA standards, yielded robust discrimination between the panels and between standards with varying fold change ratios, showing no systematic effects due to different labelling and hybridization runs. Also, comparison of microarray and RT-qPCR data for fold changes showed agreement for the two platforms. ERCC RNA standards provide a generic means of evaluating different aspects of platform performance, and can provide information on the technical variation associated with quantification of biomarkers expressed at different levels of physiological abundance. Distinct panels of standards serve as an ideal quality control

Identification of candidate cerebrospinal fluid biomarkers in parkinsonism using quantitative proteomics.

PubMed

Magdalinou, N K; Noyce, A J; Pinto, R; Lindstrom, E; Holmén-Larsson, J; Holtta, M; Blennow, K; Morris, H R; Skillbäck, T; Warner, T T; Lees, A J; Pike, I; Ward, M; Zetterberg, H; Gobom, J

2017-04-01

Neurodegenerative parkinsonian syndromes have significant clinical and pathological overlap, making early diagnosis difficult. Cerebrospinal fluid (CSF) biomarkers may aid the differentiation of these disorders, but other than α-synuclein and neurofilament light chain protein, which have limited diagnostic power, specific protein biomarkers remain elusive. To study disease mechanisms and identify possible CSF diagnostic biomarkers through discovery proteomics, which discriminate parkinsonian syndromes from healthy controls. CSF was collected consecutively from 134 participants; Parkinson's disease (n = 26), atypical parkinsonian syndromes (n = 78, including progressive supranuclear palsy (n = 36), multiple system atrophy (n = 28), corticobasal syndrome (n = 14)), and elderly healthy controls (n = 30). Participants were divided into a discovery and a validation set for analysis. The samples were subjected to tryptic digestion, followed by liquid chromatography-mass spectrometry analysis for identification and relative quantification by isobaric labelling. Candidate protein biomarkers were identified based on the relative abundances of the identified tryptic peptides. Their predictive performance was evaluated by analysis of the validation set. 79 tryptic peptides, derived from 26 proteins were found to differ significantly between atypical parkinsonism patients and controls. They included acute phase/inflammatory markers and neuronal/synaptic markers, which were respectively increased or decreased in atypical parkinsonism, while their levels in PD subjects were intermediate between controls and atypical parkinsonism. Using an unbiased proteomic approach, proteins were identified that were able to differentiate atypical parkinsonian syndrome patients from healthy controls. Our study indicates that markers that may reflect neuronal function and/or plasticity, such as the amyloid precursor protein, and inflammatory markers may hold future promise as

A new strategy for faster urinary biomarkers identification by Nano-LC-MALDI-TOF/TOF mass spectrometry

PubMed Central

Benkali, K; Marquet, P; Rérolle, JP; Le Meur, Y; Gastinel, LN

2008-01-01

Background LC-MALDI-TOF/TOF analysis is a potent tool in biomarkers discovery characterized by its high sensitivity and high throughput capacity. However, methods based on MALDI-TOF/TOF for biomarkers discovery still need optimization, in particular to reduce analysis time and to evaluate their reproducibility for peak intensities measurement. The aims of this methodological study were: (i) to optimize and critically evaluate each step of urine biomarker discovery method based on Nano-LC coupled off-line to MALDI-TOF/TOF, taking full advantage of the dual decoupling between Nano-LC, MS and MS/MS to reduce the overall analysis time; (ii) to evaluate the quantitative performance and reproducibility of nano-LC-MALDI analysis in biomarker discovery; and (iii) to evaluate the robustness of biomarkers selection. Results A pool of urine sample spiked at increasing concentrations with a mixture of standard peptides was used as a specimen for biological samples with or without biomarkers. Extraction and nano-LC-MS variabilities were estimated by analyzing in triplicates and hexaplicates, respectively. The stability of chromatographic fractions immobilised with MALDI matrix on MALDI plates was evaluated by successive MS acquisitions after different storage times at different temperatures. Low coefficient of variation (CV%: 10–22%) and high correlation (R2 > 0.96) values were obtained for the quantification of the spiked peptides, allowing quantification of these peptides in the low fentomole range, correct group discrimination and selection of "specific" markers using principal component analysis. Excellent peptide integrity and stable signal intensity were found when MALDI plates were stored for periods of up to 2 months at +4°C. This allowed storage of MALDI plates between LC separation and MS acquisition (first decoupling), and between MS and MSMS acquisitions while the selection of inter-group discriminative ions is done (second decoupling). Finally the recording of

Developing High-Affinity Protein Capture Agents and Nanotechnology-Based Platforms for In Vitro Diagnostics

NASA Astrophysics Data System (ADS)

Rohde, Rosemary Dyane

In this thesis, I describe projects that were aimed at improving ways to capture proteins for clinical diagnostics. Nanoelectronic sensors, such as silicon nanowires (SiNWs), can provide label-free quantitative measurements of protein biomarkers in real time. One technical challenge for SiNWs is to develop chemistry that can be applied for selectively encoding the nanowire surfaces with capture agents, thus making them sensors that have selectivity for specific proteins. Furthermore, because of the nature of how the sensor works, it is desirable to achieve this spatially selective chemical functionalization without having the silicon undergo oxidation. This method is described here and provides a general platform that can incorporate organic and biological molecules on Si (111) with minimal oxidation of the silicon surface. The development of these devices is, in part, driven by early diagnosis, treatment, monitoring, and personalized medicine---all of which are increasingly requiring quantitative, rapid, and multiparameter measurements. To begin achieving this goal, a large number of protein biomarkers need to be captured and quantitatively measured to create a diagnostic panel. One of the greatest challenges towards making protein-biomarker-based in vitro diagnostics inexpensive involves developing capture agents to detect the proteins. A major thrust of this thesis is to develop multi-valent, high-affinity and high-selectivity protein capture agents using in situ click chemistry. In situ click chemistry is a tool that utilizes the protein itself to catalyze the formation of a biligand from individual azide and alkyne ligands that are co-localized. Large one-bead one-compound (OBOC) libraries of peptides are used to form the body of these ligands, also providing high chemical diversity with minimal synthetic effort. This process can be repeated to identify a triligand, tetraligand, and so forth. Moreover, the resulting multiligand protein capture agents can be

Towards High-throughput Immunomics for Infectious Diseases: Use of Next-generation Peptide Microarrays for Rapid Discovery and Mapping of Antigenic Determinants*

PubMed Central

Carmona, Santiago J.; Nielsen, Morten; Schafer-Nielsen, Claus; Mucci, Juan; Altcheh, Jaime; Balouz, Virginia; Tekiel, Valeria; Frasch, Alberto C.; Campetella, Oscar; Buscaglia, Carlos A.; Agüero, Fernán

2015-01-01

Complete characterization of antibody specificities associated to natural infections is expected to provide a rich source of serologic biomarkers with potential applications in molecular diagnosis, follow-up of chemotherapeutic treatments, and prioritization of targets for vaccine development. Here, we developed a highly-multiplexed platform based on next-generation high-density peptide microarrays to map these specificities in Chagas Disease, an exemplar of a human infectious disease caused by the protozoan Trypanosoma cruzi. We designed a high-density peptide microarray containing more than 175,000 overlapping 15mer peptides derived from T. cruzi proteins. Peptides were synthesized in situ on microarray slides, spanning the complete length of 457 parasite proteins with fully overlapped 15mers (1 residue shift). Screening of these slides with antibodies purified from infected patients and healthy donors demonstrated both a high technical reproducibility as well as epitope mapping consistency when compared with earlier low-throughput technologies. Using a conservative signal threshold to classify positive (reactive) peptides we identified 2,031 disease-specific peptides and 97 novel parasite antigens, effectively doubling the number of known antigens and providing a 10-fold increase in the number of fine mapped antigenic determinants for this disease. Finally, further analysis of the chip data showed that optimizing the amount of sequence overlap of displayed peptides can increase the protein space covered in a single chip by at least ∼threefold without sacrificing sensitivity. In conclusion, we show the power of high-density peptide chips for the discovery of pathogen-specific linear B-cell epitopes from clinical samples, thus setting the stage for high-throughput biomarker discovery screenings and proteome-wide studies of immune responses against pathogens. PMID:25922409

Payload canister for Discovery is lifted in place for transfer

NASA Technical Reports Server (NTRS)

1998-01-01

At left, the payload canister for Space Shuttle Discovery is lifted from its canister movement vehicle to the top of the Rotating Service Structure on Launch Pad 39-B. Discovery (right), sitting atop the Mobile Launch Platform and next to the Fixed Service Structure (FSS), is scheduled for launch on Oct. 29, 1998, for the STS-95 mission. That mission includes the International Extreme Ultraviolet Hitchhiker (IEH-3), the Hubble Space Telescope Orbital Systems Test Platform, the Spartan solar- observing deployable spacecraft, and the SPACEHAB single module with experiments on space flight and the aging process. At the top of the FSS can be seen the 80-foot lightning mast . The 4- foot-high lightning rod on top helps prevent lightning current from passing directly through the Space Shuttle and the structures on the pad.

Potential biomarker panels in overall breast cancer management: advancements by multilevel diagnostics.

PubMed

Girotra, Shantanu; Yeghiazaryan, Kristina; Golubnitschaja, Olga

2016-09-01

Breast cancer (BC) prevalence has reached an epidemic scale with half a million deaths annually. Current deficits in BC management include predictive and preventive approaches, optimized screening programs, individualized patient profiling, highly sensitive detection technologies for more precise diagnostics and therapy monitoring, individualized prediction and effective treatment of BC metastatic disease. To advance BC management, paradigm shift from delayed to predictive, preventive and personalized medical services is essential. Corresponding step forwards requires innovative multilevel diagnostics procuring specific panels of validated biomarkers. Here, we discuss current instrumental advancements including genomics, proteomics, epigenetics, miRNA, metabolomics, circulating tumor cells and cancer stem cells with a focus on biomarker discovery and multilevel diagnostic panels. A list of the recommended biomarker candidates is provided.

Metabolomics: beyond biomarkers and towards mechanisms

PubMed Central

Johnson, Caroline H.; Ivanisevic, Julijana; Siuzdak, Gary

2017-01-01

Metabolomics, which is the profiling of metabolites in biofluids, cells and tissues, is routinely applied as a tool for biomarker discovery. Owing to innovative developments in informatics and analytical technologies, and the integration of orthogonal biological approaches, it is now possible to expand metabolomic analyses to understand the systems-level effects of metabolites. Moreover, because of the inherent sensitivity of metabolomics, subtle alterations in biological pathways can be detected to provide insight into the mechanisms that underlie various physiological conditions and aberrant processes, including diseases. PMID:26979502

[Recent advances in metabonomics].

PubMed

Xu, Guo-Wang; Lu, Xin; Yang, Sheng-Li

2007-12-01

Metabonomics (or metabolomics) aims at the comprehensive and quantitative analysis of the wide arrays of metabolites in biological samples. Metabonomics has been labeled as one of the new" -omics" joining genomics, transcriptomics, and proteomics as a science employed toward the understanding of global systems biology. It has been widely applied in many research areas including drug toxicology, biomarker discovery, functional genomics, and molecular pathology etc. The comprehensive analysis of the metabonome is particularly challenging due to the diverse chemical natures of metabolites. Metabonomics investigations require special approaches for sample preparation, data-rich analytical chemical measurements, and information mining. The outputs from a metabonomics study allow sample classification, biomarker discovery, and interpretation of the reasons for classification information. This review focuses on the currently new advances in various technical platforms of metabonomics and its applications in drug discovery and development, disease biomarker identification, plant and microbe related fields.

Biomarkers of Nutrition for Development—Iodine Review1234

PubMed Central

Rohner, Fabian; Zimmermann, Michael; Jooste, Pieter; Pandav, Chandrakant; Caldwell, Kathleen; Raghavan, Ramkripa; Raiten, Daniel J.

2014-01-01

The objective of the Biomarkers of Nutrition for Development (BOND) project is to provide state-of-the-art information and service with regard to selection, use, and interpretation of biomarkers of nutrient exposure, status, function, and effect. Specifically, the BOND project seeks to develop consensus on accurate assessment methodologies that are applicable to researchers (laboratory/clinical/surveillance), clinicians, programmers, and policy makers (data consumers). The BOND project is also intended to develop targeted research agendas to support the discovery and development of biomarkers through improved understanding of nutrient biology within relevant biologic systems. In phase I of the BOND project, 6 nutrients (iodine, vitamin A, iron, zinc, folate, and vitamin B-12) were selected for their high public health importance because they typify the challenges faced by users in the selection, use, and interpretation of biomarkers. For each nutrient, an expert panel was constituted and charged with the development of a comprehensive review covering the respective nutrient’s biology, existing biomarkers, and specific issues of use with particular reference to the needs of the individual user groups. In addition to the publication of these reviews, materials from each will be extracted to support the BOND interactive Web site (http://www.nichd.nih.gov/global_nutrition/programs/bond/pages/index.aspx). This review represents the first in the series of reviews and covers all relevant aspects of iodine biology and biomarkers. The article is organized to provide the reader with a full appreciation of iodine’s background history as a public health issue, its biology, and an overview of available biomarkers and specific considerations for the use and interpretation of iodine biomarkers across a range of clinical and population-based uses. The review also includes a detailed research agenda to address priority gaps in our understanding of iodine biology and assessment

Biomarkers of Alzheimer’s Disease Among Mexican Americans

PubMed Central

O’Bryant, Sid E.; Xiao, Guanghua; Edwards, Melissa; Devous, Michael; Gupta, Veer Bala; Martins, Ralph; Zhang, Fan; Barber, Robert

2013-01-01

Background Mexican Americans are the fastest aging segment of the U.S. population yet little scientific literature exists regarding the Alzheimer disease (AD) among this segment of the population. The extant literature suggests that biomarkers of AD will vary according to race/ethnicity though no prior work has explicitly studied this possibility. The aim of this study was to create a serum-based biomarker profile of AD among Mexican American. Methods Data were analyzed from 363 Mexican American participants (49 AD and 314 normal controls) enrolled in the Texas Alzheimer’s Research & Care Consortium (TARCC). Non-fasting serum samples were analyzed using a luminex-based multi-plex platform. A biomarker profile was generated using random forest analyses. Results The biomarker profile of AD among Mexican Americans was different from prior work from non-Hispanic populations with regards to the variable importance plots. In fact, many of the top markers were related to metabolic factors (e.g. FABP, GLP-1, CD40, pancreatic polypeptide, insulin-like-growth factor, and insulin). The biomarker profile was a significant classifier of AD status yielding an area under the receiver operating characteristic curve (AUC), sensitivity (SN) and specificity (SP) of 0.77, 0.92 and 0.64, respectively. Combining biomarkers with clinical variables yielded a better balance of SN and SP. Conclusion The biomarker profile for AD among Mexican American cases is significantly different from that previously identified among non-Hispanic cases from many large-scale studies. This is the first study to explicitly examine and provide support for blood-based biomarkers of AD among Mexican Americans. Areas for future research are highlighted. PMID:23313927

Assessment of cisplatin-induced kidney injury using an integrated rodent platform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Yafei; Brott, David; Luo, Wenli

Current diagnosis of drug-induced kidney injury (DIKI) primarily relies on detection of elevated plasma creatinine (Cr) or blood urea nitrogen (BUN) levels; however, both are indices of overall kidney function and changes are delayed with respect to onset of nephron injury. Our aim was to investigate whether early changes in new urinary DIKI biomarkers predict plasma Cr, BUN, renal hemodynamic and kidney morphological changes associated with kidney injury following a single dose of cisplatin (CDDP) using an integrated platform in rodent. Conscious surgically prepared male Han Wistar rats were given a single intraperitoneal dose of CDDP (15 mg/kg). Glomerular filtrationmore » rate (GFR), effective renal plasma flow (ERPF), urinalysis, DIKI biomarkers, CDDP pharmacokinetics, blood pressures, heart rate, body temperature and electroencephalogram (EEG) were measured in the same vehicle- or CDDP-treated animals over 72 h. Plasma chemistry (including Cr and BUN) and renal tissues were examined at study termination. Cisplatin caused progressive reductions of GFR, ERPF, heart rate and body temperature from day 1 (0–24 h). DIKI biomarkers including alpha-glutathione S-transferase (α-GST) significantly increased as early as 6 h post-dose, which preceded significant declines of GFR and ERPF (24 h), increased plasma Cr and BUN (72 h), and associated with renal acute tubular necrosis at 72 h post-dose. The present study adds to the current understanding of CDDP action by demonstrating that early increases in urinary excretion of α-GST predict DIKI risk following acute exposure to CDDP in rats, before changes in traditional DIKI markers are evident. - Highlights: ► CDDP causes direct damage to kidneys without affecting EEG or CVS function. ► α-GST and albumin detect DIKI earlier when compared with traditional indices. ► Integrated “cardiovascular-EEG-renal” model to better understand DIKI mechanisms ► Promotes 3R's principles in drug discovery and development.« less

The Matchmaker Exchange: A Platform for Rare Disease Gene Discovery

PubMed Central

Philippakis, Anthony A.; Azzariti, Danielle R.; Beltran, Sergi; Brookes, Anthony J.; Brownstein, Catherine A.; Brudno, Michael; Brunner, Han G.; Buske, Orion J.; Carey, Knox; Doll, Cassie; Dumitriu, Sergiu; Dyke, Stephanie O.M.; den Dunnen, Johan T.; Firth, Helen V.; Gibbs, Richard A.; Girdea, Marta; Gonzalez, Michael; Haendel, Melissa A.; Hamosh, Ada; Holm, Ingrid A.; Huang, Lijia; Hurles, Matthew E.; Hutton, Ben; Krier, Joel B.; Misyura, Andriy; Mungall, Christopher J.; Paschall, Justin; Paten, Benedict; Robinson, Peter N.; Schiettecatte, François; Sobreira, Nara L.; Swaminathan, Ganesh J.; Taschner, Peter E.; Terry, Sharon F.; Washington, Nicole L.; Züchner, Stephan; Boycott, Kym M.; Rehm, Heidi L.

2015-01-01

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for “the needle in a haystack” to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can “match” these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow. PMID:26295439

Enrichment of serum biomarkers by magnetic metal-organic framework composites.

PubMed

Wei, Ji-Ping; Wang, Heng; Luo, Tao; Zhou, Zhi-Jiang; Huang, Yan-Feng; Qiao, Bin

2017-03-01

Highly efficient extraction of peptides from serum is critical for finding serum biomarkers using mass spectrometry, which still remains a great challenge. Currently, a bottom-up proteomics approach has been applied to discover serum biomarkers. However, the approach was labor intensive, time and cost consuming, and cannot meet the requirements for clinical application. In this work, Fe 3 O 4 /C@MIL-100 composites were synthesized to efficiently capture peptides from microwave-assisted formic acid digests of BSA and human serum prior to MALDI-TOF MS analysis. Fe 3 O 4 /C@MIL-100 composites exhibited size-selective adsorption performance, thus providing a rapid and convenient approach to enrich low-abundance peptides. Notably, the peptides' mass fingerprinting of serum digestions between type 2 diabetes mellitus (T2DM) and healthy persons were distinguishable, which indicated the potential ability of this technique for T2DM diagnosis and rapid biomarker discovery. Graphical Abstract Efficient extraction and identification of serum biomarkers using Fe 3 O 4 /C@MIL-100 composites from acid hydrolysate.

Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.

PubMed

Geeleher, Paul; Zhang, Zhenyu; Wang, Fan; Gruener, Robert F; Nath, Aritro; Morrison, Gladys; Bhutra, Steven; Grossman, Robert L; Huang, R Stephanie

2017-10-01

Obtaining accurate drug response data in large cohorts of cancer patients is very challenging; thus, most cancer pharmacogenomics discovery is conducted in preclinical studies, typically using cell lines and mouse models. However, these platforms suffer from serious limitations, including small sample sizes. Here, we have developed a novel computational method that allows us to impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). The approach works by creating statistical models relating gene expression to drug response in large panels of cancer cell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA). This yields an imputed drug response for every drug in each patient. These imputed drug response data are then associated with somatic genetic variants measured in the clinical cohort, such as copy number changes or mutations in protein coding genes. These analyses recapitulated drug associations for known clinically actionable somatic genetic alterations and identified new predictive biomarkers for existing drugs. © 2017 Geeleher et al.; Published by Cold Spring Harbor Laboratory Press.

Distinguishing prognostic and predictive biomarkers: An information theoretic approach.

PubMed

Sechidis, Konstantinos; Papangelou, Konstantinos; Metcalfe, Paul D; Svensson, David; Weatherall, James; Brown, Gavin

2018-05-02

The identification of biomarkers to support decision-making is central to personalised medicine, in both clinical and research scenarios. The challenge can be seen in two halves: identifying predictive markers, which guide the development/use of tailored therapies; and identifying prognostic markers, which guide other aspects of care and clinical trial planning, i.e. prognostic markers can be considered as covariates for stratification. Mistakenly assuming a biomarker to be predictive, when it is in fact largely prognostic (and vice-versa) is highly undesirable, and can result in financial, ethical and personal consequences. We present a framework for data-driven ranking of biomarkers on their prognostic/predictive strength, using a novel information theoretic method. This approach provides a natural algebra to discuss and quantify the individual predictive and prognostic strength, in a self-consistent mathematical framework. Our contribution is a novel procedure, INFO+, which naturally distinguishes the prognostic vs predictive role of each biomarker and handles higher order interactions. In a comprehensive empirical evaluation INFO+ outperforms more complex methods, most notably when noise factors dominate, and biomarkers are likely to be falsely identified as predictive, when in fact they are just strongly prognostic. Furthermore, we show that our methods can be 1-3 orders of magnitude faster than competitors, making it useful for biomarker discovery in 'big data' scenarios. Finally, we apply our methods to identify predictive biomarkers on two real clinical trials, and introduce a new graphical representation that provides greater insight into the prognostic and predictive strength of each biomarker. R implementations of the suggested methods are available at https://github.com/sechidis. konstantinos.sechidis@manchester.ac.uk. Supplementary data are available at Bioinformatics online.

Chromatography/Mass Spectrometry-Based Biomarkers in the Field of Obstructive Sleep Apnea

PubMed Central

Xu, Huajun; Zheng, Xiaojiao; Jia, Wei; Yin, Shankai

2015-01-01

Abstract Biomarker assessment is based on quantifying several proteins and metabolites. Recent developments in proteomics and metabolomics have enabled detection of these small molecules in biological samples and exploration of the underlying disease mechanisms in obstructive sleep apnea (OSA). This systemic review was performed to identify biomarkers, which were only detected by chromatography and/or mass spectrometry (MS) and to discuss the role of these biomarkers in the field of OSA. We systemically reviewed relevant articles from PubMed and EMBASE referring to proteins and metabolite profiles of biological samples in patients with OSA. The analytical platforms in this review were focused on chromatography and/or MS. In total, 30 studies evaluating biomarkers in patients with OSA using chromatography and/or MS methods were included. Numerous proteins and metabolites, including lipid profiles, adrenergic/dopaminergic biomarkers and derivatives, amino acids, oxidative stress biomarkers, and other micromolecules were identified in patients with OSA. Applying chromatography and/or MS methods to detect biomarkers helps develop an understanding of OSA mechanisms. More proteomic and metabolomic studies are warranted to develop potential diagnostic and clinical monitoring methods for OSA. PMID:26448002

SpirPep: an in silico digestion-based platform to assist bioactive peptides discovery from a genome-wide database.

PubMed

Anekthanakul, Krittima; Hongsthong, Apiradee; Senachak, Jittisak; Ruengjitchatchawalya, Marasri

2018-04-20

web and database server are available at http://spirpepapp.sbi.kmutt.ac.th . SpirPep, a web-based bioactive peptide discovery application, is an in silico-based tool with an overview of the results. The platform is a one-stop analysis and visualization facility; and offers advantages over the currently available tools. This tool may be useful for further bioactivity analysis and the quantitative discovery of desirable peptides.

Biomarkers in Sports and Exercise: Tracking Health, Performance, and Recovery in Athletes

PubMed Central

Fragala, Maren S.; Kavouras, Stavros A.; Queen, Robin M.; Pryor, John Luke; Casa, Douglas J.

2017-01-01

Abstract Lee, EC, Fragala, MS, Kavouras, SA, Queen, RM, Pryor, JL, and Casa, DJ. Biomarkers in sports and exercise: tracking health, performance, and recovery in athletes. J Strength Cond Res 31(10): 2920–2937, 2017—Biomarker discovery and validation is a critical aim of the medical and scientific community. Research into exercise and diet-related biomarkers aims to improve health, performance, and recovery in military personnel, athletes, and lay persons. Exercise physiology research has identified individual biomarkers for assessing health, performance, and recovery during exercise training. However, there are few recommendations for biomarker panels for tracking changes in individuals participating in physical activity and exercise training programs. Our approach was to review the current literature and recommend a collection of validated biomarkers in key categories of health, performance, and recovery that could be used for this purpose. We determined that a comprehensive performance set of biomarkers should include key markers of (a) nutrition and metabolic health, (b) hydration status, (c) muscle status, (d) endurance performance, (e) injury status and risk, and (f) inflammation. Our review will help coaches, clinical sport professionals, researchers, and athletes better understand how to comprehensively monitor physiologic changes, as they design training cycles that elicit maximal improvements in performance while minimizing overtraining and injury risk. PMID:28737585

Urine Metabonomics Reveals Early Biomarkers in Diabetic Cognitive Dysfunction.

PubMed

Song, Lili; Zhuang, Pengwei; Lin, Mengya; Kang, Mingqin; Liu, Hongyue; Zhang, Yuping; Yang, Zhen; Chen, Yunlong; Zhang, Yanjun

2017-09-01

Recently, increasing attention has been paid to diabetic encephalopathy, which is a frequent diabetic complication and affects nearly 30% of diabetics. Because cognitive dysfunction from diabetic encephalopathy might develop into irreversible dementia, early diagnosis and detection of this disease is of great significance for its prevention and treatment. This study is to investigate the early specific metabolites biomarkers in urine prior to the onset of diabetic cognitive dysfunction (DCD) by using metabolomics technology. An ultra-high performance liquid-chromatography-quadrupole time-of-flight-mass spectrometry (UPLC-Q/TOF-MS) platform was used to analyze the urine samples from diabetic mice that were associated with mild cognitive impairment (MCI) and nonassociated with MCI in the stage of diabetes (prior to the onset of DCD). We then screened and validated the early biomarkers using OPLS-DA model and support vector machine (SVM) method. Following multivariate statistical and integration analysis, we found that seven metabolites could be accepted as early biomarkers of DCD, and the SVM results showed that the prediction accuracy is as high as 91.66%. The identities of four biomarkers were determined by mass spectrometry. The identified biomarkers were largely involved in nicotinate and nicotinamide metabolism, glutathione metabolism, tryptophan metabolism, and sphingolipid metabolism. The present study first revealed reliable biomarkers for early diagnosis of DCD. It provides new insight and strategy for the early diagnosis and treatment of DCD.

Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis.

PubMed

Emwas, Abdul-Hamid; Roy, Raja; McKay, Ryan T; Ryan, Danielle; Brennan, Lorraine; Tenori, Leonardo; Luchinat, Claudio; Gao, Xin; Zeri, Ana Carolina; Gowda, G A Nagana; Raftery, Daniel; Steinbeck, Christoph; Salek, Reza M; Wishart, David S

2016-02-05

NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to nondestructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Precise metabolite quantification is a prerequisite to move any chemical biomarker or biomarker panel from the lab to the clinic. Among the biofluids commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, and easily obtained, needs little sample preparation, and does not require invasive medical procedures for collection. Furthermore, urine captures and concentrates many "unwanted" or "undesirable" compounds throughout the body, providing a rich source of potentially useful disease biomarkers; however, incredible variation in urine chemical concentrations makes analysis of urine and identification of useful urinary biomarkers by NMR challenging. We discuss a number of the most significant issues regarding NMR-based urinary metabolomics with specific emphasis on metabolite quantification for disease biomarker applications and propose data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, sample preparation, and biomarker assessment.

Risk factors and biomarkers of life-threatening cancers

PubMed Central

Autier, Philippe

2015-01-01

There is growing evidence that risk factors for cancer occurrence and for cancer death are not necessarily the same. Knowledge of cancer aggressiveness risk factors (CARF) may help in identifying subjects at high risk of developing a potentially deadly cancer (and not just any cancer). The availability of CARFs may have positive consequences for health policies, medical practice, and the search for biomarkers. For instance, cancer chemoprevention and cancer screening of subjects with CARFs would probably be more ethical and cost-effective than recommending chemoprevention and screening to entire segments of the population. Also, the harmful consequences of chemoprevention and of screening would be reduced while effectiveness would be optimised. We present examples of CARF already in use (e.g. mutations of the breast cancer (BRCA) gene), of promising avenues for the discovery of biomarkers thanks to the investigation of CARFs (e.g. breast radiological density and systemic inflammation), and of biomarkers commonly used that are not real CARFs (e.g. certain mammography images, prostate-specific antigen (PSA) concentration, nevus number). PMID:26635900

Biomarker Discovery for Early Detection of Hepatocellular Carcinoma in Hepatitis C–infected Patients*

PubMed Central

Mustafa, Mehnaz G.; Petersen, John R.; Ju, Hyunsu; Cicalese, Luca; Snyder, Ned; Haidacher, Sigmund J.; Denner, Larry; Elferink, Cornelis

2013-01-01

Chronic hepatic disease damages the liver, and the resulting wound-healing process leads to liver fibrosis and the subsequent development of cirrhosis. The leading cause of hepatic fibrosis and cirrhosis is infection with hepatitis C virus (HCV), and of the patients with HCV-induced cirrhosis, 2% to 5% develop hepatocellular carcinoma (HCC), with a survival rate of 7%. HCC is one of the leading causes of cancer-related death worldwide, and the poor survival rate is largely due to late-stage diagnosis, which makes successful intervention difficult, if not impossible. The lack of sensitive and specific diagnostic tools and the urgent need for early-stage diagnosis prompted us to discover new candidate biomarkers for HCV and HCC. We used aptamer-based fractionation technology to reduce serum complexity, differentially labeled samples (six HCV and six HCC) with fluorescent dyes, and resolved proteins in pairwise two-dimensional difference gel electrophoresis. DeCyder software was used to identify differentially expressed proteins and spots picked, and MALDI-MS/MS was used to determine that ApoA1 was down-regulated by 22% (p < 0.004) in HCC relative to HCV. Differential expression quantified via two-dimensional difference gel electrophoresis was confirmed by means of 18O/16O stable isotope differential labeling with LC-MS/MS zoom scans. Technically independent confirmation was demonstrated by triple quadrupole LC-MS/MS selected reaction monitoring (SRM) assays with three peptides specific to human ApoA1 (DLATVYVDVLK, WQEEMELYR, and VSFLSALEEYTK) using 18O/16O-labeled samples and further verified with AQUA peptides as internal standards for quantification. In 50 patient samples (24 HCV and 26 HCC), all three SRM assays yielded highly similar differential expression of ApoA1 in HCC and HCV patients. These results validated the SRM assays, which were independently confirmed by Western blotting. Thus, ApoA1 is a candidate member of an SRM biomarker panel for early diagnosis

Current advances in biomarkers for targeted therapy in triple-negative breast cancer

PubMed Central

Fleisher, Brett; Clarke, Charlotte; Ait-Oudhia, Sihem

2016-01-01

Triple-negative breast cancer (TNBC) is a complex heterogeneous disease characterized by the absence of three hallmark receptors: human epidermal growth factor receptor 2, estrogen receptor, and progesterone receptor. Compared to other breast cancer subtypes, TNBC is more aggressive, has a higher prevalence in African-Americans, and more frequently affects younger patients. Currently, TNBC lacks clinically accepted targets for tailored therapy, warranting the need for candidate biomarkers. BiomarkerBase, an online platform used to find biomarkers reported in clinical trials, was utilized to screen all potential biomarkers for TNBC and select only the ones registered in completed TNBC trials through clinicaltrials.gov. The selected candidate biomarkers were classified as surrogate, prognostic, predictive, or pharmacodynamic (PD) and organized by location in the blood, on the cell surface, in the cytoplasm, or in the nucleus. Blood biomarkers include vascular endothelial growth factor/vascular endothelial growth factor receptor and interleukin-8 (IL-8); cell surface biomarkers include EGFR, insulin-like growth factor binding protein, c-Kit, c-Met, and PD-L1; cytoplasm biomarkers include PIK3CA, pAKT/S6/p4E-BP1, PTEN, ALDH1, and the PIK3CA/AKT/mTOR-related metabolites; and nucleus biomarkers include BRCA1, the gluco-corticoid receptor, TP53, and Ki67. Candidate biomarkers were further organized into a “cellular protein network” that demonstrates potential connectivity. This review provides an inventory and reference point for promising biomarkers for breakthrough targeted therapies in TNBC. PMID:27785100

Three-Dimensionally Functionalized Reverse Phase Glycoprotein Array for Cancer Biomarker Discovery and Validation.

PubMed

Pan, Li; Aguilar, Hillary Andaluz; Wang, Linna; Iliuk, Anton; Tao, W Andy

2016-11-30

Glycoproteins have vast structural diversity that plays an important role in many biological processes and have great potential as disease biomarkers. Here, we report a novel functionalized reverse phase protein array (RPPA), termed polymer-based reverse phase glycoprotein array (polyGPA), to capture and profile glycoproteomes specifically, and validate glycoproteins. Nitrocellulose membrane functionalized with globular hydroxyaminodendrimers was used to covalently capture preoxidized glycans on glycoproteins from complex protein samples such as biofluids. The captured glycoproteins were subsequently detected using the same validated antibodies as in RPPA. We demonstrated the outstanding specificity, sensitivity, and quantitative capabilities of polyGPA by capturing and detecting purified as well as endogenous α-1-acid glycoprotein (AGP) in human plasma. We further applied quantitative N-glycoproteomics and the strategy to validate a panel of glycoproteins identified as potential biomarkers for bladder cancer by analyzing urine glycoproteins from bladder cancer patients or matched healthy individuals.

Roadmap and standard operating procedures for biobanking and discovery of neurochemical markers in ALS.

PubMed

Otto, Markus; Bowser, Robert; Turner, Martin; Berry, James; Brettschneider, Johannes; Connor, James; Costa, Júlia; Cudkowicz, Merit; Glass, Jonathan; Jahn, Olaf; Lehnert, Stefan; Malaspina, Andrea; Parnetti, Lucilla; Petzold, Axel; Shaw, Pamela; Sherman, Alexander; Steinacker, Petra; Süssmuth, Sigurd; Teunissen, Charlotte; Tumani, Hayrettin; Wuolikainen, Anna; Ludolph, Albert

2012-01-01

Despite major advances in deciphering the neuropathological hallmarks of amyotrophic lateral sclerosis (ALS), validated neurochemical biomarkers for monitoring disease activity, earlier diagnosis, defining prognosis and unlocking key pathophysiological pathways are lacking. Although several candidate biomarkers exist, translation into clinical application is hindered by small sample numbers, especially longitudinal, for independent verification. This review considers the potential routes to the discovery of neurochemical markers in ALS, and provides a consensus statement on standard operating procedures that will facilitate multicenter collaboration, validation and ultimately clinical translation.

The Matchmaker Exchange: a platform for rare disease gene discovery.

PubMed

Philippakis, Anthony A; Azzariti, Danielle R; Beltran, Sergi; Brookes, Anthony J; Brownstein, Catherine A; Brudno, Michael; Brunner, Han G; Buske, Orion J; Carey, Knox; Doll, Cassie; Dumitriu, Sergiu; Dyke, Stephanie O M; den Dunnen, Johan T; Firth, Helen V; Gibbs, Richard A; Girdea, Marta; Gonzalez, Michael; Haendel, Melissa A; Hamosh, Ada; Holm, Ingrid A; Huang, Lijia; Hurles, Matthew E; Hutton, Ben; Krier, Joel B; Misyura, Andriy; Mungall, Christopher J; Paschall, Justin; Paten, Benedict; Robinson, Peter N; Schiettecatte, François; Sobreira, Nara L; Swaminathan, Ganesh J; Taschner, Peter E; Terry, Sharon F; Washington, Nicole L; Züchner, Stephan; Boycott, Kym M; Rehm, Heidi L

2015-10-01

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow. © 2015 WILEY PERIODICALS, INC.

The Matchmaker Exchange: A Platform for Rare Disease Gene Discovery

DOE PAGES

Philippakis, Anthony A.; Azzariti, Danielle R.; Beltran, Sergi; ...

2015-09-17

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be amore » reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. In conclusion, three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.« less

The Matchmaker Exchange: A Platform for Rare Disease Gene Discovery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Philippakis, Anthony A.; Azzariti, Danielle R.; Beltran, Sergi

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be amore » reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. In conclusion, three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.« less

Translating Metabolomics to Cardiovascular Biomarkers

PubMed Central

Senn, Todd; Hazen, Stanley L.; Tang, W. H. Wilson

2012-01-01