A four-gene signature predicts survival in clear-cell renal-cell carcinoma.

PubMed

Dai, Jun; Lu, Yuchao; Wang, Jinyu; Yang, Lili; Han, Yingyan; Wang, Ying; Yan, Dan; Ruan, Qiurong; Wang, Shaogang

2016-12-13

Clear-cell renal-cell carcinoma (ccRCC) is the most common pathological subtype of renal cell carcinoma (RCC), accounting for about 80% of RCC. In order to find potential prognostic biomarkers in ccRCC, we presented a four-gene signature to evaluate the prognosis of ccRCC. SurvExpress and immunohistochemical (IHC) staining of tissue microarrays were used to analyze the association between the four genes and the prognosis of ccRCC. Data from TCGA dataset revealed a prognostic prompt function of the four genes (PTEN, PIK3C2A, ITPA and BCL3). Further discovery suggested that the four-gene signature predicted survival better than any of the four genes alone. Moreover, IHC staining demonstrated a consistent result with TCGA, indicating that the signature was an independent prognostic factor of survival in ccRCC. Univariate and multivariate Cox proportional hazard regression analysis were conducted to verify the association of clinicopathological variables and the four genes' expression levels with survival. The results further testified that the risk (four-gene signature) was an independent prognostic factors of both Overall Survival (OS) and Disease-free Survival (DFS) (P<0.05). In conclusion, the four-gene signature was correlated with the survival of ccRCC, and therefore, may help to provide significant clinical implications for predicting the prognosis of patients.

Ion channel gene expression predicts survival in glioma patients

PubMed Central

Wang, Rong; Gurguis, Christopher I.; Gu, Wanjun; Ko, Eun A; Lim, Inja; Bang, Hyoweon; Zhou, Tong; Ko, Jae-Hong

2015-01-01

Ion channels are important regulators in cell proliferation, migration, and apoptosis. The malfunction and/or aberrant expression of ion channels may disrupt these important biological processes and influence cancer progression. In this study, we investigate the expression pattern of ion channel genes in glioma. We designate 18 ion channel genes that are differentially expressed in high-grade glioma as a prognostic molecular signature. This ion channel gene expression based signature predicts glioma outcome in three independent validation cohorts. Interestingly, 16 of these 18 genes were down-regulated in high-grade glioma. This signature is independent of traditional clinical, molecular, and histological factors. Resampling tests indicate that the prognostic power of the signature outperforms random gene sets selected from human genome in all the validation cohorts. More importantly, this signature performs better than the random gene signatures selected from glioma-associated genes in two out of three validation datasets. This study implicates ion channels in brain cancer, thus expanding on knowledge of their roles in other cancers. Individualized profiling of ion channel gene expression serves as a superior and independent prognostic tool for glioma patients. PMID:26235283

A peripheral blood transcriptomic signature predicts autoantibody development in infants at risk of type 1 diabetes.

PubMed

Mehdi, Ahmed M; Hamilton-Williams, Emma E; Cristino, Alexandre; Ziegler, Anette; Bonifacio, Ezio; Le Cao, Kim-Anh; Harris, Mark; Thomas, Ranjeny

2018-03-08

Autoimmune-mediated destruction of pancreatic islet β cells results in type 1 diabetes (T1D). Serum islet autoantibodies usually develop in genetically susceptible individuals in early childhood before T1D onset, with multiple islet autoantibodies predicting diabetes development. However, most at-risk children remain islet-antibody negative, and no test currently identifies those likely to seroconvert. We sought a genomic signature predicting seroconversion risk by integrating longitudinal peripheral blood gene expression profiles collected in high-risk children included in the BABYDIET and DIPP cohorts, of whom 50 seroconverted. Subjects were followed for 10 years to determine time of seroconversion. Any cohort effect and the time of seroconversion were corrected to uncover genes differentially expressed (DE) in seroconverting children. Gene expression signatures associated with seroconversion were evident during the first year of life, with 67 DE genes identified in seroconverting children relative to those remaining antibody negative. These genes contribute to T cell-, DC-, and B cell-related immune responses. Near-birth expression of ADCY9, PTCH1, MEX3B, IL15RA, ZNF714, TENM1, and PLEKHA5, along with HLA risk score predicted seroconversion (AUC 0.85). The ubiquitin-proteasome pathway linked DE genes and T1D susceptibility genes. Therefore, a gene expression signature in infancy predicts risk of seroconversion. Ubiquitination may play a mechanistic role in diabetes progression.

A peripheral blood transcriptomic signature predicts autoantibody development in infants at risk of type 1 diabetes

PubMed Central

Mehdi, Ahmed M.; Hamilton-Williams, Emma E.; Cristino, Alexandre; Ziegler, Anette; Harris, Mark

2018-01-01

Autoimmune-mediated destruction of pancreatic islet β cells results in type 1 diabetes (T1D). Serum islet autoantibodies usually develop in genetically susceptible individuals in early childhood before T1D onset, with multiple islet autoantibodies predicting diabetes development. However, most at-risk children remain islet-antibody negative, and no test currently identifies those likely to seroconvert. We sought a genomic signature predicting seroconversion risk by integrating longitudinal peripheral blood gene expression profiles collected in high-risk children included in the BABYDIET and DIPP cohorts, of whom 50 seroconverted. Subjects were followed for 10 years to determine time of seroconversion. Any cohort effect and the time of seroconversion were corrected to uncover genes differentially expressed (DE) in seroconverting children. Gene expression signatures associated with seroconversion were evident during the first year of life, with 67 DE genes identified in seroconverting children relative to those remaining antibody negative. These genes contribute to T cell–, DC-, and B cell–related immune responses. Near-birth expression of ADCY9, PTCH1, MEX3B, IL15RA, ZNF714, TENM1, and PLEKHA5, along with HLA risk score predicted seroconversion (AUC 0.85). The ubiquitin-proteasome pathway linked DE genes and T1D susceptibility genes. Therefore, a gene expression signature in infancy predicts risk of seroconversion. Ubiquitination may play a mechanistic role in diabetes progression. PMID:29515040

Changes in Gene Expression Predicting Local Control in Cervical Cancer: Results from Radiation Therapy Oncology Group 0128

PubMed Central

Weidhaas, Joanne B.; Li, Shu-Xia; Winter, Kathryn; Ryu, Janice; Jhingran, Anuja; Miller, Bridgette; Dicker, Adam P.; Gaffney, David

2009-01-01

Purpose To evaluate the potential of gene expression signatures to predict response to treatment in locally advanced cervical cancer treated with definitive chemotherapy and radiation. Experimental Design Tissue biopsies were collected from patients participating in Radiation Therapy Oncology Group (RTOG) 0128, a phase II trial evaluating the benefit of celecoxib in addition to cisplatin chemotherapy and radiation for locally advanced cervical cancer. Gene expression profiling was done and signatures of pretreatment, mid-treatment (before the first implant), and “changed” gene expression patterns between pre- and mid-treatment samples were determined. The ability of the gene signatures to predict local control versus local failure was evaluated. Two-group t test was done to identify the initial gene set separating these end points. Supervised classification methods were used to enrich the gene sets. The results were further validated by leave-one-out and 2-fold cross-validation. Results Twenty-two patients had suitable material from pretreatment samples for analysis, and 13 paired pre- and mid-treatment samples were obtained. The changed gene expression signatures between the pre- and mid-treatment biopsies predicted response to treatment, separating patients with local failures from those who achieved local control with a seven-gene signature. The in-sample prediction rate, leave-one-out prediction rate, and 2-fold prediction rate are 100% for this seven-gene signature. This signature was enriched for cell cycle genes. Conclusions Changed gene expression signatures during therapy in cervical cancer can predict outcome as measured by local control. After further validation, such findings could be applied to direct additional therapy for cervical cancer patients treated with chemotherapy and radiation. PMID:19509178

Minimising Immunohistochemical False Negative ER Classification Using a Complementary 23 Gene Expression Signature of ER Status

PubMed Central

Li, Qiyuan; Eklund, Aron C.; Juul, Nicolai; Haibe-Kains, Benjamin; Workman, Christopher T.; Richardson, Andrea L.; Szallasi, Zoltan; Swanton, Charles

2010-01-01

Background Expression of the oestrogen receptor (ER) in breast cancer predicts benefit from endocrine therapy. Minimising the frequency of false negative ER status classification is essential to identify all patients with ER positive breast cancers who should be offered endocrine therapies in order to improve clinical outcome. In routine oncological practice ER status is determined by semi-quantitative methods such as immunohistochemistry (IHC) or other immunoassays in which the ER expression level is compared to an empirical threshold[1], [2]. The clinical relevance of gene expression-based ER subtypes as compared to IHC-based determination has not been systematically evaluated. Here we attempt to reduce the frequency of false negative ER status classification using two gene expression approaches and compare these methods to IHC based ER status in terms of predictive and prognostic concordance with clinical outcome. Methodology/Principal Findings Firstly, ER status was discriminated by fitting the bimodal expression of ESR1 to a mixed Gaussian model. The discriminative power of ESR1 suggested bimodal expression as an efficient way to stratify breast cancer; therefore we identified a set of genes whose expression was both strongly bimodal, mimicking ESR expression status, and highly expressed in breast epithelial cell lines, to derive a 23-gene ER expression signature-based classifier. We assessed our classifiers in seven published breast cancer cohorts by comparing the gene expression-based ER status to IHC-based ER status as a predictor of clinical outcome in both untreated and tamoxifen treated cohorts. In untreated breast cancer cohorts, the 23 gene signature-based ER status provided significantly improved prognostic power compared to IHC-based ER status (P = 0.006). In tamoxifen-treated cohorts, the 23 gene ER expression signature predicted clinical outcome (HR = 2.20, P = 0.00035). These complementary ER signature-based strategies estimated that

A reported 20-gene expression signature to predict lymph node-positive disease at radical cystectomy for muscle-invasive bladder cancer is clinically not applicable.

PubMed

van Kessel, Kim E M; van de Werken, Harmen J G; Lurkin, Irene; Ziel-van der Made, Angelique C J; Zwarthoff, Ellen C; Boormans, Joost L

2017-01-01

Neoadjuvant chemotherapy (NAC) for muscle-invasive bladder cancer (MIBC) provides a small but significant survival benefit. Nevertheless, controversies on applying NAC remain because the limited benefit must be weight against chemotherapy-related toxicity and the delay of definitive local treatment. Therefore, there is a clear clinical need for tools to guide treatment decisions on NAC in MIBC. Here, we aimed to validate a previously reported 20-gene expression signature that predicted lymph node-positive disease at radical cystectomy in clinically node-negative MIBC patients, which would be a justification for upfront chemotherapy. We studied diagnostic transurethral resection of bladder tumors (dTURBT) of 150 MIBC patients (urothelial carcinoma) who were subsequently treated by radical cystectomy and pelvic lymph node dissection. RNA was isolated and the expression level of the 20 genes was determined on a qRT-PCR platform. Normalized Ct values were used to calculate a risk score to predict the presence of node-positive disease. The Cancer Genome Atlas (TCGA) RNA expression data was analyzed to subsequently validate the results. In a univariate regression analysis, none of the 20 genes significantly correlated with node-positive disease. The area under the curve of the risk score calculated by the 20-gene expression signature was 0.54 (95% Confidence Interval: 0.44-0.65) versus 0.67 for the model published by Smith et al. Node-negative patients had a significantly lower tumor grade at TURBT (p = 0.03), a lower pT stage (p<0.01) and less frequent lymphovascular invasion (13% versus 38%, p<0.01) at radical cystectomy than node-positive patients. In addition, in the TCGA data, none of the 20 genes was differentially expressed in node-negative versus node-positive patients. We conclude that a 20-gene expression signature developed for nodal staging of MIBC at radical cystectomy could not be validated on a qRT-PCR platform in a large cohort of dTURBT specimens.

FARO server: Meta-analysis of gene expression by matching gene expression signatures to a compendium of public gene expression data.

PubMed

Manijak, Mieszko P; Nielsen, Henrik B

2011-06-11

Although, systematic analysis of gene annotation is a powerful tool for interpreting gene expression data, it sometimes is blurred by incomplete gene annotation, missing expression response of key genes and secondary gene expression responses. These shortcomings may be partially circumvented by instead matching gene expression signatures to signatures of other experiments. To facilitate this we present the Functional Association Response by Overlap (FARO) server, that match input signatures to a compendium of 242 gene expression signatures, extracted from more than 1700 Arabidopsis microarray experiments. Hereby we present a publicly available tool for robust characterization of Arabidopsis gene expression experiments which can point to similar experimental factors in other experiments. The server is available at http://www.cbs.dtu.dk/services/faro/.

Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information.

PubMed

Hieke, Stefanie; Benner, Axel; Schlenl, Richard F; Schumacher, Martin; Bullinger, Lars; Binder, Harald

2016-08-30

High-throughput technology allows for genome-wide measurements at different molecular levels for the same patient, e.g. single nucleotide polymorphisms (SNPs) and gene expression. Correspondingly, it might be beneficial to also integrate complementary information from different molecular levels when building multivariable risk prediction models for a clinical endpoint, such as treatment response or survival. Unfortunately, such a high-dimensional modeling task will often be complicated by a limited overlap of molecular measurements at different levels between patients, i.e. measurements from all molecular levels are available only for a smaller proportion of patients. We propose a sequential strategy for building clinical risk prediction models that integrate genome-wide measurements from two molecular levels in a complementary way. To deal with partial overlap, we develop an imputation approach that allows us to use all available data. This approach is investigated in two acute myeloid leukemia applications combining gene expression with either SNP or DNA methylation data. After obtaining a sparse risk prediction signature e.g. from SNP data, an automatically selected set of prognostic SNPs, by componentwise likelihood-based boosting, imputation is performed for the corresponding linear predictor by a linking model that incorporates e.g. gene expression measurements. The imputed linear predictor is then used for adjustment when building a prognostic signature from the gene expression data. For evaluation, we consider stability, as quantified by inclusion frequencies across resampling data sets. Despite an extremely small overlap in the application example with gene expression and SNPs, several genes are seen to be more stably identified when taking the (imputed) linear predictor from the SNP data into account. In the application with gene expression and DNA methylation, prediction performance with respect to survival also indicates that the proposed approach might

An MEG signature corresponding to an axiomatic model of reward prediction error.

PubMed

Talmi, Deborah; Fuentemilla, Lluis; Litvak, Vladimir; Duzel, Emrah; Dolan, Raymond J

2012-01-02

Optimal decision-making is guided by evaluating the outcomes of previous decisions. Prediction errors are theoretical teaching signals which integrate two features of an outcome: its inherent value and prior expectation of its occurrence. To uncover the magnetic signature of prediction errors in the human brain we acquired magnetoencephalographic (MEG) data while participants performed a gambling task. Our primary objective was to use formal criteria, based upon an axiomatic model (Caplin and Dean, 2008a), to determine the presence and timing profile of MEG signals that express prediction errors. We report analyses at the sensor level, implemented in SPM8, time locked to outcome onset. We identified, for the first time, a MEG signature of prediction error, which emerged approximately 320 ms after an outcome and expressed as an interaction between outcome valence and probability. This signal followed earlier, separate signals for outcome valence and probability, which emerged approximately 200 ms after an outcome. Strikingly, the time course of the prediction error signal, as well as the early valence signal, resembled the Feedback-Related Negativity (FRN). In simultaneously acquired EEG data we obtained a robust FRN, but the win and loss signals that comprised this difference wave did not comply with the axiomatic model. Our findings motivate an explicit examination of the critical issue of timing embodied in computational models of prediction errors as seen in human electrophysiological data. Copyright © 2011 Elsevier Inc. All rights reserved.

An integrated mRNA and microRNA expression signature for glioblastoma multiforme prognosis.

PubMed

Xiong, Jie; Bing, Zhitong; Su, Yanlin; Deng, Defeng; Peng, Xiaoning

2014-01-01

Although patients with Glioblastoma multiforme (GBM) have grave prognosis, significant variability in patient outcome is observed. The objective of this study is to identify a molecular signature for GBM prognosis. We subjected 355 mRNA and microRNA expression profiles to elastic net-regulated Cox regression for identification of an integrated RNA signature for GBM prognosis. A prognostic index (PI) was generated for patient stratification. Survival comparison was conducted by Kaplan-Meier method and a general multivariate Cox regression procedure was applied to evaluate the independence of the PI. The abilities and efficiencies of signatures to predict GBM patient outcome was assessed and compared by the area under the curve (AUC) of the receiver-operator characteristic (ROC). An integrated RNA prognostic signature consisted by 4 protective mRNAs, 12 risky mRNAs, and 1 risky microRNA was identified. Decreased survival was associated with being in the high-risk group (hazard ratio = 2.864, P<0.0001). The prognostic value of the integrated signature was validated in five independent GBM expression datasets (n = 201, hazard ratio = 2.453, P<0.0001). The PI outperformed the known clinical factors, mRNA-only, and miRNA-only prognostic signatures for GBM prognosis (area under the ROC curve for the integrated RNA, mRNA-only, and miRNA-only signatures were 0.828, 0.742, and 0.757 at 3 years of overall survival, respectively, P<0.0001 by permutation test). We describe the first, to our knowledge, robust transcriptome-based integrated RNA signature that improves the current GBM prognosis based on clinical variables, mRNA-only, and miRNA-only signatures.

An Integrated mRNA and microRNA Expression Signature for Glioblastoma Multiforme Prognosis

PubMed Central

Xiong, Jie; Bing, Zhitong; Su, Yanlin; Deng, Defeng; Peng, Xiaoning

2014-01-01

Although patients with Glioblastoma multiforme (GBM) have grave prognosis, significant variability in patient outcome is observed. The objective of this study is to identify a molecular signature for GBM prognosis. We subjected 355 mRNA and microRNA expression profiles to elastic net-regulated Cox regression for identification of an integrated RNA signature for GBM prognosis. A prognostic index (PI) was generated for patient stratification. Survival comparison was conducted by Kaplan-Meier method and a general multivariate Cox regression procedure was applied to evaluate the independence of the PI. The abilities and efficiencies of signatures to predict GBM patient outcome was assessed and compared by the area under the curve (AUC) of the receiver-operator characteristic (ROC). An integrated RNA prognostic signature consisted by 4 protective mRNAs, 12 risky mRNAs, and 1 risky microRNA was identified. Decreased survival was associated with being in the high-risk group (hazard ratio = 2.864, P<0.0001). The prognostic value of the integrated signature was validated in five independent GBM expression datasets (n = 201, hazard ratio = 2.453, P<0.0001). The PI outperformed the known clinical factors, mRNA-only, and miRNA-only prognostic signatures for GBM prognosis (area under the ROC curve for the integrated RNA, mRNA-only, and miRNA-only signatures were 0.828, 0.742, and 0.757 at 3 years of overall survival, respectively, P<0.0001 by permutation test). We describe the first, to our knowledge, robust transcriptome-based integrated RNA signature that improves the current GBM prognosis based on clinical variables, mRNA-only, and miRNA-only signatures. PMID:24871302

eXpression2Kinases (X2K) Web: linking expression signatures to upstream cell signaling networks.

PubMed

Clarke, Daniel J B; Kuleshov, Maxim V; Schilder, Brian M; Torre, Denis; Duffy, Mary E; Keenan, Alexandra B; Lachmann, Alexander; Feldmann, Axel S; Gundersen, Gregory W; Silverstein, Moshe C; Wang, Zichen; Ma'ayan, Avi

2018-05-25

While gene expression data at the mRNA level can be globally and accurately measured, profiling the activity of cell signaling pathways is currently much more difficult. eXpression2Kinases (X2K) computationally predicts involvement of upstream cell signaling pathways, given a signature of differentially expressed genes. X2K first computes enrichment for transcription factors likely to regulate the expression of the differentially expressed genes. The next step of X2K connects these enriched transcription factors through known protein-protein interactions (PPIs) to construct a subnetwork. The final step performs kinase enrichment analysis on the members of the subnetwork. X2K Web is a new implementation of the original eXpression2Kinases algorithm with important enhancements. X2K Web includes many new transcription factor and kinase libraries, and PPI networks. For demonstration, thousands of gene expression signatures induced by kinase inhibitors, applied to six breast cancer cell lines, are provided for fetching directly into X2K Web. The results are displayed as interactive downloadable vector graphic network images and bar graphs. Benchmarking various settings via random permutations enabled the identification of an optimal set of parameters to be used as the default settings in X2K Web. X2K Web is freely available from http://X2K.cloud.

Correlated miR-mRNA expression signatures of mouse hematopoietic stem and progenitor cell subsets predict "Stemness" and "Myeloid" interaction networks.

PubMed

Heiser, Diane; Tan, Yee Sun; Kaplan, Ian; Godsey, Brian; Morisot, Sebastien; Cheng, Wen-Chih; Small, Donald; Civin, Curt I

2014-01-01

Several individual miRNAs (miRs) have been implicated as potent regulators of important processes during normal and malignant hematopoiesis. In addition, many miRs have been shown to fine-tune intricate molecular networks, in concert with other regulatory elements. In order to study hematopoietic networks as a whole, we first created a map of global miR expression during early murine hematopoiesis. Next, we determined the copy number per cell for each miR in each of the examined stem and progenitor cell types. As data is emerging indicating that miRs function robustly mainly when they are expressed above a certain threshold (∼100 copies per cell), our database provides a resource for determining which miRs are expressed at a potentially functional level in each cell type. Finally, we combine our miR expression map with matched mRNA expression data and external prediction algorithms, using a Bayesian modeling approach to create a global landscape of predicted miR-mRNA interactions within each of these hematopoietic stem and progenitor cell subsets. This approach implicates several interaction networks comprising a "stemness" signature in the most primitive hematopoietic stem cell (HSC) populations, as well as "myeloid" patterns associated with two branches of myeloid development.

Radiation Gene-expression Signatures in Primary Breast Cancer Cells.

PubMed

Minafra, Luigi; Bravatà, Valentina; Cammarata, Francesco P; Russo, Giorgio; Gilardi, Maria C; Forte, Giusi I

2018-05-01

In breast cancer (BC) care, radiation therapy (RT) is an efficient treatment to control localized tumor. Radiobiological research is needed to understand molecular differences that affect radiosensitivity of different tumor subtypes and the response variability. The aim of this study was to analyze gene expression profiling (GEP) in primary BC cells following irradiation with doses of 9 Gy and 23 Gy delivered by intraoperative electron radiation therapy (IOERT) in order to define gene signatures of response to high doses of ionizing radiation. We performed GEP by cDNA microarrays and evaluated cell survival after IOERT treatment in primary BC cell cultures. Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to validate candidate genes. We showed, for the first time, a 4-gene and a 6-gene signature, as new molecular biomarkers, in two primary BC cell cultures after exposure at 9 Gy and 23 Gy respectively, for which we observed a significantly high survival rate. Gene signatures activated by different doses of ionizing radiation may predict response to RT and contribute to defining a personalized biological-driven treatment plan. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Discovery of metabolic signatures for predicting whole organism toxicology.

PubMed

Hines, Adam; Staff, Fred J; Widdows, John; Compton, Russell M; Falciani, Francesco; Viant, Mark R

2010-06-01

Toxicological studies in sentinel organisms frequently use biomarkers to assess biological effect. Development of "omic" technologies has enhanced biomarker discovery at the molecular level, providing signatures unique to toxicant mode-of-action (MOA). However, these signatures often lack relevance to organismal responses, such as growth or reproduction, limiting their value for environmental monitoring. Our primary objective was to discover metabolic signatures in chemically exposed organisms that can predict physiological toxicity. Marine mussels (Mytilus edulis) were exposed for 7 days to 12 and 50 microg/l copper and 50 and 350 microg/l pentachlorophenol (PCP), toxicants with unique MOAs. Physiological responses comprised an established measure of organism energetic fitness, scope for growth (SFG). Metabolic fingerprints were measured in the same individuals using nuclear magnetic resonance-based metabolomics. Metabolic signatures predictive of SFG were sought using optimal variable selection strategies and multivariate regression and then tested upon independently field-sampled mussels from rural and industrialized sites. Copper and PCP induced rational metabolic and physiological changes. Measured and predicted SFG were highly correlated for copper (r(2) = 0.55, P = 2.82 x 10(-7)) and PCP (r(2) = 0.66, P = 3.20 x 10(-6)). Predictive metabolites included methionine and arginine/phosphoarginine for copper and allantoin, valine, and methionine for PCP. When tested on field-sampled animals, metabolic signatures predicted considerably reduced fitness of mussels from the contaminated (SFG = 6.0 J/h/g) versus rural (SFG = 15.2 J/h/g) site. We report the first successful discovery of metabolic signatures in chemically exposed environmental organisms that inform on molecular MOA and that can predict physiological toxicity. This could have far-reaching implications for monitoring impacts on environmental health.

A potential prognostic long non-coding RNA signature to predict metastasis-free survival of breast cancer patients.

PubMed

Sun, Jie; Chen, Xihai; Wang, Zhenzhen; Guo, Maoni; Shi, Hongbo; Wang, Xiaojun; Cheng, Liang; Zhou, Meng

2015-11-09

Long non-coding RNAs (lncRNAs) have been implicated in a variety of biological processes, and dysregulated lncRNAs have demonstrated potential roles as biomarkers and therapeutic targets for cancer prognosis and treatment. In this study, by repurposing microarray probes, we analyzed lncRNA expression profiles of 916 breast cancer patients from the Gene Expression Omnibus (GEO). Nine lncRNAs were identified to be significantly associated with metastasis-free survival (MFS) in the training dataset of 254 patients using the Cox proportional hazards regression model. These nine lncRNAs were then combined to form a single prognostic signature for predicting metastatic risk in breast cancer patients that was able to classify patients in the training dataset into high- and low-risk subgroups with significantly different MFSs (median 2.4 years versus 3.0 years, log-rank test p < 0.001). This nine-lncRNA signature was similarly effective for prognosis in a testing dataset and two independent datasets. Further analysis showed that the predictive ability of the signature was independent of clinical variables, including age, ER status, ESR1 status and ERBB2 status. Our results indicated that lncRNA signature could be a useful prognostic marker to predict metastatic risk in breast cancer patients and may improve upon our understanding of the molecular mechanisms underlying breast cancer metastasis.

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Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-02

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Predictions of runoff signatures in ungauged basins: Austrian case study

NASA Astrophysics Data System (ADS)

Viglione, A.; Parajka, J.; Salinas, J.; Rogger, M.; Sivapalan, M.; Bloeschl, G.

2012-12-01

Runoff variability can be broken up into several components, each of them meaningful of a certain class of applications of societal relevance: annual runoff, seasonal runoff, flow duration curve, low flows, floods and hydrographs. We call them runoff signatures and we view them as a manifestation of catchment functioning at different time scales, as emergent properties of the complex systems that catchments are. Just as a medical doctor has many different options for studying the state and functioning of a patient, we can infer the state and functioning of a catchment observing its runoff signatures. But what can we do in the absence of runoff data? This study aims to understand how well one can predict runoff signatures in ungauged catchments. The comparison across signatures is based on one consistent data set (Austria) and one regionalisation method (Top-Kriging) in order to explore the relative performance of the predictions of each of the signatures. Results indicate that the performance, assessed by cross-validation, is best for annual and seasonal runoff, it degrades as one moves to low flows and floods and goes up again to high values for runoff hydrographs. Also, dedicated regionalisation methods, i.e. focusing on particular signatures and their characteristics, provide better predictions of the signatures than regionalisation of the entire hydrograph. These results suggest that the use of signatures in the calibration or assessment of process models can be valuable, in that this can lead to models predicting runoff correctly for the right reasons.

GeneSigDB—a curated database of gene expression signatures

PubMed Central

Culhane, Aedín C.; Schwarzl, Thomas; Sultana, Razvan; Picard, Kermshlise C.; Picard, Shaita C.; Lu, Tim H.; Franklin, Katherine R.; French, Simon J.; Papenhausen, Gerald; Correll, Mick; Quackenbush, John

2010-01-01

The primary objective of most gene expression studies is the identification of one or more gene signatures; lists of genes whose transcriptional levels are uniquely associated with a specific biological phenotype. Whilst thousands of experimentally derived gene signatures are published, their potential value to the community is limited by their computational inaccessibility. Gene signatures are embedded in published article figures, tables or in supplementary materials, and are frequently presented using non-standard gene or probeset nomenclature. We present GeneSigDB (http://compbio.dfci.harvard.edu/genesigdb) a manually curated database of gene expression signatures. GeneSigDB release 1.0 focuses on cancer and stem cells gene signatures and was constructed from more than 850 publications from which we manually transcribed 575 gene signatures. Most gene signatures (n = 560) were successfully mapped to the genome to extract standardized lists of EnsEMBL gene identifiers. GeneSigDB provides the original gene signature, the standardized gene list and a fully traceable gene mapping history for each gene from the original transcribed data table through to the standardized list of genes. The GeneSigDB web portal is easy to search, allows users to compare their own gene list to those in the database, and download gene signatures in most common gene identifier formats. PMID:19934259

Early signatures of regime shifts in gene expression dynamics

NASA Astrophysics Data System (ADS)

Pal, Mainak; Pal, Amit Kumar; Ghosh, Sayantari; Bose, Indrani

2013-06-01

Recently, a large number of studies have been carried out on the early signatures of sudden regime shifts in systems as diverse as ecosystems, financial markets, population biology and complex diseases. The signatures of regime shifts in gene expression dynamics are less systematically investigated. In this paper, we consider sudden regime shifts in the gene expression dynamics described by a fold-bifurcation model involving bistability and hysteresis. We consider two alternative models, models 1 and 2, of competence development in the bacterial population B. subtilis and determine some early signatures of the regime shifts between competence and noncompetence. We use both deterministic and stochastic formalisms for the purpose of our study. The early signatures studied include the critical slowing down as a transition point is approached, rising variance and the lag-1 autocorrelation function, skewness and a ratio of two mean first passage times. Some of the signatures could provide the experimental basis for distinguishing between bistability and excitability as the correct mechanism for the development of competence.

Copy number variation signature to predict human ancestry

PubMed Central

2012-01-01

Background Copy number variations (CNVs) are genomic structural variants that are found in healthy populations and have been observed to be associated with disease susceptibility. Existing methods for CNV detection are often performed on a sample-by-sample basis, which is not ideal for large datasets where common CNVs must be estimated by comparing the frequency of CNVs in the individual samples. Here we describe a simple and novel approach to locate genome-wide CNVs common to a specific population, using human ancestry as the phenotype. Results We utilized our previously published Genome Alteration Detection Analysis (GADA) algorithm to identify common ancestry CNVs (caCNVs) and built a caCNV model to predict population structure. We identified a 73 caCNV signature using a training set of 225 healthy individuals from European, Asian, and African ancestry. The signature was validated on an independent test set of 300 individuals with similar ancestral background. The error rate in predicting ancestry in this test set was 2% using the 73 caCNV signature. Among the caCNVs identified, several were previously confirmed experimentally to vary by ancestry. Our signature also contains a caCNV region with a single microRNA (MIR270), which represents the first reported variation of microRNA by ancestry. Conclusions We developed a new methodology to identify common CNVs and demonstrated its performance by building a caCNV signature to predict human ancestry with high accuracy. The utility of our approach could be extended to large case–control studies to identify CNV signatures for other phenotypes such as disease susceptibility and drug response. PMID:23270563

Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd

PubMed Central

Wang, Zichen; Monteiro, Caroline D.; Jagodnik, Kathleen M.; Fernandez, Nicolas F.; Gundersen, Gregory W.; Rouillard, Andrew D.; Jenkins, Sherry L.; Feldmann, Axel S.; Hu, Kevin S.; McDermott, Michael G.; Duan, Qiaonan; Clark, Neil R.; Jones, Matthew R.; Kou, Yan; Goff, Troy; Woodland, Holly; Amaral, Fabio M R.; Szeto, Gregory L.; Fuchs, Oliver; Schüssler-Fiorenza Rose, Sophia M.; Sharma, Shvetank; Schwartz, Uwe; Bausela, Xabier Bengoetxea; Szymkiewicz, Maciej; Maroulis, Vasileios; Salykin, Anton; Barra, Carolina M.; Kruth, Candice D.; Bongio, Nicholas J.; Mathur, Vaibhav; Todoric, Radmila D; Rubin, Udi E.; Malatras, Apostolos; Fulp, Carl T.; Galindo, John A.; Motiejunaite, Ruta; Jüschke, Christoph; Dishuck, Philip C.; Lahl, Katharina; Jafari, Mohieddin; Aibar, Sara; Zaravinos, Apostolos; Steenhuizen, Linda H.; Allison, Lindsey R.; Gamallo, Pablo; de Andres Segura, Fernando; Dae Devlin, Tyler; Pérez-García, Vicente; Ma'ayan, Avi

2016-01-01

Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO). Through a massive open online course on Coursera, over 70 participants from over 25 countries identify and annotate 2,460 single-gene perturbation signatures, 839 disease versus normal signatures, and 906 drug perturbation signatures. All these signatures are unique and are manually validated for quality. Global analysis of these signatures confirms known associations and identifies novel associations between genes, diseases and drugs. The manually curated signatures are used as a training set to develop classifiers for extracting similar signatures from the entire GEO repository. We develop a web portal to serve these signatures for query, download and visualization. PMID:27667448

Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd.

PubMed

Wang, Zichen; Monteiro, Caroline D; Jagodnik, Kathleen M; Fernandez, Nicolas F; Gundersen, Gregory W; Rouillard, Andrew D; Jenkins, Sherry L; Feldmann, Axel S; Hu, Kevin S; McDermott, Michael G; Duan, Qiaonan; Clark, Neil R; Jones, Matthew R; Kou, Yan; Goff, Troy; Woodland, Holly; Amaral, Fabio M R; Szeto, Gregory L; Fuchs, Oliver; Schüssler-Fiorenza Rose, Sophia M; Sharma, Shvetank; Schwartz, Uwe; Bausela, Xabier Bengoetxea; Szymkiewicz, Maciej; Maroulis, Vasileios; Salykin, Anton; Barra, Carolina M; Kruth, Candice D; Bongio, Nicholas J; Mathur, Vaibhav; Todoric, Radmila D; Rubin, Udi E; Malatras, Apostolos; Fulp, Carl T; Galindo, John A; Motiejunaite, Ruta; Jüschke, Christoph; Dishuck, Philip C; Lahl, Katharina; Jafari, Mohieddin; Aibar, Sara; Zaravinos, Apostolos; Steenhuizen, Linda H; Allison, Lindsey R; Gamallo, Pablo; de Andres Segura, Fernando; Dae Devlin, Tyler; Pérez-García, Vicente; Ma'ayan, Avi

2016-09-26

Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO). Through a massive open online course on Coursera, over 70 participants from over 25 countries identify and annotate 2,460 single-gene perturbation signatures, 839 disease versus normal signatures, and 906 drug perturbation signatures. All these signatures are unique and are manually validated for quality. Global analysis of these signatures confirms known associations and identifies novel associations between genes, diseases and drugs. The manually curated signatures are used as a training set to develop classifiers for extracting similar signatures from the entire GEO repository. We develop a web portal to serve these signatures for query, download and visualization.

A novel RNA-sequencing-based miRNA signature predicts with recurrence and outcome of hepatocellular carcinoma.

PubMed

Fumao, Bai; Zhou, Huaibin; Ma, Mengni; Guan, Chen; Lyu, Jianxin; Meng, Qing H

2018-05-02

Hepatocellular carcinoma (HCC) is the fifth most common type of cancer and the second leading cause of cancer-related deaths worldwide. Given that the rate of HCC recurrence 5 years after liver resection is as high as 70%, patient with HCC typically have a poor outcome. A biomarker or set of biomarkers that could predict disease recurrence would have a substantial clinical impact, allowing earlier detection of recurrence and more effective treatment. With the aim of identifying a new microRNA (miRNA) signature associated with HCC recurrence, we analyzed data on 306 HCC patients for whom both miRNA expression profiles and complete clinical information were available from The Cancer Genome Atlas (TCGA) database. Through this analysis, we identified a six-miRNA signature that could effectively predict patients' recurrence risk; the high-risk and low-risk groups had significantly different recurrence-free survival rates. Time-dependent receiver operating characteristic analysis indicated that this signature had a good predictive performance. Multivariable Cox regression and stratified analyses demonstrated that the six-miRNA signature was independent of other clinical features. Functional enrichment analysis of the gene targets of the six prognostic miRNAs indicated enrichment mainly in cancer-related pathways and important cell biological processes. Our results support use of this six-miRNA signature as an independent factor for predicting recurrence and outcome of patients with HCC. Molecular Oncology (2018) © 2018 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.

Hereditary family signature of facial expression

PubMed Central

Peleg, Gili; Katzir, Gadi; Peleg, Ofer; Kamara, Michal; Brodsky, Leonid; Hel-Or, Hagit; Keren, Daniel; Nevo, Eviatar

2006-01-01

Although facial expressions of emotion are universal, individual differences create a facial expression “signature” for each person; but, is there a unique family facial expression signature? Only a few family studies on the heredity of facial expressions have been performed, none of which compared the gestalt of movements in various emotional states; they compared only a few movements in one or two emotional states. No studies, to our knowledge, have compared movements of congenitally blind subjects with their relatives to our knowledge. Using two types of analyses, we show a correlation between movements of congenitally blind subjects with those of their relatives in think-concentrate, sadness, anger, disgust, joy, and surprise and provide evidence for a unique family facial expression signature. In the analysis “in-out family test,” a particular movement was compared each time across subjects. Results show that the frequency of occurrence of a movement of a congenitally blind subject in his family is significantly higher than that outside of his family in think-concentrate, sadness, and anger. In the analysis “the classification test,” in which congenitally blind subjects were classified to their families according to the gestalt of movements, results show 80% correct classification over the entire interview and 75% in anger. Analysis of the movements' frequencies in anger revealed a correlation between the movements' frequencies of congenitally blind individuals and those of their relatives. This study anticipates discovering genes that influence facial expressions, understanding their evolutionary significance, and elucidating repair mechanisms for syndromes lacking facial expression, such as autism. PMID:17043232

ADAGE signature analysis: differential expression analysis with data-defined gene sets.

PubMed

Tan, Jie; Huyck, Matthew; Hu, Dongbo; Zelaya, René A; Hogan, Deborah A; Greene, Casey S

2017-11-22

Gene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server ( http://adage.greenelab.com ) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and ∆anr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and Anr. We designed

S100A9 and EGFR gene signatures predict disease progression in muscle invasive bladder cancer patients after chemotherapy.

PubMed

Kim, W T; Kim, J; Yan, C; Jeong, P; Choi, S Y; Lee, O J; Chae, Y B; Yun, S J; Lee, S C; Kim, W J

2014-05-01

In our previous gene expression profile analysis, IL1B, S100A8, S100A9, and EGFR were shown to be important mediators of muscle invasive bladder cancer (MIBC) progression. The aim of the present study was to investigate the ability of these gene signatures to predict disease progression after chemotherapy in patients with locally recurrent or metastatic MIBC. Patients with locally advanced MIBC who received chemotherapy were enrolled. The expression signatures of four genes were measured and carried out further functional analysis to confirm our findings. Two of the four genes, S100A9 and EGFR, were determined to significantly influence disease progression (P = 0.023, 0.045, respectively). Based on a receiver operating characteristic curve, a cut-off value for disease progression was determined. Patients with the good-prognostic signature group had a significantly longer time to progression and cancer-specific survival time than those with the poor-prognostic signature group (P < 0.001, 0.042, respectively). In the multivariate Cox regression analysis, gene signature was the only factor that significantly influenced disease progression [hazard ratio: 4.726, confidence interval: 1.623-13.763, P = 0.004]. In immunohistochemical analysis, S100A9 and EGFR positivity were associated with disease progression after chemotherapy. Protein expression of S100A9/EGFR showed modest correlation with gene expression of S100A9/EGFR (r = 0.395, P = 0.014 and r = 0.453, P = 0.004). Our functional analysis provided the evidence demonstrating that expression of S100A9 and EGFR closely associated chemoresistance, and that inhibition of S100A9 and EGFR may sensitize bladder tumor cells to the cisplatin-based chemotherapy. The S100A9/EGFR level is a novel prognostic marker to predict the chemoresponsiveness of patients with locally recurrent or metastatic MIBC.

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Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-06

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Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

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

Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.

2008-10-20

One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasetsmore » having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds

Muscle myeloid type I interferon gene expression may predict therapeutic responses to rituximab in myositis patients

PubMed Central

Nagaraju, Kanneboyina; Ghimbovschi, Svetlana; Rayavarapu, Sree; Phadke, Aditi; Rider, Lisa G.; Hoffman, Eric P.

2016-01-01

Abstract Objective. To identify muscle gene expression patterns that predict rituximab responses and assess the effects of rituximab on muscle gene expression in PM and DM. Methods. In an attempt to understand the molecular mechanism of response and non-response to rituximab therapy, we performed Affymetrix gene expression array analyses on muscle biopsy specimens taken before and after rituximab therapy from eight PM and two DM patients in the Rituximab in Myositis study. We also analysed selected muscle-infiltrating cell phenotypes in these biopsies by immunohistochemical staining. Partek and Ingenuity pathway analyses assessed the gene pathways and networks. Results. Myeloid type I IFN signature genes were expressed at higher levels at baseline in the skeletal muscle of rituximab responders than in non-responders, whereas classic non-myeloid IFN signature genes were expressed at higher levels in non-responders at baseline. Also, rituximab responders have a greater reduction of the myeloid and non-myeloid type I IFN signatures than non-responders. The decrease in the type I IFN signature following administration of rituximab may be associated with the decreases in muscle-infiltrating CD19 + B cells and CD68 + macrophages in responders. Conclusion. Our findings suggest that high levels of myeloid type I IFN gene expression in skeletal muscle predict responses to rituximab in PM/DM and that rituximab responders also have a greater decrease in the expression of these genes. These data add further evidence to recent studies defining the type I IFN signature as both a predictor of therapeutic responses and a biomarker of myositis disease activity. PMID:27215813

Quantifying uncertainties in streamflow predictions through signature based inference of hydrological model parameters

NASA Astrophysics Data System (ADS)

Fenicia, Fabrizio; Reichert, Peter; Kavetski, Dmitri; Albert, Calro

2016-04-01

The calibration of hydrological models based on signatures (e.g. Flow Duration Curves - FDCs) is often advocated as an alternative to model calibration based on the full time series of system responses (e.g. hydrographs). Signature based calibration is motivated by various arguments. From a conceptual perspective, calibration on signatures is a way to filter out errors that are difficult to represent when calibrating on the full time series. Such errors may for example occur when observed and simulated hydrographs are shifted, either on the "time" axis (i.e. left or right), or on the "streamflow" axis (i.e. above or below). These shifts may be due to errors in the precipitation input (time or amount), and if not properly accounted in the likelihood function, may cause biased parameter estimates (e.g. estimated model parameters that do not reproduce the recession characteristics of a hydrograph). From a practical perspective, signature based calibration is seen as a possible solution for making predictions in ungauged basins. Where streamflow data are not available, it may in fact be possible to reliably estimate streamflow signatures. Previous research has for example shown how FDCs can be reliably estimated at ungauged locations based on climatic and physiographic influence factors. Typically, the goal of signature based calibration is not the prediction of the signatures themselves, but the prediction of the system responses. Ideally, the prediction of system responses should be accompanied by a reliable quantification of the associated uncertainties. Previous approaches for signature based calibration, however, do not allow reliable estimates of streamflow predictive distributions. Here, we illustrate how the Bayesian approach can be employed to obtain reliable streamflow predictive distributions based on signatures. A case study is presented, where a hydrological model is calibrated on FDCs and additional signatures. We propose an approach where the likelihood

Gene-expression signatures of Atlantic salmon's plastic life cycle.

PubMed

Aubin-Horth, Nadia; Letcher, Benjamin H; Hofmann, Hans A

2009-09-15

How genomic expression differs as a function of life history variation is largely unknown. Atlantic salmon exhibits extreme alternative life histories. We defined the gene-expression signatures of wild-caught salmon at two different life stages by comparing the brain expression profiles of mature sneaker males and immature males, and early migrants and late migrants. In addition to life-stage-specific signatures, we discovered a surprisingly large gene set that was differentially regulated-at similar magnitudes, yet in opposite direction-in both life history transitions. We suggest that this co-variation is not a consequence of many independent cellular and molecular switches in the same direction but rather represents the molecular equivalent of a physiological shift orchestrated by one or very few master regulators.

A 15-gene signature for prediction of colon cancer recurrence and prognosis based on SVM.

PubMed

Xu, Guangru; Zhang, Minghui; Zhu, Hongxing; Xu, Jinhua

2017-03-10

To screen the gene signature for distinguishing patients with high risks from those with low-risks for colon cancer recurrence and predicting their prognosis. Five microarray datasets of colon cancer samples were collected from Gene Expression Omnibus database and one was obtained from The Cancer Genome Atlas (TCGA). After preprocessing, data in GSE17537 were analyzed using the Linear Models for Microarray data (LIMMA) method to identify the differentially expressed genes (DEGs). The DEGs further underwent PPI network-based neighborhood scoring and support vector machine (SVM) analyses to screen the feature genes associated with recurrence and prognosis, which were then validated by four datasets GSE38832, GSE17538, GSE28814 and TCGA using SVM and Cox regression analyses. A total of 1207 genes were identified as DEGs between recurrence and no-recurrence samples, including 726 downregulated and 481 upregulated genes. Using SVM analysis and five gene expression profile data confirmation, a 15-gene signature (HES5, ZNF417, GLRA2, OR8D2, HOXA7, FABP6, MUSK, HTR6, GRIP2, KLRK1, VEGFA, AKAP12, RHEB, NCRNA00152 and PMEPA1) were identified as a predictor of recurrence risk and prognosis for colon cancer patients. Our identified 15-gene signature may be useful to classify colon cancer patients with different prognosis and some genes in this signature may represent new therapeutic targets. Copyright © 2016. Published by Elsevier B.V.

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

A VEGF-dependent gene signature enriched in mesenchymal ovarian cancer predicts patient prognosis.

PubMed

Yin, Xia; Wang, Xiaojie; Shen, Boqiang; Jing, Ying; Li, Qing; Cai, Mei-Chun; Gu, Zhuowei; Yang, Qi; Zhang, Zhenfeng; Liu, Jin; Li, Hongxia; Di, Wen; Zhuang, Guanglei

2016-08-08

We have previously reported surrogate biomarkers of VEGF pathway activities with the potential to provide predictive information for anti-VEGF therapies. The aim of this study was to systematically evaluate a new VEGF-dependent gene signature (VDGs) in relation to molecular subtypes of ovarian cancer and patient prognosis. Using microarray profiling and cross-species analysis, we identified 140-gene mouse VDGs and corresponding 139-gene human VDGs, which displayed enrichment of vasculature and basement membrane genes. In patients who received bevacizumab therapy and showed partial response, the expressions of VDGs (summarized to yield VDGs scores) were markedly decreased in post-treatment biopsies compared with pre-treatment baselines. In contrast, VDGs scores were not significantly altered following bevacizumab treatment in patients with stable or progressive disease. Analysis of VDGs in ovarian cancer showed that VDGs as a prognostic signature was able to predict patient outcome. Correlation estimation of VDGs scores and molecular features revealed that VDGs was overrepresented in mesenchymal subtype and BRCA mutation carriers. These findings highlighted the prognostic role of VEGF-mediated angiogenesis in ovarian cancer, and proposed a VEGF-dependent gene signature as a molecular basis for developing novel diagnostic strategies to aid patient selection for VEGF-targeted agents.

Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.

PubMed

Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne; Bourgeois, Stephane; Svensson, Peter J; Wadelius, Mia; Deloukas, Panos; Montgomery, Stephen B; Altman, Russ B

2017-11-24

Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

Twenty-four signature genes predict the prognosis of oral squamous cell carcinoma with high accuracy and repeatability

PubMed Central

Gao, Jianyong; Tian, Gang; Han, Xu; Zhu, Qiang

2018-01-01

Oral squamous cell carcinoma (OSCC) is the sixth most common type cancer worldwide, with poor prognosis. The present study aimed to identify gene signatures that could classify OSCC and predict prognosis in different stages. A training data set (GSE41613) and two validation data sets (GSE42743 and GSE26549) were acquired from the online Gene Expression Omnibus database. In the training data set, patients were classified based on the tumor-node-metastasis staging system, and subsequently grouped into low stage (L) or high stage (H). Signature genes between L and H stages were selected by disparity index analysis, and classification was performed by the expression of these signature genes. The established classification was compared with the L and H classification, and fivefold cross validation was used to evaluate the stability. Enrichment analysis for the signature genes was implemented by the Database for Annotation, Visualization and Integration Discovery. Two validation data sets were used to determine the precise of classification. Survival analysis was conducted followed each classification using the package ‘survival’ in R software. A set of 24 signature genes was identified based on the classification model with the Fi value of 0.47, which was used to distinguish OSCC samples in two different stages. Overall survival of patients in the H stage was higher than those in the L stage. Signature genes were primarily enriched in ‘ether lipid metabolism’ pathway and biological processes such as ‘positive regulation of adaptive immune response’ and ‘apoptotic cell clearance’. The results provided a novel 24-gene set that may be used as biomarkers to predict OSCC prognosis with high accuracy, which may be used to determine an appropriate treatment program for patients with OSCC in addition to the traditional evaluation index. PMID:29257303

Gene-expression signatures of Atlantic salmon's plastic life cycle

USGS Publications Warehouse

Aubin-Horth, N.; Letcher, B.H.; Hofmann, H.A.

2009-01-01

How genomic expression differs as a function of life history variation is largely unknown. Atlantic salmon exhibits extreme alternative life histories. We defined the gene-expression signatures of wild-caught salmon at two different life stages by comparing the brain expression profiles of mature sneaker males and immature males, and early migrants and late migrants. In addition to life-stage-specific signatures, we discovered a surprisingly large gene set that was differentially regulated-at similar magnitudes, yet in opposite direction-in both life history transitions. We suggest that this co-variation is not a consequence of many independent cellular and molecular switches in the same direction but rather represents the molecular equivalent of a physiological shift orchestrated by one or very few master regulators. ?? 2009 Elsevier Inc. All rights reserved.

L1000CDS2: LINCS L1000 characteristic direction signatures search engine.

PubMed

Duan, Qiaonan; Reid, St Patrick; Clark, Neil R; Wang, Zichen; Fernandez, Nicolas F; Rouillard, Andrew D; Readhead, Ben; Tritsch, Sarah R; Hodos, Rachel; Hafner, Marc; Niepel, Mario; Sorger, Peter K; Dudley, Joel T; Bavari, Sina; Panchal, Rekha G; Ma'ayan, Avi

2016-01-01

The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS 2 . The L1000CDS 2 search engine provides prioritization of thousands of small-molecule signatures, and their pairwise combinations, predicted to either mimic or reverse an input gene expression signature using two methods. The L1000CDS 2 search engine also predicts drug targets for all the small molecules profiled by the L1000 assay that we processed. Targets are predicted by computing the cosine similarity between the L1000 small-molecule signatures and a large collection of signatures extracted from the gene expression omnibus (GEO) for single-gene perturbations in mammalian cells. We applied L1000CDS 2 to prioritize small molecules that are predicted to reverse expression in 670 disease signatures also extracted from GEO, and prioritized small molecules that can mimic expression of 22 endogenous ligand signatures profiled by the L1000 assay. As a case study, to further demonstrate the utility of L1000CDS 2 , we collected expression signatures from human cells infected with Ebola virus at 30, 60 and 120 min. Querying these signatures with L1000CDS 2 we identified kenpaullone, a GSK3B/CDK2 inhibitor that we show, in subsequent experiments, has a dose-dependent efficacy in inhibiting Ebola infection in vitro without causing cellular toxicity in human cell lines. In summary, the L1000CDS 2 tool can be applied in many biological and biomedical settings, while improving the extraction of

L1000CDS2: LINCS L1000 characteristic direction signatures search engine

PubMed Central

Duan, Qiaonan; Reid, St Patrick; Clark, Neil R; Wang, Zichen; Fernandez, Nicolas F; Rouillard, Andrew D; Readhead, Ben; Tritsch, Sarah R; Hodos, Rachel; Hafner, Marc; Niepel, Mario; Sorger, Peter K; Dudley, Joel T; Bavari, Sina; Panchal, Rekha G; Ma’ayan, Avi

2016-01-01

The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS2. The L1000CDS2 search engine provides prioritization of thousands of small-molecule signatures, and their pairwise combinations, predicted to either mimic or reverse an input gene expression signature using two methods. The L1000CDS2 search engine also predicts drug targets for all the small molecules profiled by the L1000 assay that we processed. Targets are predicted by computing the cosine similarity between the L1000 small-molecule signatures and a large collection of signatures extracted from the gene expression omnibus (GEO) for single-gene perturbations in mammalian cells. We applied L1000CDS2 to prioritize small molecules that are predicted to reverse expression in 670 disease signatures also extracted from GEO, and prioritized small molecules that can mimic expression of 22 endogenous ligand signatures profiled by the L1000 assay. As a case study, to further demonstrate the utility of L1000CDS2, we collected expression signatures from human cells infected with Ebola virus at 30, 60 and 120 min. Querying these signatures with L1000CDS2 we identified kenpaullone, a GSK3B/CDK2 inhibitor that we show, in subsequent experiments, has a dose-dependent efficacy in inhibiting Ebola infection in vitro without causing cellular toxicity in human cell lines. In summary, the L1000CDS2 tool can be applied in many biological and biomedical settings, while improving the extraction of knowledge

GeneSigDB: a manually curated database and resource for analysis of gene expression signatures

PubMed Central

Culhane, Aedín C.; Schröder, Markus S.; Sultana, Razvan; Picard, Shaita C.; Martinelli, Enzo N.; Kelly, Caroline; Haibe-Kains, Benjamin; Kapushesky, Misha; St Pierre, Anne-Alyssa; Flahive, William; Picard, Kermshlise C.; Gusenleitner, Daniel; Papenhausen, Gerald; O'Connor, Niall; Correll, Mick; Quackenbush, John

2012-01-01

GeneSigDB (http://www.genesigdb.org or http://compbio.dfci.harvard.edu/genesigdb/) is a database of gene signatures that have been extracted and manually curated from the published literature. It provides a standardized resource of published prognostic, diagnostic and other gene signatures of cancer and related disease to the community so they can compare the predictive power of gene signatures or use these in gene set enrichment analysis. Since GeneSigDB release 1.0, we have expanded from 575 to 3515 gene signatures, which were collected and transcribed from 1604 published articles largely focused on gene expression in cancer, stem cells, immune cells, development and lung disease. We have made substantial upgrades to the GeneSigDB website to improve accessibility and usability, including adding a tag cloud browse function, facetted navigation and a ‘basket’ feature to store genes or gene signatures of interest. Users can analyze GeneSigDB gene signatures, or upload their own gene list, to identify gene signatures with significant gene overlap and results can be viewed on a dynamic editable heatmap that can be downloaded as a publication quality image. All data in GeneSigDB can be downloaded in numerous formats including .gmt file format for gene set enrichment analysis or as a R/Bioconductor data file. GeneSigDB is available from http://www.genesigdb.org. PMID:22110038

Prognostic stratification improvement by integrating ID1/ID3/IGJ gene expression signature and immunophenotypic profile in adult patients with B-ALL.

PubMed

Cruz-Rodriguez, Nataly; Combita, Alba L; Enciso, Leonardo J; Raney, Lauren F; Pinzon, Paula L; Lozano, Olga C; Campos, Alba M; Peñaloza, Niyireth; Solano, Julio; Herrera, Maria V; Zabaleta, Jovanny; Quijano, Sandra

2017-02-28

Survival of adults with B-Acute Lymphoblastic Leukemia requires accurate risk stratification of patients in order to provide the appropriate therapy. Contemporary techniques, using clinical and cytogenetic variables are incomplete for prognosis prediction. To improve the classification of adult patients diagnosed with B-ALL into prognosis groups, two strategies were examined and combined: the expression of the ID1/ID3/IGJ gene signature by RT-PCR and the immunophenotypic profile of 19 markers proposed in the EuroFlow protocol by Flow Cytometry in bone marrow samples. Both techniques were correlated to stratify patients into prognostic groups. An inverse relationship between survival and expression of the three-genes signature was observed and an immunophenotypic profile associated with clinical outcome was identified. Markers CD10 and CD20 were correlated with simultaneous overexpression of ID1, ID3 and IGJ. Patients with simultaneous expression of the poor prognosis gene signature and overexpression of CD10 or CD20, had worse Event Free Survival and Overall Survival than patients who had either the poor prognosis gene expression signature or only CD20 or CD10 overexpressed. By utilizing the combined evaluation of these two immunophenotypic markers along with the poor prognosis gene expression signature, the risk stratification can be significantly strengthened. Further studies including a large number of patients are needed to confirm these findings.

Muscle myeloid type I interferon gene expression may predict therapeutic responses to rituximab in myositis patients.

PubMed

Nagaraju, Kanneboyina; Ghimbovschi, Svetlana; Rayavarapu, Sree; Phadke, Aditi; Rider, Lisa G; Hoffman, Eric P; Miller, Frederick W

2016-09-01

To identify muscle gene expression patterns that predict rituximab responses and assess the effects of rituximab on muscle gene expression in PM and DM. In an attempt to understand the molecular mechanism of response and non-response to rituximab therapy, we performed Affymetrix gene expression array analyses on muscle biopsy specimens taken before and after rituximab therapy from eight PM and two DM patients in the Rituximab in Myositis study. We also analysed selected muscle-infiltrating cell phenotypes in these biopsies by immunohistochemical staining. Partek and Ingenuity pathway analyses assessed the gene pathways and networks. Myeloid type I IFN signature genes were expressed at higher levels at baseline in the skeletal muscle of rituximab responders than in non-responders, whereas classic non-myeloid IFN signature genes were expressed at higher levels in non-responders at baseline. Also, rituximab responders have a greater reduction of the myeloid and non-myeloid type I IFN signatures than non-responders. The decrease in the type I IFN signature following administration of rituximab may be associated with the decreases in muscle-infiltrating CD19(+) B cells and CD68(+) macrophages in responders. Our findings suggest that high levels of myeloid type I IFN gene expression in skeletal muscle predict responses to rituximab in PM/DM and that rituximab responders also have a greater decrease in the expression of these genes. These data add further evidence to recent studies defining the type I IFN signature as both a predictor of therapeutic responses and a biomarker of myositis disease activity. Published by Oxford University Press on behalf British Society for Rheumatology 2016. This work is written by US Government employees and is in the public domain in the US.

Gene-expression signatures of Atlantic salmon’s plastic life cycle

PubMed Central

Aubin-Horth, Nadia; Letcher, Benjamin H.; Hofmann, Hans A.

2009-01-01

How genomic expression differs as a function of life history variation is largely unknown. Atlantic salmon exhibits extreme alternative life histories. We defined the gene-expression signatures of wild-caught salmon at two different life stages by comparing the brain expression profiles of mature sneaker males and immature males, and early migrants and late migrants. In addition to life-stage-specific signatures, we discovered a surprisingly large gene set that was differentially regulated - at similar magnitudes, yet in opposite direction - in both life history transitions. We suggest that this co-variation is not a consequence of many independent cellular and molecular switches in the same direction but rather represents the molecular equivalent of a physiological shift orchestrated by one or very few master regulators. PMID:19401203

Multigene signature for predicting prognosis of patients with 1p19q co-deletion diffuse glioma.

PubMed

Hu, Xin; Martinez-Ledesma, Emmanuel; Zheng, Siyuan; Kim, Hoon; Barthel, Floris; Jiang, Tao; Hess, Kenneth R; Verhaak, Roel G W

2017-06-01

Co-deletion of 1p and 19q marks a diffuse glioma subtype associated with relatively favorable overall survival; however, heterogeneous clinical outcomes are observed within this category. We assembled gene expression profiles and sample annotation of 374 glioma patients carrying the 1p/19q co-deletion. We predicted 1p/19q status using gene expression when annotation was missing. A first cohort was randomly split into training (n = 170) and a validation dataset (n = 163). A second validation set consisted of 41 expression profiles. An elastic-net penalized Cox proportional hazards model was applied to build a classifier model through cross-validation within the training dataset. The selected 35-gene signature was used to identify high-risk and low-risk groups in the validation set, which showed significantly different overall survival (P = .00058, log-rank test). For time-to-death events, the high-risk group predicted by the gene signature yielded a hazard ratio of 1.78 (95% confidence interval, 1.02-3.11). The signature was also significantly associated with clinical outcome in the The Cancer Genome Atlas (CGA) IDH-mutant 1p/19q wild-type and IDH-wild-type glioma cohorts. Pathway analysis suggested that high risk was associated with increased acetylation activity and inflammatory response. Tumor purity was found to be significantly decreased in high-risk IDH-mutant with 1p/19q co-deletion gliomas and IDH-wild-type glioblastomas but not in IDH-wild-type lower grade or IDH-mutant, non-co-deleted gliomas. We identified a 35-gene signature that identifies high-risk and low-risk categories of 1p/19q positive glioma patients. We have demonstrated heterogeneity amongst a relatively new glioma subtype and provided a stepping stone towards risk stratification. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Gene signatures and expression of miRNAs associated with efficacy of panitumumab in a head and neck cancer phase II trial.

PubMed

Siano, Marco; Espeli, Vittoria; Mach, Nicolas; Bossi, Paolo; Licitra, Lisa; Ghielmini, Michele; Frattini, Milo; Canevari, Silvana; De Cecco, Loris

2018-07-01

Platinum-based chemotherapy plus the anti-EGFR monoclonal antibody (mAb) cetuximab is used to treat recurrent/metastatic (RM) head-neck squamous cell carcinoma (HNSCC). Recently, we defined Cluster3 gene-expression signature as a potential predictor of favorable progression-free survival (PFS) in cetuximab-treated RM-HNSCC patients and predictor of partial metabolic FDG-PET response in an afatinib window-of-opportunity trial. Another anti-EGFR-mAb (panitumumab) was used as the treatment agent in RM-HNSCC patients in the phase II PANI01trial. PANI01 tumor samples were analyzed using functional genomics to explore response predictors to anti-EGFR therapy. Whole-gene expression and real-time PCR analyses were applied to pre-treatment samples from 25 PANI01 patients. Three gene signatures (Cluster3 score, RAS onco-signature, microenvironment score) and seven selected miRNAs were separately analyzed for association with panitumumab efficacy. Cluster3 expression levels had a profile with a significant bimodal separation of samples (P =  3.08 E-13). Higher RAS activation, microenvironment score, and miRNA expression were associated with low-Cluster3 patients. The same biomarkers were separately associated with PFS. Patients with high-Cluster3 had significantly longer PFS than patients with low-Cluster3 (median PFS: 174 versus 51 days; log-rank P = 0.0021). ROC analysis demonstrated accuracy in predicting PFS (AUC = 0.877). Despite differences in clinical settings and anti-EGFR inhibitors used for treatment, response prediction by the Cluster3 signature and selected miRNAs was essentially the same. Translation into a useful clinical assay requires validation in a broader setting. Copyright © 2018 Elsevier Ltd. All rights reserved.

Can specific transcriptional regulators assemble a universal cancer signature?

NASA Astrophysics Data System (ADS)

Roy, Janine; Isik, Zerrin; Pilarsky, Christian; Schroeder, Michael

2013-10-01

Recently, there is a lot of interest in using biomarker signatures derived from gene expression data to predict cancer progression. We assembled signatures of 25 published datasets covering 13 types of cancers. How do these signatures compare with each other? On one hand signatures answering the same biological question should overlap, whereas signatures predicting different cancer types should differ. On the other hand, there could also be a Universal Cancer Signature that is predictive independently of the cancer type. Initially, we generate signatures for all datasets using classical approaches such as t-test and fold change and then, we explore signatures resulting from a network-based method, that applies the random surfer model of Google's PageRank algorithm. We show that the signatures as published by the authors and the signatures generated with classical methods do not overlap - not even for the same cancer type - whereas the network-based signatures strongly overlap. Selecting 10 out of 37 universal cancer genes gives the optimal prediction for all cancers thus taking a first step towards a Universal Cancer Signature. We furthermore analyze and discuss the involved genes in terms of the Hallmarks of cancer and in particular single out SP1, JUN/FOS and NFKB1 and examine their specific role in cancer progression.

Signaling protein signature predicts clinical outcome of non-small-cell lung cancer.

PubMed

Jin, Bao-Feng; Yang, Fan; Ying, Xiao-Min; Gong, Lin; Hu, Shuo-Feng; Zhao, Qing; Liao, Yi-Da; Chen, Ke-Zhong; Li, Teng; Tai, Yan-Hong; Cao, Yuan; Li, Xiao; Huang, Yan; Zhan, Xiao-Yan; Qin, Xuan-He; Wu, Jin; Chen, Shuai; Guo, Sai-Sai; Zhang, Yu-Cheng; Chen, Jing; Shen, Dan-Hua; Sun, Kun-Kun; Chen, Lu; Li, Wei-Hua; Li, Ai-Ling; Wang, Na; Xia, Qing; Wang, Jun; Zhou, Tao

2018-03-06

Non-small-cell lung cancer (NSCLC) is characterized by abnormalities of numerous signaling proteins that play pivotal roles in cancer development and progression. Many of these proteins have been reported to be correlated with clinical outcomes of NSCLC. However, none of them could provide adequate accuracy of prognosis prediction in clinical application. A total of 384 resected NSCLC specimens from two hospitals in Beijing (BJ) and Chongqing (CQ) were collected. Using immunohistochemistry (IHC) staining on stored formalin-fixed paraffin-embedded (FFPE) surgical samples, we examined the expression levels of 75 critical proteins on BJ samples. Random forest algorithm (RFA) and support vector machines (SVM) computation were applied to identify protein signatures on 2/3 randomly assigned BJ samples. The identified signatures were tested on the remaining BJ samples, and were further validated with CQ independent cohort. A 6-protein signature for adenocarcinoma (ADC) and a 5-protein signature for squamous cell carcinoma (SCC) were identified from training sets and tested in testing sets. In independent validation with CQ cohort, patients can also be divided into high- and low-risk groups with significantly different median overall survivals by Kaplan-Meier analysis, both in ADC (31 months vs. 87 months, HR 2.81; P <  0.001) and SCC patients (27 months vs. not reached, HR 9.97; P <  0.001). Cox regression analysis showed that both signatures are independent prognostic indicators and outperformed TNM staging (ADC: adjusted HR 3.07 vs. 2.43, SCC: adjusted HR 7.84 vs. 2.24). Particularly, we found that only the ADC patients in high-risk group significantly benefited from adjuvant chemotherapy (P = 0.018). Both ADC and SCC protein signatures could effectively stratify the prognosis of NSCLC patients, and may support patient selection for adjuvant chemotherapy.

NF-κB gene signature predicts prostate cancer progression

PubMed Central

Jin, Renjie; Yi, Yajun; Yull, Fiona E.; Blackwell, Timothy S.; Clark, Peter E.; Koyama, Tatsuki; Smith, Joseph A.; Matusik, Robert J.

2014-01-01

In many prostate cancer (PCa) patients, the cancer will be recurrent and eventually progress to lethal metastatic disease after primary treatment, such as surgery or radiation therapy. Therefore, it would be beneficial to better predict which patients with early-stage PCa would progress or recur after primary definitive treatment. In addition, many studies indicate that activation of NF-κB signaling correlates with PCa progression; however, the precise underlying mechanism is not fully understood. Our studies show that activation of NF-κB signaling via deletion of one allele of its inhibitor, IκBα, did not induce prostatic tumorigenesis in our mouse model. However, activation of NF-κB signaling did increase the rate of tumor progression in the Hi-Myc mouse PCa model when compared to Hi-Myc alone. Using the non-malignant NF-κB activated androgen depleted mouse prostate, a NF-κB Activated Recurrence Predictor 21 (NARP21) gene signature was generated. The NARP21 signature successfully predicted disease-specific survival and distant metastases-free survival in patients with PCa. This transgenic mouse model derived gene signature provides a useful and unique molecular profile for human PCa prognosis, which could be used on a prostatic biopsy to predict indolent versus aggressive behavior of the cancer after surgery. PMID:24686169

Quantitative comparison of microarray experiments with published leukemia related gene expression signatures.

PubMed

Klein, Hans-Ulrich; Ruckert, Christian; Kohlmann, Alexander; Bullinger, Lars; Thiede, Christian; Haferlach, Torsten; Dugas, Martin

2009-12-15

Multiple gene expression signatures derived from microarray experiments have been published in the field of leukemia research. A comparison of these signatures with results from new experiments is useful for verification as well as for interpretation of the results obtained. Currently, the percentage of overlapping genes is frequently used to compare published gene signatures against a signature derived from a new experiment. However, it has been shown that the percentage of overlapping genes is of limited use for comparing two experiments due to the variability of gene signatures caused by different array platforms or assay-specific influencing parameters. Here, we present a robust approach for a systematic and quantitative comparison of published gene expression signatures with an exemplary query dataset. A database storing 138 leukemia-related published gene signatures was designed. Each gene signature was manually annotated with terms according to a leukemia-specific taxonomy. Two analysis steps are implemented to compare a new microarray dataset with the results from previous experiments stored and curated in the database. First, the global test method is applied to assess gene signatures and to constitute a ranking among them. In a subsequent analysis step, the focus is shifted from single gene signatures to chromosomal aberrations or molecular mutations as modeled in the taxonomy. Potentially interesting disease characteristics are detected based on the ranking of gene signatures associated with these aberrations stored in the database. Two example analyses are presented. An implementation of the approach is freely available as web-based application. The presented approach helps researchers to systematically integrate the knowledge derived from numerous microarray experiments into the analysis of a new dataset. By means of example leukemia datasets we demonstrate that this approach detects related experiments as well as related molecular mutations and may

GESearch: An Interactive GUI Tool for Identifying Gene Expression Signature.

PubMed

Ye, Ning; Yin, Hengfu; Liu, Jingjing; Dai, Xiaogang; Yin, Tongming

2015-01-01

The huge amount of gene expression data generated by microarray and next-generation sequencing technologies present challenges to exploit their biological meanings. When searching for the coexpression genes, the data mining process is largely affected by selection of algorithms. Thus, it is highly desirable to provide multiple options of algorithms in the user-friendly analytical toolkit to explore the gene expression signatures. For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files. This analytical toolkit provides four models, including the mean, the regression, the delegate, and the ensemble models, to identify the coexpression genes, and enables the users to filter data and to select gene expression patterns by browsing the display window or by importing knowledge-based genes. Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments. Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

Oxidative stress/reactive metabolite gene expression signature in rat liver detects idiosyncratic hepatotoxicants

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

Leone, Angelique; Nie, Alex; Brandon Parker, J.

Previously we reported a gene expression signature in rat liver for detecting a specific type of oxidative stress (OS) related to reactive metabolites (RM). High doses of the drugs disulfiram, ethinyl estradiol and nimesulide were used with another dozen paradigm OS/RM compounds, and three other drugs flutamide, phenacetin and sulindac were identified by this signature. In a second study, antiepileptic drugs were compared for covalent binding and their effects on OS/RM; felbamate, carbamazepine, and phenobarbital produced robust OS/RM gene expression. In the present study, liver RNA samples from drug-treated rats from more recent experiments were examined for statistical fit tomore » the OS/RM signature. Of all 97 drugs examined, in addition to the nine drugs noted above, 19 more were identified as OS/RM-producing compounds—chlorpromazine, clozapine, cyproterone acetate, dantrolene, dipyridamole, glibenclamide, isoniazid, ketoconazole, methapyrilene, naltrexone, nifedipine, sulfamethoxazole, tamoxifen, coumarin, ritonavir, amitriptyline, valproic acid, enalapril, and chloramphenicol. Importantly, all of the OS/RM drugs listed above have been linked to idiosyncratic hepatotoxicity, excepting chloramphenicol, which does not have a package label for hepatotoxicity, but does have a black box warning for idiosyncratic bone marrow suppression. Most of these drugs are not acutely toxic in the rat. The OS/RM signature should be useful to avoid idiosyncratic hepatotoxicity of drug candidates. - Highlights: • 28 of 97 drugs gave a positive OS/RM gene expression signature in rat liver. • The specificity of the signature for human idiosyncratic hepatotoxicants was 98%. • The sensitivity of the signature for human idiosyncratic hepatotoxicants was 75%. • The signature can help eliminate hepatotoxicants from drug development.« less

Biological and Clinical Significance of MAD2L1 and BUB1, Genes Frequently Appearing in Expression Signatures for Breast Cancer Prognosis

PubMed Central

Wang, Zhanwei; Katsaros, Dionyssios; Shen, Yi; Fu, Yuanyuan; Canuto, Emilie Marion; Benedetto, Chiara; Lu, Lingeng; Chu, Wen-Ming; Risch, Harvey A.; Yu, Herbert

2015-01-01

To investigate the biologic relevance and clinical implication of genes involved in multiple gene expression signatures for breast cancer prognosis, we identified 16 published gene expression signatures, and selected two genes, MAD2L1 and BUB1. These genes appeared in 5 signatures and were involved in cell-cycle regulation. We analyzed the expression of these genes in relation to tumor features and disease outcomes. In vitro experiments were also performed in two breast cancer cell lines, MDA-MB-231 and MDA-MB-468, to assess cell proliferation, migration and invasion after knocking down the expression of these genes. High expression of these genes was found to be associated with aggressive tumors and poor disease-free survival of 203 breast cancer patients in our study, and the association with survival was confirmed in an online database consisting of 914 patients. In vitro experiments demonstrated that lowering the expression of these genes by siRNAs reduced tumor cell growth and inhibited cell migration and invasion. Our investigation suggests that MAD2L1 and BUB1 may play important roles in breast cancer progression, and measuring the expression of these genes may assist the prediction of breast cancer prognosis. PMID:26287798

Use of mRNA expression signatures to discover small molecule inhibitors of skeletal muscle atrophy

PubMed Central

Adams, Christopher M.; Ebert, Scott M.; Dyle, Michael C.

2017-01-01

Purpose of review Here, we discuss a recently developed experimental strategy for discovering small molecules with potential to prevent and treat skeletal muscle atrophy. Recent findings Muscle atrophy involves and requires widespread changes in skeletal muscle gene expression, which generate complex but measurable patterns of positive and negative changes in skeletal muscle mRNA levels (a.k.a. mRNA expression signatures of muscle atrophy). Many bioactive small molecules generate their own characteristic mRNA expression signatures, and by identifying small molecules whose signatures approximate mirror images of muscle atrophy signatures, one may identify small molecules with potential to prevent and/or reverse muscle atrophy. Unlike a conventional drug discovery approach, this strategy does not rely on a predefined molecular target but rather exploits the complexity of muscle atrophy to identify small molecules that counter the entire spectrum of pathological changes in atrophic muscle. We discuss how this strategy has been used to identify two natural compounds, ursolic acid and tomatidine, that reduce muscle atrophy and improve skeletal muscle function. Summary Discovery strategies based on mRNA expression signatures can elucidate new approaches for preserving and restoring muscle mass and function. PMID:25807353

Use of mRNA expression signatures to discover small molecule inhibitors of skeletal muscle atrophy.

PubMed

Adams, Christopher M; Ebert, Scott M; Dyle, Michael C

2015-05-01

Here, we discuss a recently developed experimental strategy for discovering small molecules with potential to prevent and treat skeletal muscle atrophy. Muscle atrophy involves and requires widespread changes in skeletal muscle gene expression, which generate complex but measurable patterns of positive and negative changes in skeletal muscle mRNA levels (a.k.a. mRNA expression signatures of muscle atrophy). Many bioactive small molecules generate their own characteristic mRNA expression signatures, and by identifying small molecules whose signatures approximate mirror images of muscle atrophy signatures, one may identify small molecules with potential to prevent and/or reverse muscle atrophy. Unlike a conventional drug discovery approach, this strategy does not rely on a predefined molecular target but rather exploits the complexity of muscle atrophy to identify small molecules that counter the entire spectrum of pathological changes in atrophic muscle. We discuss how this strategy has been used to identify two natural compounds, ursolic acid and tomatidine, that reduce muscle atrophy and improve skeletal muscle function. Discovery strategies based on mRNA expression signatures can elucidate new approaches for preserving and restoring muscle mass and function.

Glioma IL13Rα2 Is Associated with Mesenchymal Signature Gene Expression and Poor Patient Prognosis

PubMed Central

Starr, Renate; Deng, Xutao; Badie, Behnam; Yuan, Yate-Ching; Forman, Stephen J.; Barish, Michael E.

2013-01-01

A major challenge for successful immunotherapy against glioma is the identification and characterization of validated targets. We have taken a bioinformatics approach towards understanding the biological context of IL-13 receptor α2 (IL13Rα2) expression in brain tumors, and its functional significance for patient survival. Querying multiple gene expression databases, we show that IL13Rα2 expression increases with glioma malignancy grade, and expression for high-grade tumors is bimodal, with approximately 58% of WHO grade IV gliomas over-expressing this receptor. By several measures, IL13Rα2 expression in patient samples and low-passage primary glioma lines most consistently correlates with the expression of signature genes defining mesenchymal subclass tumors and negatively correlates with proneural signature genes as defined by two studies. Positive associations were also noted with proliferative signature genes, whereas no consistent associations were found with either classical or neural signature genes. Probing the potential functional consequences of this mesenchymal association through IPA analysis suggests that IL13Rα2 expression is associated with activation of proinflammatory and immune pathways characteristic of mesenchymal subclass tumors. In addition, survival analyses indicate that IL13Rα2 over-expression is associated with poor patient prognosis, a single gene correlation ranking IL13Rα2 in the top ~1% of total gene expression probes with regard to survival association with WHO IV gliomas. This study better defines the functional consequences of IL13Rα2 expression by demonstrating association with mesenchymal signature gene expression and poor patient prognosis. It thus highlights the utility of IL13Rα2 as a therapeutic target, and helps define patient populations most likely to respond to immunotherapy in present and future clinical trials. PMID:24204956

Glioma IL13Rα2 is associated with mesenchymal signature gene expression and poor patient prognosis.

PubMed

Brown, Christine E; Warden, Charles D; Starr, Renate; Deng, Xutao; Badie, Behnam; Yuan, Yate-Ching; Forman, Stephen J; Barish, Michael E

2013-01-01

A major challenge for successful immunotherapy against glioma is the identification and characterization of validated targets. We have taken a bioinformatics approach towards understanding the biological context of IL-13 receptor α2 (IL13Rα2) expression in brain tumors, and its functional significance for patient survival. Querying multiple gene expression databases, we show that IL13Rα2 expression increases with glioma malignancy grade, and expression for high-grade tumors is bimodal, with approximately 58% of WHO grade IV gliomas over-expressing this receptor. By several measures, IL13Rα2 expression in patient samples and low-passage primary glioma lines most consistently correlates with the expression of signature genes defining mesenchymal subclass tumors and negatively correlates with proneural signature genes as defined by two studies. Positive associations were also noted with proliferative signature genes, whereas no consistent associations were found with either classical or neural signature genes. Probing the potential functional consequences of this mesenchymal association through IPA analysis suggests that IL13Rα2 expression is associated with activation of proinflammatory and immune pathways characteristic of mesenchymal subclass tumors. In addition, survival analyses indicate that IL13Rα2 over-expression is associated with poor patient prognosis, a single gene correlation ranking IL13Rα2 in the top ~1% of total gene expression probes with regard to survival association with WHO IV gliomas. This study better defines the functional consequences of IL13Rα2 expression by demonstrating association with mesenchymal signature gene expression and poor patient prognosis. It thus highlights the utility of IL13Rα2 as a therapeutic target, and helps define patient populations most likely to respond to immunotherapy in present and future clinical trials.

Polycomb repressive complex 2 epigenomic signature defines age-associated hypermethylation and gene expression changes

PubMed Central

Dozmorov, Mikhail G

2015-01-01

Although age-associated gene expression and methylation changes have been reported throughout the literature, the unifying epigenomic principles of aging remain poorly understood. Recent explosion in availability and resolution of functional/regulatory genome annotation data (epigenomic data), such as that provided by the ENCODE and Roadmap Epigenomics projects, provides an opportunity for the identification of epigenomic mechanisms potentially altered by age-associated differentially methylated regions (aDMRs) and regulatory signatures in the promoters of age-associated genes (aGENs). In this study we found that aDMRs and aGENs identified in multiple independent studies share a common Polycomb Repressive Complex 2 signature marked by EZH2, SUZ12, CTCF binding sites, repressive H3K27me3, and activating H3K4me1 histone modification marks, and a “poised promoter” chromatin state. This signature is depleted in RNA Polymerase II-associated transcription factor binding sites, activating H3K79me2, H3K36me3, H3K27ac marks, and an “active promoter” chromatin state. The PRC2 signature was shown to be generally stable across cell types. When considering the directionality of methylation changes, we found the PRC2 signature to be associated with aDMRs hypermethylated with age, while hypomethylated aDMRs were associated with enhancers. In contrast, aGENs were associated with the PRC2 signature independently of the directionality of gene expression changes. In this study we demonstrate that the PRC2 signature is the common epigenomic context of genomic regions associated with hypermethylation and gene expression changes in aging. PMID:25880792

Association of a cytarabine chemosensitivity related gene expression signature with survival in cytogenetically normal acute myeloid leukemia.

PubMed

Yan, Han; Wen, Lu; Tan, Dan; Xie, Pan; Pang, Feng-Mei; Zhou, Hong-Hao; Zhang, Wei; Liu, Zhao-Qian; Tang, Jie; Li, Xi; Chen, Xiao-Ping

2017-01-03

The prognosis of cytogenetically normal acute myeloid leukemia (CN-AML) varies greatly among patients. Achievement of complete remission (CR) after chemotherapy is indispensable for a better prognosis. To develop a gene signature predicting overall survival (OS) in CN-AML, we performed data mining procedure based on whole genome expression data of both blood cancer cell lines and AML patients from open access database. A gene expression signature including 42 probes was derived. These probes were significantly associated with both cytarabine half maximal inhibitory concentration values in blood cancer cell lines and OS in CN-AML patients. By using cox regression analysis and linear regression analysis, a chemo-sensitive score calculated algorithm based on mRNA expression levels of the 42 probes was established. The scores were associated with OS in both the training sample (p=5.13 × 10-4, HR=2.040, 95% CI: 1.364-3.051) and the validation sample (p=0.002, HR=2.528, 95% CI: 1.393-4.591) of the GSE12417 dataset from Gene Expression Omnibus. In The Cancer Genome Atlas (TCGA) CN-AML patients, higher scores were found to be associated with both worse OS (p=0.013, HR=2.442, 95% CI: 1.205-4.950) and DFS (p=0.015, HR=2.376, 95% CI: 1.181-4.779). Results of gene ontology (GO) analysis showed that all the significant GO Terms were correlated with cellular component of mitochondrion. In summary, a novel gene set that could predict prognosis of CN-AML was identified presently, which provided a new way to identify genes impacting AML chemo-sensitivity and prognosis.

Association of a cytarabine chemosensitivity related gene expression signature with survival in cytogenetically normal acute myeloid leukemia

PubMed Central

Yan, Han; Wen, Lu; Tan, Dan; Xie, Pan; Pang, Feng-mei; Zhou, Hong-hao; Zhang, Wei; Liu, Zhao-qian; Tang, Jie; Li, Xi; Chen, Xiao-ping

2017-01-01

The prognosis of cytogenetically normal acute myeloid leukemia (CN-AML) varies greatly among patients. Achievement of complete remission (CR) after chemotherapy is indispensable for a better prognosis. To develop a gene signature predicting overall survival (OS) in CN-AML, we performed data mining procedure based on whole genome expression data of both blood cancer cell lines and AML patients from open access database. A gene expression signature including 42 probes was derived. These probes were significantly associated with both cytarabine half maximal inhibitory concentration values in blood cancer cell lines and OS in CN-AML patients. By using cox regression analysis and linear regression analysis, a chemo-sensitive score calculated algorithm based on mRNA expression levels of the 42 probes was established. The scores were associated with OS in both the training sample (p=5.13 × 10−4, HR=2.040, 95% CI: 1.364-3.051) and the validation sample (p=0.002, HR=2.528, 95% CI: 1.393-4.591) of the GSE12417 dataset from Gene Expression Omnibus. In The Cancer Genome Atlas (TCGA) CN-AML patients, higher scores were found to be associated with both worse OS (p=0.013, HR=2.442, 95% CI: 1.205-4.950) and DFS (p=0.015, HR=2.376, 95% CI: 1.181-4.779). Results of gene ontology (GO) analysis showed that all the significant GO Terms were correlated with cellular component of mitochondrion. In summary, a novel gene set that could predict prognosis of CN-AML was identified presently, which provided a new way to identify genes impacting AML chemo-sensitivity and prognosis. PMID:27903973

Circulating neutrophil transcriptome may reveal intracranial aneurysm signature

PubMed Central

Tutino, Vincent M.; Poppenberg, Kerry E.; Jiang, Kaiyu; Jarvis, James N.; Sun, Yijun; Sonig, Ashish; Siddiqui, Adnan H.; Snyder, Kenneth V.; Levy, Elad I.; Kolega, John

2018-01-01

Background Unruptured intracranial aneurysms (IAs) are typically asymptomatic and undetected except for incidental discovery on imaging. Blood-based diagnostic biomarkers could lead to improvements in IA management. This exploratory study examined circulating neutrophils to determine whether they carry RNA expression signatures of IAs. Methods Blood samples were collected from patients receiving cerebral angiography. Eleven samples were collected from patients with IAs and 11 from patients without IAs as controls. Samples from the two groups were paired based on demographics and comorbidities. RNA was extracted from isolated neutrophils and subjected to next-generation RNA sequencing to obtain differential expressions for identification of an IA-associated signature. Bioinformatics analyses, including gene set enrichment analysis and Ingenuity Pathway Analysis, were used to investigate the biological function of all differentially expressed transcripts. Results Transcriptome profiling identified 258 differentially expressed transcripts in patients with and without IAs. Expression differences were consistent with peripheral neutrophil activation. An IA-associated RNA expression signature was identified in 82 transcripts (p<0.05, fold-change ≥2). This signature was able to separate patients with and without IAs on hierarchical clustering. Furthermore, in an independent, unpaired, replication cohort of patients with IAs (n = 5) and controls (n = 5), the 82 transcripts separated 9 of 10 patients into their respective groups. Conclusion Preliminary findings show that RNA expression from circulating neutrophils carries an IA-associated signature. These findings highlight a potential to use predictive biomarkers from peripheral blood samples to identify patients with IAs. PMID:29342213

Comprehensive evaluation of gene expression signatures in response to electroacupuncture stimulation at Zusanli (ST36) acupoint by transcriptomic analysis.

PubMed

Wu, Jing-Shan; Lo, Hsin-Yi; Li, Chia-Cheng; Chen, Feng-Yuan; Hsiang, Chien-Yun; Ho, Tin-Yun

2017-08-15

Electroacupuncture (EA) has been applied to treat and prevent diseases for years. However, molecular events happened in both the acupunctured site and the internal organs after EA stimulation have not been clarified. Here we applied transcriptomic analysis to explore the gene expression signatures after EA stimulation. Mice were applied EA stimulation at ST36 for 15 min and nine tissues were collected three hours later for microarray analysis. We found that EA affected the expression of genes not only in the acupunctured site but also in the internal organs. EA commonly affected biological networks involved in cytoskeleton and cell adhesion, and also regulated unique process networks in specific organs, such as γ-aminobutyric acid-ergic neurotransmission in brain and inflammation process in lung. In addition, EA affected the expression of genes related to various diseases, such as neurodegenerative diseases in brain and obstructive pulmonary diseases in lung. This report applied, for the first time, a global comprehensive genome-wide approach to analyze the gene expression profiling of acupunctured site and internal organs after EA stimulation. The connection between gene expression signatures, biological processes, and diseases might provide a basis for prediction and explanation on the therapeutic potentials of acupuncture in organs.

A Semi-Supervised Approach for Refining Transcriptional Signatures of Drug Response and Repositioning Predictions

PubMed Central

Iorio, Francesco; Shrestha, Roshan L.; Levin, Nicolas; Boilot, Viviane; Garnett, Mathew J.; Saez-Rodriguez, Julio; Draviam, Viji M.

2015-01-01

We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed compound). This signature can be derived from data in existing databases, such as the connectivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the principal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising agent) as seed compound. Our method predicts that glipizide and splitomicin perturb microtubule function in human cells: a result that could not be obtained through standard signature matching methods. In agreement, we find that glipizide and splitomicin reduce interphase microtubule growth rates and transiently increase the percentage of mitotic cells–consistent with our prediction. Finally, we validated the refined signatures of paclitaxel response by mining a large drug screening dataset, showing that human cancer cell lines whose basal transcriptional profile is anti-correlated to them are significantly more sensitive to paclitaxel and docetaxel. PMID:26452147

miRNA signature associated with outcome of gastric cancer patients following chemotherapy

PubMed Central

2011-01-01

Background Identification of patients who likely will or will not benefit from cytotoxic chemotherapy through the use of biomarkers could greatly improve clinical management by better defining appropriate treatment options for patients. microRNAs may be potentially useful biomarkers that help guide individualized therapy for cancer because microRNA expression is dysregulated in cancer. In order to identify miRNA signatures for gastric cancer and for predicting clinical resistance to cisplatin/fluorouracil (CF) chemotherapy, a comprehensive miRNA microarray analysis was performed using endoscopic biopsy samples. Methods Biopsy samples were collected prior to chemotherapy from 90 gastric cancer patients treated with CF and from 34 healthy volunteers. At the time of disease progression, post-treatment samples were additionally collected from 8 clinical responders. miRNA expression was determined using a custom-designed Agilent microarray. In order to identify a miRNA signature for chemotherapy resistance, we correlated miRNA expression levels with the time to progression (TTP) of disease after CF therapy. Results A miRNA signature distinguishing gastric cancer from normal stomach epithelium was identified. 30 miRNAs were significantly inversely correlated with TTP whereas 28 miRNAs were significantly positively correlated with TTP of 82 cancer patients (P<0.05). Prominent among the upregulated miRNAs associated with chemosensitivity were miRNAs known to regulate apoptosis, including let-7g, miR-342, miR-16, miR-181, miR-1, and miR-34. When this 58-miRNA predictor was applied to a separate set of pre- and post-treatment tumor samples from the 8 clinical responders, all of the 8 pre-treatment samples were correctly predicted as low-risk, whereas samples from the post-treatment tumors that developed chemoresistance were predicted to be in the high-risk category by the 58 miRNA signature, suggesting that selection for the expression of these miRNAs occurred as

A 16-Gene Signature Distinguishes Anaplastic Astrocytoma from Glioblastoma

PubMed Central

Rao, Soumya Alige Mahabala; Srinivasan, Sujaya; Patric, Irene Rosita Pia; Hegde, Alangar Sathyaranjandas; Chandramouli, Bangalore Ashwathnarayanara; Arimappamagan, Arivazhagan; Santosh, Vani; Kondaiah, Paturu; Rao, Manchanahalli R. Sathyanarayana; Somasundaram, Kumaravel

2014-01-01

Anaplastic astrocytoma (AA; Grade III) and glioblastoma (GBM; Grade IV) are diffusely infiltrating tumors and are called malignant astrocytomas. The treatment regimen and prognosis are distinctly different between anaplastic astrocytoma and glioblastoma patients. Although histopathology based current grading system is well accepted and largely reproducible, intratumoral histologic variations often lead to difficulties in classification of malignant astrocytoma samples. In order to obtain a more robust molecular classifier, we analysed RT-qPCR expression data of 175 differentially regulated genes across astrocytoma using Prediction Analysis of Microarrays (PAM) and found the most discriminatory 16-gene expression signature for the classification of anaplastic astrocytoma and glioblastoma. The 16-gene signature obtained in the training set was validated in the test set with diagnostic accuracy of 89%. Additionally, validation of the 16-gene signature in multiple independent cohorts revealed that the signature predicted anaplastic astrocytoma and glioblastoma samples with accuracy rates of 99%, 88%, and 92% in TCGA, GSE1993 and GSE4422 datasets, respectively. The protein-protein interaction network and pathway analysis suggested that the 16-genes of the signature identified epithelial-mesenchymal transition (EMT) pathway as the most differentially regulated pathway in glioblastoma compared to anaplastic astrocytoma. In addition to identifying 16 gene classification signature, we also demonstrated that genes involved in epithelial-mesenchymal transition may play an important role in distinguishing glioblastoma from anaplastic astrocytoma. PMID:24475040

Gene Expression Signatures Diagnose Influenza and Other Symptomatic Respiratory Viral Infection in Humans

PubMed Central

Zaas, Aimee K.; Chen, Minhua; Varkey, Jay; Veldman, Timothy; Hero, Alfred O.; Lucas, Joseph; Huang, Yongsheng; Turner, Ronald; Gilbert, Anthony; Lambkin-Williams, Robert; Øien, N. Christine; Nicholson, Bradly; Kingsmore, Stephen; Carin, Lawrence; Woods, Christopher W.; Ginsburg, Geoffrey S.

2010-01-01

Summary Acute respiratory infections (ARI) are a common reason for seeking medical attention and the threat of pandemic influenza will likely add to these numbers. Using human viral challenge studies with live rhinovirus, respiratory syncytial virus, and influenza A, we developed peripheral blood gene expression signatures that distinguish individuals with symptomatic ARI from uninfected individuals with > 95% accuracy. We validated this “acute respiratory viral” signature - encompassing genes with a known role in host defense against viral infections - across each viral challenge. We also validated the signature in an independently acquired dataset for influenza A and classified infected individuals from healthy controls with 100% accuracy. In the same dataset, we could also distinguish viral from bacterial ARIs (93% accuracy). These results demonstrate that ARIs induce changes in human peripheral blood gene expression that can be used to diagnose a viral etiology of respiratory infection and triage symptomatic individuals. PMID:19664979

Integrating Radiosensitivity and Immune Gene Signatures for Predicting Benefit of Radiotherapy in Breast Cancer.

PubMed

Cui, Yi; Li, Bailiang; Pollom, Erqi Liu; Horst, Kathleen; Li, Ruijiang

2018-06-19

Breast cancer is a heterogeneous disease and not all patients respond equally to adjuvant radiotherapy. Predictive biomarkers are needed to select patients who will benefit from the treatment and spare others the toxicity and burden of radiation. We first trained and tested an intrinsic radiosensitivity gene signature to predict local recurrence after radiotherapy in three cohorts of 948 patients. Next, we developed an antigen processing and presentation-based immune signature by maximizing the treatment interaction effect in 129 patients. To test their predictive value, we matched patients treated with or without radiotherapy in an independent validation cohort for clinicopathologic factors including age, ER status, HER2 status, stage, hormone-therapy, chemotherapy, and surgery. Disease specific survival (DSS) was the primary endpoint. Our validation cohort consisted of 1,439 patients. After matching and stratification by the radiosensitivity signature, patients who received radiotherapy had better DSS than patients who did not in the radiation-sensitive group (hazard ratio [HR]=0.68, P=0.059, n=322), while a reverse trend was observed in the radiation-resistant group (HR=1.53, P=0.059, n=202). Similarly, patients treated with radiotherapy had significantly better DSS in the immuneeffective group (HR=0.46, P=0.0076, n=180), with no difference in DSS in the immunedefective group (HR=1.27, P=0.16, n=348). Both signatures were predictive of radiotherapy benefit (P interaction =0.007 and 0.005). Integration of radiosensitivity and immune signatures further stratified patients into three groups with differential outcomes for those treated with or without radiotherapy (P interaction =0.003). The proposed signatures have the potential to select patients who are most likely to benefit from radiotherapy. Copyright ©2018, American Association for Cancer Research.

Blood Gene Expression Predicts Bronchiolitis Obliterans Syndrome

PubMed Central

Danger, Richard; Royer, Pierre-Joseph; Reboulleau, Damien; Durand, Eugénie; Loy, Jennifer; Tissot, Adrien; Lacoste, Philippe; Roux, Antoine; Reynaud-Gaubert, Martine; Gomez, Carine; Kessler, Romain; Mussot, Sacha; Dromer, Claire; Brugière, Olivier; Mornex, Jean-François; Guillemain, Romain; Dahan, Marcel; Knoop, Christiane; Botturi, Karine; Foureau, Aurore; Pison, Christophe; Koutsokera, Angela; Nicod, Laurent P.; Brouard, Sophie; Magnan, Antoine; Jougon, J.

2018-01-01

Bronchiolitis obliterans syndrome (BOS), the main manifestation of chronic lung allograft dysfunction, leads to poor long-term survival after lung transplantation. Identifying predictors of BOS is essential to prevent the progression of dysfunction before irreversible damage occurs. By using a large set of 107 samples from lung recipients, we performed microarray gene expression profiling of whole blood to identify early biomarkers of BOS, including samples from 49 patients with stable function for at least 3 years, 32 samples collected at least 6 months before BOS diagnosis (prediction group), and 26 samples at or after BOS diagnosis (diagnosis group). An independent set from 25 lung recipients was used for validation by quantitative PCR (13 stables, 11 in the prediction group, and 8 in the diagnosis group). We identified 50 transcripts differentially expressed between stable and BOS recipients. Three genes, namely POU class 2 associating factor 1 (POU2AF1), T-cell leukemia/lymphoma protein 1A (TCL1A), and B cell lymphocyte kinase, were validated as predictive biomarkers of BOS more than 6 months before diagnosis, with areas under the curve of 0.83, 0.77, and 0.78 respectively. These genes allow stratification based on BOS risk (log-rank test p < 0.01) and are not associated with time posttransplantation. This is the first published large-scale gene expression analysis of blood after lung transplantation. The three-gene blood signature could provide clinicians with new tools to improve follow-up and adapt treatment of patients likely to develop BOS. PMID:29375549

Importance of correlation between gene expression levels: application to the type I interferon signature in rheumatoid arthritis.

PubMed

Reynier, Frédéric; Petit, Fabien; Paye, Malick; Turrel-Davin, Fanny; Imbert, Pierre-Emmanuel; Hot, Arnaud; Mougin, Bruno; Miossec, Pierre

2011-01-01

The analysis of gene expression data shows that many genes display similarity in their expression profiles suggesting some co-regulation. Here, we investigated the co-expression patterns in gene expression data and proposed a correlation-based research method to stratify individuals. Using blood from rheumatoid arthritis (RA) patients, we investigated the gene expression profiles from whole blood using Affymetrix microarray technology. Co-expressed genes were analyzed by a biclustering method, followed by gene ontology analysis of the relevant biclusters. Taking the type I interferon (IFN) pathway as an example, a classification algorithm was developed from the 102 RA patients and extended to 10 systemic lupus erythematosus (SLE) patients and 100 healthy volunteers to further characterize individuals. We developed a correlation-based algorithm referred to as Classification Algorithm Based on a Biological Signature (CABS), an alternative to other approaches focused specifically on the expression levels. This algorithm applied to the expression of 35 IFN-related genes showed that the IFN signature presented a heterogeneous expression between RA, SLE and healthy controls which could reflect the level of global IFN signature activation. Moreover, the monitoring of the IFN-related genes during the anti-TNF treatment identified changes in type I IFN gene activity induced in RA patients. In conclusion, we have proposed an original method to analyze genes sharing an expression pattern and a biological function showing that the activation levels of a biological signature could be characterized by its overall state of correlation.

Binding Affinity prediction with Property Encoded Shape Distribution signatures

PubMed Central

Das, Sourav; Krein, Michael P.

2010-01-01

We report the use of the molecular signatures known as “Property-Encoded Shape Distributions” (PESD) together with standard Support Vector Machine (SVM) techniques to produce validated models that can predict the binding affinity of a large number of protein ligand complexes. This “PESD-SVM” method uses PESD signatures that encode molecular shapes and property distributions on protein and ligand surfaces as features to build SVM models that require no subjective feature selection. A simple protocol was employed for tuning the SVM models during their development, and the results were compared to SFCscore – a regression-based method that was previously shown to perform better than 14 other scoring functions. Although the PESD-SVM method is based on only two surface property maps, the overall results were comparable. For most complexes with a dominant enthalpic contribution to binding (ΔH/-TΔS > 3), a good correlation between true and predicted affinities was observed. Entropy and solvent were not considered in the present approach and further improvement in accuracy would require accounting for these components rigorously. PMID:20095526

Genomic signatures predict migration and spawning failure in wild Canadian salmon.

PubMed

Miller, Kristina M; Li, Shaorong; Kaukinen, Karia H; Ginther, Norma; Hammill, Edd; Curtis, Janelle M R; Patterson, David A; Sierocinski, Thomas; Donnison, Louise; Pavlidis, Paul; Hinch, Scott G; Hruska, Kimberly A; Cooke, Steven J; English, Karl K; Farrell, Anthony P

2011-01-14

Long-term population viability of Fraser River sockeye salmon (Oncorhynchus nerka) is threatened by unusually high levels of mortality as they swim to their spawning areas before they spawn. Functional genomic studies on biopsied gill tissue from tagged wild adults that were tracked through ocean and river environments revealed physiological profiles predictive of successful migration and spawning. We identified a common genomic profile that was correlated with survival in each study. In ocean-tagged fish, a mortality-related genomic signature was associated with a 13.5-fold greater chance of dying en route. In river-tagged fish, the same genomic signature was associated with a 50% increase in mortality before reaching the spawning grounds in one of three stocks tested. At the spawning grounds, the same signature was associated with 3.7-fold greater odds of dying without spawning. Functional analysis raises the possibility that the mortality-related signature reflects a viral infection.

ColoLipidGene: signature of lipid metabolism-related genes to predict prognosis in stage-II colon cancer patients

PubMed Central

Vargas, Teodoro; Moreno-Rubio, Juan; Herranz, Jesús; Cejas, Paloma; Molina, Susana; González-Vallinas, Margarita; Mendiola, Marta; Burgos, Emilio; Aguayo, Cristina; Custodio, Ana B.; Machado, Isidro; Ramos, David; Gironella, Meritxell; Espinosa-Salinas, Isabel; Ramos, Ricardo; Martín-Hernández, Roberto; Risueño, Alberto; De Las Rivas, Javier; Reglero, Guillermo; Yaya, Ricardo; Fernández-Martos, Carlos; Aparicio, Jorge; Maurel, Joan; Feliu, Jaime; de Molina, Ana Ramírez

2015-01-01

Lipid metabolism plays an essential role in carcinogenesis due to the requirements of tumoral cells to sustain increased structural, energetic and biosynthetic precursor demands for cell proliferation. We investigated the association between expression of lipid metabolism-related genes and clinical outcome in intermediate-stage colon cancer patients with the aim of identifying a metabolic profile associated with greater malignancy and increased risk of relapse. Expression profile of 70 lipid metabolism-related genes was determined in 77 patients with stage II colon cancer. Cox regression analyses using c-index methodology was applied to identify a metabolic-related signature associated to prognosis. The metabolic signature was further confirmed in two independent validation sets of 120 patients and additionally, in a group of 264 patients from a public database. The combined analysis of these 4 genes, ABCA1, ACSL1, AGPAT1 and SCD, constitutes a metabolic-signature (ColoLipidGene) able to accurately stratify stage II colon cancer patients with 5-fold higher risk of relapse with strong statistical power in the four independent groups of patients. The identification of a group of 4 genes that predict survival in intermediate-stage colon cancer patients allows delineation of a high-risk group that may benefit from adjuvant therapy, and avoids the toxic and unnecessary chemotherapy in patients classified as low-risk group. PMID:25749516

Aging-dependent alterations in gene expression and a mitochondrial signature of responsiveness to human influenza vaccination.

PubMed

Thakar, Juilee; Mohanty, Subhasis; West, A Phillip; Joshi, Samit R; Ueda, Ikuyo; Wilson, Jean; Meng, Hailong; Blevins, Tamara P; Tsang, Sui; Trentalange, Mark; Siconolfi, Barbara; Park, Koonam; Gill, Thomas M; Belshe, Robert B; Kaech, Susan M; Shadel, Gerald S; Kleinstein, Steven H; Shaw, Albert C

2015-01-01

To elucidate gene expression pathways underlying age-associated impairment in influenza vaccine response, we screened young (age 21-30) and older (age≥65) adults receiving influenza vaccine in two consecutive seasons and identified those with strong or absent response to vaccine, including a subset of older adults meeting criteria for frailty. PBMCs obtained prior to vaccination (Day 0) and at day 2 or 4, day 7 and day 28 post-vaccine were subjected to gene expression microarray analysis. We defined a response signature and also detected induction of a type I interferon response at day 2 and a plasma cell signature at day 7 post-vaccine in young responders. The response signature was dysregulated in older adults, with the plasma cell signature induced at day 2, and was never induced in frail subjects (who were all non-responders). We also identified a mitochondrial signature in young vaccine responders containing genes mediating mitochondrial biogenesis and oxidative phosphorylation that was consistent in two different vaccine seasons and verified by analyses of mitochondrial content and protein expression. These results represent the first genome-wide transcriptional profiling analysis of age-associated dynamics following influenza vaccination, and implicate changes in mitochondrial biogenesis and function as a critical factor in human vaccine responsiveness.

CFD predictions of near-field pressure signatures of a low-boom aircraft

NASA Technical Reports Server (NTRS)

Fouladi, Kamran; Baize, Daniel G.

1992-01-01

A three dimensional Euler marching code has been utilized to predict near-field pressure signatures of an aircraft with low boom characteristics. Computations were extended to approximately six body lengths aft of the aircraft in order to obtain pressure data at three body lengths below the aircraft for a cruise Mach number of 1.6. The near-field pressure data were extrapolated to the ground using a Whitham based method. The distance below the aircraft where the pressure data are attained is defined in this paper as the 'separation distance.' The influences of separation distances and the still highly three-dimensional flow field on the predicted ground pressure signatures and boom loudness are presented in this paper.

A PRIM approach to predictive-signature development for patient stratification

PubMed Central

Chen, Gong; Zhong, Hua; Belousov, Anton; Devanarayan, Viswanath

2015-01-01

Patients often respond differently to a treatment because of individual heterogeneity. Failures of clinical trials can be substantially reduced if, prior to an investigational treatment, patients are stratified into responders and nonresponders based on biological or demographic characteristics. These characteristics are captured by a predictive signature. In this paper, we propose a procedure to search for predictive signatures based on the approach of patient rule induction method. Specifically, we discuss selection of a proper objective function for the search, present its algorithm, and describe a resampling scheme that can enhance search performance. Through simulations, we characterize conditions under which the procedure works well. To demonstrate practical uses of the procedure, we apply it to two real-world data sets. We also compare the results with those obtained from a recent regression-based approach, Adaptive Index Models, and discuss their respective advantages. In this study, we focus on oncology applications with survival responses. PMID:25345685

A PRIM approach to predictive-signature development for patient stratification.

PubMed

Chen, Gong; Zhong, Hua; Belousov, Anton; Devanarayan, Viswanath

2015-01-30

Patients often respond differently to a treatment because of individual heterogeneity. Failures of clinical trials can be substantially reduced if, prior to an investigational treatment, patients are stratified into responders and nonresponders based on biological or demographic characteristics. These characteristics are captured by a predictive signature. In this paper, we propose a procedure to search for predictive signatures based on the approach of patient rule induction method. Specifically, we discuss selection of a proper objective function for the search, present its algorithm, and describe a resampling scheme that can enhance search performance. Through simulations, we characterize conditions under which the procedure works well. To demonstrate practical uses of the procedure, we apply it to two real-world data sets. We also compare the results with those obtained from a recent regression-based approach, Adaptive Index Models, and discuss their respective advantages. In this study, we focus on oncology applications with survival responses. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

A gene expression signature of Retinoblastoma loss-of-function predicts resistance to neoadjuvant chemotherapy in ER-positive/HER2-positive breast cancer patients.

PubMed

Risi, Emanuela; Grilli, Andrea; Migliaccio, Ilenia; Biagioni, Chiara; McCartney, Amelia; Guarducci, Cristina; Bonechi, Martina; Benelli, Matteo; Vitale, Stefania; Biganzoli, Laura; Bicciato, Silvio; Di Leo, Angelo; Malorni, Luca

2018-07-01

HER2-positive (HER2+) breast cancers show heterogeneous response to chemotherapy, with the ER-positive (ER+) subgroup deriving less benefit. Loss of retinoblastoma tumor suppressor gene (RB1) function has been suggested as a cardinal feature of breast cancers that are more sensitive to chemotherapy and conversely resistant to CDK4/6 inhibitors. We performed a retrospective analysis exploring RBsig, a gene signature of RB loss, as a potential predictive marker of response to neoadjuvant chemotherapy in ER+/HER2+ breast cancer patients. We selected clinical trials of neoadjuvant chemotherapy ± anti-HER2 therapy in HER2+ breast cancer patients with available information on gene expression data, hormone receptor status, and pathological complete response (pCR) rates. RBsig expression was computed in silico and correlated with pCR. Ten studies fulfilled the inclusion criteria and were included in the analysis (514 patients). Overall, of 211 ER+/HER2+ breast cancer patients, 49 achieved pCR (23%). The pCR rate following chemotherapy ± anti-HER2 drugs in patients with RBsig low expression was significantly lower compared to patients with RBsig high expression (16% vs. 30%, respectively; Fisher's exact test p = 0.015). The area under the ROC curve (AUC) was 0.62 (p = 0.005). In the 303 ER-negative (ER-)/HER2+ patients treated with chemotherapy ± anti-HER2 drugs, the pCR rate was 43%. No correlation was found between RBsig expression and pCR rate in this group. Low expression of RBsig identifies a subset of ER+/HER2+ patients with low pCR rates following neoadjuvant chemotherapy ± anti-HER2 therapy. These patients may potentially be spared chemotherapy in favor of anti-HER2, endocrine therapy, and CDK 4/6 inhibitor combinations.

Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks.

PubMed

Tan, Jie; Doing, Georgia; Lewis, Kimberley A; Price, Courtney E; Chen, Kathleen M; Cady, Kyle C; Perchuk, Barret; Laub, Michael T; Hogan, Deborah A; Greene, Casey S

2017-07-26

Cross-experiment comparisons in public data compendia are challenged by unmatched conditions and technical noise. The ADAGE method, which performs unsupervised integration with denoising autoencoder neural networks, can identify biological patterns, but because ADAGE models, like many neural networks, are over-parameterized, different ADAGE models perform equally well. To enhance model robustness and better build signatures consistent with biological pathways, we developed an ensemble ADAGE (eADAGE) that integrated stable signatures across models. We applied eADAGE to a compendium of Pseudomonas aeruginosa gene expression profiling experiments performed in 78 media. eADAGE revealed a phosphate starvation response controlled by PhoB in media with moderate phosphate and predicted that a second stimulus provided by the sensor kinase, KinB, is required for this PhoB activation. We validated this relationship using both targeted and unbiased genetic approaches. eADAGE, which captures stable biological patterns, enables cross-experiment comparisons that can highlight measured but undiscovered relationships. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Integrated MicroRNA and mRNA Signatures Associated with Survival in Triple Negative Breast Cancer

PubMed Central

Lovat, Francesca; Carasi, Stefania; Pulvirenti, Alfredo; Ferro, Alfredo; Alder, Hansjuerg; He, Gang; Vecchione, Andrea; Croce, Carlo M.; Shapiro, Charles L.; Huebner, Kay

2013-01-01

Triple negative breast cancer (TNBC) is a heterogeneous disease at the molecular, pathologic and clinical levels. To stratify TNBCs, we determined microRNA (miRNA) expression profiles, as well as expression profiles of a cancer-focused mRNA panel, in tumor, adjacent non-tumor (normal) and lymph node metastatic lesion (mets) tissues, from 173 women with TNBCs; we linked specific miRNA signatures to patient survival and used miRNA/mRNA anti-correlations to identify clinically and genetically different TNBC subclasses. We also assessed miRNA signatures as potential regulators of TNBC subclass-specific gene expression networks defined by expression of canonical signal pathways. Tissue specific miRNAs and mRNAs were identified for normal vs tumor vs mets comparisons. miRNA signatures correlated with prognosis were identified and predicted anti-correlated targets within the mRNA profile were defined. Two miRNA signatures (miR-16, 155, 125b, 374a and miR-16, 125b, 374a, 374b, 421, 655, 497) predictive of overall survival (P = 0.05) and distant-disease free survival (P = 0.009), respectively, were identified for patients 50 yrs of age or younger. By multivariate analysis the risk signatures were independent predictors for overall survival and distant-disease free survival. mRNA expression profiling, using the cancer-focused mRNA panel, resulted in clustering of TNBCs into 4 molecular subclasses with different expression signatures anti-correlated with the prognostic miRNAs. Our findings suggest that miRNAs play a key role in triple negative breast cancer through their ability to regulate fundamental pathways such as: cellular growth and proliferation, cellular movement and migration, Extra Cellular Matrix degradation. The results define miRNA expression signatures that characterize and contribute to the phenotypic diversity of TNBC and its metastasis. PMID:23405235

Integrated microRNA and mRNA signatures associated with survival in triple negative breast cancer.

PubMed

Cascione, Luciano; Gasparini, Pierluigi; Lovat, Francesca; Carasi, Stefania; Pulvirenti, Alfredo; Ferro, Alfredo; Alder, Hansjuerg; He, Gang; Vecchione, Andrea; Croce, Carlo M; Shapiro, Charles L; Huebner, Kay

2013-01-01

Triple negative breast cancer (TNBC) is a heterogeneous disease at the molecular, pathologic and clinical levels. To stratify TNBCs, we determined microRNA (miRNA) expression profiles, as well as expression profiles of a cancer-focused mRNA panel, in tumor, adjacent non-tumor (normal) and lymph node metastatic lesion (mets) tissues, from 173 women with TNBCs; we linked specific miRNA signatures to patient survival and used miRNA/mRNA anti-correlations to identify clinically and genetically different TNBC subclasses. We also assessed miRNA signatures as potential regulators of TNBC subclass-specific gene expression networks defined by expression of canonical signal pathways.Tissue specific miRNAs and mRNAs were identified for normal vs tumor vs mets comparisons. miRNA signatures correlated with prognosis were identified and predicted anti-correlated targets within the mRNA profile were defined. Two miRNA signatures (miR-16, 155, 125b, 374a and miR-16, 125b, 374a, 374b, 421, 655, 497) predictive of overall survival (P = 0.05) and distant-disease free survival (P = 0.009), respectively, were identified for patients 50 yrs of age or younger. By multivariate analysis the risk signatures were independent predictors for overall survival and distant-disease free survival. mRNA expression profiling, using the cancer-focused mRNA panel, resulted in clustering of TNBCs into 4 molecular subclasses with different expression signatures anti-correlated with the prognostic miRNAs. Our findings suggest that miRNAs play a key role in triple negative breast cancer through their ability to regulate fundamental pathways such as: cellular growth and proliferation, cellular movement and migration, Extra Cellular Matrix degradation. The results define miRNA expression signatures that characterize and contribute to the phenotypic diversity of TNBC and its metastasis.

Peripheral Blood Signatures of Lead Exposure

PubMed Central

LaBreche, Heather G.; Meadows, Sarah K.; Nevins, Joseph R.; Chute, John P.

2011-01-01

Background Current evidence indicates that even low-level lead (Pb) exposure can have detrimental effects, especially in children. We tested the hypothesis that Pb exposure alters gene expression patterns in peripheral blood cells and that these changes reflect dose-specific alterations in the activity of particular pathways. Methodology/Principal Finding Using Affymetrix Mouse Genome 430 2.0 arrays, we examined gene expression changes in the peripheral blood of female Balb/c mice following exposure to per os lead acetate trihydrate or plain drinking water for two weeks and after a two-week recovery period. Data sets were RMA-normalized and dose-specific signatures were generated using established methods of supervised classification and binary regression. Pathway activity was analyzed using the ScoreSignatures module from GenePattern. Conclusions/Significance The low-level Pb signature was 93% sensitive and 100% specific in classifying samples a leave-one-out crossvalidation. The high-level Pb signature demonstrated 100% sensitivity and specificity in the leave-one-out crossvalidation. These two signatures exhibited dose-specificity in their ability to predict Pb exposure and had little overlap in terms of constituent genes. The signatures also seemed to reflect current levels of Pb exposure rather than past exposure. Finally, the two doses showed differential activation of cellular pathways. Low-level Pb exposure increased activity of the interferon-gamma pathway, whereas high-level Pb exposure increased activity of the E2F1 pathway. PMID:21829687

A Prognostic Gene Expression Profile That Predicts Circulating Tumor Cell Presence in Breast Cancer Patients

PubMed Central

Molloy, Timothy J.; Roepman, Paul; Naume, Bjørn; van't Veer, Laura J.

2012-01-01

The detection of circulating tumor cells (CTCs) in the peripheral blood and microarray gene expression profiling of the primary tumor are two promising new technologies able to provide valuable prognostic data for patients with breast cancer. Meta-analyses of several established prognostic breast cancer gene expression profiles in large patient cohorts have demonstrated that despite sharing few genes, their delineation of patients into “good prognosis” or “poor prognosis” are frequently very highly correlated, and combining prognostic profiles does not increase prognostic power. In the current study, we aimed to develop a novel profile which provided independent prognostic data by building a signature predictive of CTC status rather than outcome. Microarray gene expression data from an initial training cohort of 72 breast cancer patients for which CTC status had been determined in a previous study using a multimarker QPCR-based assay was used to develop a CTC-predictive profile. The generated profile was validated in two independent datasets of 49 and 123 patients and confirmed to be both predictive of CTC status, and independently prognostic. Importantly, the “CTC profile” also provided prognostic information independent of the well-established and powerful ‘70-gene’ prognostic breast cancer signature. This profile therefore has the potential to not only add prognostic information to currently-available microarray tests but in some circumstances even replace blood-based prognostic CTC tests at time of diagnosis for those patients already undergoing testing by multigene assays. PMID:22384245

Genome-Wide Prediction and Validation of Intergenic Enhancers in Arabidopsis Using Open Chromatin Signatures[OPEN

PubMed Central

Zhu, Bo; Zhang, Wenli; Jiang, Jiming

2015-01-01

Enhancers are important regulators of gene expression in eukaryotes. Enhancers function independently of their distance and orientation to the promoters of target genes. Thus, enhancers have been difficult to identify. Only a few enhancers, especially distant intergenic enhancers, have been identified in plants. We developed an enhancer prediction system based exclusively on the DNase I hypersensitive sites (DHSs) in the Arabidopsis thaliana genome. A set of 10,044 DHSs located in intergenic regions, which are away from any gene promoters, were predicted to be putative enhancers. We examined the functions of 14 predicted enhancers using the β-glucuronidase gene reporter. Ten of the 14 (71%) candidates were validated by the reporter assay. We also designed 10 constructs using intergenic sequences that are not associated with DHSs, and none of these constructs showed enhancer activities in reporter assays. In addition, the tissue specificity of the putative enhancers can be precisely predicted based on DNase I hypersensitivity data sets developed from different plant tissues. These results suggest that the open chromatin signature-based enhancer prediction system developed in Arabidopsis may serve as a universal system for enhancer identification in plants. PMID:26373455

Four-gene Pan-African Blood Signature Predicts Progression to Tuberculosis.

PubMed

Suliman, Sara; Thompson, Ethan; Sutherland, Jayne; Weiner Rd, January; Ota, Martin O C; Shankar, Smitha; Penn-Nicholson, Adam; Thiel, Bonnie; Erasmus, Mzwandile; Maertzdorf, Jeroen; Duffy, Fergal J; Hill, Philip C; Hughes, E Jane; Stanley, Kim; Downing, Katrina; Fisher, Michelle L; Valvo, Joe; Parida, Shreemanta K; van der Spuy, Gian; Tromp, Gerard; Adetifa, Ifedayo M O; Donkor, Simon; Howe, Rawleigh; Mayanja-Kizza, Harriet; Boom, W Henry; Dockrell, Hazel; Ottenhoff, Tom H M; Hatherill, Mark; Aderem, Alan; Hanekom, Willem A; Scriba, Thomas J; Kaufmann, Stefan He; Zak, Daniel E; Walzl, Gerhard

2018-04-06

Contacts of tuberculosis (TB) patients constitute an important target population for preventative measures as they are at high risk of infection with Mycobacterium tuberculosis and progression to disease. We investigated biosignatures with predictive ability for incident tuberculosis. In a case-control study nested within the Grand Challenges 6-74 longitudinal HIV-negative African cohort of exposed household contacts, we employed RNA sequencing, polymerase chain reaction (PCR) and the Pair Ratio algorithm in a training/test set approach. Overall, 79 progressors, who developed tuberculosis between 3 and 24 months following exposure, and 328 matched non-progressors, who remained healthy during 24 months of follow-up, were investigated. A four-transcript signature (RISK4), derived from samples in a South African and Gambian training set, predicted progression up to two years before onset of disease in blinded test set samples from South Africa, The Gambia and Ethiopia with little population-associated variability and also validated on an external cohort of South African adolescents with latent Mycobacterium tuberculosis infection. By contrast, published diagnostic or prognostic tuberculosis signatures predicted on samples from some but not all 3 countries, indicating site-specific variability. Post-hoc meta-analysis identified a single gene pair, C1QC/TRAV27, that would consistently predict TB progression in household contacts from multiple African sites but not in infected adolescents without known recent exposure events. Collectively, we developed a simple whole blood-based PCR test to predict tuberculosis in household contacts from diverse African populations, with potential for implementation in national TB contact investigation programs.

A radiosensitivity gene signature and PD-L1 status predict clinical outcome of patients with invasive breast carcinoma in The Cancer Genome Atlas (TCGA) dataset.

PubMed

Jang, Bum-Sup; Kim, In Ah

2017-09-01

We investigated the link between the radiosensitivity gene signature and programmed cell death ligand 1 (PD-L1) status and clinical outcome in order to identify a group of patients that would possibly receive clinical benefit of radiotherapy (RT) combined with anti-PD1/PD-L1 therapy. We validated the identified gene signature related to radiosensitivity and analyzed the PD-L1 status of invasive breast cancer in The Cancer Genome Atlas (TCGA) dataset. To validate the gene signature, 1045 patients were selected and divided into two clusters using a consensus clustering algorithm based on their radiosensitive (RS) or radioresistant (RR) designation according to their prognosis. Patients were also stratified as PD-L1-high or PD-L1-low based on the median value of CD274 mRNA expression level as surrogates of PD-L1. Patents assigned to the RS group had decreased risk of recurrence-free survival (RFS) rate than patients in the RR group by univariate analysis (HR 0.45, 95% CI 0.25-0.81, p=0.008) only when treated with RT. The RS group was independently associated with the PD-L1-high group, and CD274 mRNA expression was significantly higher in the RS group (p<0.001) than the RR group. In the PD-L1-high group, the RS group was associated with better RFS compared to the RR group (HR 0.37, 95% CI 0.16-0.87, p=0.022) in multivariate analysis. The level of PD-L1 expression may represent the immunogenicity of tumors, and thus, we speculated that the PD-L1-high group had more immunogenic tumors, which could be more sensitive to radiation-induced immunologic cell death. We first evaluated the predictive value of the radiosensitivity gene signature and described a relationship with this radiosensitivity gene signature and PD-L1. The radiosensitivity gene signature and PD-L1 status were important factors for prediction of the clinical outcome of RT in patients with invasive breast cancer and may be used for selecting patients who will benefit from RT combined with anti-PD1/PDL1

KRAS driven expression signature has prognostic power superior to mutation status in non-small cell lung cancer.

PubMed

Nagy, Ádám; Pongor, Lőrinc Sándor; Szabó, András; Santarpia, Mariacarmela; Győrffy, Balázs

2017-02-15

KRAS is the most frequently mutated oncogene in non-small cell lung cancer (NSCLC). However, the prognostic role of KRAS mutation status in NSCLC still remains controversial. We hypothesize that the expression changes of genes affected by KRAS mutation status will have the most prominent effect and could be used as a prognostic signature in lung cancer. We divided NSCLC patients with mutation and RNA-seq data into KRAS mutated and wild type groups. Mann-Whitney test was used to identify genes showing altered expression between these cohorts. Mean expression of the top five genes was designated as a "transcriptomic fingerprint" of the mutation. We evaluated the effect of this signature on clinical outcome in 2,437 NSCLC patients using univariate and multivariate Cox regression analysis. Mutation of KRAS was most common in adenocarcinoma. Mutation status and KRAS expression were not correlated to prognosis. The transcriptomic fingerprint of KRAS include FOXRED2, KRAS, TOP1, PEX3 and ABL2. The KRAS signature had a high prognostic power. Similar results were achieved when using the second and third set of strongest genes. Moreover, all cutoff values delivered significant prognostic power (p < 0.01). The KRAS signature also remained significant (p < 0.01) in a multivariate analysis including age, gender, smoking history and tumor stage. We generated a "surrogate signature" of KRAS mutation status in NSCLC patients by computationally linking genotype and gene expression. We show that secondary effects of a mutation can have a higher prognostic relevance than the primary genetic alteration itself. © 2016 The Authors International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.

Four-miRNA signature as a prognostic tool for lung adenocarcinoma.

PubMed

Lin, Yan; Lv, Yufeng; Liang, Rong; Yuan, Chunling; Zhang, Jinyan; He, Dan; Zheng, Xiaowen; Zhang, Jianfeng

2018-01-01

The aim of this study was to generate a novel miRNA expression signature to accurately predict prognosis for patients with lung adenocarcinoma (LUAD). Using expression profiles downloaded from The Cancer Genome Atlas database, we identified multiple miRNAs with differential expression between LUAD and paired healthy tissues. We then evaluated the prognostic values of the differentially expressed miRNAs using univariate/multivariate Cox regression analysis. This analysis was ultimately used to construct a four-miRNA signature that effectively predicted patient survival. Finally, we analyzed potential functional roles of the target genes for these four miRNAs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Based on our cutoff criteria ( P <0.05 and |log2FC| >1.0), we identified a total of 187 differentially expressed miRNAs, including 148 that were upregulated in LUAD tissues and 39 that were downregulated. Four miRNAs (miR-148a-5p, miR-31-5p, miR-548v, and miR-550a-5p) were independently associated with survival based on Kaplan-Meier analysis. We generated a signature index based on the expression of these four miRNAs and stratified patients into low- and high-risk groups. Patients in the high-risk group had significantly shorter survival times than those in the low-risk group ( P =0.002). A functional enrichment analysis suggested that the target genes of these four miRNAs were involved in protein phosphorylation and the Hippo and sphingolipid signaling pathways. Taken together, our results suggest that our four-miRNA signature can be used as a prognostic tool for patients with LUAD.

Microbial forensics: predicting phenotypic characteristics and environmental conditions from large-scale gene expression profiles.

PubMed

Kim, Minseung; Zorraquino, Violeta; Tagkopoulos, Ilias

2015-03-01

A tantalizing question in cellular physiology is whether the cellular state and environmental conditions can be inferred by the expression signature of an organism. To investigate this relationship, we created an extensive normalized gene expression compendium for the bacterium Escherichia coli that was further enriched with meta-information through an iterative learning procedure. We then constructed an ensemble method to predict environmental and cellular state, including strain, growth phase, medium, oxygen level, antibiotic and carbon source presence. Results show that gene expression is an excellent predictor of environmental structure, with multi-class ensemble models achieving balanced accuracy between 70.0% (±3.5%) to 98.3% (±2.3%) for the various characteristics. Interestingly, this performance can be significantly boosted when environmental and strain characteristics are simultaneously considered, as a composite classifier that captures the inter-dependencies of three characteristics (medium, phase and strain) achieved 10.6% (±1.0%) higher performance than any individual models. Contrary to expectations, only 59% of the top informative genes were also identified as differentially expressed under the respective conditions. Functional analysis of the respective genetic signatures implicates a wide spectrum of Gene Ontology terms and KEGG pathways with condition-specific information content, including iron transport, transferases, and enterobactin synthesis. Further experimental phenotypic-to-genotypic mapping that we conducted for knock-out mutants argues for the information content of top-ranked genes. This work demonstrates the degree at which genome-scale transcriptional information can be predictive of latent, heterogeneous and seemingly disparate phenotypic and environmental characteristics, with far-reaching applications.

Common disease signatures from gene expression analysis in Huntington's disease human blood and brain.

PubMed

Mina, Eleni; van Roon-Mom, Willeke; Hettne, Kristina; van Zwet, Erik; Goeman, Jelle; Neri, Christian; A C 't Hoen, Peter; Mons, Barend; Roos, Marco

2016-08-01

Huntington's disease (HD) is a devastating brain disorder with no effective treatment or cure available. The scarcity of brain tissue makes it hard to study changes in the brain and impossible to perform longitudinal studies. However, peripheral pathology in HD suggests that it is possible to study the disease using peripheral tissue as a monitoring tool for disease progression and/or efficacy of novel therapies. In this study, we investigated if blood can be used to monitor disease severity and progression in brain. Since previous attempts using only gene expression proved unsuccessful, we compared blood and brain Huntington's disease signatures in a functional context. Microarray HD gene expression profiles from three brain regions were compared to the transcriptome of HD blood generated by next generation sequencing. The comparison was performed with a combination of weighted gene co-expression network analysis and literature based functional analysis (Concept Profile Analysis). Uniquely, our comparison of blood and brain datasets was not based on (the very limited) gene overlap but on the similarity between the gene annotations in four different semantic categories: "biological process", "cellular component", "molecular function" and "disease or syndrome". We identified signatures in HD blood reflecting a broad pathophysiological spectrum, including alterations in the immune response, sphingolipid biosynthetic processes, lipid transport, cell signaling, protein modification, spliceosome, RNA splicing, vesicle transport, cell signaling and synaptic transmission. Part of this spectrum was reminiscent of the brain pathology. The HD signatures in caudate nucleus and BA4 exhibited the highest similarity with blood, irrespective of the category of semantic annotations used. BA9 exhibited an intermediate similarity, while cerebellum had the least similarity. We present two signatures that were shared between blood and brain: immune response and spinocerebellar ataxias

Identification of gene expression signature for cigarette smoke exposure response--from man to mouse.

PubMed

Martin, F; Talikka, M; Hoeng, J; Peitsch, M C

2015-12-01

Gene expression profiling data can be used in toxicology to assess both the level and impact of toxicant exposure, aligned with a vision of 21st century toxicology. Here, we present a whole blood-derived gene signature that can distinguish current smokers from either nonsmokers or former smokers with high specificity and sensitivity. Such a signature that can be measured in a surrogate tissue (whole blood) may help in monitoring smoking exposure as well as discontinuation of exposure when the primarily impacted tissue (e.g., lung) is not readily accessible. The signature consisted of LRRN3, SASH1, PALLD, RGL1, TNFRSF17, CDKN1C, IGJ, RRM2, ID3, SERPING1, and FUCA1. Several members of this signature have been previously described in the context of smoking. The signature translated well across species and could distinguish mice that were exposed to cigarette smoke from ones exposed to air only or had been withdrawn from cigarette smoke exposure. Finally, the small signature of only 11 genes could be converted into a polymerase chain reaction-based assay that could serve as a marker to monitor compliance with a smoking abstinence protocol. © The Author(s) 2015.

MicroRNA signature of the human developing pancreas.

PubMed

Rosero, Samuel; Bravo-Egana, Valia; Jiang, Zhijie; Khuri, Sawsan; Tsinoremas, Nicholas; Klein, Dagmar; Sabates, Eduardo; Correa-Medina, Mayrin; Ricordi, Camillo; Domínguez-Bendala, Juan; Diez, Juan; Pastori, Ricardo L

2010-09-22

MicroRNAs are non-coding RNAs that regulate gene expression including differentiation and development by either inhibiting translation or inducing target degradation. The aim of this study is to determine the microRNA expression signature during human pancreatic development and to identify potential microRNA gene targets calculating correlations between the signature microRNAs and their corresponding mRNA targets, predicted by bioinformatics, in genome-wide RNA microarray study. The microRNA signature of human fetal pancreatic samples 10-22 weeks of gestational age (wga), was obtained by PCR-based high throughput screening with Taqman Low Density Arrays. This method led to identification of 212 microRNAs. The microRNAs were classified in 3 groups: Group number I contains 4 microRNAs with the increasing profile; II, 35 microRNAs with decreasing profile and III with 173 microRNAs, which remain unchanged. We calculated Pearson correlations between the expression profile of microRNAs and target mRNAs, predicted by TargetScan 5.1 and miRBase algorithms, using genome-wide mRNA expression data. Group I correlated with the decreasing expression of 142 target mRNAs and Group II with the increasing expression of 876 target mRNAs. Most microRNAs correlate with multiple targets, just as mRNAs are targeted by multiple microRNAs. Among the identified targets are the genes and transcription factors known to play an essential role in pancreatic development. We have determined specific groups of microRNAs in human fetal pancreas that change the degree of their expression throughout the development. A negative correlative analysis suggests an intertwined network of microRNAs and mRNAs collaborating with each other. This study provides information leading to potential two-way level of combinatorial control regulating gene expression through microRNAs targeting multiple mRNAs and, conversely, target mRNAs regulated in parallel by other microRNAs as well. This study may further the

MicroRNA signature of the human developing pancreas

PubMed Central

2010-01-01

Background MicroRNAs are non-coding RNAs that regulate gene expression including differentiation and development by either inhibiting translation or inducing target degradation. The aim of this study is to determine the microRNA expression signature during human pancreatic development and to identify potential microRNA gene targets calculating correlations between the signature microRNAs and their corresponding mRNA targets, predicted by bioinformatics, in genome-wide RNA microarray study. Results The microRNA signature of human fetal pancreatic samples 10-22 weeks of gestational age (wga), was obtained by PCR-based high throughput screening with Taqman Low Density Arrays. This method led to identification of 212 microRNAs. The microRNAs were classified in 3 groups: Group number I contains 4 microRNAs with the increasing profile; II, 35 microRNAs with decreasing profile and III with 173 microRNAs, which remain unchanged. We calculated Pearson correlations between the expression profile of microRNAs and target mRNAs, predicted by TargetScan 5.1 and miRBase altgorithms, using genome-wide mRNA expression data. Group I correlated with the decreasing expression of 142 target mRNAs and Group II with the increasing expression of 876 target mRNAs. Most microRNAs correlate with multiple targets, just as mRNAs are targeted by multiple microRNAs. Among the identified targets are the genes and transcription factors known to play an essential role in pancreatic development. Conclusions We have determined specific groups of microRNAs in human fetal pancreas that change the degree of their expression throughout the development. A negative correlative analysis suggests an intertwined network of microRNAs and mRNAs collaborating with each other. This study provides information leading to potential two-way level of combinatorial control regulating gene expression through microRNAs targeting multiple mRNAs and, conversely, target mRNAs regulated in parallel by other microRNAs as well

Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

PubMed

Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

2016-02-19

Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

Gene expression markers in circulating tumor cells may predict bone metastasis and response to hormonal treatment in breast cancer.

PubMed

Wang, Haiying; Molina, Julian; Jiang, John; Ferber, Matthew; Pruthi, Sandhya; Jatkoe, Timothy; Derecho, Carlo; Rajpurohit, Yashoda; Zheng, Jian; Wang, Yixin

2013-11-01

Circulating tumor cells (CTCs) have recently attracted attention due to their potential as prognostic and predictive markers for the clinical management of metastatic breast cancer patients. The isolation of CTCs from patients may enable the molecular characterization of these cells, which may help establish a minimally invasive assay for the prediction of metastasis and further optimization of treatment. Molecular markers of proven clinical value may therefore be useful in predicting disease aggressiveness and response to treatment. In our earlier study, we identified a gene signature in breast cancer that appears to be significantly associated with bone metastasis. Among the genes that constitute this signature, trefoil factor 1 (TFF1) was identified as the most differentially expressed gene associated with bone metastasis. In this study, we investigated 25 candidate gene markers in the CTCs of metastatic breast cancer patients with different metastatic sites. The panel of the 25 markers was investigated in 80 baseline samples (first blood draw of CTCs) and 30 follow-up samples. In addition, 40 healthy blood donors (HBDs) were analyzed as controls. The assay was performed using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) with RNA extracted from CTCs captured by the CellSearch system. Our study indicated that 12 of the genes were uniquely expressed in CTCs and 10 were highly expressed in the CTCs obtained from patients compared to those obtained from HBDs. Among these genes, the expression of keratin 19 was highly correlated with the CTC count. The TFF1 expression in CTCs was a strong predictor of bone metastasis and the patients with a high expression of estrogen receptor β in CTCs exhibited a better response to hormonal treatment. Molecular characterization of these genes in CTCs may provide a better understanding of the mechanism underlying tumor metastasis and identify gene markers in CTCs for predicting disease progression and

Adipose Gene Expression Prior to Weight Loss Can Differentiate and Weakly Predict Dietary Responders

PubMed Central

Mutch, David M.; Temanni, M. Ramzi; Henegar, Corneliu; Combes, Florence; Pelloux, Véronique; Holst, Claus; Sørensen, Thorkild I. A.; Astrup, Arne; Martinez, J. Alfredo; Saris, Wim H. M.; Viguerie, Nathalie; Langin, Dominique; Zucker, Jean-Daniel; Clément, Karine

2007-01-01

Background The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot. Methodology/Principal Findings The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8–12 kgs weight loss) could always be differentiated from non-responders (<4 kgs weight loss). We also assessed whether this differentiation was sufficient for prediction. Using a bottom-up (i.e. black-box) approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%±8.1% accuracy. Using a top-down approach (i.e. using differentially expressed genes to build a classifier) improved prediction accuracy to 80.9%±2.2%. Conclusion Adipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition. PMID:18094752

Discovery and Validation of Novel Expression Signature for Postcystectomy Recurrence in High-Risk Bladder Cancer

PubMed Central

Lam, Lucia L.; Ghadessi, Mercedeh; Erho, Nicholas; Vergara, Ismael A.; Alshalalfa, Mohammed; Buerki, Christine; Haddad, Zaid; Sierocinski, Thomas; Triche, Timothy J.; Skinner, Eila C.; Davicioni, Elai; Daneshmand, Siamak; Black, Peter C.

2014-01-01

Background Nearly half of muscle-invasive bladder cancer patients succumb to their disease following cystectomy. Selecting candidates for adjuvant therapy is currently based on clinical parameters with limited predictive power. This study aimed to develop and validate genomic-based signatures that can better identify patients at risk for recurrence than clinical models alone. Methods Transcriptome-wide expression profiles were generated using 1.4 million feature-arrays on archival tumors from 225 patients who underwent radical cystectomy and had muscle-invasive and/or node-positive bladder cancer. Genomic (GC) and clinical (CC) classifiers for predicting recurrence were developed on a discovery set (n = 133). Performances of GC, CC, an independent clinical nomogram (IBCNC), and genomic-clinicopathologic classifiers (G-CC, G-IBCNC) were assessed in the discovery and independent validation (n = 66) sets. GC was further validated on four external datasets (n = 341). Discrimination and prognostic abilities of classifiers were compared using area under receiver-operating characteristic curves (AUCs). All statistical tests were two-sided. Results A 15-feature GC was developed on the discovery set with area under curve (AUC) of 0.77 in the validation set. This was higher than individual clinical variables, IBCNC (AUC = 0.73), and comparable to CC (AUC = 0.78). Performance was improved upon combining GC with clinical nomograms (G-IBCNC, AUC = 0.82; G-CC, AUC = 0.86). G-CC high-risk patients had elevated recurrence probabilities (P < .001), with GC being the best predictor by multivariable analysis (P = .005). Genomic-clinicopathologic classifiers outperformed clinical nomograms by decision curve and reclassification analyses. GC performed the best in validation compared with seven prior signatures. GC markers remained prognostic across four independent datasets. Conclusions The validated genomic-based classifiers outperform clinical models for predicting postcystectomy

A predictive signature gene set for discriminating active from latent tuberculosis in Warao Amerindian children.

PubMed

Verhagen, Lilly M; Zomer, Aldert; Maes, Mailis; Villalba, Julian A; Del Nogal, Berenice; Eleveld, Marc; van Hijum, Sacha Aft; de Waard, Jacobus H; Hermans, Peter Wm

2013-02-01

Tuberculosis (TB) continues to cause a high toll of disease and death among children worldwide. The diagnosis of childhood TB is challenged by the paucibacillary nature of the disease and the difficulties in obtaining specimens. Whereas scientific and clinical research efforts to develop novel diagnostic tools have focused on TB in adults, childhood TB has been relatively neglected. Blood transcriptional profiling has improved our understanding of disease pathogenesis of adult TB and may offer future leads for diagnosis and treatment. No studies applying gene expression profiling of children with TB have been published so far. We identified a 116-gene signature set that showed an average prediction error of 11% for TB vs. latent TB infection (LTBI) and for TB vs. LTBI vs. healthy controls (HC) in our dataset. A minimal gene set of only 9 genes showed the same prediction error of 11% for TB vs. LTBI in our dataset. Furthermore, this minimal set showed a significant discriminatory value for TB vs. LTBI for all previously published adult studies using whole blood gene expression, with average prediction errors between 17% and 23%. In order to identify a robust representative gene set that would perform well in populations of different genetic backgrounds, we selected ten genes that were highly discriminative between TB, LTBI and HC in all literature datasets as well as in our dataset. Functional annotation of these genes highlights a possible role for genes involved in calcium signaling and calcium metabolism as biomarkers for active TB. These ten genes were validated by quantitative real-time polymerase chain reaction in an additional cohort of 54 Warao Amerindian children with LTBI, HC and non-TB pneumonia. Decision tree analysis indicated that five of the ten genes were sufficient to classify 78% of the TB cases correctly with no LTBI subjects wrongly classified as TB (100% specificity). Our data justify the further exploration of our signature set as biomarkers

A predictive signature gene set for discriminating active from latent tuberculosis in Warao Amerindian children

PubMed Central

2013-01-01

Background Tuberculosis (TB) continues to cause a high toll of disease and death among children worldwide. The diagnosis of childhood TB is challenged by the paucibacillary nature of the disease and the difficulties in obtaining specimens. Whereas scientific and clinical research efforts to develop novel diagnostic tools have focused on TB in adults, childhood TB has been relatively neglected. Blood transcriptional profiling has improved our understanding of disease pathogenesis of adult TB and may offer future leads for diagnosis and treatment. No studies applying gene expression profiling of children with TB have been published so far. Results We identified a 116-gene signature set that showed an average prediction error of 11% for TB vs. latent TB infection (LTBI) and for TB vs. LTBI vs. healthy controls (HC) in our dataset. A minimal gene set of only 9 genes showed the same prediction error of 11% for TB vs. LTBI in our dataset. Furthermore, this minimal set showed a significant discriminatory value for TB vs. LTBI for all previously published adult studies using whole blood gene expression, with average prediction errors between 17% and 23%. In order to identify a robust representative gene set that would perform well in populations of different genetic backgrounds, we selected ten genes that were highly discriminative between TB, LTBI and HC in all literature datasets as well as in our dataset. Functional annotation of these genes highlights a possible role for genes involved in calcium signaling and calcium metabolism as biomarkers for active TB. These ten genes were validated by quantitative real-time polymerase chain reaction in an additional cohort of 54 Warao Amerindian children with LTBI, HC and non-TB pneumonia. Decision tree analysis indicated that five of the ten genes were sufficient to classify 78% of the TB cases correctly with no LTBI subjects wrongly classified as TB (100% specificity). Conclusions Our data justify the further exploration of our

Genetically diverse CC-founder mouse strains replicate the human influenza gene expression signature.

PubMed

Elbahesh, Husni; Schughart, Klaus

2016-05-19

Influenza A viruses (IAV) are zoonotic pathogens that pose a major threat to human and animal health. Influenza virus disease severity is influenced by viral virulence factors as well as individual differences in host response. We analyzed gene expression changes in the blood of infected mice using a previously defined set of signature genes that was derived from changes in the blood transcriptome of IAV-infected human volunteers. We found that the human signature was reproduced well in the founder strains of the Collaborative Cross (CC) mice, thus demonstrating the relevance and importance of mouse experimental model systems for studying human influenza disease.

Inflammatory macrophage-associated 3-gene signature predicts subclinical allograft injury and graft survival.

PubMed

Azad, Tej D; Donato, Michele; Heylen, Line; Liu, Andrew B; Shen-Orr, Shai S; Sweeney, Timothy E; Maltzman, Jonathan Scott; Naesens, Maarten; Khatri, Purvesh

2018-01-25

Late allograft failure is characterized by cumulative subclinical insults manifesting over many years. Although immunomodulatory therapies targeting host T cells have improved short-term survival rates, rates of chronic allograft loss remain high. We hypothesized that other immune cell types may drive subclinical injury, ultimately leading to graft failure. We collected whole-genome transcriptome profiles from 15 independent cohorts composed of 1,697 biopsy samples to assess the association of an inflammatory macrophage polarization-specific gene signature with subclinical injury. We applied penalized regression to a subset of the data sets and identified a 3-gene inflammatory macrophage-derived signature. We validated discriminatory power of the 3-gene signature in 3 independent renal transplant data sets with mean AUC of 0.91. In a longitudinal cohort, the 3-gene signature strongly correlated with extent of injury and accurately predicted progression of subclinical injury 18 months before clinical manifestation. The 3-gene signature also stratified patients at high risk of graft failure as soon as 15 days after biopsy. We found that the 3-gene signature also distinguished acute rejection (AR) accurately in 3 heart transplant data sets but not in lung transplant. Overall, we identified a parsimonious signature capable of diagnosing AR, recognizing subclinical injury, and risk-stratifying renal transplant patients. Our results strongly suggest that inflammatory macrophages may be a viable therapeutic target to improve long-term outcomes for organ transplantation patients.

A microRNA expression signature of the postprandial state in response to a high-saturated-fat challenge.

PubMed

Lopez, Sergio; Bermudez, Beatriz; Montserrat-de la Paz, Sergio; Abia, Rocio; Muriana, Francisco J G

2018-07-01

The postprandial hypertriglyceridemia is an important and largely silent disturbance involved in the genesis of numerous pathological conditions. Exaggerated and prolonged states of postprandial hypertriglyceridemia are frequently related to the ingestion of meals enriched in saturated fatty acids (SFAs). MicroRNAs are noncoding RNAs that function as gene regulators and play significant roles in both health and disease. However, differential miRNA expression between fasting and postprandial states has never been elucidated. Here, we studied the impact of a high-saturated-fat meal, mainly rich in palmitic acid, on the miRNA signature in peripheral blood mononuclear cells (PBMCs) of nine male healthy individuals in the postprandial period by using a two-step analysis: miRNA array and validation through quantitative real-time polymerase chain reaction. Compared with miRNA expression signature in PBMCs at fasting, 36 miRNAs were down-regulated and 43 miRNAs were up-regulated in PBMCs at postprandial hypertriglyceridemic peak. Six chromosomes (3, 7, 8, 12, 14 and 19) had nearly half (48.1%) of dysregulated miRNA-gene-containing regions. Down-regulated miR-300 and miR-369-3p and up-regulated miR-495-3p, miR-129-5p and miR-7-2-3p had the highest number of target genes. The differentially expressed miRNAs and their predicted target genes involved pathways in cancer, MAPK signaling pathway, endocytosis and axon guidance. Only down-regulated miRNAs notably targeted PI3K-Akt signaling pathways, whereas only up-regulated miRNAs targeted focal adhesion, Wnt signaling pathway, transcriptional misregulation in cancer and ubiquitin-mediated proteolysis. This is the first study of miRNA expression analysis of human PBMCs during postprandial hypertriglyceridemia and offers insight into new potential mechanisms by which dietary SFAs influence health or disease. Copyright © 2018 Elsevier Inc. All rights reserved.

Identifying developmental vascular disruptor compounds using a predictive signature and alternative toxicity models

EPA Science Inventory

Identifying Developmental Vascular Disruptor Compounds Using a Predictive Signature and Alternative Toxicity Models Presenting Author: Tamara Tal Affiliation: U.S. EPA/ORD/ISTD, RTP, NC, USA Chemically induced vascular toxicity during embryonic development can result in a wide...

Irma 5.1 multisensor signature prediction model

NASA Astrophysics Data System (ADS)

Savage, James; Coker, Charles; Edwards, Dave; Thai, Bea; Aboutalib, Omar; Chow, Anthony; Yamaoka, Neil; Kim, Charles

2006-05-01

The Irma synthetic signature prediction code is being developed to facilitate the research and development of multi-sensor systems. Irma was one of the first high resolution, physics-based Infrared (IR) target and background signature models to be developed for tactical weapon applications. Originally developed in 1980 by the Munitions Directorate of the Air Force Research Laboratory (AFRL/MN), the Irma model was used exclusively to generate IR scenes. In 1988, a number of significant upgrades to Irma were initiated including the addition of a laser (or active) channel. This two-channel version was released to the user community in 1990. In 1992, an improved scene generator was incorporated into the Irma model, which supported correlated frame-to-frame imagery. A passive IR/millimeter wave (MMW) code was completed in 1994. This served as the cornerstone for the development of the co-registered active/passive IR/MMW model, Irma 4.0. In 2000, Irma version 5.0 was released which encompassed several upgrades to both the physical models and software. Circular polarization was added to the passive channel, and a Doppler capability was added to the active MMW channel. In 2002, the multibounce technique was added to the Irma passive channel. In the ladar channel, a user-friendly Ladar Sensor Assistant (LSA) was incorporated which provides capability and flexibility for sensor modeling. Irma 5.0 runs on several platforms including Windows, Linux, Solaris, and SGI Irix. Irma is currently used to support a number of civilian and military applications. The Irma user base includes over 130 agencies within the Air Force, Army, Navy, DARPA, NASA, Department of Transportation, academia, and industry. In 2005, Irma version 5.1 was released to the community. In addition to upgrading the Ladar channel code to an object oriented language (C++) and providing a new graphical user interface to construct scenes, this new release significantly improves the modeling of the ladar channel and

Irma 5.2 multi-sensor signature prediction model

NASA Astrophysics Data System (ADS)

Savage, James; Coker, Charles; Thai, Bea; Aboutalib, Omar; Chow, Anthony; Yamaoka, Neil; Kim, Charles

2007-04-01

The Irma synthetic signature prediction code is being developed by the Munitions Directorate of the Air Force Research Laboratory (AFRL/MN) to facilitate the research and development of multi-sensor systems. There are over 130 users within the Department of Defense, NASA, Department of Transportation, academia, and industry. Irma began as a high-resolution, physics-based Infrared (IR) target and background signature model for tactical weapon applications and has grown to include: a laser (or active) channel (1990), improved scene generator to support correlated frame-to-frame imagery (1992), and passive IR/millimeter wave (MMW) channel for a co-registered active/passive IR/MMW model (1994). Irma version 5.0 was released in 2000 and encompassed several upgrades to both the physical models and software; host support was expanded to Windows, Linux, Solaris, and SGI Irix platforms. In 2005, version 5.1 was released after an extensive verification and validation of an upgraded and reengineered active channel. Since 2005, the reengineering effort has focused on the Irma passive channel. Field measurements for the validation effort include the unpolarized data collection. Irma 5.2 is scheduled for release in the summer of 2007. This paper will report the validation test results of the Irma passive models and discuss the new features in Irma 5.2.

Gene expression signature of tolerance and lymphocyte subsets in stable renal transplants: results of a cross-sectional study.

PubMed

Moreso, Francesc; Torres, Irina B; Martínez-Gallo, Monica; Benlloch, Susana; Cantarell, Carme; Perelló, Manel; Jimeno, José; Pujol-Borrell, Ricardo; Seron, Daniel

2014-06-01

In kidney transplants operational tolerance has been associated with up-regulation of B cell differentiation genes and an increased number of total, naive and transitional peripheral B cells. The aim is to evaluate tolerance biomarkers in different cohorts of stable renal transplants under immunosuppression. This is a cross-sectional study conducted in renal transplants. We evaluate genetic tolerance signature and lymphocyte subsets in stable transplants treated with calcineurin inhibitors (CNI) at 1 (n=15), 5 (n=14) and 10 (n=16) years, and azathioprine-treated transplants followed 30 years (n=8). Healthy volunteers (n=10) and patients with chronic rejection (n=15) served as controls. We confirm that peripheral expression of IGKV1D-13 and IGKV4-1 genes by RT-PCR distinguish tolerant (n=10) from stable transplants (n=10) provided by the International Tolerance Network. Tolerance signature was defined as the lowest expression for both genes in tolerant patients. In CNI-treated patients, genetic signature of tolerance and B cells showed a time-dependent increase not observed in azathioprine-treated patients (p<0.01). Genetic tolerance signature was observed in 0% at 1, 7% at 5 and 25% at 10-years while it was not observed in azathioprine-treated and chronic rejection patients. Fifteen out of 16 CNI-treated transplants at 10 years were revaluated 3 months apart. Nine did not show the tolerance signature in any determination, 4 in one and 2 in both determinations. Genetic signature of tolerance was associated with an increase of total, naive and transitional B cells (p<0.05). IGKV1D-13 and IGKV4-1 gene expression and its linked B cell populations increase during follow up in CNI-treated patients. At 10 years, 2 out of 15 CNI treated patients consistently express biomarkers associated with true tolerance. In azathioprine-treated patients these biomarkers were down-regulated. Copyright © 2014 Elsevier B.V. All rights reserved.

Application of connectivity mapping in predictive toxicology based on gene-expression similarity.

PubMed

Smalley, Joshua L; Gant, Timothy W; Zhang, Shu-Dong

2010-02-09

Connectivity mapping is the process of establishing connections between different biological states using gene-expression profiles or signatures. There are a number of applications but in toxicology the most pertinent is for understanding mechanisms of toxicity. In its essence the process involves comparing a query gene signature generated as a result of exposure of a biological system to a chemical to those in a database that have been previously derived. In the ideal situation the query gene-expression signature is characteristic of the event and will be matched to similar events in the database. Key criteria are therefore the means of choosing the signature to be matched and the means by which the match is made. In this article we explore these concepts with examples applicable to toxicology. (c) 2009 Elsevier Ireland Ltd. All rights reserved.

Microrna expression signatures predict patient progression and disease outcome in pediatric embryonal central nervous system neoplasms.

PubMed

Braoudaki, Maria; Lambrou, George I; Giannikou, Krinio; Milionis, Vasilis; Stefanaki, Kalliopi; Birks, Diane K; Prodromou, Neophytos; Kolialexi, Aggeliki; Kattamis, Antonis; Spiliopoulou, Chara A; Tzortzatou-Stathopoulou, Fotini; Kanavakis, Emmanouel

2014-12-31

pediatric embryonal CNS neoplasms studied. Deeper understanding of the aberrant miRNA expression in pediatric embryonal brain tumors might aid in the development of tumor-specific miRNA signatures, which could potentially afford promising biomarkers related to diagnosis, prognosis and patient targeted therapy.

An independent validation of a gene expression signature to differentiate malignant melanoma from benign melanocytic nevi.

PubMed

Clarke, Loren E; Flake, Darl D; Busam, Klaus; Cockerell, Clay; Helm, Klaus; McNiff, Jennifer; Reed, Jon; Tschen, Jaime; Kim, Jinah; Barnhill, Raymond; Elenitsas, Rosalie; Prieto, Victor G; Nelson, Jonathan; Kimbrell, Hillary; Kolquist, Kathryn A; Brown, Krystal L; Warf, M Bryan; Roa, Benjamin B; Wenstrup, Richard J

2017-02-15

Recently, a 23-gene signature was developed to produce a melanoma diagnostic score capable of differentiating malignant and benign melanocytic lesions. The primary objective of this study was to independently assess the ability of the gene signature to differentiate melanoma from benign nevi in clinically relevant lesions. A set of 1400 melanocytic lesions was selected from samples prospectively submitted for gene expression testing at a clinical laboratory. Each sample was tested and subjected to an independent histopathologic evaluation by 3 experienced dermatopathologists. A primary diagnosis (benign or malignant) was assigned to each sample, and diagnostic concordance among the 3 dermatopathologists was required for inclusion in analyses. The sensitivity and specificity of the score in differentiating benign and malignant melanocytic lesions were calculated to assess the association between the score and the pathologic diagnosis. The gene expression signature differentiated benign nevi from malignant melanoma with a sensitivity of 91.5% and a specificity of 92.5%. These results reflect the performance of the gene signature in a diverse array of samples encountered in routine clinical practice. Cancer 2017;123:617-628. © 2016 American Cancer Society. © 2016 Myriad Genetics, Inc. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society.

Optimization Of Engine Heat Transfer Mechanisms For Ground Combat Vehicle Signature Models

NASA Astrophysics Data System (ADS)

Gonda, T.; Rogers, P.; Gerhart, G.; Reynolds, W. R.

1988-08-01

A thermodynamic model for predicting the behavior of selected internal thermal sources of an M2 Bradley Infantry Fighting Vehicle is described. The modeling methodology is expressed in terms of first principle heat transfer equations along with a brief history of TACOM's experience with thermal signature modeling techniques. The dynamic operation of the internal thermal sources is presented along with limited test data and an examination of their effect on the vehicle signature.

Stromal signatures in endometrioid endometrial carcinomas.

PubMed

Espinosa, Iñigo; Catasus, Lluis; D' Angelo, Emanuela; Mozos, Ana; Pedrola, Nuria; Bértolo, Cristina; Ferrer, Irene; Zannoni, Gian Franco; West, Robert B; van de Rijn, Matt; Matias-Guiu, Xavier; Prat, Jaime

2014-04-01

The pattern of myometrial invasion in endometrioid endometrial carcinomas varies considerably; ie, from widely scattered glands and cell nests, often associated with a fibromyxoid stromal reaction (desmoplasia) and/or a lymphocytic infiltrate, to invasive glands with little or no stromal response. Recently, two distinct stromal signatures derived from a macrophage response (colony-stimulating factor 1, CSF1) and a fibroblastic response (desmoid-type fibromatosis, DTF) were identified in breast carcinomas and correlated with clinicopathologic features including outcome. In this study, we explored whether these stromal signatures also apply to endometrioid carcinomas and how their expression patterns correlated with morphologic changes. We studied the stromal signatures both by immunohistochemistry and in situ hybridization in 98 primary endometrioid carcinomas with (87 cases) and without (11 cases) myometrial invasion as well as in the corresponding regional lymph nodes metatases of 9 myoinvasive tumors. Desmoplasia correlated positively with the DTF expression signature. Likewise, mononuclear infiltrates were found in the stroma of tumors expressing CSF1. Twenty-four out of eighty-seven (27%) myoinvasive endometrioid carcinomas were positive for the macrophage signature and thirteen out of eighty-seven (15%) expressed the fibroblast signature. Eleven additional cases were positive for both DTF and CSF1 signatures (11/87; 13%). However, over half of the cases (39/87; 45%) and the majority of the non-myoinvasive tumors (8/11; 73%) failed to express any of the two stromal signatures. The macrophage response (CSF1) was associated with higher tumor grade, lymphovascular invasion, and PIK3CA mutations (P<0.05). There was a concordance in the expression of the CSF1 signature in the primary tumors and their corresponding lymph node metastases. This study is the first characterization of stromal signatures in endometrioid carcinomas. Our findings shed new light on the

Species-Specific Predictive Signatures of Developmental Toxicity Using the ToxCast Chemical Library

EPA Science Inventory

EPA’s ToxCastTM project is profiling the in vitro bioactivity of chemicals to generate predictive signatures that correlate with observed in vivo toxicity. In vitro profiling methods from ToxCast data consist of over 600 high-throughput screening (HTS) and high-content screening ...

Genotype-based gene signature of glioma risk.

PubMed

Huang, Yen-Tsung; Zhang, Yi; Wu, Zhijin; Michaud, Dominique S

2017-07-01

Glioma accounts for 80% of malignant brain tumors, but its etiologic determinants remain elusive. Despite genetic susceptibility loci identified by genome-wide association study (GWAS), the agnostic approach leaves open the possibility that other susceptibility genes remain to be discovered. Here we conduct a gene-centric integrative GWAS (iGWAS) of glioma risk that combines transcriptomics and genetics. We synthesized a brain transcriptomics dataset (n = 354), a GWAS dataset (n = 4203), and an advanced glioma tumor transcriptomic dataset (n = 483) to conduct an iGWAS. Using the expression quantitative trait loci (eQTL) dataset, we built models to predict gene expression for the GWAS data, based on eQTL genotypes. With the predicted gene expression, iGWAS analyses were performed using a novel statistical method. Gene signature risk score was constructed using a penalized logistic regression model. A total of 30527 transcripts were analyzed using the iGWAS approach. Four novel glioma susceptibility genes were identified with internal and external validation, including DRD5 (P = 3.0 × 10-79), WDR1 (P = 8.4 × 10-77), NOMO1 (P = 1.3 × 10-25), and PDXDC1 (P = 8.3 × 10-24). The genotype-predicted transcription pattern between cases and controls is consistent with that between tumor and its matched normal tissue. The genotype-based 4-gene signature improved the classification between glioma cases and controls based on age, gender, and population stratification, with area under the receiver operating characteristic curve increasing from 0.77 to 0.85 (P = 8.1 × 10-23). A new genotype-based gene signature of glioma was identified using a novel iGWAS approach, which integrates multiplatform genomic data as well as different genetic association studies. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Using a Stem Cell-Based Signature to Guide Therapeutic Selection in Cancer

PubMed Central

Shats, Igor; Gatza, Michael L.; Chang, Jeffrey T.; Mori, Seiichi; Wang, Jialiang; Rich, Jeremy; Nevins, Joseph R.

2010-01-01

Given the very substantial heterogeneity of most human cancers, it is likely that most cancer therapeutics will be active in only a small fraction of any population of patients. As such, the development of new therapeutics, coupled with methods to match a therapy with the individual patient, will be critical to achieving significant gains in disease outcome. One such opportunity is the use of expression signatures to identify key oncogenic phenotypes that can serve not only as biomarkers but also as a means of identifying therapeutic compounds that might specifically target these phenotypes. Given the potential importance of targeting tumors exhibiting a stem-like phenotype, we have developed an expression signature that reflects common biological aspects of various stem-like characteristics. The Consensus Stemness Ranking (CSR) signature is upregulated in cancer stem cell enriched samples, at advanced tumor stages and is associated with poor prognosis in multiple cancer types. Using two independent computational approaches we utilized the CSR signature to identify clinically useful compounds that could target the CSR phenotype. In vitro assays confirmed selectivity of several predicted compounds including topoisomerase inhibitors and resveratrol towards breast cancer cell lines that exhibit a high-CSR phenotype. Importantly, the CSR signature could predict clinical response of breast cancer patients to a neoadjuvant regimen that included a CSR-specific agent. Collectively, these results suggest therapeutic opportunities to target the CSR phenotype in a relevant cohort of cancer patients. PMID:21169407

Molecular Signature for Lymphatic Invasion Associated with Survival of Epithelial Ovarian Cancer.

PubMed

Paik, E Sun; Choi, Hyun Jin; Kim, Tae-Joong; Lee, Jeong-Won; Kim, Byoung-Gie; Bae, Duk-Soo; Choi, Chel Hun

2018-04-01

We aimed to develop molecular classifier that can predict lymphatic invasion and their clinical significance in epithelial ovarian cancer (EOC) patients. We analyzed gene expression (mRNA, methylated DNA) in data from The Cancer Genome Atlas. To identify molecular signatures for lymphatic invasion, we found differentially expressed genes. The performance of classifier was validated by receiver operating characteristics analysis, logistic regression, linear discriminant analysis (LDA), and support vector machine (SVM). We assessed prognostic role of classifier using random survival forest (RSF) model and pathway deregulation score (PDS). For external validation,we analyzed microarray data from 26 EOC samples of Samsung Medical Center and curatedOvarianData database. We identified 21 mRNAs, and seven methylated DNAs from primary EOC tissues that predicted lymphatic invasion and created prognostic models. The classifier predicted lymphatic invasion well, which was validated by logistic regression, LDA, and SVM algorithm (C-index of 0.90, 0.71, and 0.74 for mRNA and C-index of 0.64, 0.68, and 0.69 for DNA methylation). Using RSF model, incorporating molecular data with clinical variables improved prediction of progression-free survival compared with using only clinical variables (p < 0.001 and p=0.008). Similarly, PDS enabled us to classify patients into high-risk and low-risk group, which resulted in survival difference in mRNA profiles (log-rank p-value=0.011). In external validation, gene signature was well correlated with prediction of lymphatic invasion and patients' survival. Molecular signature model predicting lymphatic invasion was well performed and also associated with survival of EOC patients.

A BAP1 Mutation-specific MicroRNA Signature Predicts Clinical Outcomes in Clear Cell Renal Cell Carcinoma Patients with Wild-type BAP1

PubMed Central

Ge, Yu-Zheng; Xu, Lu-Wei; Zhou, Chang-Cheng; Lu, Tian-Ze; Yao, Wen-Tao; Wu, Ran; Zhao, You-Cai; Xu, Xiao; Hu, Zhi-Kai; Wang, Min; Yang, Xiao-Bing; Zhou, Liu-Hua; Zhong, Bing; Xu, Zheng; Li, Wen-Cheng; Zhu, Jia-Geng; Jia, Rui-Peng

2017-01-01

Background: Clear cell renal cell carcinoma (ccRCC) is the most prevalent histologic subtype of kidney cancers in adults, which could be divided into two distinct subgroups according to the BRCA1 associated protein-1 (BAP1) mutation status. In the current study, we comprehensively analyzed the genome-wide microRNA (miRNA) expression profiles in ccRCC, with the aim to identify the differentially expressed miRNAs between BAP1 mutant and wild-type tumors, and generate a BAP1 mutation-specific miRNA signature for ccRCC patients with wild-type BAP1. Methods: The BAP1 mutation status and miRNA profiles in BAP1 mutant and wild-type tumors were analyzed. Subsequently, the association of the differentially expressed miRNAs with patient survival was examined, and a BAP1 mutation-specific miRNA signature was generated and examined with Kaplan-Meier survival, univariate and multivariate Cox regression analyses. Finally, the bioinformatics methods were adopted for the target prediction of selected miRNAs and functional annotation analyses. Results: A total of 350 treatment-naïve primary ccRCC patients were selected from The Cancer Genome Atlas project, among which 35 (10.0%) subjects carried mutant BAP1 and had a shorter overall survival (OS) time. Furthermore, 33 miRNAs were found to be differentially expressed between BAP1 mutant and wild-type tumors, among which 11 (miR-149, miR-29b-2, miR-182, miR-183, miR-21, miR-365-2, miR-671, miR-365-1, miR-10b, miR-139, and miR-181a-2) were significantly associated with OS in ccRCC patients with wild-type BAP1. Finally, a BAP1 mutation-specific miRNA signature consisting of 11 miRNAs was generated and validated as an independent prognostic parameter. Conclusions: In summary, our study identified a total of 33 miRNAs differentially expressed between BAP1 mutant and wild-type tumors, and generated a BAP1 mutation-specific miRNA signature including eleven miRNAs, which could serve as a novel prognostic biomarker for ccRCC patients with

Long non-coding RNAs as novel expression signatures modulate DNA damage and repair in cadmium toxicology

NASA Astrophysics Data System (ADS)

Zhou, Zhiheng; Liu, Haibai; Wang, Caixia; Lu, Qian; Huang, Qinhai; Zheng, Chanjiao; Lei, Yixiong

2015-10-01

Increasing evidence suggests that long non-coding RNAs (lncRNAs) are involved in a variety of physiological and pathophysiological processes. Our study was to investigate whether lncRNAs as novel expression signatures are able to modulate DNA damage and repair in cadmium(Cd) toxicity. There were aberrant expression profiles of lncRNAs in 35th Cd-induced cells as compared to untreated 16HBE cells. siRNA-mediated knockdown of ENST00000414355 inhibited the growth of DNA-damaged cells and decreased the expressions of DNA-damage related genes (ATM, ATR and ATRIP), while increased the expressions of DNA-repair related genes (DDB1, DDB2, OGG1, ERCC1, MSH2, RAD50, XRCC1 and BARD1). Cadmium increased ENST00000414355 expression in the lung of Cd-exposed rats in a dose-dependent manner. A significant positive correlation was observed between blood ENST00000414355 expression and urinary/blood Cd concentrations, and there were significant correlations of lncRNA-ENST00000414355 expression with the expressions of target genes in the lung of Cd-exposed rats and the blood of Cd exposed workers. These results indicate that some lncRNAs are aberrantly expressed in Cd-treated 16HBE cells. lncRNA-ENST00000414355 may serve as a signature for DNA damage and repair related to the epigenetic mechanisms underlying the cadmium toxicity and become a novel biomarker of cadmium toxicity.

Gene expression markers in circulating tumor cells may predict bone metastasis and response to hormonal treatment in breast cancer

PubMed Central

WANG, HAIYING; MOLINA, JULIAN; JIANG, JOHN; FERBER, MATTHEW; PRUTHI, SANDHYA; JATKOE, TIMOTHY; DERECHO, CARLO; RAJPUROHIT, YASHODA; ZHENG, JIAN; WANG, YIXIN

2013-01-01

Circulating tumor cells (CTCs) have recently attracted attention due to their potential as prognostic and predictive markers for the clinical management of metastatic breast cancer patients. The isolation of CTCs from patients may enable the molecular characterization of these cells, which may help establish a minimally invasive assay for the prediction of metastasis and further optimization of treatment. Molecular markers of proven clinical value may therefore be useful in predicting disease aggressiveness and response to treatment. In our earlier study, we identified a gene signature in breast cancer that appears to be significantly associated with bone metastasis. Among the genes that constitute this signature, trefoil factor 1 (TFF1) was identified as the most differentially expressed gene associated with bone metastasis. In this study, we investigated 25 candidate gene markers in the CTCs of metastatic breast cancer patients with different metastatic sites. The panel of the 25 markers was investigated in 80 baseline samples (first blood draw of CTCs) and 30 follow-up samples. In addition, 40 healthy blood donors (HBDs) were analyzed as controls. The assay was performed using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) with RNA extracted from CTCs captured by the CellSearch system. Our study indicated that 12 of the genes were uniquely expressed in CTCs and 10 were highly expressed in the CTCs obtained from patients compared to those obtained from HBDs. Among these genes, the expression of keratin 19 was highly correlated with the CTC count. The TFF1 expression in CTCs was a strong predictor of bone metastasis and the patients with a high expression of estrogen receptor β in CTCs exhibited a better response to hormonal treatment. Molecular characterization of these genes in CTCs may provide a better understanding of the mechanism underlying tumor metastasis and identify gene markers in CTCs for predicting disease progression and

Gene expression signatures of energetic acclimatisation in the reef building coral Acropora millepora.

PubMed

Bay, Line K; Guérécheau, Aurélie; Andreakis, Nikos; Ulstrup, Karin E; Matz, Mikhail V

2013-01-01

Understanding the mechanisms by which natural populations cope with environmental stress is paramount to predict their persistence in the face of escalating anthropogenic impacts. Reef-building corals are increasingly exposed to local and global stressors that alter nutritional status causing reduced fitness and mortality, however, these responses can vary considerably across species and populations. We compare the expression of 22 coral host genes in individuals from an inshore and an offshore reef location using quantitative Reverse Transcription-PCR (qRT-PCR) over the course of 26 days following translocation into a shaded, filtered seawater environment. Declines in lipid content and PSII activity of the algal endosymbionts (Symbiodinium ITS-1 type C2) over the course of the experiment indicated that heterotrophic uptake and photosynthesis were limited, creating nutritional deprivation conditions. Regulation of coral host genes involved in metabolism, CO2 transport and oxidative stress could be detected already after five days, whereas PSII activity took twice as long to respond. Opposing expression trajectories of Tgl, which releases fatty acids from the triacylglycerol storage, and Dgat1, which catalyses the formation of triglycerides, indicate that the decline in lipid content can be attributed, at least in part, by mobilisation of triacylglycerol stores. Corals from the inshore location had initially higher lipid content and showed consistently elevated expression levels of two genes involved in metabolism (aldehyde dehydrogenase) and calcification (carbonic anhydrase). Coral host gene expression adjusts rapidly upon change in nutritional conditions, and therefore can serve as an early signature of imminent coral stress. Consistent gene expression differences between populations indicate that corals acclimatize and/or adapt to local environments. Our results set the stage for analysis of these processes in natural coral populations, to better understand the

REDD1 induction regulates the skeletal muscle gene expression signature following acute aerobic exercise.

PubMed

Gordon, Bradley S; Steiner, Jennifer L; Rossetti, Michael L; Qiao, Shuxi; Ellisen, Leif W; Govindarajan, Subramaniam S; Eroshkin, Alexey M; Williamson, David L; Coen, Paul M

2017-12-01

The metabolic stress placed on skeletal muscle by aerobic exercise promotes acute and long-term health benefits in part through changes in gene expression. However, the transducers that mediate altered gene expression signatures have not been completely elucidated. Regulated in development and DNA damage 1 (REDD1) is a stress-induced protein whose expression is transiently increased in skeletal muscle following acute aerobic exercise. However, the role of this induction remains unclear. Because REDD1 altered gene expression in other model systems, we sought to determine whether REDD1 induction following acute exercise altered the gene expression signature in muscle. To do this, wild-type and REDD1-null mice were randomized to remain sedentary or undergo a bout of acute treadmill exercise. Exercised mice recovered for 1, 3, or 6 h before euthanization. Acute exercise induced a transient increase in REDD1 protein expression within the plantaris only at 1 h postexercise, and the induction occurred in both cytosolic and nuclear fractions. At this time point, global changes in gene expression were surveyed using microarray. REDD1 induction was required for the exercise-induced change in expression of 24 genes. Validation by RT-PCR confirmed that the exercise-mediated changes in genes related to exercise capacity, muscle protein metabolism, neuromuscular junction remodeling, and Metformin action were negated in REDD1-null mice. Finally, the exercise-mediated induction of REDD1 was partially dependent upon glucocorticoid receptor activation. In all, these data show that REDD1 induction regulates the exercise-mediated change in a distinct set of genes within skeletal muscle. Copyright © 2017 the American Physiological Society.

Common and specific signatures of gene expression and protein-protein interactions in autoimmune diseases.

PubMed

Tuller, T; Atar, S; Ruppin, E; Gurevich, M; Achiron, A

2013-03-01

different subsignaling pathways. Analyses of the expression levels of dozens of genes and the protein-protein interactions among them demonstrated that CD and UC have relatively similar gene expression signatures, whereas the gene expression signatures of T1D and JRA relatively differ from the signatures of the other autoimmune diseases. These diseases are the only ones activated via the Fcɛ pathway. The relevant genes and pathways reported in this study are discussed at length, and may be helpful in the diagnoses and understanding of autoimmunity and/or specific autoimmune diseases.

Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest.

PubMed

Eng, Christine L P; Tong, Joo Chuan; Tan, Tin Wee

2017-05-25

Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak.

A Gene Signature to Determine Metastatic Behavior in Thymomas

PubMed Central

Gökmen-Polar, Yesim; Wilkinson, Jeff; Maetzold, Derek; Stone, John F.; Oelschlager, Kristen M.; Vladislav, Ioan Tudor; Shirar, Kristen L.; Kesler, Kenneth A.; Loehrer, Patrick J.; Badve, Sunil

2013-01-01

Purpose Thymoma represents one of the rarest of all malignancies. Stage and completeness of resection have been used to ascertain postoperative therapeutic strategies albeit with limited prognostic accuracy. A molecular classifier would be useful to improve the assessment of metastatic behaviour and optimize patient management. Methods qRT-PCR assay for 23 genes (19 test and four reference genes) was performed on multi-institutional archival primary thymomas (n = 36). Gene expression levels were used to compute a signature, classifying tumors into classes 1 and 2, corresponding to low or high likelihood for metastases. The signature was validated in an independent multi-institutional cohort of patients (n = 75). Results A nine-gene signature that can predict metastatic behavior of thymomas was developed and validated. Using radial basis machine modeling in the training set, 5-year and 10-year metastasis-free survival rates were 77% and 26% for predicted low (class 1) and high (class 2) risk of metastasis (P = 0.0047, log-rank), respectively. For the validation set, 5-year metastasis-free survival rates were 97% and 30% for predicted low- and high-risk patients (P = 0.0004, log-rank), respectively. The 5-year metastasis-free survival rates for the validation set were 49% and 41% for Masaoka stages I/II and III/IV (P = 0.0537, log-rank), respectively. In univariate and multivariate Cox models evaluating common prognostic factors for thymoma metastasis, the nine-gene signature was the only independent indicator of metastases (P = 0.036). Conclusion A nine-gene signature was established and validated which predicts the likelihood of metastasis more accurately than traditional staging. This further underscores the biologic determinants of the clinical course of thymoma and may improve patient management. PMID:23894276

Signatures from Tissue-specific MPSS Libraries Identify Transcripts Preferentially Expressed in the Mouse Inner Ear

PubMed Central

Peters, Linda M.; Belyantseva, Inna A.; Lagziel, Ayala; Battey, James F.; Friedman, Thomas B.; Morell, Robert J.

2007-01-01

Specialization in cell function and morphology is influenced by the differential expression of mRNAs, many of which are expressed at low abundance and restricted to certain cell types. Detecting such transcripts in cDNA libraries may require sequencing millions of clones. Massively parallel signature sequencing (MPSS) is well-suited for identifying transcripts that are expressed in discrete cell types and in low abundance. We have made MPSS libraries from microdissections of three inner ear tissues. By comparing these MPSS libraries to those of 87 other tissues included in the Mouse Reference Transcriptome (MRT) online resource, we have identified genes that are highly enriched in, or specific to, the inner ear. We show by RT-PCR and in situ hybridization that signatures unique to the inner ear libraries identify transcripts with highly specific cell-type localizations. These transcripts serve to illustrate the utility of a resource that is available to the research community. Utilization of these resources will increase the number of known transcription units and expand our knowledge of the tissue-specific regulation of the transcriptome. PMID:17049805

Novel immunohistochemistry-based signatures to predict metastatic site of triple-negative breast cancers.

PubMed

Klimov, Sergey; Rida, Padmashree Cg; Aleskandarany, Mohammed A; Green, Andrew R; Ellis, Ian O; Janssen, Emiel Am; Rakha, Emad A; Aneja, Ritu

2017-09-05

Although distant metastasis (DM) in breast cancer (BC) is the most lethal form of recurrence and the most common underlying cause of cancer related deaths, the outcome following the development of DM is related to the site of metastasis. Triple negative BC (TNBC) is an aggressive form of BC characterised by early recurrences and high mortality. Athough multiple variables can be used to predict the risk of metastasis, few markers can predict the specific site of metastasis. This study aimed at identifying a biomarker signature to predict particular sites of DM in TNBC. A clinically annotated series of 322 TNBC were immunohistochemically stained with 133 biomarkers relevant to BC, to develop multibiomarker models for predicting metastasis to the bone, liver, lung and brain. Patients who experienced metastasis to each site were compared with those who did not, by gradually filtering the biomarker set via a two-tailed t-test and Cox univariate analyses. Biomarker combinations were finally ranked based on statistical significance, and evaluated in multivariable analyses. Our final models were able to stratify TNBC patients into high risk groups that showed over 5, 6, 7 and 8 times higher risk of developing metastasis to the bone, liver, lung and brain, respectively, than low-risk subgroups. These models for predicting site-specific metastasis retained significance following adjustment for tumour size, patient age and chemotherapy status. Our novel IHC-based biomarkers signatures, when assessed in primary TNBC tumours, enable prediction of specific sites of metastasis, and potentially unravel biomarkers previously unknown in site tropism.

Autophagy-related prognostic signature for breast cancer.

PubMed

Gu, Yunyan; Li, Pengfei; Peng, Fuduan; Zhang, Mengmeng; Zhang, Yuanyuan; Liang, Haihai; Zhao, Wenyuan; Qi, Lishuang; Wang, Hongwei; Wang, Chenguang; Guo, Zheng

2016-03-01

Autophagy is a process that degrades intracellular constituents, such as long-lived or damaged proteins and organelles, to buffer metabolic stress under starvation conditions. Deregulation of autophagy is involved in the progression of cancer. However, the predictive value of autophagy for breast cancer prognosis remains unclear. First, based on gene expression profiling, we found that autophagy genes were implicated in breast cancer. Then, using the Cox proportional hazard regression model, we detected autophagy prognostic signature for breast cancer in a training dataset. We identified a set of eight autophagy genes (BCL2, BIRC5, EIF4EBP1, ERO1L, FOS, GAPDH, ITPR1 and VEGFA) that were significantly associated with overall survival in breast cancer. The eight autophagy genes were assigned as a autophagy-related prognostic signature for breast cancer. Based on the autophagy-related signature, the training dataset GSE21653 could be classified into high-risk and low-risk subgroups with significantly different survival times (HR = 2.72, 95% CI = (1.91, 3.87); P = 1.37 × 10(-5)). Inactivation of autophagy was associated with shortened survival of breast cancer patients. The prognostic value of the autophagy-related signature was confirmed in the testing dataset GSE3494 (HR = 2.12, 95% CI = (1.48, 3.03); P = 1.65 × 10(-3)) and GSE7390 (HR = 1.76, 95% CI = (1.22, 2.54); P = 9.95 × 10(-4)). Further analysis revealed that the prognostic value of the autophagy signature was independent of known clinical prognostic factors, including age, tumor size, grade, estrogen receptor status, progesterone receptor status, ERBB2 status, lymph node status and TP53 mutation status. Finally, we demonstrated that the autophagy signature could also predict distant metastasis-free survival for breast cancer. © 2015 Wiley Periodicals, Inc.

Signature pathway expression of xylose utilization in the genetically engineered industrial yeast Saccharomyces cerevisiae

PubMed Central

Feng, Quanzhou; Weber, Scott A.; Li, Shizhong

2018-01-01

Haploid laboratory strains of Saccharomyces cerevisiae are commonly used for genetic engineering to enable their xylose utilization but little is known about the industrial yeast which is often recognized as diploid and as well as haploid and tetraploid. Here we report three unique signature pathway expression patterns and gene interactions in the centre metabolic pathways that signify xylose utilization of genetically engineered industrial yeast S. cerevisiae NRRL Y-50463, a diploid yeast. Quantitative expression analysis revealed outstanding high levels of constitutive expression of YXI, a synthesized yeast codon-optimized xylose isomerase gene integrated into chromosome XV of strain Y-50463. Comparative expression analysis indicated that the YXI was necessary to initiate the xylose metabolic pathway along with a set of heterologous xylose transporter and utilization facilitating genes including XUT4, XUT6, XKS1 and XYL2. The highly activated transketolase and transaldolase genes TKL1, TKL2, TAL1 and NQM1 as well as their complex interactions in the non-oxidative pentose phosphate pathway branch were critical for the serial of sugar transformation to drive the metabolic flow into glycolysis for increased ethanol production. The significantly increased expression of the entire PRS gene family facilitates functions of the life cycle and biosynthesis superpathway for the yeast. The outstanding higher levels of constitutive expression of YXI and the first insight into the signature pathway expression and the gene interactions in the closely related centre metabolic pathways from the industrial yeast aid continued efforts for development of the next-generation biocatalyst. Our results further suggest the industrial yeast is a desirable delivery vehicle for new strain development for efficient lignocellulose-to-advanced biofuels production. PMID:29621349

Signature pathway expression of xylose utilization in the genetically engineered industrial yeast Saccharomyces cerevisiae.

PubMed

Feng, Quanzhou; Liu, Z Lewis; Weber, Scott A; Li, Shizhong

2018-01-01

Haploid laboratory strains of Saccharomyces cerevisiae are commonly used for genetic engineering to enable their xylose utilization but little is known about the industrial yeast which is often recognized as diploid and as well as haploid and tetraploid. Here we report three unique signature pathway expression patterns and gene interactions in the centre metabolic pathways that signify xylose utilization of genetically engineered industrial yeast S. cerevisiae NRRL Y-50463, a diploid yeast. Quantitative expression analysis revealed outstanding high levels of constitutive expression of YXI, a synthesized yeast codon-optimized xylose isomerase gene integrated into chromosome XV of strain Y-50463. Comparative expression analysis indicated that the YXI was necessary to initiate the xylose metabolic pathway along with a set of heterologous xylose transporter and utilization facilitating genes including XUT4, XUT6, XKS1 and XYL2. The highly activated transketolase and transaldolase genes TKL1, TKL2, TAL1 and NQM1 as well as their complex interactions in the non-oxidative pentose phosphate pathway branch were critical for the serial of sugar transformation to drive the metabolic flow into glycolysis for increased ethanol production. The significantly increased expression of the entire PRS gene family facilitates functions of the life cycle and biosynthesis superpathway for the yeast. The outstanding higher levels of constitutive expression of YXI and the first insight into the signature pathway expression and the gene interactions in the closely related centre metabolic pathways from the industrial yeast aid continued efforts for development of the next-generation biocatalyst. Our results further suggest the industrial yeast is a desirable delivery vehicle for new strain development for efficient lignocellulose-to-advanced biofuels production.

Identifying prognostic signature in ovarian cancer using DirGenerank

PubMed Central

Wang, Jian-Yong; Chen, Ling-Ling; Zhou, Xiong-Hui

2017-01-01

Identifying the prognostic genes in cancer is essential not only for the treatment of cancer patients, but also for drug discovery. However, it's still a big challenge to select the prognostic genes that can distinguish the risk of cancer patients across various data sets because of tumor heterogeneity. In this situation, the selected genes whose expression levels are statistically related to prognostic risks may be passengers. In this paper, based on gene expression data and prognostic data of ovarian cancer patients, we used conditional mutual information to construct gene dependency network in which the nodes (genes) with more out-degrees have more chances to be the modulators of cancer prognosis. After that, we proposed DirGenerank (Generank in direct netowrk) algorithm, which concerns both the gene dependency network and genes’ correlations to prognostic risks, to identify the gene signature that can predict the prognostic risks of ovarian cancer patients. Using ovarian cancer data set from TCGA (The Cancer Genome Atlas) as training data set, 40 genes with the highest importance were selected as prognostic signature. Survival analysis of these patients divided by the prognostic signature in testing data set and four independent data sets showed the signature can distinguish the prognostic risks of cancer patients significantly. Enrichment analysis of the signature with curated cancer genes and the drugs selected by CMAP showed the genes in the signature may be drug targets for therapy. In summary, we have proposed a useful pipeline to identify prognostic genes of cancer patients. PMID:28615526

Multi-walled carbon nanotube-induced gene signatures in the mouse lung: potential predictive value for human lung cancer risk and prognosis

PubMed Central

Guo, Nancy L; Wan, Ying-Wooi; Denvir, James; Porter, Dale W; Pacurari, Maricica; Wolfarth, Michael G; Castranova, Vincent; Qian, Yong

2012-01-01

Concerns over the potential for multi-walled carbon nanotubes (MWCNT) to induce lung carcinogenesis have emerged. This study sought to (1) identify gene expression signatures in the mouse lungs following pharyngeal aspiration of well-dispersed MWCNT and (2) determine if these genes were associated with human lung cancer risk and progression. Genome-wide mRNA expression profiles were analyzed in mouse lungs (n=160) exposed to 0, 10, 20, 40, or 80 µg of MWCNT by pharyngeal aspiration at 1, 7, 28, and 56 days post-exposure. By using pairwise-Statistical Analysis of Microarray (SAM) and linear modeling, 24 genes were selected, which have significant changes in at least two time points, have a more than 1.5 fold change at all doses, and are significant in the linear model for the dose or the interaction of time and dose. Additionally, a 38-gene set was identified as related to cancer from 330 genes differentially expressed at day 56 post-exposure in functional pathway analysis. Using the expression profiles of the cancer-related gene set in 8 mice at day 56 post-exposure to 10 µg of MWCNT, a nearest centroid classification accurately predicts human lung cancer survival with a significant hazard ratio in training set (n=256) and test set (n=186). Furthermore, both gene signatures were associated with human lung cancer risk (n=164) with significant odds ratios. These results may lead to development of a surveillance approach for early detection of lung cancer and prognosis associated with MWCNT in the workplace. PMID:22891886

Dynamic thermal signature prediction for real-time scene generation

NASA Astrophysics Data System (ADS)

Christie, Chad L.; Gouthas, Efthimios (Themie); Williams, Owen M.; Swierkowski, Leszek

2013-05-01

At DSTO, a real-time scene generation framework, VIRSuite, has been developed in recent years, within which trials data are predominantly used for modelling the radiometric properties of the simulated objects. Since in many cases the data are insufficient, a physics-based simulator capable of predicting the infrared signatures of objects and their backgrounds has been developed as a new VIRSuite module. It includes transient heat conduction within the materials, and boundary conditions that take into account the heat fluxes due to solar radiation, wind convection and radiative transfer. In this paper, an overview is presented, covering both the steady-state and transient performance.

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

DOE PAGES

Wang, Pin; Wang, Yunshan; Hang, Bo; ...

2016-07-11

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A networkmore » was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.« less

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

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

Wang, Pin; Wang, Yunshan; Hang, Bo

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A networkmore » was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.« less

Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest

PubMed Central

Eng, Christine L. P.; Tong, Joo Chuan; Tan, Tin Wee

2017-01-01

Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak. PMID:28587080

Comparative expression profiling in grape (Vitis vinifera) berries derived from frequency analysis of ESTs and MPSS signatures.

PubMed

Iandolino, Alberto; Nobuta, Kan; da Silva, Francisco Goes; Cook, Douglas R; Meyers, Blake C

2008-05-12

Vitis vinifera (V. vinifera) is the primary grape species cultivated for wine production, with an industry valued annually in the billions of dollars worldwide. In order to sustain and increase grape production, it is necessary to understand the genetic makeup of grape species. Here we performed mRNA profiling using Massively Parallel Signature Sequencing (MPSS) and combined it with available Expressed Sequence Tag (EST) data. These tag-based technologies, which do not require a priori knowledge of genomic sequence, are well-suited for transcriptional profiling. The sequence depth of MPSS allowed us to capture and quantify almost all the transcripts at a specific stage in the development of the grape berry. The number and relative abundance of transcripts from stage II grape berries was defined using Massively Parallel Signature Sequencing (MPSS). A total of 2,635,293 17-base and 2,259,286 20-base signatures were obtained, representing at least 30,737 and 26,878 distinct sequences. The average normalized abundance per signature was approximately 49 TPM (Transcripts Per Million). Comparisons of the MPSS signatures with available Vitis species' ESTs and a unigene set demonstrated that 6,430 distinct contigs and 2,190 singletons have a perfect match to at least one MPSS signature. Among the matched sequences, ESTs were identified from tissues other than berries or from berries at different developmental stages. Additional MPSS signatures not matching to known grape ESTs can extend our knowledge of the V. vinifera transcriptome, particularly when these data are used to assist in annotation of whole genome sequences from Vitis vinifera. The MPSS data presented here not only achieved a higher level of saturation than previous EST based analyses, but in doing so, expand the known set of transcripts of grape berries during the unique stage in development that immediately precedes the onset of ripening. The MPSS dataset also revealed evidence of antisense expression not

Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer

PubMed Central

Kim, Yunee; Jeon, Jouhyun; Mejia, Salvador; Yao, Cindy Q; Ignatchenko, Vladimir; Nyalwidhe, Julius O; Gramolini, Anthony O; Lance, Raymond S; Troyer, Dean A; Drake, Richard R; Boutros, Paul C; Semmes, O. John; Kislinger, Thomas

2016-01-01

Biomarkers are rapidly gaining importance in personalized medicine. Although numerous molecular signatures have been developed over the past decade, there is a lack of overlap and many biomarkers fail to validate in independent patient cohorts and hence are not useful for clinical application. For these reasons, identification of novel and robust biomarkers remains a formidable challenge. We combine targeted proteomics with computational biology to discover robust proteomic signatures for prostate cancer. Quantitative proteomics conducted in expressed prostatic secretions from men with extraprostatic and organ-confined prostate cancers identified 133 differentially expressed proteins. Using synthetic peptides, we evaluate them by targeted proteomics in a 74-patient cohort of expressed prostatic secretions in urine. We quantify a panel of 34 candidates in an independent 207-patient cohort. We apply machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis. Our results demonstrate that computationally guided proteomics can discover highly accurate non-invasive biomarkers. PMID:27350604

A New Gene Expression Signature for Triple-Negative Breast Cancer Using Frozen Fresh Tissue before Neoadjuvant Chemotherapy

PubMed Central

Santuario-Facio, Sandra K; Cardona-Huerta, Servando; Perez-Paramo, Yadira X; Trevino, Victor; Hernandez-Cabrera, Francisco; Rojas-Martinez, Augusto; Uscanga-Perales, Grecia; Martinez-Rodriguez, Jorge L; Martinez-Jacobo, Lizeth; Padilla-Rivas, Gerardo; Muñoz-Maldonado, Gerardo; Gonzalez-Guerrero, Juan Francisco; Valero-Gomez, Javier; Vazquez-Guerrero, Ana L; Martinez-Rodriguez, Herminia G; Barboza-Quintana, Alvaro; Barboza-Quintana, Oralia; Garza-Guajardo, Raquel; Ortiz-Lopez, Rocio

2017-01-01

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer tumors. Comparisons between TNBC and non–triple-negative breast cancer (nTNBC) may help to differentiate key components involved in TNBC neoplasms. The purpose of the study was to analyze the expression profile of TNBC versus nTNBC tumors in a homogeneous population from northeastern Mexico. A prospective study of 50 patients (25 TNBC and 25 nTNBC) was conducted. Clinic parameters were equally distributed for TNBC and nTNBC: age at diagnosis (51 versus 47 years, p = 0.1), glucose level (107 mg/dl versus 104 mg/dl, p = 0.64), and body mass index (28 versus 29, p = 0.14). Core biopsies were collected for histopathological diagnosis and gene expression analysis. Total RNA was isolated and expression profiling was performed. Forty genes showed differential expression pattern in TNBC tumors. Among these, nine overexpressed genes (PRKX/PRKY, UGT8, HMGA1, LPIN1, HAPLN3, FAM171A1, BCL141A, FOXC1, and ANKRD11), and one underexpressed gene (ANX9) are involved in general metabolism. Based on this biochemical peculiarity and the overexpression of BCL11A and FOXC1 (involved in tumor growth and metastasis, respectively), we validated by quantitative polymerase chain reaction the expression profiles of seven genes out of the signature. In this report, a new gene signature for TNBC is proposed. To our knowledge, this is the first TNBC signature that describes genes involved in general metabolism. The findings may be pertinent for Mexican patients and require evaluation in other ethnic groups and populations. PMID:28474731

Gene Expression Signature in Adipose Tissue of Acromegaly Patients

PubMed Central

Hochberg, Irit; Tran, Quynh T.; Barkan, Ariel L.; Saltiel, Alan R.; Chandler, William F.; Bridges, Dave

2015-01-01

To study the effect of chronic excess growth hormone on adipose tissue, we performed RNA sequencing in adipose tissue biopsies from patients with acromegaly (n = 7) or non-functioning pituitary adenomas (n = 11). The patients underwent clinical and metabolic profiling including assessment of HOMA-IR. Explants of adipose tissue were assayed ex vivo for lipolysis and ceramide levels. Patients with acromegaly had higher glucose, higher insulin levels and higher HOMA-IR score. We observed several previously reported transcriptional changes (IGF1, IGFBP3, CISH, SOCS2) that are known to be induced by GH/IGF-1 in liver but are also induced in adipose tissue. We also identified several novel transcriptional changes, some of which may be important for GH/IGF responses (PTPN3 and PTPN4) and the effects of acromegaly on growth and proliferation. Several differentially expressed transcripts may be important in GH/IGF-1-induced metabolic changes. Specifically, induction of LPL, ABHD5, and NRIP1 can contribute to enhanced lipolysis and may explain the elevated adipose tissue lipolysis in acromegalic patients. Higher expression of TCF7L2 and the fatty acid desaturases FADS1, FADS2 and SCD could contribute to insulin resistance. Ceramides were not different between the two groups. In summary, we have identified the acromegaly gene expression signature in human adipose tissue. The significance of altered expression of specific transcripts will enhance our understanding of the metabolic and proliferative changes associated with acromegaly. PMID:26087292

Immune signatures of protective spleen memory CD8 T cells.

PubMed

Brinza, Lilia; Djebali, Sophia; Tomkowiak, Martine; Mafille, Julien; Loiseau, Céline; Jouve, Pierre-Emmanuel; de Bernard, Simon; Buffat, Laurent; Lina, Bruno; Ottmann, Michèle; Rosa-Calatrava, Manuel; Schicklin, Stéphane; Bonnefoy, Nathalie; Lauvau, Grégoire; Grau, Morgan; Wencker, Mélanie; Arpin, Christophe; Walzer, Thierry; Leverrier, Yann; Marvel, Jacqueline

2016-11-24

Memory CD8 T lymphocyte populations are remarkably heterogeneous and differ in their ability to protect the host. In order to identify the whole range of qualities uniquely associated with protective memory cells we compared the gene expression signatures of two qualities of memory CD8 T cells sharing the same antigenic-specificity: protective (Influenza-induced, Flu-TM) and non-protective (peptide-induced, TIM) spleen memory CD8 T cells. Although Flu-TM and TIM express classical phenotypic memory markers and are polyfunctional, only Flu-TM protects against a lethal viral challenge. Protective memory CD8 T cells express a unique set of genes involved in migration and survival that correlate with their unique capacity to rapidly migrate within the infected lung parenchyma in response to influenza infection. We also enlighten a new set of poised genes expressed by protective cells that is strongly enriched in cytokines and chemokines such as Ccl1, Ccl9 and Gm-csf. CCL1 and GM-CSF genes are also poised in human memory CD8 T cells. These immune signatures are also induced by two other pathogens (vaccinia virus and Listeria monocytogenes). The immune signatures associated with immune protection were identified on circulating cells, i.e. those that are easily accessible for immuno-monitoring and could help predict vaccines efficacy.

Prediction of Chemical Respiratory Sensitizers Using GARD, a Novel In Vitro Assay Based on a Genomic Biomarker Signature

PubMed Central

Albrekt, Ann-Sofie; Borrebaeck, Carl A. K.; Lindstedt, Malin

2015-01-01

Background Repeated exposure to certain low molecular weight (LMW) chemical compounds may result in development of allergic reactions in the skin or in the respiratory tract. In most cases, a certain LMW compound selectively sensitize the skin, giving rise to allergic contact dermatitis (ACD), or the respiratory tract, giving rise to occupational asthma (OA). To limit occurrence of allergic diseases, efforts are currently being made to develop predictive assays that accurately identify chemicals capable of inducing such reactions. However, while a few promising methods for prediction of skin sensitization have been described, to date no validated method, in vitro or in vivo, exists that is able to accurately classify chemicals as respiratory sensitizers. Results Recently, we presented the in vitro based Genomic Allergen Rapid Detection (GARD) assay as a novel testing strategy for classification of skin sensitizing chemicals based on measurement of a genomic biomarker signature. We have expanded the applicability domain of the GARD assay to classify also respiratory sensitizers by identifying a separate biomarker signature containing 389 differentially regulated genes for respiratory sensitizers in comparison to non-respiratory sensitizers. By using an independent data set in combination with supervised machine learning, we validated the assay, showing that the identified genomic biomarker is able to accurately classify respiratory sensitizers. Conclusions We have identified a genomic biomarker signature for classification of respiratory sensitizers. Combining this newly identified biomarker signature with our previously identified biomarker signature for classification of skin sensitizers, we have developed a novel in vitro testing strategy with a potent ability to predict both skin and respiratory sensitization in the same sample. PMID:25760038

sscMap: an extensible Java application for connecting small-molecule drugs using gene-expression signatures.

PubMed

Zhang, Shu-Dong; Gant, Timothy W

2009-07-31

Connectivity mapping is a process to recognize novel pharmacological and toxicological properties in small molecules by comparing their gene expression signatures with others in a database. A simple and robust method for connectivity mapping with increased specificity and sensitivity was recently developed, and its utility demonstrated using experimentally derived gene signatures. This paper introduces sscMap (statistically significant connections' map), a Java application designed to undertake connectivity mapping tasks using the recently published method. The software is bundled with a default collection of reference gene-expression profiles based on the publicly available dataset from the Broad Institute Connectivity Map 02, which includes data from over 7000 Affymetrix microarrays, for over 1000 small-molecule compounds, and 6100 treatment instances in 5 human cell lines. In addition, the application allows users to add their custom collections of reference profiles and is applicable to a wide range of other 'omics technologies. The utility of sscMap is two fold. First, it serves to make statistically significant connections between a user-supplied gene signature and the 6100 core reference profiles based on the Broad Institute expanded dataset. Second, it allows users to apply the same improved method to custom-built reference profiles which can be added to the database for future referencing. The software can be freely downloaded from http://purl.oclc.org/NET/sscMap.

Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index

PubMed Central

Tsai, Yu-Shuen; Aguan, Kripamoy; Pal, Nikhil R.; Chung, I-Fang

2011-01-01

Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing

Expression profiles of loneliness-associated genes for survival prediction in cancer patients.

PubMed

You, Liang-Fu; Yeh, Jia-Rong; Su, Mu-Chun

2014-01-01

Influence of loneliness on human survival has been established epidemiologically, but genomic research remains undeveloped. We identified 34 loneliness-associated genes which were statistically significant for high- lonely and low-lonely individuals. With the univariate Cox proportional hazards regression model, we obtained corresponding regression coefficients for loneliness-associated genes fo individual cancer patients. Furthermore, risk scores could be generated with the combination of gene expression level multiplied by corresponding regression coefficients of loneliness-associated genes. We verified that high-risk score cancer patients had shorter mean survival time than their low-risk score counterparts. Then we validated the loneliness-associated gene signature in three independent brain cancer cohorts with Kaplan-Meier survival curves (n=77, 85 and 191), significantly separable by log-rank test with hazard ratios (HR) >1 and p-values <0.0001 (HR=2.94, 3.82, and 1.78). Moreover, we validated the loneliness-associated gene signature in bone cancer (HR=5.10, p-value=4.69e-3), lung cancer (HR=2.86, p-value=4.71e-5), ovarian cancer (HR=1.97, p-value=3.11e-5), and leukemia (HR=2.06, p-value=1.79e-4) cohorts. The last lymphoma cohort proved to have an HR=3.50, p-value=1.15e-7. Loneliness- associated genes had good survival prediction for cancer patients, especially bone cancer patients. Our study provided the first indication that expression of loneliness-associated genes are related to survival time of cancer patients.

Prevalence of interferon type I signature in CD14 monocytes of patients with Sjögren's syndrome and association with disease activity and BAFF gene expression

PubMed Central

Brkic, Zana; Maria, Naomi I; van Helden-Meeuwsen, Cornelia G; van de Merwe, Joop P; van Daele, Paul L; Dalm, Virgil A; Wildenberg, Manon E; Beumer, Wouter; Drexhage, Hemmo A; Versnel, Marjan A

2013-01-01

Objective To determine the prevalence of upregulation of interferon (IFN) type I inducible genes, the so called ‘IFN type I signature’, in CD14 monocytes in 69 patients with primary Sjögren's syndrome (pSS) and 44 healthy controls (HC) and correlate it with disease manifestations and expression of B cell activating factor (BAFF). Methods Expression of IFI44L, IFI44, IFIT3, LY6E and MX1 was measured using real time quantitative PCR in monocytes. Expression values were used to calculate IFN type I scores for each subject. pSS patients positive for the IFN type I signature (IFN score≥10) and patients negative for the signature (IFN score<10) were then compared for clinical disease manifestations and BAFF expression. A bioassay using a monocytic cell line was performed to study whether BAFF mRNA expression was inducible by IFN type I activity in serum of patients with pSS. Results An IFN type I signature was present in 55% of patients with pSS compared with 4.5% of HC. Patients with the IFN type I signature showed: (a) higher EULAR Sjögren's Syndrome Disease Activity Index scores; higher anti-Ro52, anti-Ro60 and anti-La autoantibodies; higher rheumatoid factor; higher serum IgG; lower C3, lower absolute lymphocyte and neutrophil counts; (b)higher BAFF gene expression in monocytes. In addition, serum of signature-positive patients induced BAFF gene expression in monocytes. Conclusions The monocyte IFN type I signature identifies a subgroup of patients with pSS with a higher clinical disease activity together with higher BAFF mRNA expression. Such patients might benefit from treatment blocking IFN type I production or activity. PMID:22736090

A 16 Yin Yang gene expression ratio signature for ER+/node- breast cancer.

PubMed

Xu, Wayne; Jia, Gaofeng; Cai, Nianguang; Huang, Shujun; Davie, James R; Pitz, Marshall; Banerji, Shantanu; Murphy, Leigh

2017-03-15

Breast cancer is one of the leading causes of cancer death in women. It is a complex and heterogeneous disease with different clinical outcomes. Stratifying patients into subgroups with different outcomes could help guide clinical decision making. In this study, we used two opposing groups of genes, Yin and Yang, to develop a prognostic expression ratio signature. Using the METABRIC cohort we identified a16-gene signature capable of stratifying breast cancer patients into four risk levels with intention that low-risk patients would not undergo adjuvant systemic therapy, intermediate-low-risk patients will be treated with hormonal therapy only, and intermediate-high- and high-risk groups will be treated by chemotherapy in addition to the hormonal therapy. The 16-gene signature for four risk level stratifications of breast cancer patients has been validated using 14 independent datasets. Notably, the low-risk group (n = 51) of 205 estrogen receptor-positive and node negative (ER+/node-) patients from three different datasets who had not had any systemic adjuvant therapy had 100% 15-year disease-specific survival rate. The Concordance Index of YMR for ER+/node negative patients is close to the commercially available signatures. However, YMR showed more significance (HR = 3.7, p = 8.7e-12) in stratifying ER+/node- subgroup than OncotypeDx (HR = 2.7, p = 1.3e-7), MammaPrint (HR = 2.5, p = 5.8e-7), rorS (HR = 2.4, p = 1.4e-6), and NPI (HR = 2.6, p = 1.2e-6). YMR signature may be developed as a clinical tool to select a subgroup of low-risk ER+/node- patients who do not require any adjuvant hormonal therapy (AHT). © 2016 UICC.

Gene Expression Profiling Specifies Chemokine, Mitochondrial and Lipid Metabolism Signatures in Leprosy

PubMed Central

Guerreiro, Luana Tatiana Albuquerque; Robottom-Ferreira, Anna Beatriz; Ribeiro-Alves, Marcelo; Toledo-Pinto, Thiago Gomes; Rosa Brito, Tiana; Rosa, Patrícia Sammarco; Sandoval, Felipe Galvan; Jardim, Márcia Rodrigues; Antunes, Sérgio Gomes; Shannon, Edward J.; Sarno, Euzenir Nunes; Pessolani, Maria Cristina Vidal; Williams, Diana Lynn; Moraes, Milton Ozório

2013-01-01

Herein, we performed microarray experiments in Schwann cells infected with live M. leprae and identified novel differentially expressed genes (DEG) in M. leprae infected cells. Also, we selected candidate genes associated or implicated with leprosy in genetic studies and biological experiments. Forty-seven genes were selected for validation in two independent types of samples by multiplex qPCR. First, an in vitro model using THP-1 cells was infected with live Mycobacterium leprae and M. bovis bacillus Calmette-Guérin (BCG). In a second situation, mRNA obtained from nerve biopsies from patients with leprosy or other peripheral neuropathies was tested. We detected DEGs that discriminate M. bovis BCG from M. leprae infection. Specific signatures of susceptible responses after M. leprae infection when compared to BCG lead to repression of genes, including CCL2, CCL3, IL8 and SOD2. The same 47-gene set was screened in nerve biopsies, which corroborated the down-regulation of CCL2 and CCL3 in leprosy, but also evidenced the down-regulation of genes involved in mitochondrial metabolism, and the up-regulation of genes involved in lipid metabolism and ubiquitination. Finally, a gene expression signature from DEG was identified in patients confirmed of having leprosy. A classification tree was able to ascertain 80% of the cases as leprosy or non-leprous peripheral neuropathy based on the expression of only LDLR and CCL4. A general immune and mitochondrial hypo-responsive state occurs in response to M. leprae infection. Also, the most important genes and pathways have been highlighted providing new tools for early diagnosis and treatment of leprosy. PMID:23798993

Characteristics of genomic signatures derived using univariate methods and mechanistically anchored functional descriptors for predicting drug- and xenobiotic-induced nephrotoxicity.

PubMed

Shi, Weiwei; Bugrim, Andrej; Nikolsky, Yuri; Nikolskya, Tatiana; Brennan, Richard J

2008-01-01

ABSTRACT The ideal toxicity biomarker is composed of the properties of prediction (is detected prior to traditional pathological signs of injury), accuracy (high sensitivity and specificity), and mechanistic relationships to the endpoint measured (biological relevance). Gene expression-based toxicity biomarkers ("signatures") have shown good predictive power and accuracy, but are difficult to interpret biologically. We have compared different statistical methods of feature selection with knowledge-based approaches, using GeneGo's database of canonical pathway maps, to generate gene sets for the classification of renal tubule toxicity. The gene set selection algorithms include four univariate analyses: t-statistics, fold-change, B-statistics, and RankProd, and their combination and overlap for the identification of differentially expressed probes. Enrichment analysis following the results of the four univariate analyses, Hotelling T-square test, and, finally out-of-bag selection, a variant of cross-validation, were used to identify canonical pathway maps-sets of genes coordinately involved in key biological processes-with classification power. Differentially expressed genes identified by the different statistical univariate analyses all generated reasonably performing classifiers of tubule toxicity. Maps identified by enrichment analysis or Hotelling T-square had lower classification power, but highlighted perturbed lipid homeostasis as a common discriminator of nephrotoxic treatments. The out-of-bag method yielded the best functionally integrated classifier. The map "ephrins signaling" performed comparably to a classifier derived using sparse linear programming, a machine learning algorithm, and represents a signaling network specifically involved in renal tubule development and integrity. Such functional descriptors of toxicity promise to better integrate predictive toxicogenomics with mechanistic analysis, facilitating the interpretation and risk assessment of

Gene Expression Signatures Characterized by Longitudinal Stability and Interindividual Variability Delineate Baseline Phenotypic Groups with Distinct Responses to Immune Stimulation.

PubMed

Scheid, Adam D; Van Keulen, Virginia P; Felts, Sara J; Neier, Steven C; Middha, Sumit; Nair, Asha A; Techentin, Robert W; Gilbert, Barry K; Jen, Jin; Neuhauser, Claudia; Zhang, Yuji; Pease, Larry R

2018-03-01

Human immunity exhibits remarkable heterogeneity among individuals, which engenders variable responses to immune perturbations in human populations. Population studies reveal that, in addition to interindividual heterogeneity, systemic immune signatures display longitudinal stability within individuals, and these signatures may reliably dictate how given individuals respond to immune perturbations. We hypothesize that analyzing relationships among these signatures at the population level may uncover baseline immune phenotypes that correspond with response outcomes to immune stimuli. To test this, we quantified global gene expression in peripheral blood CD4 + cells from healthy individuals at baseline and following CD3/CD28 stimulation at two time points 1 mo apart. Systemic CD4 + cell baseline and poststimulation molecular immune response signatures (MIRS) were defined by identifying genes expressed at levels that were stable between time points within individuals and differential among individuals in each state. Iterative differential gene expression analyses between all possible phenotypic groupings of at least three individuals using the baseline and stimulated MIRS gene sets revealed shared baseline and response phenotypic groupings, indicating the baseline MIRS contained determinants of immune responsiveness. Furthermore, significant numbers of shared phenotype-defining sets of determinants were identified in baseline data across independent healthy cohorts. Combining the cohorts and repeating the analyses resulted in identification of over 6000 baseline immune phenotypic groups, implying that the MIRS concept may be useful in many immune perturbation contexts. These findings demonstrate that patterns in complex gene expression variability can be used to define immune phenotypes and discover determinants of immune responsiveness. Copyright © 2018 by The American Association of Immunologists, Inc.

Gene Expression-Based Survival Prediction in Lung Adenocarcinoma: A Multi-Site, Blinded Validation Study

PubMed Central

Shedden, Kerby; Taylor, Jeremy M.G.; Enkemann, Steve A.; Tsao, Ming S.; Yeatman, Timothy J.; Gerald, William L.; Eschrich, Steve; Jurisica, Igor; Venkatraman, Seshan E.; Meyerson, Matthew; Kuick, Rork; Dobbin, Kevin K.; Lively, Tracy; Jacobson, James W.; Beer, David G.; Giordano, Thomas J.; Misek, David E.; Chang, Andrew C.; Zhu, Chang Qi; Strumpf, Dan; Hanash, Samir; Shepherd, Francis A.; Ding, Kuyue; Seymour, Lesley; Naoki, Katsuhiko; Pennell, Nathan; Weir, Barbara; Verhaak, Roel; Ladd-Acosta, Christine; Golub, Todd; Gruidl, Mike; Szoke, Janos; Zakowski, Maureen; Rusch, Valerie; Kris, Mark; Viale, Agnes; Motoi, Noriko; Travis, William; Sharma, Anupama

2009-01-01

Although prognostic gene expression signatures for survival in early stage lung cancer have been proposed, for clinical application it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) can be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas. PMID:18641660

EG-05COMBINATION OF GENE COPY GAIN AND EPIGENETIC DEREGULATION ARE ASSOCIATED WITH THE ABERRANT EXPRESSION OF A STEM CELL RELATED HOX-SIGNATURE IN GLIOBLASTOMA

PubMed Central

Kurscheid, Sebastian; Bady, Pierre; Sciuscio, Davide; Samarzija, Ivana; Shay, Tal; Vassallo, Irene; Van Criekinge, Wim; Domany, Eytan; Stupp, Roger; Delorenzi, Mauro; Hegi, Monika

2014-01-01

We previously reported a stem cell related HOX gene signature associated with resistance to chemo-radiotherapy (TMZ/RT- > TMZ) in glioblastoma. However, underlying mechanisms triggering overexpression remain mostly elusive. Interestingly, HOX genes are neither involved in the developing brain, nor expressed in normal brain, suggestive of an acquired gene expression signature during gliomagenesis. HOXA genes are located on CHR 7 that displays trisomy in most glioblastoma which strongly impacts gene expression on this chromosome, modulated by local regulatory elements. Furthermore we observed more pronounced DNA methylation across the HOXA locus as compared to non-tumoral brain (Human methylation 450K BeadChip Illumina; 59 glioblastoma, 5 non-tumoral brain sampes). CpG probes annotated for HOX-signature genes, contributing most to the variability, served as input into the analysis of DNA methylation and expression to identify key regulatory regions. The structural similarity of the observed correlation matrices between DNA methylation and gene expression in our cohort and an independent data-set from TCGA (106 glioblastoma) was remarkable (RV-coefficient, 0.84; p-value < 0.0001). We identified a CpG located in the promoter region of the HOXA10 locus exerting the strongest mean negative correlation between methylation and expression of the whole HOX-signature. Applying this analysis the same CpG emerged in the external set. We then determined the contribution of both, gene copy aberration (CNA) and methylation at the selected probe to explain expression of the HOX-signature using a linear model. Statistically significant results suggested an additive effect between gene dosage and methylation at the key CpG identified. Similarly, such an additive effect was also observed in the external data-set. Taken together, we hypothesize that overexpression of the stem-cell related HOX signature is triggered by gain of trisomy 7 and escape from compensatory DNA methylation at

Genomic scar signatures associated with homologous recombination deficiency predict adverse clinical outcomes in patients with ovarian clear cell carcinoma.

PubMed

Chao, Angel; Lai, Chyong-Huey; Wang, Tzu-Hao; Jung, Shih-Ming; Lee, Yun-Shien; Chang, Wei-Yang; Yang, Lan-Yang; Ku, Fei-Chun; Huang, Huei-Jean; Chao, An-Shine; Wang, Chin-Jung; Chang, Ting-Chang; Wu, Ren-Chin

2018-05-03

We investigated whether genomic scar signatures associated with homologous recombination deficiency (HRD), which include telomeric allelic imbalance (TAI), large-scale transition (LST), and loss of heterozygosity (LOH), can predict clinical outcomes in patients with ovarian clear cell carcinoma (OCCC). We enrolled patients with OCCC (n = 80) and high-grade serous carcinoma (HGSC; n = 92) subjected to primary cytoreductive surgery, most of whom received platinum-based adjuvant chemotherapy. Genomic scar signatures based on genome-wide copy number data were determined in all participants and investigated in relation to prognosis. OCCC had significantly lower genomic scar signature scores than HGSC (p < 0.001). Near-triploid OCCC specimens showed higher TAI and LST scores compared with diploid tumors (p < 0.001). While high scores of these genomic scar signatures were significantly associated with better clinical outcomes in patients with HGSC, the opposite was evident for OCCC. Multivariate survival analysis in patients with OCCC identified high LOH scores as the main independent adverse predictor for both cancer-specific (hazard ratio [HR] = 3.22, p = 0.005) and progression-free survival (HR = 2.54, p = 0.01). In conclusion, genomic scar signatures associated with HRD predict adverse clinical outcomes in patients with OCCC. The LOH score was identified as the strongest prognostic indicator in this patient group. Genomic scar signatures associated with HRD are less frequent in OCCC than in HGSC. Genomic scar signatures associated with HRD have an adverse prognostic impact in patients with OCCC. LOH score is the strongest adverse prognostic factor in patients with OCCC.

A BRCA1 deficient-like signature is enriched in breast cancer brain metastases and predicts DNA damage-induced poly (ADP-ribose) polymerase inhibitor sensitivity.

PubMed

McMullin, Ryan P; Wittner, Ben S; Yang, Chuanwei; Denton-Schneider, Benjamin R; Hicks, Daniel; Singavarapu, Raj; Moulis, Sharon; Lee, Jeongeun; Akbari, Mohammad R; Narod, Steven A; Aldape, Kenneth D; Steeg, Patricia S; Ramaswamy, Sridhar; Sgroi, Dennis C

2014-03-14

There is an unmet clinical need for biomarkers to identify breast cancer patients at an increased risk of developing brain metastases. The objective is to identify gene signatures and biological pathways associated with human epidermal growth factor receptor 2-positive (HER2+) brain metastasis. We combined laser capture microdissection and gene expression microarrays to analyze malignant epithelium from HER2+ breast cancer brain metastases with that from HER2+ nonmetastatic primary tumors. Differential gene expression was performed including gene set enrichment analysis (GSEA) using publicly available breast cancer gene expression data sets. In a cohort of HER2+ breast cancer brain metastases, we identified a gene expression signature that anti-correlates with overexpression of BRCA1. Sequence analysis of the HER2+ brain metastases revealed no pathogenic mutations of BRCA1, and therefore the aforementioned signature was designated BRCA1 Deficient-Like (BD-L). Evaluation of an independent cohort of breast cancer metastases demonstrated that BD-L values are significantly higher in brain metastases as compared to other metastatic sites. Although the BD-L signature is present in all subtypes of breast cancer, it is significantly higher in BRCA1 mutant primary tumors as compared with sporadic breast tumors. Additionally, BD-L signature values are significantly higher in HER2-/ER- primary tumors as compared with HER2+/ER + and HER2-/ER + tumors. The BD-L signature correlates with breast cancer cell line pharmacologic response to a combination of poly (ADP-ribose) polymerase (PARP) inhibitor and temozolomide, and the signature outperformed four published gene signatures of BRCA1/2 deficiency. A BD-L signature is enriched in HER2+ breast cancer brain metastases without pathogenic BRCA1 mutations. Unexpectedly, elevated BD-L values are found in a subset of primary tumors across all breast cancer subtypes. Evaluation of pharmacological sensitivity in breast cancer

A BRCA1 deficient-like signature is enriched in breast cancer brain metastases and predicts DNA damage-induced poly (ADP-ribose) polymerase inhibitor sensitivity

PubMed Central

2014-01-01

Introduction There is an unmet clinical need for biomarkers to identify breast cancer patients at an increased risk of developing brain metastases. The objective is to identify gene signatures and biological pathways associated with human epidermal growth factor receptor 2-positive (HER2+) brain metastasis. Methods We combined laser capture microdissection and gene expression microarrays to analyze malignant epithelium from HER2+ breast cancer brain metastases with that from HER2+ nonmetastatic primary tumors. Differential gene expression was performed including gene set enrichment analysis (GSEA) using publicly available breast cancer gene expression data sets. Results In a cohort of HER2+ breast cancer brain metastases, we identified a gene expression signature that anti-correlates with overexpression of BRCA1. Sequence analysis of the HER2+ brain metastases revealed no pathogenic mutations of BRCA1, and therefore the aforementioned signature was designated BRCA1 Deficient-Like (BD-L). Evaluation of an independent cohort of breast cancer metastases demonstrated that BD-L values are significantly higher in brain metastases as compared to other metastatic sites. Although the BD-L signature is present in all subtypes of breast cancer, it is significantly higher in BRCA1 mutant primary tumors as compared with sporadic breast tumors. Additionally, BD-L signature values are significantly higher in HER2-/ER- primary tumors as compared with HER2+/ER + and HER2-/ER + tumors. The BD-L signature correlates with breast cancer cell line pharmacologic response to a combination of poly (ADP-ribose) polymerase (PARP) inhibitor and temozolomide, and the signature outperformed four published gene signatures of BRCA1/2 deficiency. Conclusions A BD-L signature is enriched in HER2+ breast cancer brain metastases without pathogenic BRCA1 mutations. Unexpectedly, elevated BD-L values are found in a subset of primary tumors across all breast cancer subtypes. Evaluation of

BMI1 induces an invasive signature in melanoma that promotes metastasis and chemoresistance

PubMed Central

Ferretti, Roberta; Bhutkar, Arjun; McNamara, Molly C.; Lees, Jacqueline A.

2016-01-01

Melanoma can switch between proliferative and invasive states, which have identifying gene expression signatures that correlate with good and poor prognosis, respectively. However, the mechanisms controlling these signatures are poorly understood. In this study, we identify BMI1 as a key determinant of melanoma metastasis by which its overexpression enhanced and its deletion impaired dissemination. Remarkably, in this tumor type, BMI1 had no effect on proliferation or primary tumor growth but enhanced every step of the metastatic cascade. Consistent with the broad spectrum of effects, BMI1 activated widespread gene expression changes, which are characteristic of melanoma progression and also chemoresistance. Accordingly, we showed that up-regulation or down-regulation of BMI1 induced resistance or sensitivity to BRAF inhibitor treatment and that induction of noncanonical Wnt by BMI1 is required for this resistance. Finally, we showed that our BMI1-induced gene signature encompasses all of the hallmarks of the previously described melanoma invasive signature. Moreover, our signature is predictive of poor prognosis in human melanoma and is able to identify primary tumors that are likely to become metastatic. These data yield key insights into melanoma biology and establish BMI1 as a compelling drug target whose inhibition would suppress both metastasis and chemoresistance of melanoma. PMID:26679841

An Approach for Assessing the Signature Quality of Various Chemical Assays when Predicting the Culture Media Used to Grow Microorganisms

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

Holmes, Aimee E.; Sego, Landon H.; Webb-Robertson, Bobbie-Jo M.

We demonstrate an approach for assessing the quality of a signature system designed to predict the culture medium used to grow a microorganism. The system was comprised of four chemical assays designed to identify various ingredients that could be used to produce the culture medium. The analytical measurements resulting from any combination of these four assays can be used in a Bayesian network to predict the probabilities that the microorganism was grown using one of eleven culture media. We evaluated combinations of the signature system by removing one or more of the assays from the Bayes network. We measured andmore » compared the quality of the various Bayes nets in terms of fidelity, cost, risk, and utility, a method we refer to as Signature Quality Metrics« less

Genome signature analysis of thermal virus metagenomes reveals Archaea and thermophilic signatures.

PubMed

Pride, David T; Schoenfeld, Thomas

2008-09-17

Metagenomic analysis provides a rich source of biological information for otherwise intractable viral communities. However, study of viral metagenomes has been hampered by its nearly complete reliance on BLAST algorithms for identification of DNA sequences. We sought to develop algorithms for examination of viral metagenomes to identify the origin of sequences independent of BLAST algorithms. We chose viral metagenomes obtained from two hot springs, Bear Paw and Octopus, in Yellowstone National Park, as they represent simple microbial populations where comparatively large contigs were obtained. Thermal spring metagenomes have high proportions of sequences without significant Genbank homology, which has hampered identification of viruses and their linkage with hosts. To analyze each metagenome, we developed a method to classify DNA fragments using genome signature-based phylogenetic classification (GSPC), where metagenomic fragments are compared to a database of oligonucleotide signatures for all previously sequenced Bacteria, Archaea, and viruses. From both Bear Paw and Octopus hot springs, each assembled contig had more similarity to other metagenome contigs than to any sequenced microbial genome based on GSPC analysis, suggesting a genome signature common to each of these extreme environments. While viral metagenomes from Bear Paw and Octopus share some similarity, the genome signatures from each locale are largely unique. GSPC using a microbial database predicts most of the Octopus metagenome has archaeal signatures, while bacterial signatures predominate in Bear Paw; a finding consistent with those of Genbank BLAST. When using a viral database, the majority of the Octopus metagenome is predicted to belong to archaeal virus Families Globuloviridae and Fuselloviridae, while none of the Bear Paw metagenome is predicted to belong to archaeal viruses. As expected, when microbial and viral databases are combined, each of the Octopus and Bear Paw metagenomic contigs

Longitudinal Transcriptome Analysis Reveals a Sustained Differential Gene Expression Signature in Patients Treated for Acute Lyme Disease

PubMed Central

Bouquet, Jerome; Soloski, Mark J.; Swei, Andrea; Cheadle, Chris; Federman, Scot; Billaud, Jean-Noel; Rebman, Alison W.; Kabre, Beniwende; Halpert, Richard; Boorgula, Meher

2016-01-01

ABSTRACT Lyme disease is a tick-borne illness caused by the bacterium Borrelia burgdorferi, and approximately 10 to 20% of patients report persistent symptoms lasting months to years despite appropriate treatment with antibiotics. To gain insights into the molecular basis of acute Lyme disease and the ensuing development of post-treatment symptoms, we conducted a longitudinal transcriptome study of 29 Lyme disease patients (and 13 matched controls) enrolled at the time of diagnosis and followed for up to 6 months. The differential gene expression signature of Lyme disease following the acute phase of infection persisted for at least 3 weeks and had fewer than 44% differentially expressed genes (DEGs) in common with other infectious or noninfectious syndromes. Early Lyme disease prior to antibiotic therapy was characterized by marked upregulation of Toll-like receptor signaling but lack of activation of the inflammatory T-cell apoptotic and B-cell developmental pathways seen in other acute infectious syndromes. Six months after completion of therapy, Lyme disease patients were found to have 31 to 60% of their pathways in common with three different immune-mediated chronic diseases. No differential gene expression signature was observed between Lyme disease patients with resolved illness to those with persistent symptoms at 6 months post-treatment. The identification of a sustained differential gene expression signature in Lyme disease suggests that a panel of selected human host-based biomarkers may address the need for sensitive clinical diagnostics during the “window period” of infection prior to the appearance of a detectable antibody response and may also inform the development of new therapeutic targets. PMID:26873097

Advanced colorectal adenoma related gene expression signature may predict prognostic for colorectal cancer patients with adenoma-carcinoma sequence.

PubMed

Li, Bing; Shi, Xiao-Yu; Liao, Dai-Xiang; Cao, Bang-Rong; Luo, Cheng-Hua; Cheng, Shu-Jun

2015-01-01

There are still no absolute parameters predicting progression of adenoma into cancer. The present study aimed to characterize functional differences on the multistep carcinogenetic process from the adenoma-carcinoma sequence. All samples were collected and mRNA expression profiling was performed by using Agilent Microarray high-throughput gene-chip technology. Then, the characteristics of mRNA expression profiles of adenoma-carcinoma sequence were described with bioinformatics software, and we analyzed the relationship between gene expression profiles of adenoma-adenocarcinoma sequence and clinical prognosis of colorectal cancer. The mRNA expressions of adenoma-carcinoma sequence were significantly different between high-grade intraepithelial neoplasia group and adenocarcinoma group. The biological process of gene ontology function enrichment analysis on differentially expressed genes between high-grade intraepithelial neoplasia group and adenocarcinoma group showed that genes enriched in the extracellular structure organization, skeletal system development, biological adhesion and itself regulated growth regulation, with the P value after FDR correction of less than 0.05. In addition, IPR-related protein mainly focused on the insulin-like growth factor binding proteins. The variable trends of gene expression profiles for adenoma-carcinoma sequence were mainly concentrated in high-grade intraepithelial neoplasia and adenocarcinoma. The differentially expressed genes are significantly correlated between high-grade intraepithelial neoplasia group and adenocarcinoma group. Bioinformatics analysis is an effective way to study the gene expression profiles in the adenoma-carcinoma sequence, and may provide an effective tool to involve colorectal cancer research strategy into colorectal adenoma or advanced adenoma.

Irma 5.1 multisensor signature prediction model

NASA Astrophysics Data System (ADS)

Savage, James; Coker, Charles; Thai, Bea; Aboutalib, Omar; Yamaoka, Neil; Kim, Charles

2005-05-01

The Irma synthetic signature prediction code is being developed to facilitate the research and development of multisensor systems. Irma was one of the first high resolution Infrared (IR) target and background signature models to be developed for tactical weapon application. Originally developed in 1980 by the Munitions Directorate of the Air Force Research Laboratory (AFRL/MN), the Irma model was used exclusively to generate IR scenes. In 1988, a number of significant upgrades to Irma were initiated including the addition of a laser (or active) channel. This two-channel version was released to the user community in 1990. In 1992, an improved scene generator was incorporated into the Irma model, which supported correlated frame-to-frame imagery. A passive IR/millimeter wave (MMW) code was completed in 1994. This served as the cornerstone for the development of the co-registered active/passive IR/MMW model, Irma 4.0. In 2000, Irma version 5.0 was released which encompassed several upgrades to both the physical models and software. Circular polarization was added to the passive channel and the doppler capability was added to the active MMW channel. In 2002, the multibounce technique was added to the Irma passive channel. In the ladar channel, a user-friendly Ladar Sensor Assistant (LSA) was incorporated which provides capability and flexibility for sensor modeling. Irma 5.0 runs on several platforms including Windows, Linux, Solaris, and SGI Irix. Since 2000, additional capabilities and enhancements have been added to the ladar channel including polarization and speckle effect. Work is still ongoing to add time-jittering model to the ladar channel. A new user interface has been introduced to aid users in the mechanism of scene generation and running the Irma code. The user interface provides a canvas where a user can add and remove objects using mouse clicks to construct a scene. The scene can then be visualized to find the desired sensor position. The synthetic ladar

Development and validation of a gene expression-based signature to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma: a retrospective, multicentre, cohort study.

PubMed

Tang, Xin-Ran; Li, Ying-Qin; Liang, Shao-Bo; Jiang, Wei; Liu, Fang; Ge, Wen-Xiu; Tang, Ling-Long; Mao, Yan-Ping; He, Qing-Mei; Yang, Xiao-Jing; Zhang, Yuan; Wen, Xin; Zhang, Jian; Wang, Ya-Qin; Zhang, Pan-Pan; Sun, Ying; Yun, Jing-Ping; Zeng, Jing; Li, Li; Liu, Li-Zhi; Liu, Na; Ma, Jun

2018-03-01

Gene expression patterns can be used as prognostic biomarkers in various types of cancers. We aimed to identify a gene expression pattern for individual distant metastatic risk assessment in patients with locoregionally advanced nasopharyngeal carcinoma. In this multicentre, retrospective, cohort analysis, we included 937 patients with locoregionally advanced nasopharyngeal carcinoma from three Chinese hospitals: the Sun Yat-sen University Cancer Center (Guangzhou, China), the Affiliated Hospital of Guilin Medical University (Guilin, China), and the First People's Hospital of Foshan (Foshan, China). Using microarray analysis, we profiled mRNA gene expression between 24 paired locoregionally advanced nasopharyngeal carcinoma tumours from patients at Sun Yat-sen University Cancer Center with or without distant metastasis after radical treatment. Differentially expressed genes were examined using digital expression profiling in a training cohort (Guangzhou training cohort; n=410) to build a gene classifier using a penalised regression model. We validated the prognostic accuracy of this gene classifier in an internal validation cohort (Guangzhou internal validation cohort, n=204) and two external independent cohorts (Guilin cohort, n=165; Foshan cohort, n=158). The primary endpoint was distant metastasis-free survival. Secondary endpoints were disease-free survival and overall survival. We identified 137 differentially expressed genes between metastatic and non-metastatic locoregionally advanced nasopharyngeal carcinoma tissues. A distant metastasis gene signature for locoregionally advanced nasopharyngeal carcinoma (DMGN) that consisted of 13 genes was generated to classify patients into high-risk and low-risk groups in the training cohort. Patients with high-risk scores in the training cohort had shorter distant metastasis-free survival (hazard ratio [HR] 4·93, 95% CI 2·99-8·16; p<0·0001), disease-free survival (HR 3·51, 2·43-5·07; p<0·0001), and overall

Visualization and analysis for multidimensional gene expressions signature of cigarette smoking

NASA Astrophysics Data System (ADS)

Wang, Changbo; Xiao, Zhao; Zhang, Tianlun; Cui, Jin; Pang, Chenming

2011-11-01

Biologists often use gene chip to get massive experimental data in the field of bioscience and chemical sciences. Facing a large amount of experimental data, researchers often need to find out a few interesting data or simple regulations. This paper presents a set of methods to visualize and analyze the data for gene expression signatures of people who smoke. We use the latest research data from National Center for Biotechnology Information. Totally, there are more than 400 thousand expressions data. Using these data, we can use parallel coordinates method to visualize the different gene expressions between smokers and nonsmokers and we can distinguish non-smokers, former smokers and current smokers by using the different colors. It can be easy to find out which gene is more important during the lung cancer angiogenesis in the smoking people. In another way, we can use a hierarchical model to visualize the inner relation of different genes. The location of the nodes shows different expression moment and the distance to the root shows the sequence of the expression. We can use the ring layout to represent all the nodes, and connect the different nodes which are related with color lines. Combined with the parallel coordinates method, the visualization result show the important genes and some inner relation obviously, which is useful for examination and prevention of lung cancer.

Analysis of Gene Expression Profiles of Multiple Skin Diseases Identifies a Conserved Signature of Disrupted Homeostasis.

PubMed

Mills, Kevin J; Robinson, Michael K; Sherrill, Joseph D; Schnell, Daniel J; Xu, Jun

2018-05-28

Triggers of skin disease pathogenesis vary, but events associated with the elicitation of a lesion share many features in common. Our objective was to examine gene expression patterns in skin disease to develop a molecular signature of disruption of cutaneous homeostasis. Gene expression data from common inflammatory skin diseases (e.g., psoriasis, atopic dermatitis, seborrheic dermatitis and acne), and a novel statistical algorithm were used to define a unifying molecular signature referred to as the "Unhealthy Skin Signature" (USS). Using a pattern matching algorithm, analysis of public data repositories revealed that the USS is found in diverse epithelial diseases. Studies of milder disruptions of epidermal homeostasis have also shown that these conditions converge, to varying degrees, on the USS and that the degree of convergence is related directly to the severity of homeostatic disruption. The USS contains genes that had no prior published association with skin, but that play important roles in many different disease processes, supporting the importance of the USS to homeostasis. Finally, we show through pattern matching that the USS can be used to discover new potential dermatologic therapeutics. The USS provides a new means to further interrogate epithelial homeostasis and potentially develop novel therapeutics with efficacy across a spectrum of skin conditions. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

A Gene Expression Signature Associated with Overall Survival in Patients with Hepatocellular Carcinoma Suggests a New Treatment Strategy.

PubMed

Gillet, Jean-Pierre; Andersen, Jesper B; Madigan, James P; Varma, Sudhir; Bagni, Rachel K; Powell, Katie; Burgan, William E; Wu, Chung-Pu; Calcagno, Anna Maria; Ambudkar, Suresh V; Thorgeirsson, Snorri S; Gottesman, Michael M

2016-02-01

Despite improvements in the management of liver cancer, the survival rate for patients with hepatocellular carcinoma (HCC) remains dismal. The survival benefit of systemic chemotherapy for the treatment of liver cancer is only marginal. Although the reasons for treatment failure are multifactorial, intrinsic resistance to chemotherapy plays a primary role. Here, we analyzed the expression of 377 multidrug resistance (MDR)-associated genes in two independent cohorts of patients with advanced HCC, with the aim of finding ways to improve survival in this poor-prognosis cancer. Taqman-based quantitative polymerase chain reaction revealed a 45-gene signature that predicts overall survival (OS) in patients with HCC. Using the Connectivity Map Tool, we were able to identify drugs that converted the gene expression profiles of HCC cell lines from ones matching patients with poor OS to profiles associated with good OS. We found three compounds that convert the gene expression profiles of three HCC cell lines to gene expression profiles associated with good OS. These compounds increase histone acetylation, which correlates with the synergistic sensitization of those MDR tumor cells to conventional chemotherapeutic agents, including cisplatin, sorafenib, and 5-fluorouracil. Our results indicate that it is possible to modulate gene expression profiles in HCC cell lines to those associated with better outcome. This approach also increases sensitization of HCC cells toward conventional chemotherapeutic agents. This work suggests new treatment strategies for a disease for which few therapeutic options exist. U.S. Government work not protected by U.S. copyright.

A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests

PubMed Central

2011-01-01

Background Allergic contact dermatitis is an inflammatory skin disease that affects a significant proportion of the population. This disease is caused by an adverse immune response towards chemical haptens, and leads to a substantial economic burden for society. Current test of sensitizing chemicals rely on animal experimentation. New legislations on the registration and use of chemicals within pharmaceutical and cosmetic industries have stimulated significant research efforts to develop alternative, human cell-based assays for the prediction of sensitization. The aim is to replace animal experiments with in vitro tests displaying a higher predictive power. Results We have developed a novel cell-based assay for the prediction of sensitizing chemicals. By analyzing the transcriptome of the human cell line MUTZ-3 after 24 h stimulation, using 20 different sensitizing chemicals, 20 non-sensitizing chemicals and vehicle controls, we have identified a biomarker signature of 200 genes with potent discriminatory ability. Using a Support Vector Machine for supervised classification, the prediction performance of the assay revealed an area under the ROC curve of 0.98. In addition, categorizing the chemicals according to the LLNA assay, this gene signature could also predict sensitizing potency. The identified markers are involved in biological pathways with immunological relevant functions, which can shed light on the process of human sensitization. Conclusions A gene signature predicting sensitization, using a human cell line in vitro, has been identified. This simple and robust cell-based assay has the potential to completely replace or drastically reduce the utilization of test systems based on experimental animals. Being based on human biology, the assay is proposed to be more accurate for predicting sensitization in humans, than the traditional animal-based tests. PMID:21824406

First Generation Gene Expression Signature for Early Prediction of Late Occurring Hematological Acute Radiation Syndrome in Baboons.

PubMed

Port, M; Herodin, F; Valente, M; Drouet, M; Lamkowski, A; Majewski, M; Abend, M

2016-07-01

We implemented a two-stage study to predict late occurring hematologic acute radiation syndrome (HARS) in a baboon model based on gene expression changes measured in peripheral blood within the first two days after irradiation. Eighteen baboons were irradiated to simulate different patterns of partial-body and total-body exposure, which corresponded to an equivalent dose of 2.5 or 5 Gy. According to changes in blood cell counts the surviving baboons (n = 17) exhibited mild (H1-2, n = 4) or more severe (H2-3, n = 13) HARS. Blood samples taken before irradiation served as unexposed control (H0, n = 17). For stage I of this study, a whole genome screen (mRNA microarrays) was performed using a portion of the samples (H0, n = 5; H1-2, n = 4; H2-3, n = 5). For stage II, using the remaining samples and the more sensitive methodology, qRT-PCR, validation was performed on candidate genes that were differentially up- or down-regulated during the first two days after irradiation. Differential gene expression was defined as significant (P < 0.05) and greater than or equal to a twofold difference above a H0 classification. From approximately 20,000 genes, on average 46% appeared to be expressed. On day 1 postirradiation for H2-3, approximately 2-3 times more genes appeared up-regulated (1,418 vs. 550) or down-regulated (1,603 vs. 735) compared to H1-2. This pattern became more pronounced at day 2 while the number of differentially expressed genes decreased. The specific genes showed an enrichment of biological processes coding for immune system processes, natural killer cell activation and immune response (P = 1 × E-06 up to 9 × E-14). Based on the P values, magnitude and sustained differential gene expression over time, we selected 89 candidate genes for validation using qRT-PCR. Ultimately, 22 genes were confirmed for identification of H1-3 classifications and seven genes for identification of H2-3 classifications using qRT-PCR. For H1-3 classifications, most genes were

TransCONFIRM: Identification of a Genetic Signature of Response to Fulvestrant in Advanced Hormone Receptor-Positive Breast Cancer.

PubMed

Jeselsohn, Rinath; Barry, William T; Migliaccio, Ilenia; Biagioni, Chiara; Zhao, Jin; De Tribolet-Hardy, Jonas; Guarducci, Cristina; Bonechi, Martina; Laing, Naomi; Winer, Eric P; Brown, Myles; Leo, Angelo Di; Malorni, Luca

2016-12-01

Fulvestrant is an estrogen receptor (ER) antagonist and an approved treatment for metastatic estrogen receptor-positive (ER + ) breast cancer. With the exception of ER levels, there are no established predictive biomarkers of response to single-agent fulvestrant. We attempted to identify a gene signature of response to fulvestrant in advanced breast cancer. Primary tumor samples from 134 patients enrolled in the phase III CONFIRM study of patients with metastatic ER + breast cancer comparing treatment with either 250 mg or 500 mg fulvestrant were collected for genome-wide transcriptomic analysis. Gene expression profiling was performed using Affymetrix microarrays. An exploratory analysis was performed to identify biologic pathways and new signatures associated with response to fulvestrant. Pathway analysis demonstrated that increased EGF pathway and FOXA1 transcriptional signaling is associated with decreased response to fulvestrant. Using a multivariate Cox model, we identified a novel set of 37 genes with an expression that is independently associated with progression-free survival (PFS). TFAP2C, a known regulator of ER activity, was ranked second in this gene set, and high expression was associated with a decreased response to fulvestrant. The negative predictive value of TFAP2C expression at the protein level was confirmed by IHC. We identified biologic pathways and a novel gene signature in primary ER + breast cancers that predicts for response to treatment in the CONFIRM study. These results suggest potential new therapeutic targets and warrant further validation as predictive biomarkers of fulvestrant treatment in metastatic breast cancer. Clin Cancer Res; 22(23); 5755-64. ©2016 AACR. ©2016 American Association for Cancer Research.

Stromal-Based Signatures for the Classification of Gastric Cancer.

PubMed

Uhlik, Mark T; Liu, Jiangang; Falcon, Beverly L; Iyer, Seema; Stewart, Julie; Celikkaya, Hilal; O'Mahony, Marguerita; Sevinsky, Christopher; Lowes, Christina; Douglass, Larry; Jeffries, Cynthia; Bodenmiller, Diane; Chintharlapalli, Sudhakar; Fischl, Anthony; Gerald, Damien; Xue, Qi; Lee, Jee-Yun; Santamaria-Pang, Alberto; Al-Kofahi, Yousef; Sui, Yunxia; Desai, Keyur; Doman, Thompson; Aggarwal, Amit; Carter, Julia H; Pytowski, Bronislaw; Jaminet, Shou-Ching; Ginty, Fiona; Nasir, Aejaz; Nagy, Janice A; Dvorak, Harold F; Benjamin, Laura E

2016-05-01

Treatment of metastatic gastric cancer typically involves chemotherapy and monoclonal antibodies targeting HER2 (ERBB2) and VEGFR2 (KDR). However, reliable methods to identify patients who would benefit most from a combination of treatment modalities targeting the tumor stroma, including new immunotherapy approaches, are still lacking. Therefore, we integrated a mouse model of stromal activation and gastric cancer genomic information to identify gene expression signatures that may inform treatment strategies. We generated a mouse model in which VEGF-A is expressed via adenovirus, enabling a stromal response marked by immune infiltration and angiogenesis at the injection site, and identified distinct stromal gene expression signatures. With these data, we designed multiplexed IHC assays that were applied to human primary gastric tumors and classified each tumor to a dominant stromal phenotype representative of the vascular and immune diversity found in gastric cancer. We also refined the stromal gene signatures and explored their relation to the dominant patient phenotypes identified by recent large-scale studies of gastric cancer genomics (The Cancer Genome Atlas and Asian Cancer Research Group), revealing four distinct stromal phenotypes. Collectively, these findings suggest that a genomics-based systems approach focused on the tumor stroma can be used to discover putative predictive biomarkers of treatment response, especially to antiangiogenesis agents and immunotherapy, thus offering an opportunity to improve patient stratification. Cancer Res; 76(9); 2573-86. ©2016 AACR. ©2016 American Association for Cancer Research.

Longitudinal Transcriptome Analysis Reveals a Sustained Differential Gene Expression Signature in Patients Treated for Acute Lyme Disease.

PubMed

Bouquet, Jerome; Soloski, Mark J; Swei, Andrea; Cheadle, Chris; Federman, Scot; Billaud, Jean-Noel; Rebman, Alison W; Kabre, Beniwende; Halpert, Richard; Boorgula, Meher; Aucott, John N; Chiu, Charles Y

2016-02-12

Lyme disease is a tick-borne illness caused by the bacterium Borrelia burgdorferi, and approximately 10 to 20% of patients report persistent symptoms lasting months to years despite appropriate treatment with antibiotics. To gain insights into the molecular basis of acute Lyme disease and the ensuing development of post-treatment symptoms, we conducted a longitudinal transcriptome study of 29 Lyme disease patients (and 13 matched controls) enrolled at the time of diagnosis and followed for up to 6 months. The differential gene expression signature of Lyme disease following the acute phase of infection persisted for at least 3 weeks and had fewer than 44% differentially expressed genes (DEGs) in common with other infectious or noninfectious syndromes. Early Lyme disease prior to antibiotic therapy was characterized by marked upregulation of Toll-like receptor signaling but lack of activation of the inflammatory T-cell apoptotic and B-cell developmental pathways seen in other acute infectious syndromes. Six months after completion of therapy, Lyme disease patients were found to have 31 to 60% of their pathways in common with three different immune-mediated chronic diseases. No differential gene expression signature was observed between Lyme disease patients with resolved illness to those with persistent symptoms at 6 months post-treatment. The identification of a sustained differential gene expression signature in Lyme disease suggests that a panel of selected human host-based biomarkers may address the need for sensitive clinical diagnostics during the "window period" of infection prior to the appearance of a detectable antibody response and may also inform the development of new therapeutic targets. Lyme disease is the most common tick-borne infection in the United States, and some patients report lingering symptoms lasting months to years despite antibiotic treatment. To better understand the role of the human host response in acute Lyme disease and the

Hormone-induced protection against mammary tumorigenesis is conserved in multiple rat strains and identifies a core gene expression signature induced by pregnancy.

PubMed

Blakely, Collin M; Stoddard, Alexander J; Belka, George K; Dugan, Katherine D; Notarfrancesco, Kathleen L; Moody, Susan E; D'Cruz, Celina M; Chodosh, Lewis A

2006-06-15

Women who have their first child early in life have a substantially lower lifetime risk of breast cancer. The mechanism for this is unknown. Similar to humans, rats exhibit parity-induced protection against mammary tumorigenesis. To explore the basis for this phenomenon, we identified persistent pregnancy-induced changes in mammary gene expression that are tightly associated with protection against tumorigenesis in multiple inbred rat strains. Four inbred rat strains that exhibit marked differences in their intrinsic susceptibilities to carcinogen-induced mammary tumorigenesis were each shown to display significant protection against methylnitrosourea-induced mammary tumorigenesis following treatment with pregnancy levels of estradiol and progesterone. Microarray expression profiling of parous and nulliparous mammary tissue from these four strains yielded a common 70-gene signature. Examination of the genes constituting this signature implicated alterations in transforming growth factor-beta signaling, the extracellular matrix, amphiregulin expression, and the growth hormone/insulin-like growth factor I axis in pregnancy-induced alterations in breast cancer risk. Notably, related molecular changes have been associated with decreased mammographic density, which itself is strongly associated with decreased breast cancer risk. Our findings show that hormone-induced protection against mammary tumorigenesis is widely conserved among divergent rat strains and define a gene expression signature that is tightly correlated with reduced mammary tumor susceptibility as a consequence of a normal developmental event. Given the conservation of this signature, these pathways may contribute to pregnancy-induced protection against breast cancer.

A comparison of machine learning techniques for survival prediction in breast cancer

PubMed Central

2011-01-01

Background The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. Results We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Conclusions Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data. PMID:21569330

Microarray-based gene expression profiling in patients with cryopyrin-associated periodic syndromes defines a disease-related signature and IL-1-responsive transcripts.

PubMed

Balow, James E; Ryan, John G; Chae, Jae Jin; Booty, Matthew G; Bulua, Ariel; Stone, Deborah; Sun, Hong-Wei; Greene, James; Barham, Beverly; Goldbach-Mansky, Raphaela; Kastner, Daniel L; Aksentijevich, Ivona

2013-06-01

To analyse gene expression patterns and to define a specific gene expression signature in patients with the severe end of the spectrum of cryopyrin-associated periodic syndromes (CAPS). The molecular consequences of interleukin 1 inhibition were examined by comparing gene expression patterns in 16 CAPS patients before and after treatment with anakinra. We collected peripheral blood mononuclear cells from 22 CAPS patients with active disease and from 14 healthy children. Transcripts that passed stringent filtering criteria (p values≤false discovery rate 1%) were considered as differentially expressed genes (DEG). A set of DEG was validated by quantitative reverse transcription PCR and functional studies with primary cells from CAPS patients and healthy controls. We used 17 CAPS and 66 non-CAPS patient samples to create a set of gene expression models that differentiates CAPS patients from controls and from patients with other autoinflammatory conditions. Many DEG include transcripts related to the regulation of innate and adaptive immune responses, oxidative stress, cell death, cell adhesion and motility. A set of gene expression-based models comprising the CAPS-specific gene expression signature correctly classified all 17 samples from an independent dataset. This classifier also correctly identified 15 of 16 post-anakinra CAPS samples despite the fact that these CAPS patients were in clinical remission. We identified a gene expression signature that clearly distinguished CAPS patients from controls. A number of DEG were in common with other systemic inflammatory diseases such as systemic onset juvenile idiopathic arthritis. The CAPS-specific gene expression classifiers also suggest incomplete suppression of inflammation at low doses of anakinra.

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures.

PubMed

Duan, Qiaonan; Flynn, Corey; Niepel, Mario; Hafner, Marc; Muhlich, Jeremy L; Fernandez, Nicolas F; Rouillard, Andrew D; Tan, Christopher M; Chen, Edward Y; Golub, Todd R; Sorger, Peter K; Subramanian, Aravind; Ma'ayan, Avi

2014-07-01

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Validation of the Complexity INdex in SARComas prognostic signature on formalin-fixed, paraffin-embedded, soft tissue sarcomas.

PubMed

Le Guellec, S; Lesluyes, T; Sarot, E; Valle, C; Filleron, T; Rochaix, P; Valentin, T; Pérot, G; Coindre, J-M; Chibon, F

2018-05-31

Prediction of metastatic outcome in sarcomas is challenging for clinical management since they are aggressive and carry a high metastatic risk. A 67-gene expression signature, the Complexity INdex in SARComas (CINSARC), has been identified as a better prognostic factor than the reference pathological grade. Since it cannot be applied easily in standard laboratory practice, we assessed its prognostic value using nanoString on formalin-fixed, paraffin-embedded (FFPE) blocks to evaluate its potential in clinical routine practice and guided therapeutic management. A code set consisting of 67 probes derived from the 67 genes of the CINSARC signature was built and named NanoCind®. To compare the performance of RNA-seq and nanoString (NanoCind®), we used expressions of various sarcomas (n=124, frozen samples) using both techniques and compared predictive values based on CINSARC risk groups and clinical annotations. We also used nanoString on FFPE blocks (n=67) and matching frozen and FFPE samples (n=45) to compare their level of agreement. Metastasis-free survival and agreement values in classification groups were evaluated. CINSARC strongly predicted metastatic outcome using nanoString on frozen samples (HR = 2.9, 95% CI 1.23-6.82) with similar risk-group classifications (86%). While more than 50% of FFPE blocks were not analyzable by RNA-seq owing to poor RNA quality, all samples were analyzable with nanoString. When similar (risk-group) classifications were measured with frozen tumors (RNA-seq) compared to FFPE blocks (84% agreement), the CINSARC signature was still a predictive factor of metastatic outcome with nanoString on FFPE samples (HR = 4.43, 95% CI 1.25-15.72). CINSARC is a material-independent prognostic signature for metastatic outcome in sarcomas and outperforms histological grade. Unlike RNA-seq, nanoString is not influenced by the poor quality of RNA extracted from FFPE blocks. The CINSARC signature can potentially be used in combination with

A whole blood gene expression-based signature for smoking status

PubMed Central

2012-01-01

Background Smoking is the leading cause of preventable death worldwide and has been shown to increase the risk of multiple diseases including coronary artery disease (CAD). We sought to identify genes whose levels of expression in whole blood correlate with self-reported smoking status. Methods Microarrays were used to identify gene expression changes in whole blood which correlated with self-reported smoking status; a set of significant genes from the microarray analysis were validated by qRT-PCR in an independent set of subjects. Stepwise forward logistic regression was performed using the qRT-PCR data to create a predictive model whose performance was validated in an independent set of subjects and compared to cotinine, a nicotine metabolite. Results Microarray analysis of whole blood RNA from 209 PREDICT subjects (41 current smokers, 4 quit ≤ 2 months, 64 quit > 2 months, 100 never smoked; NCT00500617) identified 4214 genes significantly correlated with self-reported smoking status. qRT-PCR was performed on 1,071 PREDICT subjects across 256 microarray genes significantly correlated with smoking or CAD. A five gene (CLDND1, LRRN3, MUC1, GOPC, LEF1) predictive model, derived from the qRT-PCR data using stepwise forward logistic regression, had a cross-validated mean AUC of 0.93 (sensitivity=0.78; specificity=0.95), and was validated using 180 independent PREDICT subjects (AUC=0.82, CI 0.69-0.94; sensitivity=0.63; specificity=0.94). Plasma from the 180 validation subjects was used to assess levels of cotinine; a model using a threshold of 10 ng/ml cotinine resulted in an AUC of 0.89 (CI 0.81-0.97; sensitivity=0.81; specificity=0.97; kappa with expression model = 0.53). Conclusion We have constructed and validated a whole blood gene expression score for the evaluation of smoking status, demonstrating that clinical and environmental factors contributing to cardiovascular disease risk can be assessed by gene expression. PMID:23210427

Artificial neural network classifier predicts neuroblastoma patients' outcome.

PubMed

Cangelosi, Davide; Pelassa, Simone; Morini, Martina; Conte, Massimo; Bosco, Maria Carla; Eva, Alessandra; Sementa, Angela Rita; Varesio, Luigi

2016-11-08

More than fifty percent of neuroblastoma (NB) patients with adverse prognosis do not benefit from treatment making the identification of new potential targets mandatory. Hypoxia is a condition of low oxygen tension, occurring in poorly vascularized tissues, which activates specific genes and contributes to the acquisition of the tumor aggressive phenotype. We defined a gene expression signature (NB-hypo), which measures the hypoxic status of the neuroblastoma tumor. We aimed at developing a classifier predicting neuroblastoma patients' outcome based on the assessment of the adverse effects of tumor hypoxia on the progression of the disease. Multi-layer perceptron (MLP) was trained on the expression values of the 62 probe sets constituting NB-hypo signature to develop a predictive model for neuroblastoma patients' outcome. We utilized the expression data of 100 tumors in a leave-one-out analysis to select and construct the classifier and the expression data of the remaining 82 tumors to test the classifier performance in an external dataset. We utilized the Gene set enrichment analysis (GSEA) to evaluate the enrichment of hypoxia related gene sets in patients predicted with "Poor" or "Good" outcome. We utilized the expression of the 62 probe sets of the NB-Hypo signature in 182 neuroblastoma tumors to develop a MLP classifier predicting patients' outcome (NB-hypo classifier). We trained and validated the classifier in a leave-one-out cross-validation analysis on 100 tumor gene expression profiles. We externally tested the resulting NB-hypo classifier on an independent 82 tumors' set. The NB-hypo classifier predicted the patients' outcome with the remarkable accuracy of 87 %. NB-hypo classifier prediction resulted in 2 % classification error when applied to clinically defined low-intermediate risk neuroblastoma patients. The prediction was 100 % accurate in assessing the death of five low/intermediated risk patients. GSEA of tumor gene expression profile

Prediction of chemo-response in serous ovarian cancer.

PubMed

Gonzalez Bosquet, Jesus; Newtson, Andreea M; Chung, Rebecca K; Thiel, Kristina W; Ginader, Timothy; Goodheart, Michael J; Leslie, Kimberly K; Smith, Brian J

2016-10-19

Nearly one-third of serous ovarian cancer (OVCA) patients will not respond to initial treatment with surgery and chemotherapy and die within one year of diagnosis. If patients who are unlikely to respond to current standard therapy can be identified up front, enhanced tumor analyses and treatment regimens could potentially be offered. Using the Cancer Genome Atlas (TCGA) serous OVCA database, we previously identified a robust molecular signature of 422-genes associated with chemo-response. Our objective was to test whether this signature is an accurate and sensitive predictor of chemo-response in serous OVCA. We first constructed prediction models to predict chemo-response using our previously described 422-gene signature that was associated with response to treatment in serous OVCA. Performance of all prediction models were measured with area under the curves (AUCs, a measure of the model's accuracy) and their respective confidence intervals (CIs). To optimize the prediction process, we determined which elements of the signature most contributed to chemo-response prediction. All prediction models were replicated and validated using six publicly available independent gene expression datasets. The 422-gene signature prediction models predicted chemo-response with AUCs of ~70 %. Optimization of prediction models identified the 34 most important genes in chemo-response prediction. These 34-gene models had improved performance, with AUCs approaching 80 %. Both 422-gene and 34-gene prediction models were replicated and validated in six independent datasets. These prediction models serve as the foundation for the future development and implementation of a diagnostic tool to predict response to chemotherapy for serous OVCA patients.

Predictive Outcomes for HER2-enriched Cancer Using Growth and Metastasis Signatures Driven By SPARC.

PubMed

Güttlein, Leandro N; Benedetti, Lorena G; Fresno, Cristóbal; Spallanzani, Raúl G; Mansilla, Sabrina F; Rotondaro, Cecilia; Raffo Iraolagoitia, Ximena L; Salvatierra, Edgardo; Bravo, Alicia I; Fernández, Elmer A; Gottifredi, Vanesa; Zwirner, Norberto W; Llera, Andrea S; Podhajcer, Osvaldo L

2017-03-01

Understanding the mechanism of metastatic dissemination is crucial for the rational design of novel therapeutics. The secreted protein acidic and rich in cysteine (SPARC) is a matricellular glycoprotein which has been extensively associated with human breast cancer aggressiveness although the underlying mechanisms are still unclear. Here, shRNA-mediated SPARC knockdown greatly reduced primary tumor growth and completely abolished lung colonization of murine 4T1 and LM3 breast malignant cells implanted in syngeneic BALB/c mice. A comprehensive study including global transcriptomic analysis followed by biological validations confirmed that SPARC induces primary tumor growth by enhancing cell cycle and by promoting a COX-2-mediated expansion of myeloid-derived suppressor cells (MDSC). The role of SPARC in metastasis involved a COX-2-independent enhancement of cell disengagement from the primary tumor and adherence to the lungs that fostered metastasis implantation. Interestingly, SPARC-driven gene expression signatures obtained from these murine models predicted the clinical outcome of patients with HER2-enriched breast cancer subtypes. In total, the results reveal that SPARC and its downstream effectors are attractive targets for antimetastatic therapies in breast cancer. Implications: These findings shed light on the prometastatic role of SPARC, a key protein expressed by breast cancer cells and surrounding stroma, with important consequences for disease outcome. Mol Cancer Res; 15(3); 304-16. ©2016 AACR . ©2016 American Association for Cancer Research.

Histone methylation mediates plasticity of human FOXP3(+) regulatory T cells by modulating signature gene expressions.

PubMed

He, Haiqi; Ni, Bing; Tian, Yi; Tian, Zhiqiang; Chen, Yanke; Liu, Zhengwen; Yang, Xiaomei; Lv, Yi; Zhang, Yong

2014-03-01

CD4(+) FOXP3(+) regulatory T (Treg) cells constitute a heterogeneous and plastic T-cell lineage that plays a pivotal role in maintaining immune homeostasis and immune tolerance. However, the fate of human Treg cells after loss of FOXP3 expression and the epigenetic mechanisms contributing to such a phenotype switch remain to be fully elucidated. In the current study, we demonstrate that human CD4(+) CD25(high) CD127(low/-) Treg cells convert to two subpopulations with distinctive FOXP3(+) and FOXP3(-) phenotypes following in vitro culture with anti-CD3/CD28 and interleukin-2. Digital gene expression analysis showed that upon in vitro expansion, human Treg cells down-regulated Treg cell signature genes, such as FOXP3, CTLA4, ICOS, IKZF2 and LRRC32, but up-regulated a set of T helper lineage-associated genes, especially T helper type 2 (Th2)-associated, such as GATA3, GFI1 and IL13. Subsequent chromatin immunoprecipitation-sequencing of these subpopulations yielded genome-wide maps of their H3K4me3 and H3K27me3 profiles. Surprisingly, reprogramming of Treg cells was associated with differential histone modifications, as evidenced by decreased abundance of permissive H3K4me3 within the down-regulated Treg cell signature genes, such as FOXP3, CTLA4 and LRRC32 loci, and increased abundance of H3K4me3 within the Th2-associated genes, such as IL4 and IL5; however, the H3K27me3 modification profile was not significantly different between the two subpopulations. In conclusion, this study revealed that loss of FOXP3 expression from human Treg cells during in vitro expansion can induce reprogramming to a T helper cell phenotype with a gene expression signature dominated by Th2 lineage-associated genes, and that this cell type conversion may be mediated by histone methylation events. © 2013 John Wiley & Sons Ltd.

Histone methylation mediates plasticity of human FOXP3+ regulatory T cells by modulating signature gene expressions

PubMed Central

He, Haiqi; Ni, Bing; Tian, Yi; Tian, Zhiqiang; Chen, Yanke; Liu, Zhengwen; Yang, Xiaomei; Lv, Yi; Zhang, Yong

2014-01-01

CD4+ FOXP3+ regulatory T (Treg) cells constitute a heterogeneous and plastic T-cell lineage that plays a pivotal role in maintaining immune homeostasis and immune tolerance. However, the fate of human Treg cells after loss of FOXP3 expression and the epigenetic mechanisms contributing to such a phenotype switch remain to be fully elucidated. In the current study, we demonstrate that human CD4+ CD25high CD127low/− Treg cells convert to two subpopulations with distinctive FOXP3+ and FOXP3− phenotypes following in vitro culture with anti-CD3/CD28 and interleukin-2. Digital gene expression analysis showed that upon in vitro expansion, human Treg cells down-regulated Treg cell signature genes, such as FOXP3, CTLA4, ICOS, IKZF2 and LRRC32, but up-regulated a set of T helper lineage-associated genes, especially T helper type 2 (Th2)-associated, such as GATA3, GFI1 and IL13. Subsequent chromatin immunoprecipitation-sequencing of these subpopulations yielded genome-wide maps of their H3K4me3 and H3K27me3 profiles. Surprisingly, reprogramming of Treg cells was associated with differential histone modifications, as evidenced by decreased abundance of permissive H3K4me3 within the down-regulated Treg cell signature genes, such as FOXP3, CTLA4 and LRRC32 loci, and increased abundance of H3K4me3 within the Th2-associated genes, such as IL4 and IL5; however, the H3K27me3 modification profile was not significantly different between the two subpopulations. In conclusion, this study revealed that loss of FOXP3 expression from human Treg cells during in vitro expansion can induce reprogramming to a T helper cell phenotype with a gene expression signature dominated by Th2 lineage-associated genes, and that this cell type conversion may be mediated by histone methylation events. PMID:24152290

Genome signature analysis of thermal virus metagenomes reveals Archaea and thermophilic signatures

PubMed Central

Pride, David T; Schoenfeld, Thomas

2008-01-01

Background Metagenomic analysis provides a rich source of biological information for otherwise intractable viral communities. However, study of viral metagenomes has been hampered by its nearly complete reliance on BLAST algorithms for identification of DNA sequences. We sought to develop algorithms for examination of viral metagenomes to identify the origin of sequences independent of BLAST algorithms. We chose viral metagenomes obtained from two hot springs, Bear Paw and Octopus, in Yellowstone National Park, as they represent simple microbial populations where comparatively large contigs were obtained. Thermal spring metagenomes have high proportions of sequences without significant Genbank homology, which has hampered identification of viruses and their linkage with hosts. To analyze each metagenome, we developed a method to classify DNA fragments using genome signature-based phylogenetic classification (GSPC), where metagenomic fragments are compared to a database of oligonucleotide signatures for all previously sequenced Bacteria, Archaea, and viruses. Results From both Bear Paw and Octopus hot springs, each assembled contig had more similarity to other metagenome contigs than to any sequenced microbial genome based on GSPC analysis, suggesting a genome signature common to each of these extreme environments. While viral metagenomes from Bear Paw and Octopus share some similarity, the genome signatures from each locale are largely unique. GSPC using a microbial database predicts most of the Octopus metagenome has archaeal signatures, while bacterial signatures predominate in Bear Paw; a finding consistent with those of Genbank BLAST. When using a viral database, the majority of the Octopus metagenome is predicted to belong to archaeal virus Families Globuloviridae and Fuselloviridae, while none of the Bear Paw metagenome is predicted to belong to archaeal viruses. As expected, when microbial and viral databases are combined, each of the Octopus and Bear Paw

Early and long-standing rheumatoid arthritis: distinct molecular signatures identified by gene-expression profiling in synovia

PubMed Central

Lequerré, Thierry; Bansard, Carine; Vittecoq, Olivier; Derambure, Céline; Hiron, Martine; Daveau, Maryvonne; Tron, François; Ayral, Xavier; Biga, Norman; Auquit-Auckbur, Isabelle; Chiocchia, Gilles; Le Loët, Xavier; Salier, Jean-Philippe

2009-01-01

Introduction Rheumatoid arthritis (RA) is a heterogeneous disease and its underlying molecular mechanisms are still poorly understood. Because previous microarray studies have only focused on long-standing (LS) RA compared to osteoarthritis, we aimed to compare the molecular profiles of early and LS RA versus control synovia. Methods Synovial biopsies were obtained by arthroscopy from 15 patients (4 early untreated RA, 4 treated LS RA and 7 controls, who had traumatic or mechanical lesions). Extracted mRNAs were used for large-scale gene-expression profiling. The different gene-expression combinations identified by comparison of profiles of early, LS RA and healthy synovia were linked to the biological processes involved in each situation. Results Three combinations of 719, 116 and 52 transcripts discriminated, respectively, early from LS RA, and early or LS RA from healthy synovia. We identified several gene clusters and distinct molecular signatures specifically expressed during early or LS RA, thereby suggesting the involvement of different pathophysiological mechanisms during the course of RA. Conclusions Early and LS RA have distinct molecular signatures with different biological processes participating at different times during the course of the disease. These results suggest that better knowledge of the main biological processes involved at a given RA stage might help to choose the most appropriate treatment. PMID:19563633

Direct modeling parameter signature analysis and failure mode prediction of physical systems using hybrid computer optimization

NASA Technical Reports Server (NTRS)

Drake, R. L.; Duvoisin, P. F.; Asthana, A.; Mather, T. W.

1971-01-01

High speed automated identification and design of dynamic systems, both linear and nonlinear, are discussed. Special emphasis is placed on developing hardware and techniques which are applicable to practical problems. The basic modeling experiment and new results are described. Using the improvements developed successful identification of several systems, including a physical example as well as simulated systems, was obtained. The advantages of parameter signature analysis over signal signature analysis in go-no go testing of operational systems were demonstrated. The feasibility of using these ideas in failure mode prediction in operating systems was also investigated. An improved digital controlled nonlinear function generator was developed, de-bugged, and completely documented.

mRNA Expression Signatures of Human Skeletal Muscle Atrophy Identify a Natural Compound that Increases Muscle Mass

PubMed Central

Kunkel, Steven D.; Suneja, Manish; Ebert, Scott M.; Bongers, Kale S.; Fox, Daniel K.; Malmberg, Sharon E.; Alipour, Fariborz; Shields, Richard K.; Adams, Christopher M.

2011-01-01

SUMMARY Skeletal muscle atrophy is a common and debilitating condition that lacks a pharmacologic therapy. To develop a potential therapy, we identified 63 mRNAs that were regulated by fasting in both human and mouse muscle, and 29 mRNAs that were regulated by both fasting and spinal cord injury in human muscle. We used these two unbiased mRNA expression signatures of muscle atrophy to query the Connectivity Map, which singled out ursolic acid as a compound whose signature was opposite to those of atrophy-inducing stresses. A natural compound enriched in apples, ursolic acid reduced muscle atrophy and stimulated muscle hypertrophy in mice. It did so by enhancing skeletal muscle insulin/IGF-I signaling, and inhibiting atrophy-associated skeletal muscle mRNA expression. Importantly, ursolic acid’s effects on muscle were accompanied by reductions in adiposity, fasting blood glucose and plasma cholesterol and triglycerides. These findings identify a potential therapy for muscle atrophy and perhaps other metabolic diseases. PMID:21641545

A long non-coding RNA expression profile can predict early recurrence in hepatocellular carcinoma after curative resection.

PubMed

Lv, Yufeng; Wei, Wenhao; Huang, Zhong; Chen, Zhichao; Fang, Yuan; Pan, Lili; Han, Xueqiong; Xu, Zihai

2018-06-20

The aim of this study was to develop a novel long non-coding RNA (lncRNA) expression signature to accurately predict early recurrence for patients with hepatocellular carcinoma (HCC) after curative resection. Using expression profiles downloaded from The Cancer Genome Atlas database, we identified multiple lncRNAs with differential expression between early recurrence (ER) group and non-early recurrence (non-ER) group of HCC. Least absolute shrinkage and selection operator (LASSO) for logistic regression models were used to develop a lncRNA-based classifier for predicting ER in the training set. An independent test set was used to validated the predictive value of this classifier. Futhermore, a co-expression network based on these lncRNAs and its highly related genes was constructed and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of genes in the network were performed. We identified 10 differentially expressed lncRNAs, including 3 that were upregulated and 7 that were downregulated in ER group. The lncRNA-based classifier was constructed based on 7 lncRNAs (AL035661.1, PART1, AC011632.1, AC109588.1, AL365361.1, LINC00861 and LINC02084), and its accuracy was 0.83 in training set, 0.87 in test set and 0.84 in total set. And ROC curve analysis showed the AUROC was 0.741 in training set, 0.824 in the test set and 0.765 in total set. A functional enrichment analysis suggested that the genes of which is highly related to 4 lncRNAs were involved in immune system. This 7-lncRNA expression profile can effectively predict the early recurrence after surgical resection for HCC. This article is protected by copyright. All rights reserved.

Prospective assessment of a gene signature potentially predictive of clinical benefit in metastatic melanoma patients following MAGE-A3 immunotherapeutic (PREDICT).

PubMed

Saiag, P; Gutzmer, R; Ascierto, P A; Maio, M; Grob, J-J; Murawa, P; Dreno, B; Ross, M; Weber, J; Hauschild, A; Rutkowski, P; Testori, A; Levchenko, E; Enk, A; Misery, L; Vanden Abeele, C; Vojtek, I; Peeters, O; Brichard, V G; Therasse, P

2016-10-01

Genomic profiling of tumor tissue may aid in identifying predictive or prognostic gene signatures (GS) in some cancers. Retrospective gene expression profiling of melanoma and non-small-cell lung cancer led to the characterization of a GS associated with clinical benefit, including improved overall survival (OS), following immunization with the MAGE-A3 immunotherapeutic. The goal of the present study was to prospectively evaluate the predictive value of the previously characterized GS. An open-label prospective phase II trial ('PREDICT') in patients with MAGE-A3-positive unresectable stage IIIB-C/IV-M1a melanoma. Of 123 subjects who received the MAGE-A3 immunotherapeutic, 71 (58.7%) displayed the predictive GS (GS+). The 1-year OS rate was 83.1%/83.3% in the GS+/GS- populations. The rate of progression-free survival at 12 months was 5.8%/4.1% in GS+/GS- patients. The median time-to-treatment failure was 2.7/2.4 months (GS+/GS-). There was one complete response (GS-) and two partial responses (GS+). The MAGE-A3 immunotherapeutic was similarly immunogenic in both populations and had a clinically acceptable safety profile. Treatment of patients with MAGE-A3-positive unresectable stage IIIB-C/IV-M1a melanoma with the MAGE-A3 immunotherapeutic demonstrated an overall 1-year OS rate of 83.5%. GS- and GS+ patients had similar 1-year OS rates, indicating that in this study, GS was not predictive of outcome. Unexpectedly, the objective response rate was lower in this study than in other studies carried out in the same setting with the MAGE-A3 immunotherapeutic. Investigation of a GS to predict clinical benefit to adjuvant MAGE-A3 immunotherapeutic treatment is ongoing in another melanoma study.This study is registered at www.clinicatrials.gov NCT00942162. © The Author 2016. Published by Oxford University Press on behalf of the European Society for Medical Oncology.

Microarray-based gene expression profiling in patients with cryopyrin-associated periodic syndromes defines a disease-related signature and IL-1-responsive transcripts

PubMed Central

Balow, James E; Ryan, John G; Chae, Jae Jin; Booty, Matthew G; Bulua, Ariel; Stone, Deborah; Sun, Hong-Wei; Greene, James; Barham, Beverly; Goldbach-Mansky, Raphaela; Kastner, Daniel L; Aksentijevich, Ivona

2014-01-01

Objective To analyse gene expression patterns and to define a specific gene expression signature in patients with the severe end of the spectrum of cryopyrin-associated periodic syndromes (CAPS). The molecular consequences of interleukin 1 inhibition were examined by comparing gene expression patterns in 16 CAPS patients before and after treatment with anakinra. Methods We collected peripheral blood mononuclear cells from 22 CAPS patients with active disease and from 14 healthy children. Transcripts that passed stringent filtering criteria (p values ≤ false discovery rate 1%) were considered as differentially expressed genes (DEG). A set of DEG was validated by quantitative reverse transcription PCR and functional studies with primary cells from CAPS patients and healthy controls. We used 17 CAPS and 66 non-CAPS patient samples to create a set of gene expression models that differentiates CAPS patients from controls and from patients with other autoinflammatory conditions. Results Many DEG include transcripts related to the regulation of innate and adaptive immune responses, oxidative stress, cell death, cell adhesion and motility. A set of gene expression-based models comprising the CAPS-specific gene expression signature correctly classified all 17 samples from an independent dataset. This classifier also correctly identified 15 of 16 postanakinra CAPS samples despite the fact that these CAPS patients were in clinical remission. Conclusions We identified a gene expression signature that clearly distinguished CAPS patients from controls. A number of DEG were in common with other systemic inflammatory diseases such as systemic onset juvenile idiopathic arthritis. The CAPS-specific gene expression classifiers also suggest incomplete suppression of inflammation at low doses of anakinra. PMID:23223423

Identification of Gene Expression Signatures in the Chicken Intestinal Intraepithelial Lymphocytes in Response to Herb Additive Supplementations

PubMed Central

Won, Kyeong-Hye; Song, Ki-Duk; Park, Jong-Eun; Kim, Duk-Kyung; Na, Chong-Sam

2016-01-01

Anethole and garlic have an immune modulatory effects on avian coccidiosis, and these effects are correlated with gene expression changes in intestinal epithelial lymphocytes (IELs). In this study, we integrated gene expression datasets from two independent experiments and investigated gene expression profile changes by anethole and garlic respectively, and identified gene expression signatures, which are common targets of these herbs as they might be used for the evaluation of the effect of plant herbs on immunity toward avian coccidiosis. We identified 4,382 and 371 genes, which were differentially expressed in IELs of chickens supplemented with garlic and anethole respectively. The gene ontology (GO) term of differentially expressed genes (DEGs) from garlic treatment resulted in the biological processes (BPs) related to proteolysis, e.g., “modification-dependent protein catabolic process”, “proteolysis involved in cellular protein catabolic process”, “cellular protein catabolic process”, “protein catabolic process”, and “ubiquitin-dependent protein catabolic process”. In GO analysis, one BP term, “Proteolysis”, was obtained. Among DEGs, 300 genes were differentially regulated in response to both garlic and anethole, and 234 and 59 genes were either up- or down-regulated in supplementation with both herbs. Pathway analysis resulted in enrichment of the pathways related to digestion such as “Starch and sucrose metabolism” and “Insulin signaling pathway”. Taken together, the results obtained in the present study could contribute to the effective development of evaluation system of plant herbs based on molecular signatures related with their immunological functions in chicken IELs. PMID:26954117

Interferon Gamma Messenger RNA Signature in Tumor Biopsies Predicts Outcomes in Patients with Non-Small-Cell Lung Carcinoma or Urothelial Cancer Treated with Durvalumab.

PubMed

Higgs, Brandon W; Morehouse, Christopher; Streicher, Katie L; Brohawn, Philip; Pilataxi, Fernanda; Gupta, Ashok; Ranade, Koustubh

2018-05-01

To identify a predictive biomarker for durvalumab, an anti-programmed death ligand 1 (PD-L1) monoclonal antibody. RNA sequencing of 97 advanced-stage non-small-cell lung carcinoma (NSCLC) biopsies from a nonrandomized phase 1b/2 clinical trial (1108/NCT01693562) were profiled to identify a predictive signature; 62 locally advanced or metastatic urothelial cancer (UC) tumors from the same study were profiled to confirm predictive utility of the signature. Thirty NSCLC patients provided pre- and posttreatment tumors for messenger RNA (mRNA) analysis. NSCLC with ≥25% tumor cells and UC with ≥25% tumor or immune cells stained for PD-L1 at any intensity were scored PD-L1 positive (PD-L1+). Kaplan-Meier and Cox proportional hazards analyses were used to adjust for gender, age, prior therapies, histology, ECOG, liver metastasis, and smoking. Tumor mutation burden (TMB) was calculated using data from The Cancer Genome Atlas (TCGA).  In the NSCLC discovery set, a four-gene interferon gamma (IFNγ)-positive (IFNγ+) signature comprising IFNγ, CD274, LAG3, and CXCL9 was associated with higher overall response rates, longer median progression-free survival, and overall survival compared with signature-low patients. IFNγ+-signature NSCLC patients had improved survival regardless of immunohistochemistry (IHC) PD-L1 status. These associations were replicated in a UC cohort. The IFNγ+ signature was induced twofold (P = 0.003) by durvalumab after 8 weeks of therapy in NSCLC patients, and baseline signature was associated with TMB but not survival in TCGA data.  The IFNγ+ mRNA signature may assist in identifying patients with improved outcomes to durvalumab, independent of PD-L1 assessed by IHC. Copyright ©2018, American Association for Cancer Research.

Expression signatures of early-stage and advanced medaka melanomas.

PubMed

Klotz, Barbara; Kneitz, Susanne; Regensburger, Martina; Hahn, Lena; Dannemann, Michael; Kelso, Janet; Nickel, Birgit; Lu, Yuan; Boswell, William; Postlethwait, John; Warren, Wesley; Kunz, Manfred; Walter, Ronald B; Schartl, Manfred

2018-06-01

Melanoma is one of the most aggressive tumors with a very low survival rate once metastasized. The incidence of newly detected cases increases every year suggesting the necessity of development and application of innovative treatment strategies. Human melanoma develops from melanocytes localized in the epidermis of the skin to malignant tumors because of deregulated effectors influencing several molecular pathways. Despite many advances in describing the molecular changes accompanying melanoma formation, many critical and clinically relevant molecular features of the transformed pigment cells and the underlying mechanisms are largely unknown. To contribute to a better understanding of the molecular processes of melanoma formation, we use a transgenic medaka melanoma model that is well suited for the investigation of melanoma tumor development because fish and human melanocytes are both localized in the epidermis. The purpose of our study was to gain insights into melanoma development from the first steps of tumor formation up to melanoma progression and to identify gene expression patterns that will be useful for monitoring treatment effects in drug screening approaches. Comparing transcriptomes from juvenile fish at the tumor initiating stage with nevi and advanced melanoma of adults, we identified stage specific expression signatures and pathways that are characteristic for the development of medaka melanoma, and are also found in human malignancies. Copyright © 2017 Elsevier Inc. All rights reserved.

High expression of ID family and IGJ genes signature as predictor of low induction treatment response and worst survival in adult Hispanic patients with B-acute lymphoblastic leukemia.

PubMed

Cruz-Rodriguez, Nataly; Combita, Alba L; Enciso, Leonardo J; Quijano, Sandra M; Pinzon, Paula L; Lozano, Olga C; Castillo, Juan S; Li, Li; Bareño, Jose; Cardozo, Claudia; Solano, Julio; Herrera, Maria V; Cudris, Jennifer; Zabaleta, Jovanny

2016-04-05

B-Acute lymphoblastic leukemia (B-ALL) represents a hematologic malignancy with poor clinical outcome and low survival rates in adult patients. Remission rates in Hispanic population are almost 30% lower and Overall Survival (OS) nearly two years inferior than those reported in other ethnic groups. Only 61% of Colombian adult patients with ALL achieve complete remission (CR), median overall survival is 11.3 months and event-free survival (EFS) is 7.34 months. Identification of prognostic factors is crucial for the application of proper treatment strategies and subsequently for successful outcome. Our goal was to identify a gene expression signature that might correlate with response to therapy and evaluate the utility of these as prognostic tool in hispanic patients. We included 43 adult patients newly diagnosed with B-ALL. We used microarray analysis in order to identify genes that distinguish poor from good response to treatment using differential gene expression analysis. The expression profile was validated by real-time PCR (RT-PCT). We identified 442 differentially expressed genes between responders and non-responders to induction treatment. Hierarchical analysis according to the expression of a 7-gene signature revealed 2 subsets of patients that differed in their clinical characteristics and outcome. Our study suggests that response to induction treatment and clinical outcome of Hispanic patients can be predicted from the onset of the disease and that gene expression profiles can be used to stratify patient risk adequately and accurately. The present study represents the first that shows the gene expression profiling of B-ALL Colombian adults and its relevance for stratification in the early course of disease.

Genome-wide screen identifies a novel prognostic signature for breast cancer survival

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

Mao, Xuan Y.; Lee, Matthew J.; Zhu, Jeffrey

Large genomic datasets in combination with clinical data can be used as an unbiased tool to identify genes important in patient survival and discover potential therapeutic targets. We used a genome-wide screen to identify 587 genes significantly and robustly deregulated across four independent breast cancer (BC) datasets compared to normal breast tissue. Gene expression of 381 genes was significantly associated with relapse-free survival (RFS) in BC patients. We used a gene co-expression network approach to visualize the genetic architecture in normal breast and BCs. In normal breast tissue, co-expression cliques were identified enriched for cell cycle, gene transcription, cell adhesion,more » cytoskeletal organization and metabolism. In contrast, in BC, only two major co-expression cliques were identified enriched for cell cycle-related processes or blood vessel development, cell adhesion and mammary gland development processes. Interestingly, gene expression levels of 7 genes were found to be negatively correlated with many cell cycle related genes, highlighting these genes as potential tumor suppressors and novel therapeutic targets. A forward-conditional Cox regression analysis was used to identify a 12-gene signature associated with RFS. A prognostic scoring system was created based on the 12-gene signature. This scoring system robustly predicted BC patient RFS in 60 sampling test sets and was further validated in TCGA and METABRIC BC data. Our integrated study identified a 12-gene prognostic signature that could guide adjuvant therapy for BC patients and includes novel potential molecular targets for therapy.« less

Genome-wide screen identifies a novel prognostic signature for breast cancer survival

DOE PAGES

Mao, Xuan Y.; Lee, Matthew J.; Zhu, Jeffrey; ...

2017-01-21

Large genomic datasets in combination with clinical data can be used as an unbiased tool to identify genes important in patient survival and discover potential therapeutic targets. We used a genome-wide screen to identify 587 genes significantly and robustly deregulated across four independent breast cancer (BC) datasets compared to normal breast tissue. Gene expression of 381 genes was significantly associated with relapse-free survival (RFS) in BC patients. We used a gene co-expression network approach to visualize the genetic architecture in normal breast and BCs. In normal breast tissue, co-expression cliques were identified enriched for cell cycle, gene transcription, cell adhesion,more » cytoskeletal organization and metabolism. In contrast, in BC, only two major co-expression cliques were identified enriched for cell cycle-related processes or blood vessel development, cell adhesion and mammary gland development processes. Interestingly, gene expression levels of 7 genes were found to be negatively correlated with many cell cycle related genes, highlighting these genes as potential tumor suppressors and novel therapeutic targets. A forward-conditional Cox regression analysis was used to identify a 12-gene signature associated with RFS. A prognostic scoring system was created based on the 12-gene signature. This scoring system robustly predicted BC patient RFS in 60 sampling test sets and was further validated in TCGA and METABRIC BC data. Our integrated study identified a 12-gene prognostic signature that could guide adjuvant therapy for BC patients and includes novel potential molecular targets for therapy.« less

Melanoma-specific MHC-II expression represents a tumour-autonomous phenotype and predicts response to anti-PD-1/PD-L1 therapy

PubMed Central

Johnson, Douglas B.; Estrada, Monica V.; Salgado, Roberto; Sanchez, Violeta; Doxie, Deon B.; Opalenik, Susan R.; Vilgelm, Anna E.; Feld, Emily; Johnson, Adam S.; Greenplate, Allison R.; Sanders, Melinda E.; Lovly, Christine M.; Frederick, Dennie T.; Kelley, Mark C.; Richmond, Ann; Irish, Jonathan M.; Shyr, Yu; Sullivan, Ryan J.; Puzanov, Igor; Sosman, Jeffrey A.; Balko, Justin M.

2016-01-01

Anti-PD-1 therapy yields objective clinical responses in 30–40% of advanced melanoma patients. Since most patients do not respond, predictive biomarkers to guide treatment selection are needed. We hypothesize that MHC-I/II expression is required for tumour antigen presentation and may predict anti-PD-1 therapy response. In this study, across 60 melanoma cell lines, we find bimodal expression patterns of MHC-II, while MHC-I expression was ubiquitous. A unique subset of melanomas are capable of expressing MHC-II under basal or IFNγ-stimulated conditions. Using pathway analysis, we show that MHC-II(+) cell lines demonstrate signatures of ‘PD-1 signalling', ‘allograft rejection' and ‘T-cell receptor signalling', among others. In two independent cohorts of anti-PD-1-treated melanoma patients, MHC-II positivity on tumour cells is associated with therapeutic response, progression-free and overall survival, as well as CD4+ and CD8+ tumour infiltrate. MHC-II+ tumours can be identified by melanoma-specific immunohistochemistry using commercially available antibodies for HLA-DR to improve anti-PD-1 patient selection. PMID:26822383

A 9-protein biomarker molecular signature for predicting histologic type in endometrial carcinoma by immunohistochemistry.

PubMed

Santacana, Maria; Maiques, Oscar; Valls, Joan; Gatius, Sònia; Abó, Ana Isabel; López-García, María Ángeles; Mota, Alba; Reventós, Jaume; Moreno-Bueno, Gema; Palacios, Jose; Bartosch, Carla; Dolcet, Xavier; Matias-Guiu, Xavier

2014-12-01

Histologic typing may be difficult in a subset of endometrial carcinoma (EC) cases. In these cases, interobserver agreement improves when immunohistochemistry (IHC) is used. A series of endometrioid type (EEC) grades 1, 2, and 3 and serous type (SC) were immunostained for p53, p16, estrogen receptor, PTEN, IMP2, IMP3, HER2, cyclin B2 and E1, HMGA2, FolR1, MSLN, Claudins 3 and 4, and NRF2. Nine biomarkers showed significant differences with thresholds in IHC value scale between both types (p53 ≥ 20, IMP2 ≥ 115, IMP3 ≥ 2, cyclin E1 ≥ 220, HMGA2 ≥ 30, FolR1 ≥ 50, p16 ≥ 170, nuclear PTEN ≥ 2 and estrogen receptor ≤ 50; P < .005). This combination led to increased discrimination when considering cases satisfying 0 to 5 conditions predicted as EEC and those satisfying 6 to 9 conditions predicted as SC. This signature correctly predicted all 48 EEC grade 1-2 cases and 18 SC cases, but 3 SC cases were wrongly predicted as EEC. Sensitivity was 86% (95% confidence interval [CI], 64%-97%), and specificity was 100% (95% CI, 89%-100%). The classifier correctly predicted all 28 EEC grade 3 cases but only identified the EEC and SC components in 4 of 9 mixed EEC-SC. An independent validation series (29 EEC grades 1-2, 28 EEC grade 3, and 31 SC) showed 100% sensitivity (95% CI, 84%-100%) and 83% specificity (95% CI, 64%-94%). We propose an internally and externally validated 9-protein biomarker signature to predict the histologic type of EC (EEC or SC) by IHC. The results also suggest that mixed EEC-SC is molecularly ambiguous. Copyright © 2014 Elsevier Inc. All rights reserved.

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer.

PubMed

Tamez-Peña, Jose-Gerardo; Rodriguez-Rojas, Juan-Andrés; Gomez-Rueda, Hugo; Celaya-Padilla, Jose-Maria; Rivera-Prieto, Roxana-Alicia; Palacios-Corona, Rebeca; Garza-Montemayor, Margarita; Cardona-Huerta, Servando; Treviño, Victor

2018-01-01

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.

Microarray analysis of miRNA expression profiles following whole body irradiation in a mouse model.

PubMed

Aryankalayil, Molykutty J; Chopra, Sunita; Makinde, Adeola; Eke, Iris; Levin, Joel; Shankavaram, Uma; MacMillan, Laurel; Vanpouille-Box, Claire; Demaria, Sandra; Coleman, C Norman

2018-06-19

Accidental exposure to life-threatening radiation in a nuclear event is a major concern; there is an enormous need for identifying biomarkers for radiation biodosimetry to triage populations and treat critically exposed individuals. To identify dose-differentiating miRNA signatures from whole blood samples of whole body irradiated mice. Mice were whole body irradiated with X-rays (2 Gy-15 Gy); blood was collected at various time-points post-exposure; total RNA was isolated; miRNA microarrays were performed; miRNAs differentially expressed in irradiated vs. unirradiated controls were identified; feature extraction and classification models were applied to predict dose-differentiating miRNA signature. We observed a time and dose responsive alteration in the expression levels of miRNAs. Maximum number of miRNAs were altered at 24-h and 48-h time-points post-irradiation. A 23-miRNA signature was identified using feature selection algorithms and classifier models. An inverse correlation in the expression level changes of miR-17 members, and their targets were observed in whole body irradiated mice and non-human primates. Whole blood-based miRNA expression signatures might be used for predicting radiation exposures in a mass casualty nuclear incident.

EnzML: multi-label prediction of enzyme classes using InterPro signatures

PubMed Central

2012-01-01

Background Manual annotation of enzymatic functions cannot keep up with automatic genome sequencing. In this work we explore the capacity of InterPro sequence signatures to automatically predict enzymatic function. Results We present EnzML, a multi-label classification method that can efficiently account also for proteins with multiple enzymatic functions: 50,000 in UniProt. EnzML was evaluated using a standard set of 300,747 proteins for which the manually curated Swiss-Prot and KEGG databases have agreeing Enzyme Commission (EC) annotations. EnzML achieved more than 98% subset accuracy (exact match of all correct Enzyme Commission classes of a protein) for the entire dataset and between 87 and 97% subset accuracy in reannotating eight entire proteomes: human, mouse, rat, mouse-ear cress, fruit fly, the S. pombe yeast, the E. coli bacterium and the M. jannaschii archaebacterium. To understand the role played by the dataset size, we compared the cross-evaluation results of smaller datasets, either constructed at random or from specific taxonomic domains such as archaea, bacteria, fungi, invertebrates, plants and vertebrates. The results were confirmed even when the redundancy in the dataset was reduced using UniRef100, UniRef90 or UniRef50 clusters. Conclusions InterPro signatures are a compact and powerful attribute space for the prediction of enzymatic function. This representation makes multi-label machine learning feasible in reasonable time (30 minutes to train on 300,747 instances with 10,852 attributes and 2,201 class values) using the Mulan Binary Relevance Nearest Neighbours algorithm implementation (BR-kNN). PMID:22533924

Genome-wide identification of novel expression signatures reveal distinct patterns and prevalence of binding motifs for p53, nuclear factor-κB and other signal transcription factors in head and neck squamous cell carcinoma

PubMed Central

Yan, Bin; Yang, Xinping; Lee, Tin-Lap; Friedman, Jay; Tang, Jun; Van Waes, Carter; Chen, Zhong

2007-01-01

Background Differentially expressed gene profiles have previously been observed among pathologically defined cancers by microarray technologies, including head and neck squamous cell carcinomas (HNSCCs). However, the molecular expression signatures and transcriptional regulatory controls that underlie the heterogeneity in HNSCCs are not well defined. Results Genome-wide cDNA microarray profiling of ten HNSCC cell lines revealed novel gene expression signatures that distinguished cancer cell subsets associated with p53 status. Three major clusters of over-expressed genes (A to C) were defined through hierarchical clustering, Gene Ontology, and statistical modeling. The promoters of genes in these clusters exhibited different patterns and prevalence of transcription factor binding sites for p53, nuclear factor-κB (NF-κB), activator protein (AP)-1, signal transducer and activator of transcription (STAT)3 and early growth response (EGR)1, as compared with the frequency in vertebrate promoters. Cluster A genes involved in chromatin structure and function exhibited enrichment for p53 and decreased AP-1 binding sites, whereas clusters B and C, containing cytokine and antiapoptotic genes, exhibited a significant increase in prevalence of NF-κB binding sites. An increase in STAT3 and EGR1 binding sites was distributed among the over-expressed clusters. Novel regulatory modules containing p53 or NF-κB concomitant with other transcription factor binding motifs were identified, and experimental data supported the predicted transcriptional regulation and binding activity. Conclusion The transcription factors p53, NF-κB, and AP-1 may be important determinants of the heterogeneous pattern of gene expression, whereas STAT3 and EGR1 may broadly enhance gene expression in HNSCCs. Defining these novel gene signatures and regulatory mechanisms will be important for establishing new molecular classifications and subtyping, which in turn will promote development of targeted

A Simple and Efficient Computational Approach to Chafed Cable Time-Domain Reflectometry Signature Prediction

NASA Technical Reports Server (NTRS)

Kowalski, Marc Edward

2009-01-01

A method for the prediction of time-domain signatures of chafed coaxial cables is presented. The method is quasi-static in nature, and is thus efficient enough to be included in inference and inversion routines. Unlike previous models proposed, no restriction on the geometry or size of the chafe is required in the present approach. The model is validated and its speed is illustrated via comparison to simulations from a commercial, three-dimensional electromagnetic simulator.

A Gene Expression Profile of BRCAness that Predicts for Responsiveness to Platinum and PARP Inhibitors

DTIC Science & Technology

2011-08-01

tumors as BRCA-like (BL) or non-BRCA-like ( NBL ) corresponding to tumors predicted to have a BRCAness phenotype (BL tumors) or not ( NBL tumors). In...of six specimens with ATM knock down had the BL signature and six of six control specimens had the NBL signature (Fisher’s exact two sided p=0.002...control specimens had the NBL signature (Fisher’s exact two sided p=0.067). Figure 2. BRCAness profile distinguishes between BRCA1 knock down

Gene expression of Caenorhabditis elegans neurons carries information on their synaptic connectivity.

PubMed

Kaufman, Alon; Dror, Gideon; Meilijson, Isaac; Ruppin, Eytan

2006-12-08

The claim that genetic properties of neurons significantly influence their synaptic network structure is a common notion in neuroscience. The nematode Caenorhabditis elegans provides an exciting opportunity to approach this question in a large-scale quantitative manner. Its synaptic connectivity network has been identified, and, combined with cellular studies, we currently have characteristic connectivity and gene expression signatures for most of its neurons. By using two complementary analysis assays we show that the expression signature of a neuron carries significant information about its synaptic connectivity signature, and identify a list of putative genes predicting neural connectivity. The current study rigorously quantifies the relation between gene expression and synaptic connectivity signatures in the C. elegans nervous system and identifies subsets of neurons where this relation is highly marked. The results presented and the genes identified provide a promising starting point for further, more detailed computational and experimental investigations.

Uncertainty in hydrological signatures for gauged and ungauged catchments

NASA Astrophysics Data System (ADS)

Westerberg, Ida K.; Wagener, Thorsten; Coxon, Gemma; McMillan, Hilary K.; Castellarin, Attilio; Montanari, Alberto; Freer, Jim

2016-03-01

Reliable information about hydrological behavior is needed for water-resource management and scientific investigations. Hydrological signatures quantify catchment behavior as index values, and can be predicted for ungauged catchments using a regionalization procedure. The prediction reliability is affected by data uncertainties for the gauged catchments used in prediction and by uncertainties in the regionalization procedure. We quantified signature uncertainty stemming from discharge data uncertainty for 43 UK catchments and propagated these uncertainties in signature regionalization, while accounting for regionalization uncertainty with a weighted-pooling-group approach. Discharge uncertainty was estimated using Monte Carlo sampling of multiple feasible rating curves. For each sampled rating curve, a discharge time series was calculated and used in deriving the gauged signature uncertainty distribution. We found that the gauged uncertainty varied with signature type, local measurement conditions and catchment behavior, with the highest uncertainties (median relative uncertainty ±30-40% across all catchments) for signatures measuring high- and low-flow magnitude and dynamics. Our regionalization method allowed assessing the role and relative magnitudes of the gauged and regionalized uncertainty sources in shaping the signature uncertainty distributions predicted for catchments treated as ungauged. We found that (1) if the gauged uncertainties were neglected there was a clear risk of overconditioning the regionalization inference, e.g., by attributing catchment differences resulting from gauged uncertainty to differences in catchment behavior, and (2) uncertainty in the regionalization results was lower for signatures measuring flow distribution (e.g., mean flow) than flow dynamics (e.g., autocorrelation), and for average flows (and then high flows) compared to low flows.

Identification of immune signatures predictive of clinical protection from malaria.

PubMed

Valletta, John Joseph; Recker, Mario

2017-10-01

Antibodies are thought to play an essential role in naturally acquired immunity to malaria. Prospective cohort studies have frequently shown how continuous exposure to the malaria parasite Plasmodium falciparum cause an accumulation of specific responses against various antigens that correlate with a decreased risk of clinical malaria episodes. However, small effect sizes and the often polymorphic nature of immunogenic parasite proteins make the robust identification of the true targets of protective immunity ambiguous. Furthermore, the degree of individual-level protection conferred by elevated responses to these antigens has not yet been explored. Here we applied a machine learning approach to identify immune signatures predictive of individual-level protection against clinical disease. We find that commonly assumed immune correlates are poor predictors of clinical protection in children. On the other hand, antibody profiles predictive of an individual's malaria protective status can be found in data comprising responses to a large set of diverse parasite proteins. We show that this pattern emerges only after years of continuous exposure to the malaria parasite, whereas susceptibility to clinical episodes in young hosts (< 10 years) cannot be ascertained by measured antibody responses alone.

Identification of immune signatures predictive of clinical protection from malaria

PubMed Central

2017-01-01

Antibodies are thought to play an essential role in naturally acquired immunity to malaria. Prospective cohort studies have frequently shown how continuous exposure to the malaria parasite Plasmodium falciparum cause an accumulation of specific responses against various antigens that correlate with a decreased risk of clinical malaria episodes. However, small effect sizes and the often polymorphic nature of immunogenic parasite proteins make the robust identification of the true targets of protective immunity ambiguous. Furthermore, the degree of individual-level protection conferred by elevated responses to these antigens has not yet been explored. Here we applied a machine learning approach to identify immune signatures predictive of individual-level protection against clinical disease. We find that commonly assumed immune correlates are poor predictors of clinical protection in children. On the other hand, antibody profiles predictive of an individual’s malaria protective status can be found in data comprising responses to a large set of diverse parasite proteins. We show that this pattern emerges only after years of continuous exposure to the malaria parasite, whereas susceptibility to clinical episodes in young hosts (< 10 years) cannot be ascertained by measured antibody responses alone. PMID:29065113

Dynamic transcriptional signatures and network responses for clinical symptoms in influenza-infected human subjects using systems biology approaches.

PubMed

Linel, Patrice; Wu, Shuang; Deng, Nan; Wu, Hulin

2014-10-01

Recent studies demonstrate that human blood transcriptional signatures may be used to support diagnosis and clinical decisions for acute respiratory viral infections such as influenza. In this article, we propose to use a newly developed systems biology approach for time course gene expression data to identify significant dynamically response genes and dynamic gene network responses to viral infection. We illustrate the methodological pipeline by reanalyzing the time course gene expression data from a study with healthy human subjects challenged by live influenza virus. We observed clear differences in the number of significant dynamic response genes (DRGs) between the symptomatic and asymptomatic subjects and also identified DRG signatures for symptomatic subjects with influenza infection. The 505 common DRGs shared by the symptomatic subjects have high consistency with the signature genes for predicting viral infection identified in previous works. The temporal response patterns and network response features were carefully analyzed and investigated.

Network information improves cancer outcome prediction.

PubMed

Roy, Janine; Winter, Christof; Isik, Zerrin; Schroeder, Michael

2014-07-01

Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Google's PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression. © The Author 2012. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

A qualitative signature for early diagnosis of hepatocellular carcinoma based on relative expression orderings.

PubMed

Ao, Lu; Zhang, Zimei; Guan, Qingzhou; Guo, Yating; Guo, You; Zhang, Jiahui; Lv, Xingwei; Huang, Haiyan; Zhang, Huarong; Wang, Xianlong; Guo, Zheng

2018-04-23

Currently, using biopsy specimens to confirm suspicious liver lesions of early hepatocellular carcinoma are not entirely reliable because of insufficient sampling amount and inaccurate sampling location. It is necessary to develop a signature to aid early hepatocellular carcinoma diagnosis using biopsy specimens even when the sampling location is inaccurate. Based on the within-sample relative expression orderings of gene pairs, we identified a simple qualitative signature to distinguish both hepatocellular carcinoma and adjacent non-tumour tissues from cirrhosis tissues of non-hepatocellular carcinoma patients. A signature consisting of 19 gene pairs was identified in the training data sets and validated in 2 large collections of samples from biopsy and surgical resection specimens. For biopsy specimens, 95.7% of 141 hepatocellular carcinoma tissues and all (100%) of 108 cirrhosis tissues of non-hepatocellular carcinoma patients were correctly classified. Especially, all (100%) of 60 hepatocellular carcinoma adjacent normal tissues and 77.5% of 80 hepatocellular carcinoma adjacent cirrhosis tissues were classified to hepatocellular carcinoma. For surgical resection specimens, 99.7% of 733 hepatocellular carcinoma specimens were correctly classified to hepatocellular carcinoma, while 96.1% of 254 hepatocellular carcinoma adjacent cirrhosis tissues and 95.9% of 538 hepatocellular carcinoma adjacent normal tissues were classified to hepatocellular carcinoma. In contrast, 17.0% of 47 cirrhosis from non-hepatocellular carcinoma patients waiting for liver transplantation were classified to hepatocellular carcinoma, indicating that some patients with long-lasting cirrhosis could have already gained hepatocellular carcinoma characteristics. The signature can distinguish both hepatocellular carcinoma tissues and tumour-adjacent tissues from cirrhosis tissues of non-hepatocellular carcinoma patients even using inaccurately sampled biopsy specimens, which can aid early

pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures

PubMed Central

2015-01-01

Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (http://structure.bioc.cam.ac.uk/pkcsm), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties. PMID:25860834

Clinical Application of Prognostic Gene Expression Signature in Fusion Gene-Negative Rhabdomyosarcoma: A Report from the Children's Oncology Group.

PubMed

Hingorani, Pooja; Missiaglia, Edoardo; Shipley, Janet; Anderson, James R; Triche, Timothy J; Delorenzi, Mauro; Gastier-Foster, Julie; Wing, Michele; Hawkins, Douglas S; Skapek, Stephen X

2015-10-15

Pediatric rhabdomyosarcoma (RMS) has two common histologic subtypes: embryonal (ERMS) and alveolar (ARMS). PAX-FOXO1 fusion gene status is a more reliable prognostic marker than alveolar histology, whereas fusion gene-negative (FN) ARMS patients are clinically similar to ERMS patients. A five-gene expression signature (MG5) previously identified two diverse risk groups within the fusion gene-negative RMS (FN-RMS) patients, but this has not been independently validated. The goal of this study was to test whether expression of the MG5 metagene, measured using a technical platform that can be applied to routine pathology material, would correlate with outcome in a new cohort of patients with FN-RMS. Cases were taken from the Children's Oncology Group (COG) D9803 study of children with intermediate-risk RMS, and gene expression profiling for the MG5 genes was performed using the nCounter assay. The MG5 score was correlated with clinical and pathologic characteristics as well as overall and event-free survival. MG5 standardized score showed no significant association with any of the available clinicopathologic variables. The MG5 signature score showed a significant correlation with overall (N = 57; HR, 7.3; 95% CI, 1.9-27.0; P = 0.003) and failure-free survival (N = 57; HR, 6.1; 95% CI, 1.9-19.7; P = 0.002). This represents the first, validated molecular prognostic signature for children with FN-RMS who otherwise have intermediate-risk disease. The capacity to measure the expression of a small number of genes in routine pathology material and apply a simple mathematical formula to calculate the MG5 metagene score provides a clear path toward better risk stratification in future prospective clinical trials. ©2015 American Association for Cancer Research.

Core epithelial-to-mesenchymal transition interactome gene-expression signature is associated with claudin-low and metaplastic breast cancer subtypes.

PubMed

Taube, Joseph H; Herschkowitz, Jason I; Komurov, Kakajan; Zhou, Alicia Y; Gupta, Supriya; Yang, Jing; Hartwell, Kimberly; Onder, Tamer T; Gupta, Piyush B; Evans, Kurt W; Hollier, Brett G; Ram, Prahlad T; Lander, Eric S; Rosen, Jeffrey M; Weinberg, Robert A; Mani, Sendurai A

2010-08-31

The epithelial-to-mesenchymal transition (EMT) produces cancer cells that are invasive, migratory, and exhibit stem cell characteristics, hallmarks of cells that have the potential to generate metastases. Inducers of the EMT include several transcription factors (TFs), such as Goosecoid, Snail, and Twist, as well as the secreted TGF-beta1. Each of these factors is capable, on its own, of inducing an EMT in the human mammary epithelial (HMLE) cell line. However, the interactions between these regulators are poorly understood. Overexpression of each of the above EMT inducers up-regulates a subset of other EMT-inducing TFs, with Twist, Zeb1, Zeb2, TGF-beta1, and FOXC2 being commonly induced. Up-regulation of Slug and FOXC2 by either Snail or Twist does not depend on TGF-beta1 signaling. Gene expression signatures (GESs) derived by overexpressing EMT-inducing TFs reveal that the Twist GES and Snail GES are the most similar, although the Goosecoid GES is the least similar to the others. An EMT core signature was derived from the changes in gene expression shared by up-regulation of Gsc, Snail, Twist, and TGF-beta1 and by down-regulation of E-cadherin, loss of which can also trigger an EMT in certain cell types. The EMT core signature associates closely with the claudin-low and metaplastic breast cancer subtypes and correlates negatively with pathological complete response. Additionally, the expression level of FOXC1, another EMT inducer, correlates strongly with poor survival of breast cancer patients.

Core epithelial-to-mesenchymal transition interactome gene-expression signature is associated with claudin-low and metaplastic breast cancer subtypes

PubMed Central

Taube, Joseph H.; Herschkowitz, Jason I.; Komurov, Kakajan; Zhou, Alicia Y.; Gupta, Supriya; Yang, Jing; Hartwell, Kimberly; Onder, Tamer T.; Gupta, Piyush B.; Evans, Kurt W.; Hollier, Brett G.; Ram, Prahlad T.; Lander, Eric S.; Rosen, Jeffrey M.; Weinberg, Robert A.; Mani, Sendurai A.

2010-01-01

The epithelial-to-mesenchymal transition (EMT) produces cancer cells that are invasive, migratory, and exhibit stem cell characteristics, hallmarks of cells that have the potential to generate metastases. Inducers of the EMT include several transcription factors (TFs), such as Goosecoid, Snail, and Twist, as well as the secreted TGF-β1. Each of these factors is capable, on its own, of inducing an EMT in the human mammary epithelial (HMLE) cell line. However, the interactions between these regulators are poorly understood. Overexpression of each of the above EMT inducers up-regulates a subset of other EMT-inducing TFs, with Twist, Zeb1, Zeb2, TGF-β1, and FOXC2 being commonly induced. Up-regulation of Slug and FOXC2 by either Snail or Twist does not depend on TGF-β1 signaling. Gene expression signatures (GESs) derived by overexpressing EMT-inducing TFs reveal that the Twist GES and Snail GES are the most similar, although the Goosecoid GES is the least similar to the others. An EMT core signature was derived from the changes in gene expression shared by up-regulation of Gsc, Snail, Twist, and TGF-β1 and by down-regulation of E-cadherin, loss of which can also trigger an EMT in certain cell types. The EMT core signature associates closely with the claudin-low and metaplastic breast cancer subtypes and correlates negatively with pathological complete response. Additionally, the expression level of FOXC1, another EMT inducer, correlates strongly with poor survival of breast cancer patients. PMID:20713713

Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer

PubMed Central

Tamez-Peña, Jose-Gerardo; Rodriguez-Rojas, Juan-Andrés; Gomez-Rueda, Hugo; Celaya-Padilla, Jose-Maria; Rivera-Prieto, Roxana-Alicia; Palacios-Corona, Rebeca; Garza-Montemayor, Margarita; Cardona-Huerta, Servando

2018-01-01

In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures. PMID:29596496

Gene expression profiling in the early phases of DMD: a constant molecular signature characterizes DMD muscle from early postnatal life throughout disease progression.

PubMed

Pescatori, Mario; Broccolini, Aldobrando; Minetti, Carlo; Bertini, Enrico; Bruno, Claudio; D'amico, Adele; Bernardini, Camilla; Mirabella, Massimiliano; Silvestri, Gabriella; Giglio, Vincenzo; Modoni, Anna; Pedemonte, Marina; Tasca, Giorgio; Galluzzi, Giuliana; Mercuri, Eugenio; Tonali, Pietro A; Ricci, Enzo

2007-04-01

Genome-wide gene expression profiling of skeletal muscle from Duchenne muscular dystrophy (DMD) patients has been used to describe muscle tissue alterations in DMD children older than 5 years. By studying the expression profile of 19 patients younger than 2 years, we describe with high resolution the gene expression signature that characterizes DMD muscle during the initial or "presymptomatic" phase of the disease. We show that in the first 2 years of the disease, DMD muscle is already set to express a distinctive gene expression pattern considerably different from the one expressed by normal, age-matched muscle. This "dystrophic" molecular signature is characterized by a coordinate induction of genes involved in the inflammatory response, extracellular matrix (ECM) remodeling and muscle regeneration, and the reduced transcription of those involved in energy metabolism. Despite the lower degree of muscle dysfunction experienced, our younger patients showed abnormal expression of most of the genes reported as differentially expressed in more advanced stages of the disease. By analyzing our patients as a time series, we provide evidence that some genes, including members of three pathways involved in morphogenetic signaling-Wnt, Notch, and BMP-are progressively induced or repressed in the natural history of DMD.

Exploring the molecular mechanisms of Traditional Chinese Medicine components using gene expression signatures and connectivity map.

PubMed

Yoo, Minjae; Shin, Jimin; Kim, Hyunmin; Kim, Jihye; Kang, Jaewoo; Tan, Aik Choon

2018-04-04

Traditional Chinese Medicine (TCM) has been practiced over thousands of years in China and other Asian countries for treating various symptoms and diseases. However, the underlying molecular mechanisms of TCM are poorly understood, partly due to the "multi-component, multi-target" nature of TCM. To uncover the molecular mechanisms of TCM, we perform comprehensive gene expression analysis using connectivity map. We interrogated gene expression signatures obtained 102 TCM components using the next generation Connectivity Map (CMap) resource. We performed systematic data mining and analysis on the mechanism of action (MoA) of these TCM components based on the CMap results. We clustered the 102 TCM components into four groups based on their MoAs using next generation CMap resource. We performed gene set enrichment analysis on these components to provide additional supports for explaining these molecular mechanisms. We also provided literature evidence to validate the MoAs identified through this bioinformatics analysis. Finally, we developed the Traditional Chinese Medicine Drug Repurposing Hub (TCM Hub) - a connectivity map resource to facilitate the elucidation of TCM MoA for drug repurposing research. TCMHub is freely available in http://tanlab.ucdenver.edu/TCMHub. Molecular mechanisms of TCM could be uncovered by using gene expression signatures and connectivity map. Through this analysis, we identified many of the TCM components possess diverse MoAs, this may explain the applications of TCM in treating various symptoms and diseases. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Effects Of Hydrothermal Alteration On Magnetic Properties And Magnetic Signatures - Implications For Predictive Magnetic Exploration Models

NASA Astrophysics Data System (ADS)

Clark, D.

2012-12-01

Magnetics is the most widely used geophysical method in hard rock exploration and magnetic surveys are an integral part of exploration programs for many types of mineral deposit, including porphyry Cu, intrusive-related gold, volcanic-hosted epithermal Au, IOCG, VMS, and Ni sulfide deposits. However, the magnetic signatures of ore deposits and their associated mineralized systems are extremely variable and exploration that is based simply on searching for signatures that resemble those of known deposits and systems is rarely successful. Predictive magnetic exploration models are based upon well-established geological models, combined with magnetic property measurements and geological information from well-studied deposits, and guided by magnetic petrological understanding of the processes that create, destroy and modify magnetic minerals in rocks. These models are designed to guide exploration by predicting magnetic signatures that are appropriate to specific geological settings, taking into account factors such as tectonic province; protolith composition; post-formation tilting/faulting/ burial/ exhumation and partial erosion; and metamorphism. Patterns of zoned hydrothermal alteration are important indicators of potentially mineralized systems and, if properly interpreted, can provided vectors to ore. Magnetic signatures associated with these patterns at a range of scales can provide valuable information on prospectivity and can guide drilling, provided they are correctly interpreted in geological terms. This presentation reviews effects of the important types of hydrothermal alteration on magnetic properties within mineralized systems, with particular reference to porphyry copper and IOCG deposits. For example, an unmodified gold-rich porphyry copper system, emplaced into mafic-intermediate volcanic host rocks (such as Bajo de la Alumbrera, Argentina) exhibits an inner potassic zone that is strongly mineralized and magnetite-rich, which is surrounded by an outer

Integrative ChIP-seq/microarray analysis identifies a CTNNB1 target signature enriched in intestinal stem cells and colon cancer.

PubMed

Watanabe, Kazuhide; Biesinger, Jacob; Salmans, Michael L; Roberts, Brian S; Arthur, William T; Cleary, Michele; Andersen, Bogi; Xie, Xiaohui; Dai, Xing

2014-01-01

Deregulation of canonical Wnt/CTNNB1 (beta-catenin) pathway is one of the earliest events in the pathogenesis of colon cancer. Mutations in APC or CTNNB1 are highly frequent in colon cancer and cause aberrant stabilization of CTNNB1, which activates the transcription of Wnt target genes by binding to chromatin via the TCF/LEF transcription factors. Here we report an integrative analysis of genome-wide chromatin occupancy of CTNNB1 by chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) and gene expression profiling by microarray analysis upon RNAi-mediated knockdown of CTNNB1 in colon cancer cells. We observed 3629 CTNNB1 binding peaks across the genome and a significant correlation between CTNNB1 binding and knockdown-induced gene expression change. Our integrative analysis led to the discovery of a direct Wnt target signature composed of 162 genes. Gene ontology analysis of this signature revealed a significant enrichment of Wnt pathway genes, suggesting multiple feedback regulations of the pathway. We provide evidence that this gene signature partially overlaps with the Lgr5+ intestinal stem cell signature, and is significantly enriched in normal intestinal stem cells as well as in clinical colorectal cancer samples. Interestingly, while the expression of the CTNNB1 target gene set does not correlate with survival, elevated expression of negative feedback regulators within the signature predicts better prognosis. Our data provide a genome-wide view of chromatin occupancy and gene regulation of Wnt/CTNNB1 signaling in colon cancer cells.

Age gene expression and coexpression progressive signatures in peripheral blood leukocytes.

PubMed

Irizar, Haritz; Goñi, Joaquín; Alzualde, Ainhoa; Castillo-Triviño, Tamara; Olascoaga, Javier; Lopez de Munain, Adolfo; Otaegui, David

2015-12-01

Both cellular senescence and organismic aging are known to be dynamic processes that start early in life and progress constantly during the whole life of the individual. In this work, with the objective of identifying signatures of age-related progressive change at the transcriptomic level, we have performed a whole-genome gene expression analysis of peripheral blood leukocytes in a group of healthy individuals with ages ranging from 14 to 93 years. A set of genes with progressively changing gene expression (either increase or decrease with age) has been identified and contextualized in a coexpression network. A modularity analysis has been performed on this network and biological-term and pathway enrichment analyses have been used for biological interpretation of each module. In summary, the results of the present work reveal the existence of a transcriptomic component that shows progressive expression changes associated to age in peripheral blood leukocytes, highlighting both the dynamic nature of the process and the need to complement young vs. elder studies with longitudinal studies that include middle aged individuals. From the transcriptional point of view, immunosenescence seems to be occurring from a relatively early age, at least from the late 20s/early 30s, and the 49-56 year old age-range appears to be critical. In general, the genes that, according to our results, show progressive expression changes with aging are involved in pathogenic/cellular processes that have classically been linked to aging in humans: cancer, immune processes and cellular growth vs. maintenance. Copyright © 2015 Elsevier Inc. All rights reserved.

Multiclass cancer diagnosis using tumor gene expression signatures

DOE PAGES

Ramaswamy, S.; Tamayo, P.; Rifkin, R.; ...

2001-12-11

The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a supportmore » vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.« less

Expression signature as a biomarker for prenatal diagnosis of trisomy 21.

PubMed

Volk, Marija; Maver, Aleš; Lovrečić, Luca; Juvan, Peter; Peterlin, Borut

2013-01-01

A universal biomarker panel with the potential to predict high-risk pregnancies or adverse pregnancy outcome does not exist. Transcriptome analysis is a powerful tool to capture differentially expressed genes (DEG), which can be used as biomarker-diagnostic-predictive tool for various conditions in prenatal setting. In search of biomarker set for predicting high-risk pregnancies, we performed global expression profiling to find DEG in Ts21. Subsequently, we performed targeted validation and diagnostic performance evaluation on a larger group of case and control samples. Initially, transcriptomic profiles of 10 cultivated amniocyte samples with Ts21 and 9 with normal euploid constitution were determined using expression microarrays. Datasets from Ts21 transcriptomic studies from GEO repository were incorporated. DEG were discovered using linear regression modelling and validated using RT-PCR quantification on an independent sample of 16 cases with Ts21 and 32 controls. The classification performance of Ts21 status based on expression profiling was performed using supervised machine learning algorithm and evaluated using a leave-one-out cross validation approach. Global gene expression profiling has revealed significant expression changes between normal and Ts21 samples, which in combination with data from previously performed Ts21 transcriptomic studies, were used to generate a multi-gene biomarker for Ts21, comprising of 9 gene expression profiles. In addition to biomarker's high performance in discriminating samples from global expression profiling, we were also able to show its discriminatory performance on a larger sample set 2, validated using RT-PCR experiment (AUC=0.97), while its performance on data from previously published studies reached discriminatory AUC values of 1.00. Our results show that transcriptomic changes might potentially be used to discriminate trisomy of chromosome 21 in the prenatal setting. As expressional alterations reflect both, causal

A gene expression signature associated with survival in metastatic melanoma

PubMed Central

Mandruzzato, Susanna; Callegaro, Andrea; Turcatel, Gianluca; Francescato, Samuela; Montesco, Maria C; Chiarion-Sileni, Vanna; Mocellin, Simone; Rossi, Carlo R; Bicciato, Silvio; Wang, Ena; Marincola, Francesco M; Zanovello, Paola

2006-01-01

Background Current clinical and histopathological criteria used to define the prognosis of melanoma patients are inadequate for accurate prediction of clinical outcome. We investigated whether genome screening by means of high-throughput gene microarray might provide clinically useful information on patient survival. Methods Forty-three tumor tissues from 38 patients with stage III and stage IV melanoma were profiled with a 17,500 element cDNA microarray. Expression data were analyzed using significance analysis of microarrays (SAM) to identify genes associated with patient survival, and supervised principal components (SPC) to determine survival prediction. Results SAM analysis revealed a set of 80 probes, corresponding to 70 genes, associated with survival, i.e. 45 probes characterizing longer and 35 shorter survival times, respectively. These transcripts were included in a survival prediction model designed using SPC and cross-validation which allowed identifying 30 predicting probes out of the 80 associated with survival. Conclusion The longer-survival group of genes included those expressed in immune cells, both innate and acquired, confirming the interplay between immunological mechanisms and the natural history of melanoma. Genes linked to immune cells were totally lacking in the poor-survival group, which was instead associated with a number of genes related to highly proliferative and invasive tumor cells. PMID:17129373

A whole-blood transcriptome meta-analysis identifies gene expression signatures of cigarette smoking

PubMed Central

Huan, Tianxiao; Joehanes, Roby; Schurmann, Claudia; Schramm, Katharina; Pilling, Luke C.; Peters, Marjolein J.; Mägi, Reedik; DeMeo, Dawn; O'Connor, George T.; Ferrucci, Luigi; Teumer, Alexander; Homuth, Georg; Biffar, Reiner; Völker, Uwe; Herder, Christian; Waldenberger, Melanie; Peters, Annette; Zeilinger, Sonja; Metspalu, Andres; Hofman, Albert; Uitterlinden, André G.; Hernandez, Dena G.; Singleton, Andrew B.; Bandinelli, Stefania; Munson, Peter J.; Lin, Honghuang; Benjamin, Emelia J.; Esko, Tõnu; Grabe, Hans J.; Prokisch, Holger; van Meurs, Joyce B.J.; Melzer, David; Levy, Daniel

2016-01-01

Abstract Cigarette smoking is a leading modifiable cause of death worldwide. We hypothesized that cigarette smoking induces extensive transcriptomic changes that lead to target-organ damage and smoking-related diseases. We performed a meta-analysis of transcriptome-wide gene expression using whole blood-derived RNA from 10,233 participants of European ancestry in six cohorts (including 1421 current and 3955 former smokers) to identify associations between smoking and altered gene expression levels. At a false discovery rate (FDR) <0.1, we identified 1270 differentially expressed genes in current vs. never smokers, and 39 genes in former vs. never smokers. Expression levels of 12 genes remained elevated up to 30 years after smoking cessation, suggesting that the molecular consequence of smoking may persist for decades. Gene ontology analysis revealed enrichment of smoking-related genes for activation of platelets and lymphocytes, immune response, and apoptosis. Many of the top smoking-related differentially expressed genes, including LRRN3 and GPR15, have DNA methylation loci in promoter regions that were recently reported to be hypomethylated among smokers. By linking differential gene expression with smoking-related disease phenotypes, we demonstrated that stroke and pulmonary function show enrichment for smoking-related gene expression signatures. Mediation analysis revealed the expression of several genes (e.g. ALAS2) to be putative mediators of the associations between smoking and inflammatory biomarkers (IL6 and C-reactive protein levels). Our transcriptomic study provides potential insights into the effects of cigarette smoking on gene expression in whole blood and their relations to smoking-related diseases. The results of such analyses may highlight attractive targets for treating or preventing smoking-related health effects. PMID:28158590

Pediatric Crohn disease patients exhibit specific ileal transcriptome and microbiome signature.

PubMed

Haberman, Yael; Tickle, Timothy L; Dexheimer, Phillip J; Kim, Mi-Ok; Tang, Dora; Karns, Rebekah; Baldassano, Robert N; Noe, Joshua D; Rosh, Joel; Markowitz, James; Heyman, Melvin B; Griffiths, Anne M; Crandall, Wallace V; Mack, David R; Baker, Susan S; Huttenhower, Curtis; Keljo, David J; Hyams, Jeffrey S; Kugathasan, Subra; Walters, Thomas D; Aronow, Bruce; Xavier, Ramnik J; Gevers, Dirk; Denson, Lee A

2014-08-01

Interactions between the host and gut microbial community likely contribute to Crohn disease (CD) pathogenesis; however, direct evidence for these interactions at the onset of disease is lacking. Here, we characterized the global pattern of ileal gene expression and the ileal microbial community in 359 treatment-naive pediatric patients with CD, patients with ulcerative colitis (UC), and control individuals. We identified core gene expression profiles and microbial communities in the affected CD ilea that are preserved in the unaffected ilea of patients with colon-only CD but not present in those with UC or control individuals; therefore, this signature is specific to CD and independent of clinical inflammation. An abnormal increase of antimicrobial dual oxidase (DUOX2) expression was detected in association with an expansion of Proteobacteria in both UC and CD, while expression of lipoprotein APOA1 gene was downregulated and associated with CD-specific alterations in Firmicutes. The increased DUOX2 and decreased APOA1 gene expression signature favored oxidative stress and Th1 polarization and was maximally altered in patients with more severe mucosal injury. A regression model that included APOA1 gene expression and microbial abundance more accurately predicted month 6 steroid-free remission than a model using clinical factors alone. These CD-specific host and microbe profiles identify the ileum as the primary inductive site for all forms of CD and may direct prognostic and therapeutic approaches.

A common molecular signature of intestinal-type gastric carcinoma indicates processes related to gastric carcinogenesis.

PubMed

Binato, Renata; Santos, Everton Cruz; Boroni, Mariana; Demachki, Samia; Assumpção, Paulo; Abdelhay, Eliana

2018-01-26

Gastric carcinoma (GC) is one of the most aggressive cancers and the second leading cause of cancer death in the world. According to the Lauren classification, this adenocarcinoma is divided into two subtypes, intestinal and diffuse, which differ in their clinical, epidemiological and molecular features. Several studies have attempted to delineate the molecular signature of gastric cancer to develop new and non-invasive screening tests that improve diagnosis and lead to new treatment strategies. However, a consensus signature has not yet been identified for each condition. Thus, this work aimed to analyze the gene expression profile of Brazilian intestinal-type GC tissues using microarrays and compare the results to those of non-tumor tissue samples. Moreover, we compared our intestinal-type gastric carcinoma profile with those obtained from populations worldwide to assess their similarity. The results identified a molecular signature for intestinal-type GC and revealed that 38 genes differentially expressed in Brazilian intestinal-type gastric carcinoma samples can successfully distinguish gastric tumors from non-tumor tissue in the global population. These differentially expressed genes participate in biological processes important to cell homeostasis. Furthermore, Kaplan-Meier analysis suggested that 7 of these genes could individually be able to predict overall survival in intestinal-type gastric cancer patients.

Macaques can predict social outcomes from facial expressions.

PubMed

Waller, Bridget M; Whitehouse, Jamie; Micheletta, Jérôme

2016-09-01

There is widespread acceptance that facial expressions are useful in social interactions, but empirical demonstration of their adaptive function has remained elusive. Here, we investigated whether macaques can use the facial expressions of others to predict the future outcomes of social interaction. Crested macaques (Macaca nigra) were shown an approach between two unknown individuals on a touchscreen and were required to choose between one of two potential social outcomes. The facial expressions of the actors were manipulated in the last frame of the video. One subject reached the experimental stage and accurately predicted different social outcomes depending on which facial expressions the actors displayed. The bared-teeth display (homologue of the human smile) was most strongly associated with predicted friendly outcomes. Contrary to our predictions, screams and threat faces were not associated more with conflict outcomes. Overall, therefore, the presence of any facial expression (compared to neutral) caused the subject to choose friendly outcomes more than negative outcomes. Facial expression in general, therefore, indicated a reduced likelihood of social conflict. The findings dispute traditional theories that view expressions only as indicators of present emotion and instead suggest that expressions form part of complex social interactions where individuals think beyond the present.

4-protein signature predicting tamoxifen treatment outcome in recurrent breast cancer.

PubMed

De Marchi, Tommaso; Liu, Ning Qing; Stingl, Cristoph; Timmermans, Mieke A; Smid, Marcel; Look, Maxime P; Tjoa, Mila; Braakman, Rene B H; Opdam, Mark; Linn, Sabine C; Sweep, Fred C G J; Span, Paul N; Kliffen, Mike; Luider, Theo M; Foekens, John A; Martens, John W M; Umar, Arzu

2016-01-01

Estrogen receptor (ER) positive tumors represent the majority of breast malignancies, and are effectively treated with hormonal therapies, such as tamoxifen. However, in the recurrent disease resistance to tamoxifen therapy is common and a major cause of death. In recent years, in-depth proteome analyses have enabled identification of clinically useful biomarkers, particularly, when heterogeneity in complex tumor tissue was reduced using laser capture microdissection (LCM). In the current study, we performed high resolution proteomic analysis on two cohorts of ER positive breast tumors derived from patients who either manifested good or poor outcome to tamoxifen treatment upon recurrence. A total of 112 fresh frozen tumors were collected from multiple medical centers and divided into two sets: an in-house training and a multi-center test set. Epithelial tumor cells were enriched with LCM and analyzed by nano-LC Orbitrap mass spectrometry (MS), which yielded >3000 and >4000 quantified proteins in the training and test sets, respectively. Raw data are available via ProteomeXchange with identifiers PXD000484 and PXD000485. Statistical analysis showed differential abundance of 99 proteins, of which a subset of 4 proteins was selected through a multivariate step-down to develop a predictor for tamoxifen treatment outcome. The 4-protein signature significantly predicted poor outcome patients in the test set, independent of predictive histopathological characteristics (hazard ratio [HR] = 2.17; 95% confidence interval [CI] = 1.15 to 4.17; multivariate Cox regression p value = 0.017). Immunohistochemical (IHC) staining of PDCD4, one of the signature proteins, on an independent set of formalin-fixed paraffin-embedded tumor tissues provided and independent technical validation (HR = 0.72; 95% CI = 0.57 to 0.92; multivariate Cox regression p value = 0.009). We hereby report the first validated protein predictor for tamoxifen treatment outcome in recurrent ER-positive breast

Perturbation of B Cell Gene Expression Persists in HIV-Infected Children Despite Effective Antiretroviral Therapy and Predicts H1N1 Response.

PubMed

Cotugno, Nicola; De Armas, Lesley; Pallikkuth, Suresh; Rinaldi, Stefano; Issac, Biju; Cagigi, Alberto; Rossi, Paolo; Palma, Paolo; Pahwa, Savita

2017-01-01

Despite effective antiretroviral therapy (ART), HIV-infected individuals with apparently similar clinical and immunological characteristics can vary in responsiveness to vaccinations. However, molecular mechanisms responsible for such impairment, as well as biomarkers able to predict vaccine responsiveness in HIV-infected children, remain unknown. Following the hypothesis that a B cell qualitative impairment persists in HIV-infected children (HIV) despite effective ART and phenotypic B cell immune reconstitution, the aim of the current study was to investigate B cell gene expression of HIV compared to age-matched healthy controls (HCs) and to determine whether distinct gene expression patterns could predict the ability to respond to influenza vaccine. To do so, we analyzed prevaccination transcriptional levels of a 96-gene panel in equal numbers of sort-purified B cell subsets (SPBS) isolated from peripheral blood mononuclear cells using multiplexed RT-PCR. Immune responses to H1N1 antigen were determined by hemaglutination inhibition and memory B cell ELISpot assays following trivalent-inactivated influenza vaccination (TIV) for all study participants. Although there were no differences in terms of cell frequencies of SPBS between HIV and HC, the groups were distinguishable based upon gene expression analyses. Indeed, a 28-gene signature, characterized by higher expression of genes involved in the inflammatory response and immune activation was observed in activated memory B cells (CD27 + CD21 - ) from HIV when compared to HC despite long-term viral control (>24 months). Further analysis, taking into account H1N1 responses after TIV in HIV participants, revealed that a 25-gene signature in resting memory (RM) B cells (CD27 + CD21 + ) was able to distinguish vaccine responders from non-responders (NR). In fact, prevaccination RM B cells of responders showed a higher expression of gene sets involved in B cell adaptive immune responses ( APRIL, BTK, BLIMP1 ) and

Perturbation of B Cell Gene Expression Persists in HIV-Infected Children Despite Effective Antiretroviral Therapy and Predicts H1N1 Response

PubMed Central

Cotugno, Nicola; De Armas, Lesley; Pallikkuth, Suresh; Rinaldi, Stefano; Issac, Biju; Cagigi, Alberto; Rossi, Paolo; Palma, Paolo; Pahwa, Savita

2017-01-01

Despite effective antiretroviral therapy (ART), HIV-infected individuals with apparently similar clinical and immunological characteristics can vary in responsiveness to vaccinations. However, molecular mechanisms responsible for such impairment, as well as biomarkers able to predict vaccine responsiveness in HIV-infected children, remain unknown. Following the hypothesis that a B cell qualitative impairment persists in HIV-infected children (HIV) despite effective ART and phenotypic B cell immune reconstitution, the aim of the current study was to investigate B cell gene expression of HIV compared to age-matched healthy controls (HCs) and to determine whether distinct gene expression patterns could predict the ability to respond to influenza vaccine. To do so, we analyzed prevaccination transcriptional levels of a 96-gene panel in equal numbers of sort-purified B cell subsets (SPBS) isolated from peripheral blood mononuclear cells using multiplexed RT-PCR. Immune responses to H1N1 antigen were determined by hemaglutination inhibition and memory B cell ELISpot assays following trivalent-inactivated influenza vaccination (TIV) for all study participants. Although there were no differences in terms of cell frequencies of SPBS between HIV and HC, the groups were distinguishable based upon gene expression analyses. Indeed, a 28-gene signature, characterized by higher expression of genes involved in the inflammatory response and immune activation was observed in activated memory B cells (CD27+CD21−) from HIV when compared to HC despite long-term viral control (>24 months). Further analysis, taking into account H1N1 responses after TIV in HIV participants, revealed that a 25-gene signature in resting memory (RM) B cells (CD27+CD21+) was able to distinguish vaccine responders from non-responders (NR). In fact, prevaccination RM B cells of responders showed a higher expression of gene sets involved in B cell adaptive immune responses (APRIL, BTK, BLIMP1) and BCR

Integrative ChIP-seq/Microarray Analysis Identifies a CTNNB1 Target Signature Enriched in Intestinal Stem Cells and Colon Cancer

PubMed Central

Watanabe, Kazuhide; Biesinger, Jacob; Salmans, Michael L.; Roberts, Brian S.; Arthur, William T.; Cleary, Michele; Andersen, Bogi; Xie, Xiaohui; Dai, Xing

2014-01-01

Background Deregulation of canonical Wnt/CTNNB1 (beta-catenin) pathway is one of the earliest events in the pathogenesis of colon cancer. Mutations in APC or CTNNB1 are highly frequent in colon cancer and cause aberrant stabilization of CTNNB1, which activates the transcription of Wnt target genes by binding to chromatin via the TCF/LEF transcription factors. Here we report an integrative analysis of genome-wide chromatin occupancy of CTNNB1 by chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) and gene expression profiling by microarray analysis upon RNAi-mediated knockdown of CTNNB1 in colon cancer cells. Results We observed 3629 CTNNB1 binding peaks across the genome and a significant correlation between CTNNB1 binding and knockdown-induced gene expression change. Our integrative analysis led to the discovery of a direct Wnt target signature composed of 162 genes. Gene ontology analysis of this signature revealed a significant enrichment of Wnt pathway genes, suggesting multiple feedback regulations of the pathway. We provide evidence that this gene signature partially overlaps with the Lgr5+ intestinal stem cell signature, and is significantly enriched in normal intestinal stem cells as well as in clinical colorectal cancer samples. Interestingly, while the expression of the CTNNB1 target gene set does not correlate with survival, elevated expression of negative feedback regulators within the signature predicts better prognosis. Conclusion Our data provide a genome-wide view of chromatin occupancy and gene regulation of Wnt/CTNNB1 signaling in colon cancer cells. PMID:24651522

MARQ: an online tool to mine GEO for experiments with similar or opposite gene expression signatures.

PubMed

Vazquez, Miguel; Nogales-Cadenas, Ruben; Arroyo, Javier; Botías, Pedro; García, Raul; Carazo, Jose M; Tirado, Francisco; Pascual-Montano, Alberto; Carmona-Saez, Pedro

2010-07-01

The enormous amount of data available in public gene expression repositories such as Gene Expression Omnibus (GEO) offers an inestimable resource to explore gene expression programs across several organisms and conditions. This information can be used to discover experiments that induce similar or opposite gene expression patterns to a given query, which in turn may lead to the discovery of new relationships among diseases, drugs or pathways, as well as the generation of new hypotheses. In this work, we present MARQ, a web-based application that allows researchers to compare a query set of genes, e.g. a set of over- and under-expressed genes, against a signature database built from GEO datasets for different organisms and platforms. MARQ offers an easy-to-use and integrated environment to mine GEO, in order to identify conditions that induce similar or opposite gene expression patterns to a given experimental condition. MARQ also includes additional functionalities for the exploration of the results, including a meta-analysis pipeline to find genes that are differentially expressed across different experiments. The application is freely available at http://marq.dacya.ucm.es.

High BAALC expression associates with other molecular prognostic markers, poor outcome, and a distinct gene-expression signature in cytogenetically normal patients younger than 60 years with acute myeloid leukemia: a Cancer and Leukemia Group B (CALGB) study

PubMed Central

Langer, Christian; Radmacher, Michael D.; Ruppert, Amy S.; Whitman, Susan P.; Paschka, Peter; Mrózek, Krzysztof; Baldus, Claudia D.; Vukosavljevic, Tamara; Liu, Chang-Gong; Ross, Mary E.; Powell, Bayard L.; de la Chapelle, Albert; Kolitz, Jonathan E.; Larson, Richard A.; Marcucci, Guido

2008-01-01

BAALC expression is considered an independent prognostic factor in cytogenetically normal acute myeloid leukemia (CN-AML), but has yet to be investigated together with multiple other established prognostic molecular markers in CN-AML. We analyzed BAALC expression in 172 primary CN-AML patients younger than 60 years of age, treated similarly on CALGB protocols. High BAALC expression was associated with FLT3-ITD (P = .04), wild-type NPM1 (P < .001), mutated CEBPA (P = .003), MLL-PTD (P = .009), absent FLT3-TKD (P = .005), and high ERG expression (P = .05). In multivariable analysis, high BAALC expression independently predicted lower complete remission rates (P = .04) when adjusting for ERG expression and age, and shorter survival (P = .04) when adjusting for FLT3-ITD, NPM1, CEBPA, and white blood cell count. A gene-expression signature of 312 probe sets differentiating high from low BAALC expressers was identified. High BAALC expression was associated with overexpression of genes involved in drug resistance (MDR1) and stem cell markers (CD133, CD34, KIT). Global microRNA-expression analysis did not reveal significant differences between BAALC expression groups. However, an analysis of microRNAs that putatively target BAALC revealed a potentially interesting inverse association between expression of miR-148a and BAALC. We conclude that high BAALC expression is an independent adverse prognostic factor and is associated with a specific gene-expression profile. PMID:18378853

Sorghum Expressed Sequence Tags Identify Signature Genes for Drought, Pathogenesis, and Skotomorphogenesis from a Milestone Set of 16,801 Unique Transcripts1[w

PubMed Central

Pratt, Lee H.; Liang, Chun; Shah, Manish; Sun, Feng; Wang, Haiming; Reid, St. Patrick; Gingle, Alan R.; Paterson, Andrew H.; Wing, Rod; Dean, Ralph; Klein, Robert; Nguyen, Henry T.; Ma, Hong-mei; Zhao, Xin; Morishige, Daryl T.; Mullet, John E.; Cordonnier-Pratt, Marie-Michèle

2005-01-01

Improved knowledge of the sorghum transcriptome will enhance basic understanding of how plants respond to stresses and serve as a source of genes of value to agriculture. Toward this goal, Sorghum bicolor L. Moench cDNA libraries were prepared from light- and dark-grown seedlings, drought-stressed plants, Colletotrichum-infected seedlings and plants, ovaries, embryos, and immature panicles. Other libraries were prepared with meristems from Sorghum propinquum (Kunth) Hitchc. that had been photoperiodically induced to flower, and with rhizomes from S. propinquum and johnsongrass (Sorghum halepense L. Pers.). A total of 117,682 expressed sequence tags (ESTs) were obtained representing both 3′ and 5′ sequences from about half that number of cDNA clones. A total of 16,801 unique transcripts, representing tentative UniScripts (TUs), were identified from 55,783 3′ ESTs. Of these TUs, 9,032 are represented by two or more ESTs. Collectively, these libraries were predicted to contain a total of approximately 31,000 TUs. Individual libraries, however, were predicted to contain no more than about 6,000 to 9,000, with the exception of light-grown seedlings, which yielded an estimate of close to 13,000. In addition, each library exhibits about the same level of complexity with respect to both the number of TUs preferentially expressed in that library and the frequency with which two or more ESTs is found in only that library. These results indicate that the sorghum genome is expressed in highly selective fashion in the individual organs and in response to the environmental conditions surveyed here. Close to 2,000 differentially expressed TUs were identified among the cDNA libraries examined, of which 775 were differentially expressed at a confidence level of 98%. From these 775 TUs, signature genes were identified defining drought, Colletotrichum infection, skotomorphogenesis (etiolation), ovary, immature panicle, and embryo. PMID:16169961

Computational Modeling of Meteor-Generated Ground Pressure Signatures

NASA Technical Reports Server (NTRS)

Nemec, Marian; Aftosmis, Michael J.; Brown, Peter G.

2017-01-01

We present a thorough validation of a computational approach to predict infrasonic signatures of centimeter-sized meteoroids. We assume that the energy deposition along the meteor trail is dominated by atmospheric drag and simulate the steady, inviscid flow of air in thermochemical equilibrium to compute the meteoroid's near-body pressure signature. This signature is then propagated through a stratified and windy atmosphere to the ground using a methodology adapted from aircraft sonic-boom analysis. An assessment of the numerical accuracy of the near field and the far field solver is presented. The results show that when the source of the signature is the cylindrical Mach-cone, the simulations closely match the observations. The prediction of the shock rise-time, the zero-peak amplitude of the waveform, and the duration of the positive pressure phase are consistently within 10% of the measurements. Uncertainty in the shape of the meteoroid results in a poorer prediction of the trailing part of the waveform. Overall, our results independently verify energy deposition estimates deduced from optical observations.

Phenotypic Robustness and the Assortativity Signature of Human Transcription Factor Networks

PubMed Central

Pechenick, Dov A.; Payne, Joshua L.; Moore, Jason H.

2014-01-01

Many developmental, physiological, and behavioral processes depend on the precise expression of genes in space and time. Such spatiotemporal gene expression phenotypes arise from the binding of sequence-specific transcription factors (TFs) to DNA, and from the regulation of nearby genes that such binding causes. These nearby genes may themselves encode TFs, giving rise to a transcription factor network (TFN), wherein nodes represent TFs and directed edges denote regulatory interactions between TFs. Computational studies have linked several topological properties of TFNs — such as their degree distribution — with the robustness of a TFN's gene expression phenotype to genetic and environmental perturbation. Another important topological property is assortativity, which measures the tendency of nodes with similar numbers of edges to connect. In directed networks, assortativity comprises four distinct components that collectively form an assortativity signature. We know very little about how a TFN's assortativity signature affects the robustness of its gene expression phenotype to perturbation. While recent theoretical results suggest that increasing one specific component of a TFN's assortativity signature leads to increased phenotypic robustness, the biological context of this finding is currently limited because the assortativity signatures of real-world TFNs have not been characterized. It is therefore unclear whether these earlier theoretical findings are biologically relevant. Moreover, it is not known how the other three components of the assortativity signature contribute to the phenotypic robustness of TFNs. Here, we use publicly available DNaseI-seq data to measure the assortativity signatures of genome-wide TFNs in 41 distinct human cell and tissue types. We find that all TFNs share a common assortativity signature and that this signature confers phenotypic robustness to model TFNs. Lastly, we determine the extent to which each of the four components of

Penicillin-Binding Protein Transpeptidase Signatures for Tracking and Predicting β-Lactam Resistance Levels in Streptococcus pneumoniae

PubMed Central

Metcalf, Benjamin J.; Chochua, Sopio; Li, Zhongya; Gertz, Robert E.; Walker, Hollis; Hawkins, Paulina A.; Tran, Theresa; Whitney, Cynthia G.; McGee, Lesley; Beall, Bernard W.

2016-01-01

ABSTRACT β-Lactam antibiotics are the drugs of choice to treat pneumococcal infections. The spread of β-lactam-resistant pneumococci is a major concern in choosing an effective therapy for patients. Systematically tracking β-lactam resistance could benefit disease surveillance. Here we developed a classification system in which a pneumococcal isolate is assigned to a “PBP type” based on sequence signatures in the transpeptidase domains (TPDs) of the three critical penicillin-binding proteins (PBPs), PBP1a, PBP2b, and PBP2x. We identified 307 unique PBP types from 2,528 invasive pneumococcal isolates, which had known MICs to six β-lactams based on broth microdilution. We found that increased β-lactam MICs strongly correlated with PBP types containing divergent TPD sequences. The PBP type explained 94 to 99% of variation in MICs both before and after accounting for genomic backgrounds defined by multilocus sequence typing, indicating that genomic backgrounds made little independent contribution to β-lactam MICs at the population level. We further developed and evaluated predictive models of MICs based on PBP type. Compared to microdilution MICs, MICs predicted by PBP type showed essential agreement (MICs agree within 1 dilution) of >98%, category agreement (interpretive results agree) of >94%, a major discrepancy (sensitive isolate predicted as resistant) rate of <3%, and a very major discrepancy (resistant isolate predicted as sensitive) rate of <2% for all six β-lactams. Thus, the PBP transpeptidase signatures are robust indicators of MICs to different β-lactam antibiotics in clinical pneumococcal isolates and serve as an accurate alternative to phenotypic susceptibility testing. PMID:27302760

Proton radiation-induced miRNA signatures in mouse blood: Characterization and comparison with 56Fe-ion and gamma radiation

PubMed Central

Templin, Thomas; Young, Erik F.; Smilenov, Lubomir B.

2013-01-01

Purpose Previously, we showed that microRNA (miRNA) signatures derived from the peripheral blood of mice are highly specific for both radiation energy (γ-rays or high linear energy transfer [LET] 56Fe ions) and radiation dose. Here, we investigate to what extent miRNA expression signatures derived from mouse blood can be used as biomarkers for exposure to 600 MeV proton radiation. Materials and methods We exposed mice to 600 MeV protons, using doses of 0.5 or 1.0 Gy, isolated total RNA at 6 h or 24 h after irradiation, and used quantitative real-time polymerase chain reaction (PCR) to determine the changes in miRNA expression. Results A total of 26 miRNA were differentially expressed after proton irradiation, in either one (77%) or multiple conditions (23%). Statistical classifiers based on proton, γ, and 56Fe-ion miRNA expression signatures predicted radiation type and proton dose with accuracies of 81% and 88%, respectively. Importantly, gene ontology analysis for proton-irradiated cells shows that genes targeted by radiation-induced miRNA are involved in biological processes and molecular functions similar to those controlled by miRNA in γ ray- and 56Fe-irradiated cells. Conclusions Mouse blood miRNA signatures induced by proton, γ, or 56Fe irradiation are radiation type- and dose-specific. These findings underline the complexity of the miRNA-mediated radiation response. PMID:22551419

Developing a Predictive Capability for Bioluminescence Signatures

DTIC Science & Technology

2012-09-30

dinoflagellates, common unicellular plankton that are also known to form red tides. Dinoflagellate bioluminescence is stimulated by flow stress of...bioluminescence signature of a moving object depends on the bioluminescence potential of the organisms (related to their species abundance), the...WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval Surface Warfare Center Panama City, FL 32407 8. PERFORMING ORGANIZATION

Developing a Predictive Capability for Bioluminescence Signatures

DTIC Science & Technology

2012-09-30

sources of bioluminescence are dinoflagellates, common unicellular plankton that are also known to form red tides. Dinoflagellate bioluminescence is...to locate their prey. The bioluminescence signature of a moving object depends on the bioluminescence potential of the organisms (related to...characteristics of the source organisms and the measurement of their bioluminescence by ocean Report Documentation Page Form ApprovedOMB No. 0704-0188 Public

Developing a Predictive Capability for Bioluminescence Signatures

DTIC Science & Technology

2010-01-01

sources of bioluminescence are dinoflagellates, common unicellular plankton that are also known to form red tides. Dinoflagellate bioluminescence is...wakes to locate their prey. The bioluminescence signature of a moving object depends on the bioluminescence potential of the organisms (related...PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) University of California, San Diego,Scripps Institution of Oceanography,La Jolla,CA,92093-0202 8

Stem Cell-Like Gene Expression in Ovarian Cancer Predicts Type II Subtype and Prognosis

PubMed Central

Schwede, Matthew; Spentzos, Dimitrios; Bentink, Stefan; Hofmann, Oliver; Haibe-Kains, Benjamin; Harrington, David; Quackenbush, John; Culhane, Aedín C.

2013-01-01

Although ovarian cancer is often initially chemotherapy-sensitive, the vast majority of tumors eventually relapse and patients die of increasingly aggressive disease. Cancer stem cells are believed to have properties that allow them to survive therapy and may drive recurrent tumor growth. Cancer stem cells or cancer-initiating cells are a rare cell population and difficult to isolate experimentally. Genes that are expressed by stem cells may characterize a subset of less differentiated tumors and aid in prognostic classification of ovarian cancer. The purpose of this study was the genomic identification and characterization of a subtype of ovarian cancer that has stem cell-like gene expression. Using human and mouse gene signatures of embryonic, adult, or cancer stem cells, we performed an unsupervised bipartition class discovery on expression profiles from 145 serous ovarian tumors to identify a stem-like and more differentiated subgroup. Subtypes were reproducible and were further characterized in four independent, heterogeneous ovarian cancer datasets. We identified a stem-like subtype characterized by a 51-gene signature, which is significantly enriched in tumors with properties of Type II ovarian cancer; high grade, serous tumors, and poor survival. Conversely, the differentiated tumors share properties with Type I, including lower grade and mixed histological subtypes. The stem cell-like signature was prognostic within high-stage serous ovarian cancer, classifying a small subset of high-stage tumors with better prognosis, in the differentiated subtype. In multivariate models that adjusted for common clinical factors (including grade, stage, age), the subtype classification was still a significant predictor of relapse. The prognostic stem-like gene signature yields new insights into prognostic differences in ovarian cancer, provides a genomic context for defining Type I/II subtypes, and potential gene targets which following further validation may be valuable

Gene expression signatures differentiate ovarian/peritoneal serous carcinoma from breast carcinoma in effusions

PubMed Central

Davidson, Ben; Stavnes, Helene Tuft; Holth, Arild; Chen, Xu; Yang, Yanqin; Shih, Ie-Ming; Wang, Tian-Li

2011-01-01

Abstract Ovarian/primary peritoneal carcinoma and breast carcinoma are the gynaecological cancers that most frequently involve the serosal cavities. With the objective of improving on the limited diagnostic panel currently available for the differential diagnosis of these two malignancies, as well as to define tumour-specific biological targets, we compared their global gene expression patterns. Gene expression profiles of 10 serous ovarian/peritoneal and eight ductal breast carcinoma effusions were analysed using the HumanRef-8 BeadChip from Illumina. Differentially expressed candidate genes were validated using quantitative real-time PCR and immunohistochemistry. Unsupervised hierarchical clustering using all 54,675 genes in the array separated ovarian from breast carcinoma samples. We identified 288 unique probes that were significantly differentially expressed in the two cancers by greater than 3.5-fold, of which 81 and 207 were overexpressed in breast and ovarian/peritoneal carcinoma, respectively. SAM analysis identified 1078 differentially expressed probes with false discovery rate less than 0.05. Genes overexpressed in breast carcinoma included TFF1, TFF3, FOXA1, CA12, GATA3, SDC1, PITX1, TH, EHFD1, EFEMP1, TOB1 and KLF2. Genes overexpressed in ovarian/peritoneal carcinoma included SPON1, RBP1, MFGE8, TM4SF12, MMP7, KLK5/6/7, FOLR1/3, PAX8, APOL2 and NRCAM. The differential expression of 14 genes was validated by quantitative real-time PCR, and differences in 5 gene products were confirmed by immunohistochemistry. Expression profiling distinguishes ovarian/peritoneal carcinoma from breast carcinoma and identifies genes that are differentially expressed in these two tumour types. The molecular signatures unique to these cancers may facilitate their differential diagnosis and may provide a molecular basis for therapeutic target discovery. PMID:20132413

Draft versus finished sequence data for DNA and protein diagnostic signature development

PubMed Central

Gardner, Shea N.; Lam, Marisa W.; Smith, Jason R.; Torres, Clinton L.; Slezak, Tom R.

2005-01-01

Sequencing pathogen genomes is costly, demanding careful allocation of limited sequencing resources. We built a computational Sequencing Analysis Pipeline (SAP) to guide decisions regarding the amount of genomic sequencing necessary to develop high-quality diagnostic DNA and protein signatures. SAP uses simulations to estimate the number of target genomes and close phylogenetic relatives (near neighbors or NNs) to sequence. We use SAP to assess whether draft data are sufficient or finished sequencing is required using Marburg and variola virus sequences. Simulations indicate that intermediate to high-quality draft with error rates of 10−3–10−5 (∼8× coverage) of target organisms is suitable for DNA signature prediction. Low-quality draft with error rates of ∼1% (3× to 6× coverage) of target isolates is inadequate for DNA signature prediction, although low-quality draft of NNs is sufficient, as long as the target genomes are of high quality. For protein signature prediction, sequencing errors in target genomes substantially reduce the detection of amino acid sequence conservation, even if the draft is of high quality. In summary, high-quality draft of target and low-quality draft of NNs appears to be a cost-effective investment for DNA signature prediction, but may lead to underestimation of predicted protein signatures. PMID:16243783

The bioenergetic signature of isogenic colon cancer cells predicts the cell death response to treatment with 3-bromopyruvate, iodoacetate or 5-fluorouracil

PubMed Central

2011-01-01

Background Metabolic reprogramming resulting in enhanced glycolysis is a phenotypic trait of cancer cells, which is imposed by the tumor microenvironment and is linked to the down-regulation of the catalytic subunit of the mitochondrial H+-ATPase (β-F1-ATPase). The bioenergetic signature is a protein ratio (β-F1-ATPase/GAPDH), which provides an estimate of glucose metabolism in tumors and serves as a prognostic indicator for cancer patients. Targeting energetic metabolism could be a viable alternative to conventional anticancer chemotherapies. Herein, we document that the bioenergetic signature of isogenic colon cancer cells provides a gauge to predict the cell-death response to the metabolic inhibitors, 3-bromopyruvate (3BrP) and iodoacetate (IA), and the anti-metabolite, 5-fluorouracil (5-FU). Methods The bioenergetic signature of the cells was determined by western blotting. Aerobic glycolysis was determined from lactate production rates. The cell death was analyzed by fluorescence microscopy and flow cytometry. Cellular ATP concentrations were determined using bioluminiscence. Pearson's correlation coefficient was applied to assess the relationship between the bioenergetic signature and the cell death response. In vivo tumor regression activities of the compounds were assessed using a xenograft mouse model injected with the highly glycolytic HCT116 colocarcinoma cells. Results We demonstrate that the bioenergetic signature of isogenic HCT116 cancer cells inversely correlates with the potential to execute necrosis in response to 3BrP or IA treatment. Conversely, the bioenergetic signature directly correlates with the potential to execute apoptosis in response to 5-FU treatment in the same cells. However, despite the large differences observed in the in vitro cell-death responses associated with 3BrP, IA and 5-FU, the in vivo tumor regression activities of these agents were comparable. Conclusions Overall, we suggest that the determination of the bioenergetic

The bioenergetic signature of isogenic colon cancer cells predicts the cell death response to treatment with 3-bromopyruvate, iodoacetate or 5-fluorouracil.

PubMed

Sánchez-Aragó, María; Cuezva, José M

2011-02-08

Metabolic reprogramming resulting in enhanced glycolysis is a phenotypic trait of cancer cells, which is imposed by the tumor microenvironment and is linked to the down-regulation of the catalytic subunit of the mitochondrial H+-ATPase (β-F1-ATPase). The bioenergetic signature is a protein ratio (β-F1-ATPase/GAPDH), which provides an estimate of glucose metabolism in tumors and serves as a prognostic indicator for cancer patients. Targeting energetic metabolism could be a viable alternative to conventional anticancer chemotherapies. Herein, we document that the bioenergetic signature of isogenic colon cancer cells provides a gauge to predict the cell-death response to the metabolic inhibitors, 3-bromopyruvate (3BrP) and iodoacetate (IA), and the anti-metabolite, 5-fluorouracil (5-FU). The bioenergetic signature of the cells was determined by western blotting. Aerobic glycolysis was determined from lactate production rates. The cell death was analyzed by fluorescence microscopy and flow cytometry. Cellular ATP concentrations were determined using bioluminiscence. Pearson's correlation coefficient was applied to assess the relationship between the bioenergetic signature and the cell death response. In vivo tumor regression activities of the compounds were assessed using a xenograft mouse model injected with the highly glycolytic HCT116 colocarcinoma cells. We demonstrate that the bioenergetic signature of isogenic HCT116 cancer cells inversely correlates with the potential to execute necrosis in response to 3BrP or IA treatment. Conversely, the bioenergetic signature directly correlates with the potential to execute apoptosis in response to 5-FU treatment in the same cells. However, despite the large differences observed in the in vitro cell-death responses associated with 3BrP, IA and 5-FU, the in vivo tumor regression activities of these agents were comparable. Overall, we suggest that the determination of the bioenergetic signature of colon carcinomas could

A gene expression signature of retinoblastoma loss-of-function is a predictive biomarker of resistance to palbociclib in breast cancer cell lines and is prognostic in patients with ER positive early breast cancer.

PubMed

Malorni, Luca; Piazza, Silvano; Ciani, Yari; Guarducci, Cristina; Bonechi, Martina; Biagioni, Chiara; Hart, Christopher D; Verardo, Roberto; Di Leo, Angelo; Migliaccio, Ilenia

2016-09-13

Palbociclib is a CDK4/6 inhibitor that received FDA approval for treatment of hormone receptor positive (HR+) HER2 negative (HER2neg) advanced breast cancer. To better personalize patients treatment it is critical to identify subgroups that would mostly benefit from it. We hypothesize that complex alterations of the Retinoblastoma (Rb) pathway might be implicated in resistance to CDK4/6 inhibitors and aim to investigate whether signatures of Rb loss-of-function would identify breast cancer cell lines resistant to palbociclib. We established a gene expression signature of Rb loss-of-function (RBsig) by identifying genes correlated with E2F1 and E2F2 expression in breast cancers within The Cancer Genome Atlas. We assessed the RBsig prognostic role in the METABRIC and in a comprehensive breast cancer meta-dataset. Finally, we analyzed whether RBsig would discriminate palbociclib-sensitive and -resistant breast cancer cells in a large RNA sequencing-based dataset. The RBsig was associated with RB1 genetic status in all tumors (p <7e-32) and in luminal or basal subtypes (p < 7e-11 and p < 0.002, respectively). The RBsig was prognostic in the METABRIC dataset (discovery: HR = 1.93 [1.5-2.4] p = 1.4e-08; validation: HR = 2.01 [1.6-2.5] p = 1.3e-09). Untreated and endocrine treated patients with estrogen receptor positive breast cancer expressing high RBsig had significantly worse recurrence free survival compared to those with low RBsig (HR = 2.37 [1.8 - 3.2] p = 1.87e-08 and HR = 2.62 [1.9- 3.5] p = 8.6e-11, respectively). The RBsig was able to identify palbociclib resistant and sensitive breast cancer cells (ROC AUC = 0,7778). Signatures of RB loss might be helpful in personalizing treatment of patients with HR+/HER2neg breast cancer. Further validation in patients receiving palbociclib is warranted.

A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes

PubMed Central

Allalou, Amina; Nalla, Amarnadh; Prentice, Kacey J.; Liu, Ying; Zhang, Ming; Dai, Feihan F.; Ning, Xian; Osborne, Lucy R.; Cox, Brian J.

2016-01-01

Gestational diabetes mellitus (GDM) affects 3–14% of pregnancies, with 20–50% of these women progressing to type 2 diabetes (T2D) within 5 years. This study sought to develop a metabolomics signature to predict the transition from GDM to T2D. A prospective cohort of 1,035 women with GDM pregnancy were enrolled at 6–9 weeks postpartum (baseline) and were screened for T2D annually for 2 years. Of 1,010 women without T2D at baseline, 113 progressed to T2D within 2 years. T2D developed in another 17 women between 2 and 4 years. A nested case-control design used 122 incident case patients matched to non–case patients by age, prepregnancy BMI, and race/ethnicity. We conducted metabolomics with baseline fasting plasma and identified 21 metabolites that significantly differed by incident T2D status. Machine learning optimization resulted in a decision tree modeling that predicted T2D incidence with a discriminative power of 83.0% in the training set and 76.9% in an independent testing set, which is far superior to measuring fasting plasma glucose levels alone. The American Diabetes Association recommends T2D screening in the early postpartum period via oral glucose tolerance testing after GDM, which is a time-consuming and inconvenient procedure. Our metabolomics signature predicted T2D incidence from a single fasting blood sample. This study represents the first metabolomics study of the transition from GDM to T2D validated in an independent testing set, facilitating early interventions. PMID:27338739

A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes.

PubMed

Allalou, Amina; Nalla, Amarnadh; Prentice, Kacey J; Liu, Ying; Zhang, Ming; Dai, Feihan F; Ning, Xian; Osborne, Lucy R; Cox, Brian J; Gunderson, Erica P; Wheeler, Michael B

2016-09-01

Gestational diabetes mellitus (GDM) affects 3-14% of pregnancies, with 20-50% of these women progressing to type 2 diabetes (T2D) within 5 years. This study sought to develop a metabolomics signature to predict the transition from GDM to T2D. A prospective cohort of 1,035 women with GDM pregnancy were enrolled at 6-9 weeks postpartum (baseline) and were screened for T2D annually for 2 years. Of 1,010 women without T2D at baseline, 113 progressed to T2D within 2 years. T2D developed in another 17 women between 2 and 4 years. A nested case-control design used 122 incident case patients matched to non-case patients by age, prepregnancy BMI, and race/ethnicity. We conducted metabolomics with baseline fasting plasma and identified 21 metabolites that significantly differed by incident T2D status. Machine learning optimization resulted in a decision tree modeling that predicted T2D incidence with a discriminative power of 83.0% in the training set and 76.9% in an independent testing set, which is far superior to measuring fasting plasma glucose levels alone. The American Diabetes Association recommends T2D screening in the early postpartum period via oral glucose tolerance testing after GDM, which is a time-consuming and inconvenient procedure. Our metabolomics signature predicted T2D incidence from a single fasting blood sample. This study represents the first metabolomics study of the transition from GDM to T2D validated in an independent testing set, facilitating early interventions. © 2016 by the American Diabetes Association.

Gene Expression Profiling Predicts the Development of Oral Cancer

PubMed Central

Saintigny, Pierre; Zhang, Li; Fan, You-Hong; El-Naggar, Adel K.; Papadimitrakopoulou, Vali; Feng, Lei; Lee, J. Jack; Kim, Edward S.; Hong, Waun Ki; Mao, Li

2011-01-01

Patients with oral preneoplastic lesion (OPL) have high risk of developing oral cancer. Although certain risk factors such as smoking status and histology are known, our ability to predict oral cancer risk remains poor. The study objective was to determine the value of gene expression profiling in predicting oral cancer development. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course. Gene expression profiles were associated with oral cancer-free survival and used to develope multivariate predictive models for oral cancer prediction. We developed a 29-transcript predictive model which showed marked improvement in terms of prediction accuracy (with 8% predicting error rate) over the models using previously known clinico-pathological risk factors. Based on the gene expression profile data, we also identified 2182 transcripts significantly associated with oral cancer risk associated genes (P-value<0.01, single variate Cox proportional hazards model). Functional pathway analysis revealed proteasome machinery, MYC, and ribosomes components as the top gene sets associated with oral cancer risk. In multiple independent datasets, the expression profiles of the genes can differentiate head and neck cancer from normal mucosa. Our results show that gene expression profiles may improve the prediction of oral cancer risk in OPL patients and the significant genes identified may serve as potential targets for oral cancer chemoprevention. PMID:21292635

CD30 expression defines a novel subgroup of diffuse large B-cell lymphoma with favorable prognosis and distinct gene expression signature: a report from the International DLBCL Rituximab-CHOP Consortium Program Study

PubMed Central

Hu, Shimin; Xu-Monette, Zijun Y.; Balasubramanyam, Aarthi; Manyam, Ganiraju C.; Visco, Carlo; Tzankov, Alexander; Liu, Wei-min; Miranda, Roberto N.; Zhang, Li; Montes-Moreno, Santiago; Dybkær, Karen; Chiu, April; Orazi, Attilio; Zu, Youli; Bhagat, Govind; Richards, Kristy L.; Hsi, Eric D.; Choi, William W. L.; Han van Krieken, J.; Huang, Qin; Huh, Jooryung; Ai, Weiyun; Ponzoni, Maurilio; Ferreri, Andrés J. M.; Zhao, Xiaoying; Winter, Jane N.; Zhang, Mingzhi; Li, Ling; Møller, Michael B.; Piris, Miguel A.; Li, Yong; Go, Ronald S.; Wu, Lin; Medeiros, L. Jeffrey; Young, Ken H.

2013-01-01

CD30, originally identified as a cell-surface marker of Reed-Sternberg and Hodgkin cells of classical Hodgkin lymphoma, is also expressed by several types of non-Hodgkin lymphoma, including a subset of diffuse large B-cell lymphoma (DLBCL). However, the prognostic and biological importance of CD30 expression in DLBCL is unknown. Here we report that CD30 expression is a favorable prognostic factor in a cohort of 903 de novo DLBCL patients. CD30 was expressed in ∼14% of DLBCL patients. Patients with CD30+ DLBCL had superior 5-year overall survival (CD30+, 79% vs CD30–, 59%; P = .001) and progression-free survival (P = .003). The favorable outcome of CD30 expression was maintained in both the germinal center B-cell and activated B-cell subtypes. Gene expression profiling revealed the upregulation of genes encoding negative regulators of nuclear factor κB activation and lymphocyte survival, and downregulation of genes encoding B-cell receptor signaling and proliferation, as well as prominent cytokine and stromal signatures in CD30+ DLBCL patients, suggesting a distinct molecular basis for its favorable outcome. Given the superior prognostic value, unique gene expression signature, and significant value of CD30 as a therapeutic target for brentuximab vedotin in ongoing successful clinical trials, it seems appropriate to consider CD30+ DLBCL as a distinct subgroup of DLBCL. PMID:23343832

A Host-Based RT-PCR Gene Expression Signature to Identify Acute Respiratory Viral Infection

PubMed Central

Zaas, Aimee K.; Burke, Thomas; Chen, Minhua; McClain, Micah; Nicholson, Bradly; Veldman, Timothy; Tsalik, Ephraim L.; Fowler, Vance; Rivers, Emanuel P.; Otero, Ronny; Kingsmore, Stephen F.; Voora, Deepak; Lucas, Joseph; Hero, Alfred O.; Carin, Lawrence; Woods, Christopher W.; Ginsburg, Geoffrey S.

2014-01-01

Improved ways to diagnose acute respiratory viral infections could decrease inappropriate antibacterial use and serve as a vital triage mechanism in the event of a potential viral pandemic. Measurement of the host response to infection is an alternative to pathogen-based diagnostic testing and may improve diagnostic accuracy. We have developed a host-based assay with a reverse transcription polymerase chain reaction (RT-PCR) TaqMan low-density array (TLDA) platform for classifying respiratory viral infection. We developed the assay using two cohorts experimentally infected with influenza A H3N2/Wisconsin or influenza A H1N1/Brisbane, and validated the assay in a sample of adults presenting to the emergency department with fever (n = 102) and in healthy volunteers (n = 41). Peripheral blood RNA samples were obtained from individuals who underwent experimental viral challenge or who presented to the emergency department and had microbiologically proven viral respiratory infection or systemic bacterial infection. The selected gene set on the RT-PCR TLDA assay classified participants with experimentally induced influenza H3N2 and H1N1 infection with 100 and 87% accuracy, respectively. We validated this host gene expression signature in a cohort of 102 individuals arriving at the emergency department. The sensitivity of the RT-PCR test was 89% [95% confidence interval (CI), 72 to 98%], and the specificity was 94% (95% CI, 86 to 99%). These results show that RT-PCR–based detection of a host gene expression signature can classify individuals with respiratory viral infection and sets the stage for prospective evaluation of this diagnostic approach in a clinical setting. PMID:24048524

Genomic signatures characterize leukocyte infiltration in myositis muscles

PubMed Central

2012-01-01

Background Leukocyte infiltration plays an important role in the pathogenesis and progression of myositis, and is highly associated with disease severity. Currently, there is a lack of: efficacious therapies for myositis; understanding of the molecular features important for disease pathogenesis; and potential molecular biomarkers for characterizing inflammatory myopathies to aid in clinical development. Methods In this study, we developed a simple model and predicted that 1) leukocyte-specific transcripts (including both protein-coding transcripts and microRNAs) should be coherently overexpressed in myositis muscle and 2) the level of over-expression of these transcripts should be correlated with leukocyte infiltration. We applied this model to assess immune cell infiltration in myositis by examining mRNA and microRNA (miRNA) expression profiles in muscle biopsies from 31 myositis patients and 5 normal controls. Results Several gene signatures, including a leukocyte index, type 1 interferon (IFN), MHC class I, and immunoglobulin signature, were developed to characterize myositis patients at the molecular level. The leukocyte index, consisting of genes predominantly associated with immune function, displayed strong concordance with pathological assessment of immune cell infiltration. This leukocyte index was subsequently utilized to differentiate transcriptional changes due to leukocyte infiltration from other alterations in myositis muscle. Results from this differentiation revealed biologically relevant differences in the relationship between the type 1 IFN pathway, miR-146a, and leukocyte infiltration within various myositis subtypes. Conclusions Results indicate that a likely interaction between miR-146a expression and the type 1 IFN pathway is confounded by the level of leukocyte infiltration into muscle tissue. Although the role of miR-146a in myositis remains uncertain, our results highlight the potential benefit of deconvoluting the source of

Genomic signatures characterize leukocyte infiltration in myositis muscles.

PubMed

Zhu, Wei; Streicher, Katie; Shen, Nan; Higgs, Brandon W; Morehouse, Chris; Greenlees, Lydia; Amato, Anthony A; Ranade, Koustubh; Richman, Laura; Fiorentino, David; Jallal, Bahija; Greenberg, Steven A; Yao, Yihong

2012-11-21

Leukocyte infiltration plays an important role in the pathogenesis and progression of myositis, and is highly associated with disease severity. Currently, there is a lack of: efficacious therapies for myositis; understanding of the molecular features important for disease pathogenesis; and potential molecular biomarkers for characterizing inflammatory myopathies to aid in clinical development. In this study, we developed a simple model and predicted that 1) leukocyte-specific transcripts (including both protein-coding transcripts and microRNAs) should be coherently overexpressed in myositis muscle and 2) the level of over-expression of these transcripts should be correlated with leukocyte infiltration. We applied this model to assess immune cell infiltration in myositis by examining mRNA and microRNA (miRNA) expression profiles in muscle biopsies from 31 myositis patients and 5 normal controls. Several gene signatures, including a leukocyte index, type 1 interferon (IFN), MHC class I, and immunoglobulin signature, were developed to characterize myositis patients at the molecular level. The leukocyte index, consisting of genes predominantly associated with immune function, displayed strong concordance with pathological assessment of immune cell infiltration. This leukocyte index was subsequently utilized to differentiate transcriptional changes due to leukocyte infiltration from other alterations in myositis muscle. Results from this differentiation revealed biologically relevant differences in the relationship between the type 1 IFN pathway, miR-146a, and leukocyte infiltration within various myositis subtypes. Results indicate that a likely interaction between miR-146a expression and the type 1 IFN pathway is confounded by the level of leukocyte infiltration into muscle tissue. Although the role of miR-146a in myositis remains uncertain, our results highlight the potential benefit of deconvoluting the source of transcriptional changes in myositis muscle or other

Expression profiles analysis of long non-coding RNAs identified novel lncRNA biomarkers with predictive value in outcome of cutaneous melanoma.

PubMed

Ma, Xu; He, Zhijuan; Li, Ling; Yang, Daping; Liu, Guofeng

2017-09-29

Recent advancements in cancer biology have identified a large number of lncRNAs that are dysregulated expression in the development and tumorigenesis of cancers, highlighting the importance of lncRNAs as a key player for human cancers. However, the prognostic value of lncRNAs still remains unclear and needs to be further investigated. In the present study, we aim to assess the prognostic value of lncRNAs in cutaneous melanoma by integrated lncRNA expression profiles from TCGA database and matched clinical information from a large cohort of patients with cutaneous melanoma. We finally identified a set of six lncRNAs that are significantly associated with survival of patients with cutaneous melanoma. A linear combination of six lncRNAs ( LINC01260, HCP5, PIGBOS1, RP11-247L20.4, CTA-292E10.6 and CTB-113P19.5 ) was constructed as a six-lncRNA signature which classified patients of training cohort into the high-risk group and low-risk group with significantly different survival time. The prognostic value of the six-lncRNA signature was validated in both the validation cohort and entire TCGA cohort. Moreover, the six-lncRNA signature is independent of known clinic-pathological factors by multivariate Cox regression analysis and demonstrated good performance for predicting three- and five-year overall survival by time-dependent receiver operating characteristic (ROC) analysis. Our study provides novel insights into the molecular heterogeneity of cutaneous melanoma and also shows potentially important implications of lncRNAs for prognosis and therapy for cutaneous melanoma.

Molecular and clinical characterization of IDH associated immune signature in lower-grade gliomas.

PubMed

Qian, Zenghui; Li, Yiming; Fan, Xing; Zhang, Chuanbao; Wang, Yinyan; Jiang, Tao; Liu, Xing

2018-01-01

Background : Mutations in isocitrate dehydrogenase (IDH) affect the development and prognosis of gliomas. We investigated the role of IDH mutations in the regulation of immune phenotype in lower-grade gliomas (LGGs). Method and patients : A total of 1,008 cases with clinical and IDH mutation data from five cohorts were enrolled. Samples with RNA sequencing data from the Chinese Glioma Genome Atlas (CGGA) were used as training set, whereas RNA data from the Cancer Genome Atlas, Repository for Molecular Brain Neoplasia, GSE16011, and CGGA microarray databases were used for validation. R language tools and bioinformatics analysis were used for gene signature construction and biological function annotation. Results : We found that IDH mutations caused down-regulation of local immune response as among 332 immune system-related genes, 196(59.0%) were differentially expressed according to IDH mutation status. Nearly 70% of those differentially expressed genes exhibited prognostic value in LGGs. An immune response-based gene signature was constructed that distinguished cases with high- or low-risk of unfavorable prognosis and remained an independent prognostic factor in multivariate analyses in both training and validation cohorts. Samples from high-risk cases exhibited elevated expression of genes involved in immune response and NF-κB pathway activation. Furthermore, we found a strong correlation between the risk score and T cells, macrophage-related immune response, and expression of several prominent immune checkpoints. Conclusion : Our results indicated that mutant IDH is highly associated with the regulation of the immune microenvironment in LGGs. The observed immune system gene signature, which was sensitive to IDH mutation status, efficiently predicted patient survival.

Comparison of transcriptomic signature of post-Chernobyl and postradiotherapy thyroid tumors.

PubMed

Ory, Catherine; Ugolin, Nicolas; Hofman, Paul; Schlumberger, Martin; Likhtarev, Illya A; Chevillard, Sylvie

2013-11-01

We previously identified two highly discriminating and predictive radiation-induced transcriptomic signatures by comparing series of sporadic and postradiotherapy thyroid tumors (322-gene signature), and by reanalyzing a previously published data set of sporadic and post-Chernobyl thyroid tumors (106-gene signature). The aim of the present work was (i) to compare the two signatures in terms of gene expression deregulations and molecular features/pathways, and (ii) to test the capacity of the postradiotherapy signature in classifying the post-Chernobyl series of tumors and reciprocally of the post-Chernobyl signature in classifying the postradiotherapy-induced tumors. We now explored if postradiotherapy and post-Chernobyl papillary thyroid carcinomas (PTC) display common molecular features by comparing molecular pathways deregulated in the two tumor series, and tested the potential of gene subsets of the postradiotherapy signature to classify the post-Chernobyl series (14 sporadic and 12 post-Chernobyl PTC), and reciprocally of gene subsets of the post-Chernobyl signature to classify the postradiotherapy series (15 sporadic and 12 postradiotherapy PTC), by using conventional principal component analysis. We found that the five genes common to the two signatures classified the learning/training tumors (used to search these signatures) of both the postradiotherapy (seven PTC) and the post-Chernobyl (six PTC) thyroid tumor series as compared with the sporadic tumors (seven sporadic PTC in each series). Importantly, these five genes were also effective for classifying independent series of postradiotherapy (five PTC) and post-Chernobyl (six PTC) tumors compared to independent series of sporadic tumors (eight PTC and six PTC respectively; testing tumors). Moreover, part of each postradiotherapy (32 genes) and post-Chernobyl signature (16 genes) cross-classified the respective series of thyroid tumors. Finally, several molecular pathways deregulated in post

Sample entropy analysis of cervical neoplasia gene-expression signatures

PubMed Central

Botting, Shaleen K; Trzeciakowski, Jerome P; Benoit, Michelle F; Salama, Salama A; Diaz-Arrastia, Concepcion R

2009-01-01

Background We introduce Approximate Entropy as a mathematical method of analysis for microarray data. Approximate entropy is applied here as a method to classify the complex gene expression patterns resultant of a clinical sample set. Since Entropy is a measure of disorder in a system, we believe that by choosing genes which display minimum entropy in normal controls and maximum entropy in the cancerous sample set we will be able to distinguish those genes which display the greatest variability in the cancerous set. Here we describe a method of utilizing Approximate Sample Entropy (ApSE) analysis to identify genes of interest with the highest probability of producing an accurate, predictive, classification model from our data set. Results In the development of a diagnostic gene-expression profile for cervical intraepithelial neoplasia (CIN) and squamous cell carcinoma of the cervix, we identified 208 genes which are unchanging in all normal tissue samples, yet exhibit a random pattern indicative of the genetic instability and heterogeneity of malignant cells. This may be measured in terms of the ApSE when compared to normal tissue. We have validated 10 of these genes on 10 Normal and 20 cancer and CIN3 samples. We report that the predictive value of the sample entropy calculation for these 10 genes of interest is promising (75% sensitivity, 80% specificity for prediction of cervical cancer over CIN3). Conclusion The success of the Approximate Sample Entropy approach in discerning alterations in complexity from biological system with such relatively small sample set, and extracting biologically relevant genes of interest hold great promise. PMID:19232110

Developing a Predictive Capability for Bioluminescence Signatures

DTIC Science & Technology

2011-09-30

sources of bioluminescence are dinoflagellates, common unicellular plankton that are also known to form red tides. Dinoflagellate bioluminescence is...wakes to locate their prey. The bioluminescence signature of a moving object depends on the bioluminescence potential of the organisms (related to...AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) University of California, San

The PAX2-null immunophenotype defines multiple lineages with common expression signatures in benign and neoplastic oviductal epithelium

PubMed Central

Ning, Gang; Bijron, Jonathan G.; Yamamoto, Yusuke; Wang, Xia; Howitt, Brooke E.; Herfs, Michael; Yang, Eric; Hong, Yue; Cornille, Maxence; Wu, Lingyan; Hanamornroongruang, Suchanan; McKeon, Frank D.; Crum, Christopher P.; Xian, Wa

2014-01-01

The oviducts contain high grade serous cancer (HGSC) precursors (serous tubal intraepithelial neoplasia or STINs), which are γ-H2AXp- and TP53 mutation-positive. Although they express wild type p53, secretory cell outgrowths (SCOUTs) are associated with older age and serous cancer; moreover both STINs and SCOUTs share a loss of PAX2 expression (PAX2n). We evaluated PAX2 expression in proliferating adult and embryonic oviductal cells, normal mucosa, SCOUTs, Walthard cell nests (WCNs), STINs and HGSCs, and the expression of genes chosen empirically or from SCOUT expression arrays. Clones generated in vitro from embryonic gynecologic tract and adult fallopian tube were Krt7p/PAX2n/EZH2p and underwent ciliated (PAX2n/EZH2n/FOXJ1p) and basal (Krt7n/EZH2n/Krt5p) differentiation. Similarly non-ciliated cells in normal mucosa were PAX2p but became PAX2n in multilayered epithelium undergoing ciliated or basal (Walthard cell nests or WCN) cell differentiation. PAX2n SCOUTs fell into two groups; Type I were secretory or secretory/ciliated with a “tubal” phenotype and were ALDH1n and β-cateninmem (membraneous only). Type II displayed a columnar to pseudostratified (endometrioid) phenotype, with an EZH2p, ALDH1p, β-cateninnc (nuclear and cytoplasmic), stathminp, LEF1p, RCN1p and RUNX2p expression signature. STINs and HGSCs shared the Type I immunophenotype of PAX2n, ALDH1n, β-cateninmem, but highly expressed EZH2p, LEF1p, RCN1p, and stathminp. This study, for the first time, links PAX2n with proliferating fetal and adult oviductal cells undergoing basal and ciliated differentiation and shows that this expression state is maintained in SCOUTs, STINs and HGSCs. All three entities can demonstrate a consistent perturbation of genes involved in potential tumor suppressor gene silencing (EZH2), transcriptional regulation (LEF1), regulation of differentiation (RUNX2), calcium binding (RCN1) and oncogenesis (stathmin). This shared expression signature between benign and

Identification and substrate prediction of new Fragaria x ananassa aquaporins and expression in different tissues and during strawberry fruit development.

PubMed

Merlaen, Britt; De Keyser, Ellen; Van Labeke, Marie-Christine

2018-01-01

The newly identified aquaporin coding sequences presented here pave the way for further insights into the plant-water relations in the commercial strawberry ( Fragaria x ananassa ). Aquaporins are water channel proteins that allow water to cross (intra)cellular membranes. In Fragaria x ananassa , few of them have been identified hitherto, hampering the exploration of the water transport regulation at cellular level. Here, we present new aquaporin coding sequences belonging to different subclasses: plasma membrane intrinsic proteins subtype 1 and subtype 2 (PIP1 and PIP2) and tonoplast intrinsic proteins (TIP). The classification is based on phylogenetic analysis and is confirmed by the presence of conserved residues. Substrate-specific signature sequences (SSSSs) and specificity-determining positions (SDPs) predict the substrate specificity of each new aquaporin. Expression profiling in leaves, petioles and developing fruits reveals distinct patterns, even within the same (sub)class. Expression profiles range from leaf-specific expression over constitutive expression to fruit-specific expression. Both upregulation and downregulation during fruit ripening occur. Substrate specificity and expression profiles suggest that functional specialization exists among aquaporins belonging to a different but also to the same (sub)class.

Specific molecular signatures predict decitabine response in chronic myelomonocytic leukemia

PubMed Central

Meldi, Kristen; Qin, Tingting; Buchi, Francesca; Droin, Nathalie; Sotzen, Jason; Micol, Jean-Baptiste; Selimoglu-Buet, Dorothée; Masala, Erico; Allione, Bernardino; Gioia, Daniela; Poloni, Antonella; Lunghi, Monia; Solary, Eric; Abdel-Wahab, Omar; Santini, Valeria; Figueroa, Maria E.

2015-01-01

Myelodysplastic syndromes and chronic myelomonocytic leukemia (CMML) are characterized by mutations in genes encoding epigenetic modifiers and aberrant DNA methylation. DNA methyltransferase inhibitors (DMTis) are used to treat these disorders, but response is highly variable, with few means to predict which patients will benefit. Here, we examined baseline differences in mutations, DNA methylation, and gene expression in 40 CMML patients who were responsive or resistant to decitabine (DAC) in order to develop a molecular means of predicting response at diagnosis. While somatic mutations did not differentiate responders from nonresponders, we identified 167 differentially methylated regions (DMRs) of DNA at baseline that distinguished responders from nonresponders using next-generation sequencing. These DMRs were primarily localized to nonpromoter regions and overlapped with distal regulatory enhancers. Using the methylation profiles, we developed an epigenetic classifier that accurately predicted DAC response at the time of diagnosis. Transcriptional analysis revealed differences in gene expression at diagnosis between responders and nonresponders. In responders, the upregulated genes included those that are associated with the cell cycle, potentially contributing to effective DAC incorporation. Treatment with CXCL4 and CXCL7, which were overexpressed in nonresponders, blocked DAC effects in isolated normal CD34+ and primary CMML cells, suggesting that their upregulation contributes to primary DAC resistance. PMID:25822018

Numerical Predictions of Sonic Boom Signatures for a Straight Line Segmented Leading Edge Model

NASA Technical Reports Server (NTRS)

Elmiligui, Alaa A.; Wilcox, Floyd J.; Cliff, Susan; Thomas, Scott

2012-01-01

A sonic boom wind tunnel test was conducted on a straight-line segmented leading edge (SLSLE) model in the NASA Langley 4- by 4- Foot Unitary Plan Wind Tunnel (UPWT). The purpose of the test was to determine whether accurate sonic boom measurements could be obtained while continuously moving the SLSLE model past a conical pressure probe. Sonic boom signatures were also obtained using the conventional move-pause data acquisition method for comparison. The continuous data acquisition approach allows for accurate signatures approximately 15 times faster than a move-pause technique. These successful results provide an incentive for future testing with greatly increased efficiency using the continuous model translation technique with the single probe to measure sonic boom signatures. Two widely used NASA codes, USM3D (Navier-Stokes) and CART3D-AERO (Euler, adjoint-based adaptive mesh), were used to compute off-body sonic boom pressure signatures of the SLSLE model at several different altitudes below the model at Mach 2.0. The computed pressure signatures compared well with wind tunnel data. The effect of the different altitude for signature extraction was evaluated by extrapolating the near field signatures to the ground and comparing pressure signatures and sonic boom loudness levels.

Predicting features of breast cancer with gene expression patterns.

PubMed

Lu, Xuesong; Lu, Xin; Wang, Zhigang C; Iglehart, J Dirk; Zhang, Xuegong; Richardson, Andrea L

2008-03-01

Data from gene expression arrays hold an enormous amount of biological information. We sought to determine if global gene expression in primary breast cancers contained information about biologic, histologic, and anatomic features of the disease in individual patients. Microarray data from the tumors of 129 patients were analyzed for the ability to predict biomarkers [estrogen receptor (ER) and HER2], histologic features [grade and lymphatic-vascular invasion (LVI)], and stage parameters (tumor size and lymph node metastasis). Multiple statistical predictors were used and the prediction accuracy was determined by cross-validation error rate; multidimensional scaling (MDS) allowed visualization of the predicted states under study. Models built from gene expression data accurately predict ER and HER2 status, and divide tumor grade into high-grade and low-grade clusters; intermediate-grade tumors are not a unique group. In contrast, gene expression data is inaccurate at predicting tumor size, lymph node status or LVI. The best model for prediction of nodal status included tumor size, LVI status and pathologically defined tumor subtype (based on combinations of ER, HER2, and grade); the addition of microarray-based prediction to this model failed to improve the prediction accuracy. Global gene expression supports a binary division of ER, HER2, and grade, clearly separating tumors into two categories; intermediate values for these bio-indicators do not define intermediate tumor subsets. Results are consistent with a model of regional metastasis that depends on inherent biologic differences in metastatic propensity between breast cancer subtypes, upon which time and chance then operate.

Adaptation of a RAS pathway activation signature from FF to FFPE tissues in colorectal cancer.

PubMed

Omolo, Bernard; Yang, Mingli; Lo, Fang Yin; Schell, Michael J; Austin, Sharon; Howard, Kellie; Madan, Anup; Yeatman, Timothy J

2016-10-19

The KRAS gene is mutated in about 40 % of colorectal cancer (CRC) cases, which has been clinically validated as a predictive mutational marker of intrinsic resistance to anti-EGFR inhibitor (EGFRi) therapy. Since nearly 60 % of patients with a wild type KRAS fail to respond to EGFRi combination therapies, there is a need to develop more reliable molecular signatures to better predict response. Here we address the challenge of adapting a gene expression signature predictive of RAS pathway activation, created using fresh frozen (FF) tissues, for use with more widely available formalin fixed paraffin-embedded (FFPE) tissues. In this study, we evaluated the translation of an 18-gene RAS pathway signature score from FF to FFPE in 54 CRC cases, using a head-to-head comparison of five technology platforms. FFPE-based technologies included the Affymetrix GeneChip (Affy), NanoString nCounter™ (NanoS), Illumina whole genome RNASeq (RNA-Acc), Illumina targeted RNASeq (t-RNA), and Illumina stranded Total RNA-rRNA-depletion (rRNA). Using Affy_FF as the "gold" standard, initial analysis of the 18-gene RAS scores on all 54 samples shows varying pairwise Spearman correlations, with (1) Affy_FFPE (r = 0.233, p = 0.090); (2) NanoS_FFPE (r = 0.608, p < 0.0001); (3) RNA-Acc_FFPE (r = 0.175, p = 0.21); (4) t-RNA_FFPE (r = -0.237, p = 0.085); (5) and t-RNA (r = -0.012, p = 0.93). These results suggest that only NanoString has successful FF to FFPE translation. The subsequent removal of identified "problematic" samples (n = 15) and genes (n = 2) further improves the correlations of Affy_FF with three of the five technologies: Affy_FFPE (r = 0.672, p < 0.0001); NanoS_FFPE (r = 0.738, p < 0.0001); and RNA-Acc_FFPE (r = 0.483, p = 0.002). Of the five technology platforms tested, NanoString technology provides a more faithful translation of the RAS pathway gene expression signature from FF to FFPE than the Affymetrix

Gene expression analysis in RA: towards personalized medicine

PubMed Central

Burska, A N; Roget, K; Blits, M; Soto Gomez, L; van de Loo, F; Hazelwood, L D; Verweij, C L; Rowe, A; Goulielmos, G N; van Baarsen, L G M; Ponchel, F

2014-01-01

Gene expression has recently been at the forefront of advance in personalized medicine, notably in the field of cancer and transplantation, providing a rational for a similar approach in rheumatoid arthritis (RA). RA is a prototypic inflammatory autoimmune disease with a poorly understood etiopathogenesis. Inflammation is the main feature of RA; however, many biological processes are involved at different stages of the disease. Gene expression signatures offer management tools to meet the current needs for personalization of RA patient's care. This review analyses currently available information with respect to RA diagnostic, prognostic and prediction of response to therapy with a view to highlight the abundance of data, whose comparison is often inconclusive due to the mixed use of material source, experimental methodologies and analysis tools, reinforcing the need for harmonization if gene expression signatures are to become a useful clinical tool in personalized medicine for RA patients. PMID:24589910

Transforming RNA-Seq data to improve the performance of prognostic gene signatures.

PubMed

Zwiener, Isabella; Frisch, Barbara; Binder, Harald

2014-01-01

Gene expression measurements have successfully been used for building prognostic signatures, i.e for identifying a short list of important genes that can predict patient outcome. Mostly microarray measurements have been considered, and there is little advice available for building multivariable risk prediction models from RNA-Seq data. We specifically consider penalized regression techniques, such as the lasso and componentwise boosting, which can simultaneously consider all measurements and provide both, multivariable regression models for prediction and automated variable selection. However, they might be affected by the typical skewness, mean-variance-dependency or extreme values of RNA-Seq covariates and therefore could benefit from transformations of the latter. In an analytical part, we highlight preferential selection of covariates with large variances, which is problematic due to the mean-variance dependency of RNA-Seq data. In a simulation study, we compare different transformations of RNA-Seq data for potentially improving detection of important genes. Specifically, we consider standardization, the log transformation, a variance-stabilizing transformation, the Box-Cox transformation, and rank-based transformations. In addition, the prediction performance for real data from patients with kidney cancer and acute myeloid leukemia is considered. We show that signature size, identification performance, and prediction performance critically depend on the choice of a suitable transformation. Rank-based transformations perform well in all scenarios and can even outperform complex variance-stabilizing approaches. Generally, the results illustrate that the distribution and potential transformations of RNA-Seq data need to be considered as a critical step when building risk prediction models by penalized regression techniques.

Transforming RNA-Seq Data to Improve the Performance of Prognostic Gene Signatures

PubMed Central

Zwiener, Isabella; Frisch, Barbara; Binder, Harald

2014-01-01

Gene expression measurements have successfully been used for building prognostic signatures, i.e for identifying a short list of important genes that can predict patient outcome. Mostly microarray measurements have been considered, and there is little advice available for building multivariable risk prediction models from RNA-Seq data. We specifically consider penalized regression techniques, such as the lasso and componentwise boosting, which can simultaneously consider all measurements and provide both, multivariable regression models for prediction and automated variable selection. However, they might be affected by the typical skewness, mean-variance-dependency or extreme values of RNA-Seq covariates and therefore could benefit from transformations of the latter. In an analytical part, we highlight preferential selection of covariates with large variances, which is problematic due to the mean-variance dependency of RNA-Seq data. In a simulation study, we compare different transformations of RNA-Seq data for potentially improving detection of important genes. Specifically, we consider standardization, the log transformation, a variance-stabilizing transformation, the Box-Cox transformation, and rank-based transformations. In addition, the prediction performance for real data from patients with kidney cancer and acute myeloid leukemia is considered. We show that signature size, identification performance, and prediction performance critically depend on the choice of a suitable transformation. Rank-based transformations perform well in all scenarios and can even outperform complex variance-stabilizing approaches. Generally, the results illustrate that the distribution and potential transformations of RNA-Seq data need to be considered as a critical step when building risk prediction models by penalized regression techniques. PMID:24416353

Effects of Aesthetic Chills on a Cardiac Signature of Emotionality.

PubMed

Sumpf, Maria; Jentschke, Sebastian; Koelsch, Stefan

2015-01-01

Previous studies have shown that a cardiac signature of emotionality (referred to as EK, which can be computed from the standard 12 lead electrocardiogram, ECG), predicts inter-individual differences in the tendency to experience and express positive emotion. Here, we investigated whether EK values can be transiently modulated during stimulation with participant-selected music pieces and film scenes that elicit strongly positive emotion. The phenomenon of aesthetic chills, as indicated by measurable piloerection on the forearm, was used to accurately locate moments of peak emotional responses during stimulation. From 58 healthy participants, continuous EK values, heart rate, and respiratory frequency were recorded during stimulation with film scenes and music pieces, and were related to the aesthetic chills. EK values, as well as heart rate, increased significantly during moments of peak positive emotion accompanied by piloerection. These results are the first to provide evidence for an influence of momentary psychological state on a cardiac signature of emotional personality (as reflected in EK values). The possibility to modulate ECG amplitude signatures via stimulation with emotionally significant music pieces and film scenes opens up new perspectives for the use of emotional peak experiences in the therapy of disorders characterized by flattened emotionality, such as depression or schizoid personality disorder.

Effects of Aesthetic Chills on a Cardiac Signature of Emotionality

PubMed Central

Sumpf, Maria; Jentschke, Sebastian; Koelsch, Stefan

2015-01-01

Background Previous studies have shown that a cardiac signature of emotionality (referred to as EK, which can be computed from the standard 12 lead electrocardiogram, ECG), predicts inter-individual differences in the tendency to experience and express positive emotion. Here, we investigated whether EK values can be transiently modulated during stimulation with participant-selected music pieces and film scenes that elicit strongly positive emotion. Methodology/Principal Findings The phenomenon of aesthetic chills, as indicated by measurable piloerection on the forearm, was used to accurately locate moments of peak emotional responses during stimulation. From 58 healthy participants, continuous EK values, heart rate, and respiratory frequency were recorded during stimulation with film scenes and music pieces, and were related to the aesthetic chills. EK values, as well as heart rate, increased significantly during moments of peak positive emotion accompanied by piloerection. Conclusions/Significance These results are the first to provide evidence for an influence of momentary psychological state on a cardiac signature of emotional personality (as reflected in EK values). The possibility to modulate ECG amplitude signatures via stimulation with emotionally significant music pieces and film scenes opens up new perspectives for the use of emotional peak experiences in the therapy of disorders characterized by flattened emotionality, such as depression or schizoid personality disorder. PMID:26083383

The effects of extrinsic motivation on signature authorship opinions in forensic signature blind trials.

PubMed

Dewhurst, Tahnee N; Found, Bryan; Ballantyne, Kaye N; Rogers, Doug

2014-03-01

Expertise studies in forensic handwriting examination involve comparisons of Forensic Handwriting Examiners' (FHEs) opinions with lay-persons on blind tests. All published studies of this type have reported real and demonstrable skill differences between the specialist and lay groups. However, critics have proposed that any difference shown may be indicative of a lack of motivation on the part of lay participants, rather than a real difference in skill. It has been suggested that qualified FHEs would be inherently more motivated to succeed in blinded validation trials, as their professional reputations could be at risk, should they perform poorly on the task provided. Furthermore, critics suggest that lay-persons would be unlikely to be highly motivated to succeed, as they would have no fear of negative consequences should they perform badly. In an effort to investigate this concern, a blind signature trial was designed and administered to forty lay-persons. Participants were required to compare known (exemplar) signatures of an individual to questioned signatures and asked to express an opinion regarding whether the writer of the known signatures wrote each of the questioned signatures. The questioned signatures comprised a mixture of genuine, disguised and simulated signatures. The forty participants were divided into two separate groupings. Group 'A' were requested to complete the trial as directed and were advised that for each correct answer they would be financially rewarded, for each incorrect answer they would be financially penalized, and for each inconclusive opinion they would receive neither penalty nor reward. Group 'B' was requested to complete the trial as directed, with no mention of financial recompense or penalty. The results of this study do not support the proposition that motivation rather than skill difference is the source of the statistical difference in opinions between individuals' results in blinded signature proficiency trials. Crown

A Prognostic Gene Signature for Metastasis-Free Survival of Triple Negative Breast Cancer Patients

PubMed Central

Yun, Jieun; Bevilacqua, Elena; Caldas, Carlos; Chin, Suet-Feung; Rueda, Oscar M.; Reinitz, John; Rosner, Marsha Rich

2013-01-01

Although triple negative breast cancers (TNBC) are the most aggressive subtype of breast cancer, they currently lack targeted therapies. Because this classification still includes a heterogeneous collection of tumors, new tools to classify TNBCs are urgently required in order to improve our prognostic capability for high risk patients and predict response to therapy. We previously defined a gene expression signature, RKIP Pathway Metastasis Signature (RPMS), based upon a metastasis-suppressive signaling pathway initiated by Raf Kinase Inhibitory Protein (RKIP). We have now generated a new BACH1 Pathway Metastasis gene signature (BPMS) that utilizes targets of the metastasis regulator BACH1. Specifically, we substituted experimentally validated target genes to generate a new BACH1 metagene, developed an approach to optimize patient tumor stratification, and reduced the number of signature genes to 30. The BPMS significantly and selectively stratified metastasis-free survival in basal-like and, in particular, TNBC patients. In addition, the BPMS further stratified patients identified as having a good or poor prognosis by other signatures including the Mammaprint® and Oncotype® clinical tests. The BPMS is thus complementary to existing signatures and is a prognostic tool for high risk ER-HER2- patients. We also demonstrate the potential clinical applicability of the BPMS as a single sample predictor. Together, these results reveal the potential of this pathway-based BPMS gene signature to identify high risk TNBC patients that can respond effectively to targeted therapy, and highlight BPMS genes as novel drug targets for therapeutic development. PMID:24349199

A prognostic gene signature for metastasis-free survival of triple negative breast cancer patients.

PubMed

Lee, Unjin; Frankenberger, Casey; Yun, Jieun; Bevilacqua, Elena; Caldas, Carlos; Chin, Suet-Feung; Rueda, Oscar M; Reinitz, John; Rosner, Marsha Rich

2013-01-01

Although triple negative breast cancers (TNBC) are the most aggressive subtype of breast cancer, they currently lack targeted therapies. Because this classification still includes a heterogeneous collection of tumors, new tools to classify TNBCs are urgently required in order to improve our prognostic capability for high risk patients and predict response to therapy. We previously defined a gene expression signature, RKIP Pathway Metastasis Signature (RPMS), based upon a metastasis-suppressive signaling pathway initiated by Raf Kinase Inhibitory Protein (RKIP). We have now generated a new BACH1 Pathway Metastasis gene signature (BPMS) that utilizes targets of the metastasis regulator BACH1. Specifically, we substituted experimentally validated target genes to generate a new BACH1 metagene, developed an approach to optimize patient tumor stratification, and reduced the number of signature genes to 30. The BPMS significantly and selectively stratified metastasis-free survival in basal-like and, in particular, TNBC patients. In addition, the BPMS further stratified patients identified as having a good or poor prognosis by other signatures including the Mammaprint® and Oncotype® clinical tests. The BPMS is thus complementary to existing signatures and is a prognostic tool for high risk ER-HER2- patients. We also demonstrate the potential clinical applicability of the BPMS as a single sample predictor. Together, these results reveal the potential of this pathway-based BPMS gene signature to identify high risk TNBC patients that can respond effectively to targeted therapy, and highlight BPMS genes as novel drug targets for therapeutic development.

A deep auto-encoder model for gene expression prediction.

PubMed

Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua

2017-11-17

Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.

Identification of predictive markers of cytarabine response in AML by integrative analysis of gene-expression profiles with multiple phenotypes

PubMed Central

Lamba, Jatinder K; Crews, Kristine R; Pounds, Stanley B; Cao, Xueyuan; Gandhi, Varsha; Plunkett, William; Razzouk, Bassem I; Lamba, Vishal; Baker, Sharyn D; Raimondi, Susana C; Campana, Dario; Pui, Ching-Hon; Downing, James R; Rubnitz, Jeffrey E; Ribeiro, Raul C

2011-01-01

Aim To identify gene-expression signatures predicting cytarabine response by an integrative analysis of multiple clinical and pharmacological end points in acute myeloid leukemia (AML) patients. Materials & methods We performed an integrated analysis to associate the gene expression of diagnostic bone marrow blasts from acute myeloid leukemia (AML) patients treated in the discovery set (AML97; n = 42) and in the independent validation set (AML02; n = 46) with multiple clinical and pharmacological end points. Based on prior biological knowledge, we defined a gene to show a therapeutically beneficial (detrimental) pattern of association of its expression positively (negatively) correlated with favorable phenotypes such as intracellular cytarabine 5´-triphosphate levels, morphological response and event-free survival, and negatively (positively) correlated with unfavorable end points such as post-cytarabine DNA synthesis levels, minimal residual disease and cytarabine LC50. Results We identified 240 probe sets predicting a therapeutically beneficial pattern and 97 predicting detrimental pattern (p ≤ 0.005) in the discovery set. Of these, 60 were confirmed in the independent validation set. The validated probe sets correspond to genes involved in PIK3/PTEN/AKT/mTOR signaling, G-protein-coupled receptor signaling and leukemogenesis. This suggests that targeting these pathways as potential pharmacogenomic and therapeutic candidates could be useful for improving treatment outcomes in AML. Conclusion This study illustrates the power of integrated data analysis of genomic data as well as multiple clinical and pharmacologic end points in the identification of genes and pathways of biological relevance. PMID:21449673

Longitudinal Analysis of Whole Blood Transcriptomes to Explore Molecular Signatures Associated With Acute Renal Allograft Rejection

PubMed Central

Shin, Heesun; Günther, Oliver; Hollander, Zsuzsanna; Wilson-McManus, Janet E.; Ng, Raymond T.; Balshaw, Robert; Keown, Paul A.; McMaster, Robert; McManus, Bruce M.; Isbel, Nicole M.; Knoll, Greg; Tebbutt, Scott J.

2014-01-01

In this study, we explored a time course of peripheral whole blood transcriptomes from kidney transplantation patients who either experienced an acute rejection episode or did not in order to better delineate the immunological and biological processes measureable in blood leukocytes that are associated with acute renal allograft rejection. Using microarrays, we generated gene expression data from 24 acute rejectors and 24 nonrejectors. We filtered the data to obtain the most unambiguous and robustly expressing probe sets and selected a subset of patients with the clearest phenotype. We then performed a data-driven exploratory analysis using data reduction and differential gene expression analysis tools in order to reveal gene expression signatures associated with acute allograft rejection. Using a template-matching algorithm, we then expanded our analysis to include time course data, identifying genes whose expression is modulated leading up to acute rejection. We have identified molecular phenotypes associated with acute renal allograft rejection, including a significantly upregulated signature of neutrophil activation and accumulation following transplant surgery that is common to both acute rejectors and nonrejectors. Our analysis shows that this expression signature appears to stabilize over time in nonrejectors but persists in patients who go on to reject the transplanted organ. In addition, we describe an expression signature characteristic of lymphocyte activity and proliferation. This lymphocyte signature is significantly downregulated in both acute rejectors and nonrejectors following surgery; however, patients who go on to reject the organ show a persistent downregulation of this signature relative to the neutrophil signature. PMID:24526836

Novel algorithm to identify and differentiate specific digital signature of breath sound in patients with diffuse parenchymal lung disease.

PubMed

Bhattacharyya, Parthasarathi; Mondal, Ashok; Dey, Rana; Saha, Dipanjan; Saha, Goutam

2015-05-01

Auscultation is an important part of the clinical examination of different lung diseases. Objective analysis of lung sounds based on underlying characteristics and its subsequent automatic interpretations may help a clinical practice. We collected the breath sounds from 8 normal subjects and 20 diffuse parenchymal lung disease (DPLD) patients using a newly developed instrument and then filtered off the heart sounds using a novel technology. The collected sounds were thereafter analysed digitally on several characteristics as dynamical complexity, texture information and regularity index to find and define their unique digital signatures for differentiating normality and abnormality. For convenience of testing, these characteristic signatures of normal and DPLD lung sounds were transformed into coloured visual representations. The predictive power of these images has been validated by six independent observers that include three physicians. The proposed method gives a classification accuracy of 100% for composite features for both the normal as well as lung sound signals from DPLD patients. When tested by independent observers on the visually transformed images, the positive predictive value to diagnose the normality and DPLD remained 100%. The lung sounds from the normal and DPLD subjects could be differentiated and expressed according to their digital signatures. On visual transformation to coloured images, they retain 100% predictive power. This technique may assist physicians to diagnose DPLD from visual images bearing the digital signature of the condition. © 2015 Asian Pacific Society of Respirology.

Genomic profiling of a Hepatocyte growth factor-dependent signature for MET-targeted therapy in glioblastoma.

PubMed

Johnson, Jennifer; Ascierto, Maria Libera; Mittal, Sandeep; Newsome, David; Kang, Liang; Briggs, Michael; Tanner, Kirk; Marincola, Francesco M; Berens, Michael E; Vande Woude, George F; Xie, Qian

2015-09-17

Constitutive MET signaling promotes invasiveness in most primary and recurrent GBM. However, deployment of available MET-targeting agents is confounded by lack of effective biomarkers for selecting suitable patients for treatment. Because endogenous HGF overexpression often causes autocrine MET activation, and also indicates sensitivity to MET inhibitors, we investigated whether it drives the expression of distinct genes which could serve as a signature indicating vulnerability to MET-targeted therapy in GBM. Interrogation of genomic data from TCGA GBM (Student's t test, GBM patients with high and low HGF expression, p ≤ 0.00001) referenced against patient-derived xenograft (PDX) models (Student's t test, sensitive vs. insensitive models, p ≤ 0.005) was used to identify the HGF-dependent signature. Genomic analysis of GBM xenograft models using both human and mouse gene expression microarrays (Student's t test, treated vs. vehicle tumors, p ≤ 0.01) were performed to elucidate the tumor and microenvironment cross talk. A PDX model with EGFR(amp) was tested for MET activation as a mechanism of erlotinib resistance. We identified a group of 20 genes highly associated with HGF overexpression in GBM and were up- or down-regulated only in tumors sensitive to MET inhibitor. The MET inhibitors regulate tumor (human) and host (mouse) cells within the tumor via distinct molecular processes, but overall impede tumor growth by inhibiting cell cycle progression. EGFR (amp) tumors undergo erlotinib resistance responded to a combination of MET and EGFR inhibitors. Combining TCGA primary tumor datasets (human) and xenograft tumor model datasets (human tumor grown in mice) using therapeutic efficacy as an endpoint may serve as a useful approach to discover and develop molecular signatures as therapeutic biomarkers for targeted therapy. The HGF dependent signature may serve as a candidate predictive signature for patient enrollment in clinical trials using MET inhibitors

Expression Profiling Identifies Circular RNA Signature in Hepatoblastoma.

PubMed

Liu, Bai-Hui; Zhang, Bin-Bin; Liu, Xiang-Qi; Zheng, Shan; Dong, Kui-Ran; Dong, Rui

2018-01-01

Hepatoblastoma is the most common malignant pediatric liver cancer. circular RNAs (circRNAs) play important roles in fine-tuning gene expression and are often deregulated in cancers. However, the expression profile and clinical significance of circRNAs in hepatoblastoma is still unknown. Circular RNA microarray was conducted to identify hepatoblastoma-related circRNAs. GO analysis, pathway analysis, and miRNA response elements analysis was conducted to predict the potential roles of differentially expressed circRNAs in hepatoblastoma. MTT assays, Ki67 staining, and Transwell assays were conducted to clarify the role of circRNA in hepatoblastoma in vitro. Bioinformatics analysis and in vitro experiments were conducted to clarify the mechanism of circRNA-mediated gene regulation in hepatoblastoma cell. 869 differentially expressed circRNAs were identified between hepatoblastoma and adjacent normal liver samples, including 421 up-regulated circRNAs and 448 down-regulated circRNAs. The significant enriched GO term of hepatoblastoma-related circRNAs in biological process, cellular component, and molecular function were "chromosome organization", "cytoplasm", and "organic cyclic compound binding". Tight junction signaling pathway was ranked the Top 1 potentially affected by circRNA-mediated regulatory network. circ_0015756 was significantly up-regulated in human hepatoblastoma specimens and metastatic hepatoblastoma cell lines. circ_0015756 silencing decreased hepatoblastoma cell viability, proliferation, and invasion in vitro. circ_0015756 acted as miR-1250-3p sponge to regulate hepatoblastoma cell function. circRNAs are involved in the pathogenesis of hepatoblastoma. circ_0015756 is a promising target for the prognosis, diagnosis, and treatment of hepatoblastoma. © 2018 The Author(s). Published by S. Karger AG, Basel.

Ontology based molecular signatures for immune cell types via gene expression analysis

PubMed Central

2013-01-01

Background New technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resulting data analyses. Here, we describe an ‘Ontologically BAsed Molecular Signature’ (OBAMS) method that identifies novel cellular biomarkers and infers biological functions as characteristics of particular cell types. This method finds molecular signatures for immune cell types based on mapping biological samples to the Cell Ontology (CL) and navigating the space of all possible pairwise comparisons between cell types to find genes whose expression is core to a particular cell type’s identity. Results We illustrate this ontological approach by evaluating expression data available from the Immunological Genome project (IGP) to identify unique biomarkers of mature B cell subtypes. We find that using OBAMS, candidate biomarkers can be identified at every strata of cellular identity from broad classifications to very granular. Furthermore, we show that Gene Ontology can be used to cluster cell types by shared biological processes in order to find candidate genes responsible for somatic hypermutation in germinal center B cells. Moreover, through in silico experiments based on this approach, we have identified genes sets that represent genes overexpressed in germinal center B cells and identify genes uniquely expressed in these B cells compared to other B cell types. Conclusions This work demonstrates the utility of incorporating structured ontological knowledge into biological data analysis – providing a new method for defining novel biomarkers and providing an opportunity for new biological insights. PMID:24004649

Gene signature based on degradome-related genes can predict distal metastasis in cervical cancer patients.

PubMed

Fernandez-Retana, Jorge; Zamudio-Meza, Horacio; Rodriguez-Morales, Miguel; Pedroza-Torres, Abraham; Isla-Ortiz, David; Herrera, Luis; Jacobo-Herrera, Nadia; Peralta-Zaragoza, Oscar; López-Camarillo, César; Morales-Gonzalez, Fermin; Cantu de Leon, David; Pérez-Plasencia, Carlos

2017-06-01

Cervical cancer is one of the leading causes of death in women worldwide, which mainly affects developing countries. The patients who suffer a recurrence and/or progression disease have a higher risk of developing distal metastases. Proteases comprising the degradome given its ability to promote cell growth, migration, and invasion of tissues play an important role during tumor development and progression. In this study, we used high-density microarrays and quantitative reverse transcriptase polymerase chain reaction to evaluate the degradome profile and their inhibitors in 112 samples of patients diagnosed with locally advanced cervical cancer. Clinical follow-up was done during a period of 3 years. Using a correlation analysis between the response to treatment and the development of metastasis, we established a molecular signature comprising eight degradome-related genes (FAM111B, FAM111A, CFB, PSMB8, PSMB9, CASP7, PRSS16, and CD74) with the ability to discriminate patients at risk of distal metastases. In conclusion, present results show that molecular signature obtained from degradome genes can predict the possibility of metastasis in patients with locally advanced cervical cancer.

Gene expression signatures in tree shrew choroid in response to three myopiagenic conditions

PubMed Central

He, Li; Frost, Michael R.; Siegwart, John T.; Norton, Thomas T.

2014-01-01

We examined gene expression in tree shrew choroid in response to three different myopiagenic conditions: minus lens (ML) wear, form deprivation (FD), and continuous darkness (DK). Four groups of tree shrews (n = 7 per group) were used. Starting 24 days after normal eye opening (days of visual experience [DVE]), the ML group wore a monocular −5 D lens for 2 days. The FD group wore a monocular translucent diffuser for 2 days. The DK group experienced continuous darkness binocularly for 11 days, starting at 17 DVE. An age-matched normal group was examined at 26 DVE. Quantitative PCR was used to measure the relative (treated eye vs. control eye) differences in mRNA levels in the choroid for 77 candidate genes. Small myopic changes were observed in the treated eyes (relative to the control eyes) of the ML group (−1.0 ± 0.2 D; mean ± SEM) and FD group (−1.9 ± 0.2 D). A larger myopia developed in the DK group (−4.4 ± 1.0 D) relative to Normal eyes (both groups, mean of right and left eyes). In the ML group, 28 genes showed significant differential mRNA expression; eighteen were down-regulated. A very similar pattern occurred in the FD group; twenty-seven of the same genes were similarly regulated, along with five additional genes. Fewer expression differences in the DK group were significant compared to normal or the control eyes of the ML and FD groups, but the pattern was similar to that of the ML and FD differential expression patterns. These data suggest that, at the level of the choroid, the gene expression signatures produced by “GO” emmetropization signals are highly similar despite the different visual conditions. PMID:25072854

Focused and Steady-State Characteristics of Shaped Sonic Boom Signatures: Prediction and Analysis

NASA Technical Reports Server (NTRS)

Maglieri, Domenic J.; Bobbitt, Percy J.; Massey, Steven J.; Plotkin, Kenneth J.; Kandil, Osama A.; Zheng, Xudong

2011-01-01

The objective of this study is to examine the effect of flight, at off-design conditions, on the propagated sonic boom pressure signatures of a small "low-boom" supersonic aircraft. The amplification, or focusing, of the low magnitude "shaped" signatures produced by maneuvers such as the accelerations from transonic to supersonic speeds, climbs, turns, pull-up and pushovers is the concern. To analyze these effects, new and/or improved theoretical tools have been developed, in addition to the use of existing methodology. Several shaped signatures are considered in the application of these tools to the study of selected maneuvers and off-design conditions. The results of these applications are reported in this paper as well as the details of the new analytical tools. Finally, the magnitude of the focused boom problem for "low boom" supersonic aircraft designs has been more accurately quantified and potential "mitigations" suggested. In general, "shaped boom" signatures, designed for cruise flight, such as asymmetric and symmetric flat-top and initial-shock ramp waveforms retain their basic shape during transition flight. Complex and asymmetric and symmetric initial shock ramp waveforms provide lower magnitude focus boom levels than N-waves or asymmetric and symmetric flat-top signatures.

Liverome: a curated database of liver cancer-related gene signatures with self-contained context information.

PubMed

Lee, Langho; Wang, Kai; Li, Gang; Xie, Zhi; Wang, Yuli; Xu, Jiangchun; Sun, Shaoxian; Pocalyko, David; Bhak, Jong; Kim, Chulhong; Lee, Kee-Ho; Jang, Ye Jin; Yeom, Young Il; Yoo, Hyang-Sook; Hwang, Seungwoo

2011-11-30

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. A number of molecular profiling studies have investigated the changes in gene and protein expression that are associated with various clinicopathological characteristics of HCC and generated a wealth of scattered information, usually in the form of gene signature tables. A database of the published HCC gene signatures would be useful to liver cancer researchers seeking to retrieve existing differential expression information on a candidate gene and to make comparisons between signatures for prioritization of common genes. A challenge in constructing such database is that a direct import of the signatures as appeared in articles would lead to a loss or ambiguity of their context information that is essential for a correct biological interpretation of a gene's expression change. This challenge arises because designation of compared sample groups is most often abbreviated, ad hoc, or even missing from published signature tables. Without manual curation, the context information becomes lost, leading to uninformative database contents. Although several databases of gene signatures are available, none of them contains informative form of signatures nor shows comprehensive coverage on liver cancer. Thus we constructed Liverome, a curated database of liver cancer-related gene signatures with self-contained context information. Liverome's data coverage is more than three times larger than any other signature database, consisting of 143 signatures taken from 98 HCC studies, mostly microarray and proteome, and involving 6,927 genes. The signatures were post-processed into an informative and uniform representation and annotated with an itemized summary so that all context information is unambiguously self-contained within the database. The signatures were further informatively named and meaningfully organized according to ten functional categories for guided browsing. Its web interface enables a

Transcriptomic analysis in the developing zebrafish embryo after compound exposure: Individual gene expression and pathway regulation

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

Hermsen, Sanne A.B., E-mail: Sanne.Hermsen@rivm.nl; Department of Toxicogenomics, Maastricht University, P.O. Box 616, 6200 MD, Maastricht; Institute for Risk Assessment Sciences

2013-10-01

The zebrafish embryotoxicity test is a promising alternative assay for developmental toxicity. Classically, morphological assessment of the embryos is applied to evaluate the effects of compound exposure. However, by applying differential gene expression analysis the sensitivity and predictability of the test may be increased. For defining gene expression signatures of developmental toxicity, we explored the possibility of using gene expression signatures of compound exposures based on commonly expressed individual genes as well as based on regulated gene pathways. Four developmental toxic compounds were tested in concentration-response design, caffeine, carbamazepine, retinoic acid and valproic acid, and two non-embryotoxic compounds, D-mannitol andmore » saccharin, were included. With transcriptomic analyses we were able to identify commonly expressed genes, which were mostly development related, after exposure to the embryotoxicants. We also identified gene pathways regulated by the embryotoxicants, suggestive of their modes of action. Furthermore, whereas pathways may be regulated by all compounds, individual gene expression within these pathways can differ for each compound. Overall, the present study suggests that the use of individual gene expression signatures as well as pathway regulation may be useful starting points for defining gene biomarkers for predicting embryotoxicity. - Highlights: • The zebrafish embryotoxicity test in combination with transcriptomics was used. • We explored two approaches of defining gene biomarkers for developmental toxicity. • Four compounds in concentration-response design were tested. • We identified commonly expressed individual genes as well as regulated gene pathways. • Both approaches seem suitable starting points for defining gene biomarkers.« less

Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival.

PubMed

Suo, Chen; Hrydziuszko, Olga; Lee, Donghwan; Pramana, Setia; Saputra, Dhany; Joshi, Himanshu; Calza, Stefano; Pawitan, Yudi

2015-08-15

Genome and transcriptome analyses can be used to explore cancers comprehensively, and it is increasingly common to have multiple omics data measured from each individual. Furthermore, there are rich functional data such as predicted impact of mutations on protein coding and gene/protein networks. However, integration of the complex information across the different omics and functional data is still challenging. Clinical validation, particularly based on patient outcomes such as survival, is important for assessing the relevance of the integrated information and for comparing different procedures. An analysis pipeline is built for integrating genomic and transcriptomic alterations from whole-exome and RNA sequence data and functional data from protein function prediction and gene interaction networks. The method accumulates evidence for the functional implications of mutated potential driver genes found within and across patients. A driver-gene score (DGscore) is developed to capture the cumulative effect of such genes. To contribute to the score, a gene has to be frequently mutated, with high or moderate mutational impact at protein level, exhibiting an extreme expression and functionally linked to many differentially expressed neighbors in the functional gene network. The pipeline is applied to 60 matched tumor and normal samples of the same patient from The Cancer Genome Atlas breast-cancer project. In clinical validation, patients with high DGscores have worse survival than those with low scores (P = 0.001). Furthermore, the DGscore outperforms the established expression-based signatures MammaPrint and PAM50 in predicting patient survival. In conclusion, integration of mutation, expression and functional data allows identification of clinically relevant potential driver genes in cancer. The documented pipeline including annotated sample scripts can be found in http://fafner.meb.ki.se/biostatwiki/driver-genes/. yudi.pawitan@ki.se Supplementary data are

Gene-expression signature regulated by the KEAP1-NRF2-CUL3 axis is associated with a poor prognosis in head and neck squamous cell cancer.

PubMed

Namani, Akhileshwar; Matiur Rahaman, Md; Chen, Ming; Tang, Xiuwen

2018-01-06

NRF2 is the key regulator of oxidative stress in normal cells and aberrant expression of the NRF2 pathway due to genetic alterations in the KEAP1 (Kelch-like ECH-associated protein 1)-NRF2 (nuclear factor erythroid 2 like 2)-CUL3 (cullin 3) axis leads to tumorigenesis and drug resistance in many cancers including head and neck squamous cell cancer (HNSCC). The main goal of this study was to identify specific genes regulated by the KEAP1-NRF2-CUL3 axis in HNSCC patients, to assess the prognostic value of this gene signature in different cohorts, and to reveal potential biomarkers. RNA-Seq V2 level 3 data from 279 tumor samples along with 37 adjacent normal samples from patients enrolled in the The Cancer Genome Atlas (TCGA)-HNSCC study were used to identify upregulated genes using two methods (altered KEAP1-NRF2-CUL3 versus normal, and altered KEAP1-NRF2-CUL3 versus wild-type). We then used a new approach to identify the combined gene signature by integrating both datasets and subsequently tested this signature in 4 independent HNSCC datasets to assess its prognostic value. In addition, functional annotation using the DAVID v6.8 database and protein-protein interaction (PPI) analysis using the STRING v10 database were performed on the signature. A signature composed of a subset of 17 genes regulated by the KEAP1-NRF2-CUL3 axis was identified by overlapping both the upregulated genes of altered versus normal (251 genes) and altered versus wild-type (25 genes) datasets. We showed that increased expression was significantly associated with poor survival in 4 independent HNSCC datasets, including the TCGA-HNSCC dataset. Furthermore, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and PPI analysis revealed that most of the genes in this signature are associated with drug metabolism and glutathione metabolic pathways. Altogether, our study emphasizes the discovery of a gene signature regulated by the KEAP1-NRF2-CUL3 axis which is strongly associated with

Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning.

PubMed

Rathore, Saima; Akbari, Hamed; Doshi, Jimit; Shukla, Gaurav; Rozycki, Martin; Bilello, Michel; Lustig, Robert; Davatzikos, Christos

2018-04-01

Standard surgical resection of glioblastoma, mainly guided by the enhancement on postcontrast T1-weighted magnetic resonance imaging (MRI), disregards infiltrating tumor within the peritumoral edema region (ED). Subsequent radiotherapy typically delivers uniform radiation to peritumoral FLAIR-hyperintense regions, without attempting to target areas likely to be infiltrated more heavily. Noninvasive in vivo delineation of the areas of tumor infiltration and prediction of early recurrence in peritumoral ED could assist in targeted intensification of local therapies, thereby potentially delaying recurrence and prolonging survival. This paper presents a method for estimating peritumoral edema infiltration using radiomic signatures determined via machine learning methods, and tests it on 90 patients with de novo glioblastoma. The generalizability of the proposed predictive model was evaluated via cross-validation in a discovery cohort ([Formula: see text]) and was subsequently evaluated in a replication cohort ([Formula: see text]). Spatial maps representing the likelihood of tumor infiltration and future early recurrence were compared with regions of recurrence on postresection follow-up studies with pathology confirmation. The cross-validated accuracy of our predictive infiltration model on the discovery and replication cohorts was 87.51% (odds ratio = 10.22, sensitivity = 80.65, and specificity = 87.63) and 89.54% (odds ratio = 13.66, sensitivity = 97.06, and specificity = 76.73), respectively. The radiomic signature of the recurrent tumor region revealed higher vascularity and cellularity when compared with the nonrecurrent region. The proposed model shows evidence that multiparametric pattern analysis from clinical MRI sequences can assist in in vivo estimation of the spatial extent and pattern of tumor recurrence in peritumoral edema, which may guide supratotal resection and/or intensification of postoperative radiation therapy.

Signature molecular descriptor : advanced applications.

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

Visco, Donald Patrick, Jr.

In this work we report on the development of the Signature Molecular Descriptor (or Signature) for use in the solution of inverse design problems as well as in highthroughput screening applications. The ultimate goal of using Signature is to identify novel and non-intuitive chemical structures with optimal predicted properties for a given application. We demonstrate this in three studies: green solvent design, glucocorticoid receptor ligand design and the design of inhibitors for Factor XIa. In many areas of engineering, compounds are designed and/or modified in incremental ways which rely upon heuristics or institutional knowledge. Often multiple experiments are performed andmore » the optimal compound is identified in this brute-force fashion. Perhaps a traditional chemical scaffold is identified and movement of a substituent group around a ring constitutes the whole of the design process. Also notably, a chemical being evaluated in one area might demonstrate properties very attractive in another area and serendipity was the mechanism for solution. In contrast to such approaches, computer-aided molecular design (CAMD) looks to encompass both experimental and heuristic-based knowledge into a strategy that will design a molecule on a computer to meet a given target. Depending on the algorithm employed, the molecule which is designed might be quite novel (re: no CAS registration number) and/or non-intuitive relative to what is known about the problem at hand. While CAMD is a fairly recent strategy (dating to the early 1980s), it contains a variety of bottlenecks and limitations which have prevented the technique from garnering more attention in the academic, governmental and industrial institutions. A main reason for this is how the molecules are described in the computer. This step can control how models are developed for the properties of interest on a given problem as well as how to go from an output of the algorithm to an actual chemical structure. This report

Data mining reveals a network of early-response genes as a consensus signature of drug-induced in vitro and in vivo toxicity.

PubMed

Zhang, J D; Berntenis, N; Roth, A; Ebeling, M

2014-06-01

Gene signatures of drug-induced toxicity are of broad interest, but they are often identified from small-scale, single-time point experiments, and are therefore of limited applicability. To address this issue, we performed multivariate analysis of gene expression, cell-based assays, and histopathological data in the TG-GATEs (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation system) database. Data mining highlights four genes-EGR1, ATF3, GDF15 and FGF21-that are induced 2 h after drug administration in human and rat primary hepatocytes poised to eventually undergo cytotoxicity-induced cell death. Modelling and simulation reveals that these early stress-response genes form a functional network with evolutionarily conserved structure and intrinsic dynamics. This is underlined by the fact that early induction of this network in vivo predicts drug-induced liver and kidney pathology with high accuracy. Our findings demonstrate the value of early gene-expression signatures in predicting and understanding compound-induced toxicity. The identified network can empower first-line tests that reduce animal use and costs of safety evaluation.

Simplified sonic-boom prediction. [using aerodynamic configuration charts and calculators or slide rules

NASA Technical Reports Server (NTRS)

Carlson, H. W.

1978-01-01

Sonic boom overpressures and signature duration may be predicted for the entire affected ground area for a wide variety of supersonic airplane configurations and spacecraft operating at altitudes up to 76 km in level flight or in moderate climbing or descending flight paths. The outlined procedure relies to a great extent on the use of charts to provide generation and propagation factors for use in relatively simple expressions for signature calculation. Computational requirements can be met by hand-held scientific calculators, or even by slide rules. A variety of correlations of predicted and measured sonic-boom data for airplanes and spacecraft serve to demonstrate the applicability of the simplified method.

Transcriptome profiling of the whitefly Bemisia tabaci reveals stage-specific gene expression signatures for thiamethoxam resistance

PubMed Central

Yang, N; Xie, W; Jones, CM; Bass, C; Jiao, X; Yang, X; Liu, B; Li, R; Zhang, Y

2013-01-01

Bemisia tabaci has developed high levels of resistance to many insecticides including the neonicotinoids and there is strong evidence that for some compounds resistance is stage-specific. To investigate the molecular basis of B. tabaci resistance to the neonicotinoid thiamethoxam we used a custom whitefly microarray to compare gene expression in the egg, nymph and adult stages of a thiamethoxam-resistant strain (TH-R) with a susceptible strain (TH-S). Gene ontology and bioinformatic analyses revealed that in all life stages many of the differentially expressed transcripts encoded enzymes involved in metabolic processes and/or metabolism of xenobiotics. Several of these are candidate resistance genes and include the cytochrome P450 CYP6CM1, which has been shown to confer resistance to several neonicotinoids previously, a P450 belonging to the Cytochrome P450s 4 family and a glutathione S-transferase (GST) belonging to the sigma class. Finally several ATP-binding cassette transporters of the ABCG subfamily were highly over-expressed in the adult stage of the TH-R strain and may play a role in resistance by active efflux. Here, we evaluated both common and stage-specific gene expression signatures and identified several candidate resistance genes that may underlie B. tabaci resistance to thiamethoxam. PMID:23889345

A gene expression biomarker accurately predicts estrogen ...

EPA Pesticide Factsheets

The EPA’s vision for the Endocrine Disruptor Screening Program (EDSP) in the 21st Century (EDSP21) includes utilization of high-throughput screening (HTS) assays coupled with computational modeling to prioritize chemicals with the goal of eventually replacing current Tier 1 screening tests. The ToxCast program currently includes 18 HTS in vitro assays that evaluate the ability of chemicals to modulate estrogen receptor α (ERα), an important endocrine target. We propose microarray-based gene expression profiling as a complementary approach to predict ERα modulation and have developed computational methods to identify ERα modulators in an existing database of whole-genome microarray data. The ERα biomarker consisted of 46 ERα-regulated genes with consistent expression patterns across 7 known ER agonists and 3 known ER antagonists. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression data sets from experiments in MCF-7 cells. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% or 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) OECD ER reference chemicals including “very weak” agonists and replicated predictions based on 18 in vitro ER-associated HTS assays. For 114 chemicals present in both the HTS data and the MCF-7 c

Identification of miRNA-Mediated Core Gene Module for Glioma Patient Prediction by Integrating High-Throughput miRNA, mRNA Expression and Pathway Structure

PubMed Central

Han, Junwei; Shang, Desi; Zhang, Yunpeng; Zhang, Wei; Yao, Qianlan; Han, Lei; Xu, Yanjun; Yan, Wei; Bao, Zhaoshi; You, Gan; Jiang, Tao; Kang, Chunsheng; Li, Xia

2014-01-01

The prognosis of glioma patients is usually poor, especially in patients with glioblastoma (World Health Organization (WHO) grade IV). The regulatory functions of microRNA (miRNA) on genes have important implications in glioma cell survival. However, there are not many studies that have investigated glioma survival by integrating miRNAs and genes while also considering pathway structure. In this study, we performed sample-matched miRNA and mRNA expression profilings to systematically analyze glioma patient survival. During this analytical process, we developed pathway-based random walk to identify a glioma core miRNA-gene module, simultaneously considering pathway structure information and multi-level involvement of miRNAs and genes. The core miRNA-gene module we identified was comprised of four apparent sub-modules; all four sub-modules displayed a significant correlation with patient survival in the testing set (P-values≤0.001). Notably, one sub-module that consisted of 6 miRNAs and 26 genes also correlated with survival time in the high-grade subgroup (WHO grade III and IV), P-value = 0.0062. Furthermore, the 26-gene expression signature from this sub-module had robust predictive power in four independent, publicly available glioma datasets. Our findings suggested that the expression signatures, which were identified by integration of miRNA and gene level, were closely associated with overall survival among the glioma patients with various grades. PMID:24809850

Signatures of doubly-charged Higgsinos at colliders

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

Demir, D. A.; Deutsches Elektronen-Synchrotron, DESY, D-22603 Hamburg; Frank, M.

2008-11-23

Several supersymmetric models with extended gauge structures predict light doubly-charged Higgsinos. Their distinctive signature at the large hadron collider is highlighted by studying its production and decay characteristics.

An FDA Perspective on the Regulatory Implications of Complex Signatures to Predict Response to Targeted Therapies

PubMed Central

Beaver, Julia A.; Tzou, Abraham; Blumenthal, Gideon M.; McKee, Amy E.; Kim, Geoffrey; Pazdur, Richard; Philip, Reena

2016-01-01

As technologies evolve, and diagnostics move from detection of single biomarkers toward complex signatures, an increase in the clinical use and regulatory submission of complex signatures is anticipated. However, to date, no complex signatures have been approved as companion diagnostics. In this article, we will describe the potential benefit of complex signatures and their unique regulatory challenges including analytical performance validation, complex signature simulation, and clinical performance evaluation. We also will review the potential regulatory pathways for clearance, approval, or acceptance of complex signatures by the U.S. Food and Drug Administration (FDA). These regulatory pathways include regulations applicable to in vitro diagnostic devices, including companion diagnostic devices, the potential for labeling as a complementary diagnostic, and the biomarker qualification program. PMID:27993967

EP-DNN: A Deep Neural Network-Based Global Enhancer Prediction Algorithm.

PubMed

Kim, Seong Gon; Harwani, Mrudul; Grama, Ananth; Chaterji, Somali

2016-12-08

We present EP-DNN, a protocol for predicting enhancers based on chromatin features, in different cell types. Specifically, we use a deep neural network (DNN)-based architecture to extract enhancer signatures in a representative human embryonic stem cell type (H1) and a differentiated lung cell type (IMR90). We train EP-DNN using p300 binding sites, as enhancers, and TSS and random non-DHS sites, as non-enhancers. We perform same-cell and cross-cell predictions to quantify the validation rate and compare against two state-of-the-art methods, DEEP-ENCODE and RFECS. We find that EP-DNN has superior accuracy with a validation rate of 91.6%, relative to 85.3% for DEEP-ENCODE and 85.5% for RFECS, for a given number of enhancer predictions and also scales better for a larger number of enhancer predictions. Moreover, our H1 → IMR90 predictions turn out to be more accurate than IMR90 → IMR90, potentially because H1 exhibits a richer signature set and our EP-DNN model is expressive enough to extract these subtleties. Our work shows how to leverage the full expressivity of deep learning models, using multiple hidden layers, while avoiding overfitting on the training data. We also lay the foundation for exploration of cross-cell enhancer predictions, potentially reducing the need for expensive experimentation.

EP-DNN: A Deep Neural Network-Based Global Enhancer Prediction Algorithm

NASA Astrophysics Data System (ADS)

Kim, Seong Gon; Harwani, Mrudul; Grama, Ananth; Chaterji, Somali

2016-12-01

We present EP-DNN, a protocol for predicting enhancers based on chromatin features, in different cell types. Specifically, we use a deep neural network (DNN)-based architecture to extract enhancer signatures in a representative human embryonic stem cell type (H1) and a differentiated lung cell type (IMR90). We train EP-DNN using p300 binding sites, as enhancers, and TSS and random non-DHS sites, as non-enhancers. We perform same-cell and cross-cell predictions to quantify the validation rate and compare against two state-of-the-art methods, DEEP-ENCODE and RFECS. We find that EP-DNN has superior accuracy with a validation rate of 91.6%, relative to 85.3% for DEEP-ENCODE and 85.5% for RFECS, for a given number of enhancer predictions and also scales better for a larger number of enhancer predictions. Moreover, our H1 → IMR90 predictions turn out to be more accurate than IMR90 → IMR90, potentially because H1 exhibits a richer signature set and our EP-DNN model is expressive enough to extract these subtleties. Our work shows how to leverage the full expressivity of deep learning models, using multiple hidden layers, while avoiding overfitting on the training data. We also lay the foundation for exploration of cross-cell enhancer predictions, potentially reducing the need for expensive experimentation.

Males are from Mars, females are from Venus: sex-specific fetal brain gene expression signatures in a mouse model of maternal diet-induced obesity

PubMed Central

EDLOW, Andrea G.; GUEDJ, Faycal; PENNINGS, Jeroen L.A.; SVERDLOV, Deanna; NERI, Caterina; BIANCHI, Diana W.

2016-01-01

BACKGROUND Maternal obesity is associated with adverse neurodevelopmental outcomes in children, including autism spectrum disorders, developmental delay, and attention deficit hyperactivity disorder. The underlying mechanisms remain unclear. We previously identified second trimester amniotic fluid and term cord blood gene expression patterns suggesting dysregulated brain development in fetuses of obese compared to lean women. OBJECTIVES We sought to investigate the biological significance of these findings in a mouse model of maternal diet-induced obesity. We evaluated sex-specific differences in fetal growth, brain gene expression signatures and associated pathways. STUDY DESIGN Female C57BL/6J mice were fed a 60% high-fat diet or 10% fat control diet for 12–14 weeks prior to mating. During pregnancy, obese dams continued on the high-fat diet (HFD/HFD), or transitioned to the CD (HFD/CD). Lean dams stayed on the control diet. On embryonic day 17.5, embryos were weighed and fetal brains were snap frozen. RNA was extracted from male and female forebrains (10/diet group/sex) and hybridized to whole genome expression arrays. Significantly differentially expressed genes were identified using Welch’s t-test with the Benjamini-Hochberg correction. Functional analyses were performed using Ingenuity Pathways Analysis and Gene Set Enrichment Analysis. RESULTS Embryos of HFD/HFD dams were significantly smaller than controls, with males more severely affected than females (p=0.01). Maternal obesity and maternal obesity with dietary change in pregnancy resulted in significantly more dysregulated genes in male versus female fetal brains (386 vs 66, p<0.001). Maternal obesity with and without dietary change in pregnancy was associated with unique brain gene expression signatures for each sex, with overlap of only one gene. Changing obese dams to a control diet in pregnancy resulted in more differentially expressed genes in the fetal brain than maternal obesity alone

Real-time scene and signature generation for ladar and imaging sensors

NASA Astrophysics Data System (ADS)

Swierkowski, Leszek; Christie, Chad L.; Antanovskii, Leonid; Gouthas, Efthimios

2014-05-01

This paper describes development of two key functionalities within the VIRSuite scene simulation program, broadening its scene generation capabilities and increasing accuracy of thermal signatures. Firstly, a new LADAR scene generation module has been designed. It is capable of simulating range imagery for Geiger mode LADAR, in addition to the already existing functionality for linear mode systems. Furthermore, a new 3D heat diffusion solver has been developed within the VIRSuite signature prediction module. It is capable of calculating the temperature distribution in complex three-dimensional objects for enhanced dynamic prediction of thermal signatures. With these enhancements, VIRSuite is now a robust tool for conducting dynamic simulation for missiles with multi-mode seekers.

Association of Distinct Mutational Signatures With Correlates of Increased Immune Activity in Pancreatic Ductal Adenocarcinoma.

PubMed

Connor, Ashton A; Denroche, Robert E; Jang, Gun Ho; Timms, Lee; Kalimuthu, Sangeetha N; Selander, Iris; McPherson, Treasa; Wilson, Gavin W; Chan-Seng-Yue, Michelle A; Borozan, Ivan; Ferretti, Vincent; Grant, Robert C; Lungu, Ilinca M; Costello, Eithne; Greenhalf, William; Palmer, Daniel; Ghaneh, Paula; Neoptolemos, John P; Buchler, Markus; Petersen, Gloria; Thayer, Sarah; Hollingsworth, Michael A; Sherker, Alana; Durocher, Daniel; Dhani, Neesha; Hedley, David; Serra, Stefano; Pollett, Aaron; Roehrl, Michael H A; Bavi, Prashant; Bartlett, John M S; Cleary, Sean; Wilson, Julie M; Alexandrov, Ludmil B; Moore, Malcolm; Wouters, Bradly G; McPherson, John D; Notta, Faiyaz; Stein, Lincoln D; Gallinger, Steven

2017-06-01

Outcomes for patients with pancreatic ductal adenocarcinoma (PDAC) remain poor. Advances in next-generation sequencing provide a route to therapeutic approaches, and integrating DNA and RNA analysis with clinicopathologic data may be a crucial step toward personalized treatment strategies for this disease. To classify PDAC according to distinct mutational processes, and explore their clinical significance. We performed a retrospective cohort study of resected PDAC, using cases collected between 2008 and 2015 as part of the International Cancer Genome Consortium. The discovery cohort comprised 160 PDAC cases from 154 patients (148 primary; 12 metastases) that underwent tumor enrichment prior to whole-genome and RNA sequencing. The replication cohort comprised 95 primary PDAC cases that underwent whole-genome sequencing and expression microarray on bulk biospecimens. Somatic mutations accumulate from sequence-specific processes creating signatures detectable by DNA sequencing. Using nonnegative matrix factorization, we measured the contribution of each signature to carcinogenesis, and used hierarchical clustering to subtype each cohort. We examined expression of antitumor immunity genes across subtypes to uncover biomarkers predictive of response to systemic therapies. The discovery cohort was 53% male (n = 79) and had a median age of 67 (interquartile range, 58-74) years. The replication cohort was 50% male (n = 48) and had a median age of 68 (interquartile range, 60-75) years. Five predominant mutational subtypes were identified that clustered PDAC into 4 major subtypes: age related, double-strand break repair, mismatch repair, and 1 with unknown etiology (signature 8). These were replicated and validated. Signatures were faithfully propagated from primaries to matched metastases, implying their stability during carcinogenesis. Twelve of 27 (45%) double-strand break repair cases lacked germline or somatic events in canonical homologous recombination genes

Prioritizing Therapeutics for Lung Cancer: An Integrative Meta-analysis of Cancer Gene Signatures and Chemogenomic Data

PubMed Central

Fortney, Kristen; Griesman, Joshua; Kotlyar, Max; Pastrello, Chiara; Angeli, Marc; Sound-Tsao, Ming; Jurisica, Igor

2015-01-01

Repurposing FDA-approved drugs with the aid of gene signatures of disease can accelerate the development of new therapeutics. A major challenge to developing reliable drug predictions is heterogeneity. Different gene signatures of the same disease or drug treatment often show poor overlap across studies, as a consequence of both biological and technical variability, and this can affect the quality and reproducibility of computational drug predictions. Existing algorithms for signature-based drug repurposing use only individual signatures as input. But for many diseases, there are dozens of signatures in the public domain. Methods that exploit all available transcriptional knowledge on a disease should produce improved drug predictions. Here, we adapt an established meta-analysis framework to address the problem of drug repurposing using an ensemble of disease signatures. Our computational pipeline takes as input a collection of disease signatures, and outputs a list of drugs predicted to consistently reverse pathological gene changes. We apply our method to conduct the largest and most systematic repurposing study on lung cancer transcriptomes, using 21 signatures. We show that scaling up transcriptional knowledge significantly increases the reproducibility of top drug hits, from 44% to 78%. We extensively characterize drug hits in silico, demonstrating that they slow growth significantly in nine lung cancer cell lines from the NCI-60 collection, and identify CALM1 and PLA2G4A as promising drug targets for lung cancer. Our meta-analysis pipeline is general, and applicable to any disease context; it can be applied to improve the results of signature-based drug repurposing by leveraging the large number of disease signatures in the public domain. PMID:25786242

CD137+CD154- Expression As a Regulatory T Cell (Treg)-Specific Activation Signature for Identification and Sorting of Stable Human Tregs from In Vitro Expansion Cultures.

PubMed

Nowak, Anna; Lock, Dominik; Bacher, Petra; Hohnstein, Thordis; Vogt, Katrin; Gottfreund, Judith; Giehr, Pascal; Polansky, Julia K; Sawitzki, Birgit; Kaiser, Andrew; Walter, Jörn; Scheffold, Alexander

2018-01-01

Regulatory T cells (Tregs) are an attractive therapeutic tool for several different immune pathologies. Therapeutic Treg application often requires prolonged in vitro culture to generate sufficient Treg numbers or to optimize their functionality, e.g., via genetic engineering of their antigen receptors. However, purity of clinical Treg expansion cultures is highly variable, and currently, it is impossible to identify and separate stable Tregs from contaminating effector T cells, either ex vivo or after prior expansion. This represents a major obstacle for quality assurance of expanded Tregs and raises significant safety concerns. Here, we describe a Treg activation signature that allows identification and sorting of epigenetically imprinted Tregs even after prolonged in vitro culture. We show that short-term reactivation resulted in expression of CD137 but not CD154 on stable FoxP3+ Tregs that displayed a demethylated Treg-specific demethylated region, high suppressive potential, and lack of inflammatory cytokine expression. We also applied this Treg activation signature for rapid testing of chimeric antigen receptor functionality in human Tregs and identified major differences in the signaling requirements regarding CD137 versus CD28 costimulation. Taken together, CD137+CD154- expression emerges as a universal Treg activation signature ex vivo and upon in vitro expansion allowing the identification and isolation of epigenetically stable antigen-activated Tregs and providing a means for their rapid functional testing in vitro .

A genomic copy number signature predicts radiation exposure in post-Chernobyl breast cancer.

PubMed

Wilke, Christina M; Braselmann, Herbert; Hess, Julia; Klymenko, Sergiy V; Chumak, Vadim V; Zakhartseva, Liubov M; Bakhanova, Elena V; Walch, Axel K; Selmansberger, Martin; Samaga, Daniel; Weber, Peter; Schneider, Ludmila; Fend, Falko; Bösmüller, Hans C; Zitzelsberger, Horst; Unger, Kristian

2018-04-16

Breast cancer is the second leading cause of cancer death among women worldwide and besides life style, age and genetic risk factors, exposure to ionizing radiation is known to increase the risk for breast cancer. Further, DNA copy number alterations (CNAs), which can result from radiation-induced double-strand breaks, are frequently occurring in breast cancer cells. We set out to identify a signature of CNAs discriminating breast cancers from radiation-exposed and non-exposed female patients. We analyzed resected breast cancer tissues from 68 exposed female Chernobyl clean-up workers and evacuees and 68 matched non-exposed control patients for CNAs by array comparative genomic hybridization analysis (aCGH). Using a stepwise forward-backward selection approach a non-complex CNA signature, that is, less than ten features, was identified in the training data set, which could be subsequently validated in the validation data set (p value < 0.05). The signature consisted of nine copy number regions located on chromosomal bands 7q11.22-11.23, 7q21.3, 16q24.3, 17q21.31, 20p11.23-11.21, 1p21.1, 2q35, 2q35, 6p22.2. The signature was independent of any clinical characteristics of the patients. In all, we identified a CNA signature that has the potential to allow identification of radiation-associated breast cancer at the individual level. © 2018 UICC.

Identification of a Genomic Signature Predicting for Recurrence in Early Stage Ovarian Cancer

DTIC Science & Technology

2015-12-01

early stage ovarian cancer to help researchers worldwide identify biomarkers that can aid early detection and inform novel targets for therapy. This...to detect differentially expressed genes after transformation using Voom. When using the top 5 genes to build the classifier, it predicted...to analyze expression of micro-RNA in these samples. Thus, at the end of the third year of funding we started a parallel analysis of RNAseq, DNA- CNV

Common patterns and disease-related signatures in tuberculosis and sarcoidosis.

PubMed

Maertzdorf, Jeroen; Weiner, January; Mollenkopf, Hans-Joachim; Bauer, Torsten; Prasse, Antje; Müller-Quernheim, Joachim; Kaufmann, Stefan H E

2012-05-15

In light of the marked global health impact of tuberculosis (TB), strong focus has been on identifying biosignatures. Gene expression profiles in blood cells identified so far are indicative of a persistent activation of the immune system and chronic inflammatory pathology in active TB. Definition of a biosignature with unique specificity for TB demands that identified profiles can differentiate diseases with similar pathology, like sarcoidosis (SARC). Here, we present a detailed comparison between pulmonary TB and SARC, including whole-blood gene expression profiling, microRNA expression, and multiplex serum analytes. Our analysis reveals that previously disclosed gene expression signatures in TB show highly similar patterns in SARC, with a common up-regulation of proinflammatory pathways and IFN signaling and close similarity to TB-related signatures. microRNA expression also presented a highly similar pattern in both diseases, whereas cytokines in the serum of TB patients revealed a slightly elevated proinflammatory pattern compared with SARC and controls. Our results indicate several differences in expression between the two diseases, with increased metabolic activity and significantly higher antimicrobial defense responses in TB. However, matrix metallopeptidase 14 was identified as the most distinctive marker of SARC. Described communalities as well as unique signatures in blood profiles of two distinct inflammatory pulmonary diseases not only have considerable implications for the design of TB biosignatures and future diagnosis, but they also provide insights into biological processes underlying chronic inflammatory disease entities of different etiology.

Gene signature associated with benign neurofibroma transformation to malignant peripheral nerve sheath tumors

PubMed Central

Sorzano, Carlos O. S.; Pascual-Montano, Alberto; Carazo, Jose M.

2017-01-01

Benign neurofibromas, the main phenotypic manifestations of the rare neurological disorder neurofibromatosis type 1, degenerate to malignant tumors associated to poor prognosis in about 10% of patients. Despite efforts in the field of (epi)genomics, the lack of prognostic biomarkers with which to predict disease evolution frustrates the adoption of appropriate early therapeutic measures. To identify potential biomarkers of malignant neurofibroma transformation, we integrated four human experimental studies and one for mouse, using a gene score-based meta-analysis method, from which we obtained a score-ranked signature of 579 genes. Genes with the highest absolute scores were classified as promising disease biomarkers. By grouping genes with similar neurofibromatosis-related profiles, we derived panels of potential biomarkers. The addition of promoter methylation data to gene profiles indicated a panel of genes probably silenced by hypermethylation. To identify possible therapeutic treatments, we used the gene signature to query drug expression databases. Trichostatin A and other histone deacetylase inhibitors, as well as cantharidin and tamoxifen, were retrieved as putative therapeutic means to reverse the aberrant regulation that drives to malignant cell proliferation and metastasis. This in silico prediction corroborated reported experimental results that suggested the inclusion of these compounds in clinical trials. This experimental validation supported the suitability of the meta-analysis method used to integrate several sources of public genomic information, and the reliability of the gene signature associated to the malignant evolution of neurofibromas to generate working hypotheses for prognostic and drug-responsive biomarkers or therapeutic measures, thus showing the potential of this in silico approach for biomarker discovery. PMID:28542306

CpG Methylation Signature Predicts Recurrence in Early-Stage Hepatocellular Carcinoma: Results From a Multicenter Study.

PubMed

Qiu, Jiliang; Peng, Baogang; Tang, Yunqiang; Qian, Yeben; Guo, Pi; Li, Mengfeng; Luo, Junhang; Chen, Bin; Tang, Hui; Lu, Canliang; Cai, Muyan; Ke, Zunfu; He, Wei; Zheng, Yun; Xie, Dan; Li, Binkui; Yuan, Yunfei

2017-03-01

Purpose Early-stage hepatocellular carcinoma (E-HCC) is being diagnosed increasingly, and in one half of diagnosed patients, recurrence will develop. Thus, it is urgent to identify recurrence-related markers. We investigated the effectiveness of CpG methylation in predicting recurrence for patients with E-HCCs. Patients and Methods In total, 576 patients with E-HCC from four independent centers were sorted by three phases. In the discovery phase, 66 tumor samples were analyzed using the Illumina Methylation 450k Beadchip. Two algorithms, Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination, were used to select significant CpGs. In the training phase, penalized Cox regression was used to further narrow CpGs into 140 samples. In the validation phase, candidate CpGs were validated using an internal cohort (n = 141) and two external cohorts (n = 191 and n =104). Results After combining the 46 CpGs selected by the Least Absolute Shrinkage and Selector Operation and the Support Vector Machine-Recursive Feature Elimination algorithms, three CpGs corresponding to SCAN domain containing 3, Src homology 3-domain growth factor receptor-bound 2-like interacting protein 1, and peptidase inhibitor 3 were highlighted as candidate predictors in the training phase. On the basis of the three CpGs, a methylation signature for E-HCC (MSEH) was developed to classify patients into high- and low-risk recurrence groups in the training cohort ( P < .001). The performance of MSEH was validated in the internal cohort ( P < .001) and in the two external cohorts ( P < .001; P = .002). Furthermore, a nomogram comprising MSEH, tumor differentiation, cirrhosis, hepatitis B virus surface antigen, and antivirus therapy was generated to predict the 5-year recurrence-free survival in the training cohort, and it performed well in the three validation cohorts (concordance index: 0.725, 0.697, and 0.693, respectively). Conclusion MSEH, a three

Mirage: a visible signature evaluation tool

NASA Astrophysics Data System (ADS)

Culpepper, Joanne B.; Meehan, Alaster J.; Shao, Q. T.; Richards, Noel

2017-10-01

This paper presents the Mirage visible signature evaluation tool, designed to provide a visible signature evaluation capability that will appropriately reflect the effect of scene content on the detectability of targets, providing a capability to assess visible signatures in the context of the environment. Mirage is based on a parametric evaluation of input images, assessing the value of a range of image metrics and combining them using the boosted decision tree machine learning method to produce target detectability estimates. It has been developed using experimental data from photosimulation experiments, where human observers search for vehicle targets in a variety of digital images. The images used for tool development are synthetic (computer generated) images, showing vehicles in many different scenes and exhibiting a wide variation in scene content. A preliminary validation has been performed using k-fold cross validation, where 90% of the image data set was used for training and 10% of the image data set was used for testing. The results of the k-fold validation from 200 independent tests show a prediction accuracy between Mirage predictions of detection probability and observed probability of detection of r(262) = 0:63, p < 0:0001 (Pearson correlation) and a MAE = 0:21 (mean absolute error).

Accurate Identification of Fear Facial Expressions Predicts Prosocial Behavior

PubMed Central

Marsh, Abigail A.; Kozak, Megan N.; Ambady, Nalini

2009-01-01

The fear facial expression is a distress cue that is associated with the provision of help and prosocial behavior. Prior psychiatric studies have found deficits in the recognition of this expression by individuals with antisocial tendencies. However, no prior study has shown accuracy for recognition of fear to predict actual prosocial or antisocial behavior in an experimental setting. In 3 studies, the authors tested the prediction that individuals who recognize fear more accurately will behave more prosocially. In Study 1, participants who identified fear more accurately also donated more money and time to a victim in a classic altruism paradigm. In Studies 2 and 3, participants’ ability to identify the fear expression predicted prosocial behavior in a novel task designed to control for confounding variables. In Study 3, accuracy for recognizing fear proved a better predictor of prosocial behavior than gender, mood, or scores on an empathy scale. PMID:17516803

Accurate identification of fear facial expressions predicts prosocial behavior.

PubMed

Marsh, Abigail A; Kozak, Megan N; Ambady, Nalini

2007-05-01

The fear facial expression is a distress cue that is associated with the provision of help and prosocial behavior. Prior psychiatric studies have found deficits in the recognition of this expression by individuals with antisocial tendencies. However, no prior study has shown accuracy for recognition of fear to predict actual prosocial or antisocial behavior in an experimental setting. In 3 studies, the authors tested the prediction that individuals who recognize fear more accurately will behave more prosocially. In Study 1, participants who identified fear more accurately also donated more money and time to a victim in a classic altruism paradigm. In Studies 2 and 3, participants' ability to identify the fear expression predicted prosocial behavior in a novel task designed to control for confounding variables. In Study 3, accuracy for recognizing fear proved a better predictor of prosocial behavior than gender, mood, or scores on an empathy scale.

Gut Microbiota Signatures Predict Host and Microbiota Responses to Dietary Interventions in Obese Individuals

PubMed Central

Korpela, Katri; Flint, Harry J.; Johnstone, Alexandra M.; Lappi, Jenni; Poutanen, Kaisa; Dewulf, Evelyne; Delzenne, Nathalie; de Vos, Willem M.; Salonen, Anne

2014-01-01

Background Interactions between the diet and intestinal microbiota play a role in health and disease, including obesity and related metabolic complications. There is great interest to use dietary means to manipulate the microbiota to promote health. Currently, the impact of dietary change on the microbiota and the host metabolism is poorly predictable and highly individual. We propose that the responsiveness of the gut microbiota may depend on its composition, and associate with metabolic changes in the host. Methodology Our study involved three independent cohorts of obese adults (n = 78) from Belgium, Finland, and Britain, participating in different dietary interventions aiming to improve metabolic health. We used a phylogenetic microarray for comprehensive fecal microbiota analysis at baseline and after the intervention. Blood cholesterol, insulin and inflammation markers were analyzed as indicators of host response. The data were divided into four training set – test set pairs; each intervention acted both as a part of a training set and as an independent test set. We used linear models to predict the responsiveness of the microbiota and the host, and logistic regression to predict responder vs. non-responder status, or increase vs. decrease of the health parameters. Principal Findings Our models, based on the abundance of several, mainly Firmicute species at baseline, predicted the responsiveness of the microbiota (AUC  =  0.77–1; predicted vs. observed correlation  =  0.67–0.88). Many of the predictive taxa showed a non-linear relationship with the responsiveness. The microbiota response associated with the change in serum cholesterol levels with an AUC of 0.96, highlighting the involvement of the intestinal microbiota in metabolic health. Conclusion This proof-of-principle study introduces the first potential microbial biomarkers for dietary responsiveness in obese individuals with impaired metabolic health, and reveals the potential of

Blazing Signature Filter: a library for fast pairwise similarity comparisons

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

Lee, Joon-Yong; Fujimoto, Grant M.; Wilson, Ryan

Identifying similarities between datasets is a fundamental task in data mining and has become an integral part of modern scientific investigation. Whether the task is to identify co-expressed genes in large-scale expression surveys or to predict combinations of gene knockouts which would elicit a similar phenotype, the underlying computational task is often a multi-dimensional similarity test. As datasets continue to grow, improvements to the efficiency, sensitivity or specificity of such computation will have broad impacts as it allows scientists to more completely explore the wealth of scientific data. A significant practical drawback of large-scale data mining is the vast majoritymore » of pairwise comparisons are unlikely to be relevant, meaning that they do not share a signature of interest. It is therefore essential to efficiently identify these unproductive comparisons as rapidly as possible and exclude them from more time-intensive similarity calculations. The Blazing Signature Filter (BSF) is a highly efficient pairwise similarity algorithm which enables extensive data mining within a reasonable amount of time. The algorithm transforms datasets into binary metrics, allowing it to utilize the computationally efficient bit operators and provide a coarse measure of similarity. As a result, the BSF can scale to high dimensionality and rapidly filter unproductive pairwise comparison. Two bioinformatics applications of the tool are presented to demonstrate the ability to scale to billions of pairwise comparisons and the usefulness of this approach.« less

Two microRNA signatures for malignancy and immune infiltration predict overall survival in advanced epithelial ovarian cancer.

PubMed

Korsunsky, Ilya; Parameswaran, Janaki; Shapira, Iuliana; Lovecchio, John; Menzin, Andrew; Whyte, Jill; Dos Santos, Lisa; Liang, Sharon; Bhuiya, Tawfiqul; Keogh, Mary; Khalili, Houman; Pond, Cassandra; Liew, Anthony; Shih, Andrew; Gregersen, Peter K; Lee, Annette T

2017-10-01

MicroRNAs have been established as key regulators of tumor gene expression and as prime biomarker candidates for clinical phenotypes in epithelial ovarian cancer (EOC). We analyzed the coexpression and regulatory structure of microRNAs and their co-localized gene targets in primary tumor tissue of 20 patients with advanced EOC in order to construct a regulatory signature for clinical prognosis. We performed an integrative analysis to identify two prognostic microRNA/mRNA coexpression modules, each enriched for consistent biological functions. One module, enriched for malignancy-related functions, was found to be upregulated in malignant versus benign samples. The second module, enriched for immune-related functions, was strongly correlated with imputed intratumoral immune infiltrates of T cells, natural killer cells, cytotoxic lymphocytes, and macrophages. We validated the prognostic relevance of the immunological module microRNAs in the publicly available The Cancer Genome Atlas data set. These findings provide novel functional roles for microRNAs in the progression of advanced EOC and possible prognostic signatures for survival. © American Federation for Medical Research (unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Transcriptome profiling reveals novel BMI- and sex-specific gene expression signatures for human cardiac hypertrophy.

PubMed

Newman, Mackenzie S; Nguyen, Tina; Watson, Michael J; Hull, Robert W; Yu, Han-Gang

2017-07-01

How obesity or sex may affect the gene expression profiles of human cardiac hypertrophy is unknown. We hypothesized that body-mass index (BMI) and sex can affect gene expression profiles of cardiac hypertrophy. Human heart tissues were grouped according to sex (male, female), BMI (lean<25 kg/m 2 , obese>30 kg/m 2 ), or left ventricular hypertrophy (LVH) and non-LVH nonfailed controls (NF). We identified 24 differentially expressed (DE) genes comparing female with male samples. In obese subgroup, there were 236 DE genes comparing LVH with NF; in lean subgroup, there were seven DE genes comparing LVH with NF. In female subgroup, we identified 1,320 significant genes comparing LVH with NF; in male subgroup, there were 1,383 significant genes comparing LVH with NF. There were seven significant genes comparing obese LVH with lean NF; comparing male obese LVH with male lean NF samples we found 106 significant genes; comparing female obese LVH with male lean NF, we found no significant genes. Using absolute value of log 2 fold-change > 2 or extremely small P value (10 -20 ) as a criterion, we identified nine significant genes (HBA1, HBB, HIST1H2AC, GSTT1, MYL7, NPPA, NPPB, PDK4, PLA2G2A) in LVH, also found in published data set for ischemic and dilated cardiomyopathy in heart failure. We identified a potential gene expression signature that distinguishes between patients with high BMI or between men and women with cardiac hypertrophy. Expression of established biomarkers atrial natriuretic peptide A (NPPA) and B (NPPB) were already significantly increased in hypertrophy compared with controls. Copyright © 2017 the American Physiological Society.

A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mapping.

PubMed

Wen, Qing; Kim, Chang-Sik; Hamilton, Peter W; Zhang, Shu-Dong

2016-05-11

Gene expression connectivity mapping has gained much popularity recently with a number of successful applications in biomedical research testifying its utility and promise. Previously methodological research in connectivity mapping mainly focused on two of the key components in the framework, namely, the reference gene expression profiles and the connectivity mapping algorithms. The other key component in this framework, the query gene signature, has been left to users to construct without much consensus on how this should be done, albeit it has been an issue most relevant to end users. As a key input to the connectivity mapping process, gene signature is crucially important in returning biologically meaningful and relevant results. This paper intends to formulate a standardized procedure for constructing high quality gene signatures from a user's perspective. We describe a two-stage process for making quality gene signatures using gene expression data as initial inputs. First, a differential gene expression analysis comparing two distinct biological states; only the genes that have passed stringent statistical criteria are considered in the second stage of the process, which involves ranking genes based on statistical as well as biological significance. We introduce a "gene signature progression" method as a standard procedure in connectivity mapping. Starting from the highest ranked gene, we progressively determine the minimum length of the gene signature that allows connections to the reference profiles (drugs) being established with a preset target false discovery rate. We use a lung cancer dataset and a breast cancer dataset as two case studies to demonstrate how this standardized procedure works, and we show that highly relevant and interesting biological connections are returned. Of particular note is gefitinib, identified as among the candidate therapeutics in our lung cancer case study. Our gene signature was based on gene expression data from Taiwan

Prediction of gene expression with cis-SNPs using mixed models and regularization methods.

PubMed

Zeng, Ping; Zhou, Xiang; Huang, Shuiping

2017-05-11

It has been shown that gene expression in human tissues is heritable, thus predicting gene expression using only SNPs becomes possible. The prediction of gene expression can offer important implications on the genetic architecture of individual functional associated SNPs and further interpretations of the molecular basis underlying human diseases. We compared three types of methods for predicting gene expression using only cis-SNPs, including the polygenic model, i.e. linear mixed model (LMM), two sparse models, i.e. Lasso and elastic net (ENET), and the hybrid of LMM and sparse model, i.e. Bayesian sparse linear mixed model (BSLMM). The three kinds of prediction methods have very different assumptions of underlying genetic architectures. These methods were evaluated using simulations under various scenarios, and were applied to the Geuvadis gene expression data. The simulations showed that these four prediction methods (i.e. Lasso, ENET, LMM and BSLMM) behaved best when their respective modeling assumptions were satisfied, but BSLMM had a robust performance across a range of scenarios. According to R 2 of these models in the Geuvadis data, the four methods performed quite similarly. We did not observe any clustering or enrichment of predictive genes (defined as genes with R 2  ≥ 0.05) across the chromosomes, and also did not see there was any clear relationship between the proportion of the predictive genes and the proportion of genes in each chromosome. However, an interesting finding in the Geuvadis data was that highly predictive genes (e.g. R 2  ≥ 0.30) may have sparse genetic architectures since Lasso, ENET and BSLMM outperformed LMM for these genes; and this observation was validated in another gene expression data. We further showed that the predictive genes were enriched in approximately independent LD blocks. Gene expression can be predicted with only cis-SNPs using well-developed prediction models and these predictive genes were enriched in

A prospective blood RNA signature for tuberculosis disease risk

PubMed Central

Zak, Daniel E.; Penn-Nicholson, Adam; Scriba, Thomas J.; Thompson, Ethan; Suliman, Sara; Amon, Lynn M.; Mahomed, Hassan; Erasmus, Mzwandile; Whatney, Wendy; Hussey, Gregory D.; Abrahams, Deborah; Kafaar, Fazlin; Hawkridge, Tony; Verver, Suzanne; Hughes, E. Jane; Ota, Martin; Sutherland, Jayne; Howe, Rawleigh; Dockrell, Hazel M.; Boom, W. Henry; Thiel, Bonnie; Ottenhoff, Tom H.M.; Mayanja-Kizza, Harriet; Crampin, Amelia C; Downing, Katrina; Hatherill, Mark; Valvo, Joe; Shankar, Smitha; Parida, Shreemanta K; Kaufmann, Stefan H.E.; Walzl, Gerhard; Aderem, Alan; Hanekom, Willem A.

2016-01-01

Background Identification of blood biomarkers that prospectively predict progression of Mycobacterium tuberculosis infection to tuberculosis disease may lead to interventions that impact the epidemic. Methods Healthy, M. tuberculosis infected South African adolescents were followed for 2 years; blood was collected every 6 months. A prospective signature of risk was derived from whole blood RNA-Sequencing data by comparing participants who ultimately developed active tuberculosis disease (progressors) with those who remained healthy (matched controls). After adaptation to multiplex qRT-PCR, the signature was used to predict tuberculosis disease in untouched adolescent samples and in samples from independent cohorts of South African and Gambian adult progressors and controls. The latter participants were household contacts of adults with active pulmonary tuberculosis disease. Findings Of 6,363 adolescents screened, 46 progressors and 107 matched controls were identified. A 16 gene signature of risk was identified. The signature predicted tuberculosis progression with a sensitivity of 66·1% (95% confidence interval, 63·2–68·9) and a specificity of 80·6% (79·2–82·0) in the 12 months preceding tuberculosis diagnosis. The risk signature was validated in an untouched group of adolescents (p=0·018 for RNA-Seq and p=0·0095 for qRT-PCR) and in the independent South African and Gambian cohorts (p values <0·0001 by qRT-PCR) with a sensitivity of 53·7% (42·6–64·3) and a specificity of 82·8% (76·7–86) in 12 months preceding tuberculosis. Interpretation The whole blood tuberculosis risk signature prospectively identified persons at risk of developing active tuberculosis, opening the possibility for targeted intervention to prevent the disease. Funding Bill and Melinda Gates Foundation, the National Institutes of Health, Aeras, the European Union and the South African Medical Research Council (detail at end of text). PMID:27017310

Males are from Mars, and females are from Venus: sex-specific fetal brain gene expression signatures in a mouse model of maternal diet-induced obesity.

PubMed

Edlow, Andrea G; Guedj, Faycal; Pennings, Jeroen L A; Sverdlov, Deanna; Neri, Caterina; Bianchi, Diana W

2016-05-01

Maternal obesity is associated with adverse neurodevelopmental outcomes in children, including autism spectrum disorders, developmental delay, and attention-deficit hyperactivity disorder. The underlying mechanisms remain unclear. We previously identified second-trimester amniotic fluid and term cord blood gene expression patterns suggesting dysregulated brain development in fetuses of obese compared with lean women. We sought to investigate the biological significance of these findings in a mouse model of maternal diet-induced obesity. We evaluated sex-specific differences in fetal growth, brain gene expression signatures, and associated pathways. Female C57BL/6J mice were fed a 60% high-fat diet or 10% fat control diet for 12-14 weeks prior to mating. During pregnancy, obese dams continued on the high-fat diet or transitioned to the control diet. Lean dams stayed on the control diet. On embryonic day 17.5, embryos were weighed and fetal brains were snap frozen. RNA was extracted from male and female forebrains (10 per diet group per sex) and hybridized to whole-genome expression arrays. Significantly differentially expressed genes were identified using a Welch's t test with the Benjamini-Hochberg correction. Functional analyses were performed using ingenuity pathways analysis and gene set enrichment analysis. Embryos of dams on the high-fat diet were significantly smaller than controls, with males more severely affected than females (P = .01). Maternal obesity and maternal obesity with dietary change in pregnancy resulted in significantly more dysregulated genes in male vs female fetal brains (386 vs 66, P < .001). Maternal obesity with and without dietary change in pregnancy was associated with unique brain gene expression signatures for each sex, with an overlap of only 1 gene. Changing obese dams to a control diet in pregnancy resulted in more differentially expressed genes in the fetal brain than maternal obesity alone. Functional analyses identified common

CD137+CD154− Expression As a Regulatory T Cell (Treg)-Specific Activation Signature for Identification and Sorting of Stable Human Tregs from In Vitro Expansion Cultures

PubMed Central

Nowak, Anna; Lock, Dominik; Bacher, Petra; Hohnstein, Thordis; Vogt, Katrin; Gottfreund, Judith; Giehr, Pascal; Polansky, Julia K.; Sawitzki, Birgit; Kaiser, Andrew; Walter, Jörn; Scheffold, Alexander

2018-01-01

Regulatory T cells (Tregs) are an attractive therapeutic tool for several different immune pathologies. Therapeutic Treg application often requires prolonged in vitro culture to generate sufficient Treg numbers or to optimize their functionality, e.g., via genetic engineering of their antigen receptors. However, purity of clinical Treg expansion cultures is highly variable, and currently, it is impossible to identify and separate stable Tregs from contaminating effector T cells, either ex vivo or after prior expansion. This represents a major obstacle for quality assurance of expanded Tregs and raises significant safety concerns. Here, we describe a Treg activation signature that allows identification and sorting of epigenetically imprinted Tregs even after prolonged in vitro culture. We show that short-term reactivation resulted in expression of CD137 but not CD154 on stable FoxP3+ Tregs that displayed a demethylated Treg-specific demethylated region, high suppressive potential, and lack of inflammatory cytokine expression. We also applied this Treg activation signature for rapid testing of chimeric antigen receptor functionality in human Tregs and identified major differences in the signaling requirements regarding CD137 versus CD28 costimulation. Taken together, CD137+CD154− expression emerges as a universal Treg activation signature ex vivo and upon in vitro expansion allowing the identification and isolation of epigenetically stable antigen-activated Tregs and providing a means for their rapid functional testing in vitro. PMID:29467769

Unravelling site-specific breast cancer metastasis: a microRNA expression profiling study

PubMed Central

Schrijver, Willemijne A.M.E.; van Diest, Paul J.; Moelans, Cathy B

2017-01-01

Distant metastasis is still the main cause of death from breast cancer. MicroRNAs (miRs) are important regulators of many physiological and pathological processes, including metastasis. Molecular breast cancer subtypes are known to show a site-specific pattern of metastases formation. In this study, we set out to determine the underlying molecular mechanisms of site-specific breast cancer metastasis by microRNA expression profiling. To identify a miR signature for metastatic breast carcinoma that could predict metastatic localization, we compared global miR expression in 23 primary breast cancer specimens with their corresponding multiple distant metastases to ovary (n=9), skin (n=12), lung (n=10), brain (n=4) and gastrointestinal tract (n=10) by miRCURY microRNA expression arrays. For validation, we performed quantitative real-time (qRT) PCR on the discovery cohort and on an independent validation cohort of 29 primary breast cancer specimens and their matched metastases. miR expression was highly patient specific and miR signatures in the primary tumor were largely retained in the metastases, with the exception of several differentially expressed, location specific miRs. Validation with qPCR demonstrated that hsa-miR-106b-5p was predictive for the development of lung metastases. In time, the second metastasis often showed a miR upregulation compared to the first metastasis. This study discovered a metastatic site-specific miR and found miR expression to be highly patient specific. This may lead to novel biomarkers predicting site of distant metastases, and to adjuvant, personalized targeted therapy strategies that could prevent such metastases from becoming clinically manifest. PMID:27902972

Unravelling site-specific breast cancer metastasis: a microRNA expression profiling study.

PubMed

Schrijver, Willemijne A M E; van Diest, Paul J; Moelans, Cathy B

2017-01-10

Distant metastasis is still the main cause of death from breast cancer. MicroRNAs (miRs) are important regulators of many physiological and pathological processes, including metastasis. Molecular breast cancer subtypes are known to show a site-specific pattern of metastases formation. In this study, we set out to determine the underlying molecular mechanisms of site-specific breast cancer metastasis by microRNA expression profiling.To identify a miR signature for metastatic breast carcinoma that could predict metastatic localization, we compared global miR expression in 23 primary breast cancer specimens with their corresponding multiple distant metastases to ovary (n=9), skin (n=12), lung (n=10), brain (n=4) and gastrointestinal tract (n=10) by miRCURY microRNA expression arrays. For validation, we performed quantitative real-time (qRT) PCR on the discovery cohort and on an independent validation cohort of 29 primary breast cancer specimens and their matched metastases.miR expression was highly patient specific and miR signatures in the primary tumor were largely retained in the metastases, with the exception of several differentially expressed, location specific miRs. Validation with qPCR demonstrated that hsa-miR-106b-5p was predictive for the development of lung metastases. In time, the second metastasis often showed a miR upregulation compared to the first metastasis.This study discovered a metastatic site-specific miR and found miR expression to be highly patient specific. This may lead to novel biomarkers predicting site of distant metastases, and to adjuvant, personalized targeted therapy strategies that could prevent such metastases from becoming clinically manifest.

A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect

PubMed Central

Chang, Luke J.; Gianaros, Peter J.; Manuck, Stephen B.; Krishnan, Anjali; Wager, Tor D.

2015-01-01

Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high–low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion–pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional “emotion-related” regions (e.g., amygdala, insula) or resting-state networks (e.g., “salience,” “default mode”). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes. PMID:26098873

Robust transcriptional tumor signatures applicable to both formalin-fixed paraffin-embedded and fresh-frozen samples

PubMed Central

Cheng, Jun; He, Jun; Liu, Huaping; Cai, Hao; Hong, Guini; Zhang, Jiahui; Li, Na; Ao, Lu; Guo, Zheng

2017-01-01

Formalin-fixed paraffin-embedded (FFPE) samples represent a valuable resource for clinical researches. However, FFPE samples are usually considered an unreliable source for gene expression analysis due to the partial RNA degradation. In this study, through comparing gene expression profiles between FFPE samples and paired fresh-frozen (FF) samples for three cancer types, we firstly showed that expression measurements of thousands of genes had at least two-fold change in FFPE samples compared with paired FF samples. Therefore, for a transcriptional signature based on risk scores summarized from the expression levels of the signature genes, the risk score thresholds trained from FFPE (or FF) samples could not be applied to FF (or FFPE) samples. On the other hand, we found that more than 90% of the relative expression orderings (REOs) of gene pairs in the FF samples were maintained in their paired FFPE samples and largely unaffected by the storage time. The result suggested that the REOs of gene pairs were highly robust against partial RNA degradation in FFPE samples. Finally, as a case study, we developed a REOs-based signature to distinguish liver cirrhosis from hepatocellular carcinoma (HCC) using FFPE samples. The signature was validated in four datasets of FFPE samples and eight datasets of FF samples. In conclusion, the valuable FFPE samples can be fully exploited to identify REOs-based diagnostic and prognostic signatures which could be robustly applicable to both FF samples and FFPE samples with degraded RNA. PMID:28036264

Molecular evolution and expression of oxygen transport genes in livebearing fishes (Poeciliidae) from hydrogen sulfide rich springs.

PubMed

Barts, Nicholas; Greenway, Ryan; Passow, Courtney N; Arias-Rodriguez, Lenin; Kelley, Joanna L; Tobler, Michael

2018-04-01

Hydrogen sulfide (H 2 S) is a natural toxicant in some aquatic environments that has diverse molecular targets. It binds to oxygen transport proteins, rendering them non-functional by reducing oxygen-binding affinity. Hence, organisms permanently inhabiting H 2 S-rich environments are predicted to exhibit adaptive modifications to compensate for the reduced capacity to transport oxygen. We investigated 10 lineages of fish of the family Poeciliidae that have colonized freshwater springs rich in H 2 S-along with related lineages from non-sulfidic environments-to test hypotheses about the expression and evolution of oxygen transport genes in a phylogenetic context. We predicted shifts in the expression of and signatures of positive selection on oxygen transport genes upon colonization of H 2 S-rich habitats. Our analyses indicated significant shifts in gene expression for multiple hemoglobin genes in lineages that have colonized H 2 S-rich environments, and three hemoglobin genes exhibited relaxed selection in sulfidic compared to non-sulfidic lineages. However, neither changes in gene expression nor signatures of selection were consistent among all lineages in H 2 S-rich environments. Oxygen transport genes may consequently be predictable targets of selection during adaptation to sulfidic environments, but changes in gene expression and molecular evolution of oxygen transport genes in H 2 S-rich environments are not necessarily repeatable across replicated lineages.

Development and Validation of a Novel Platform-Independent Metastasis Signature in Human Breast Cancer

PubMed Central

Speers, Corey; Liu, Meilan; Wilder-Romans, Kari; Lawrence, Theodore S.; Pierce, Lori J.; Feng, Felix Y.

2015-01-01

Purpose The molecular drivers of metastasis in breast cancer are not well understood. Therefore, we sought to identify the biological processes underlying distant progression and define a prognostic signature for metastatic potential in breast cancer. Experimental design In vivo screening for metastases was performed using Chick Chorioallantoic Membrane assays in 21 preclinical breast cancer models. Expressed genes associated with metastatic potential were identified using high-throughput analysis. Correlations with biological function were determined using the Database for Annotation, Visualization and Integrated Discovery. Results We identified a broad range of metastatic potential that was independent of intrinsic breast cancer subtypes. 146 genes were significantly associated with metastasis progression and were linked to cancer-related biological functions, including cell migration/adhesion, Jak-STAT, TGF-beta, and Wnt signaling. These genes were used to develop a platform-independent gene expression signature (M-Sig), which was trained and subsequently validated on 5 independent cohorts totaling nearly 1800 breast cancer patients with all p-values < 0.005 and hazard ratios ranging from approximately 2.5 to 3. On multivariate analysis accounting for standard clinicopathologic prognostic variables, M-Sig remained the strongest prognostic factor for metastatic progression, with p-values < 0.001 and hazard ratios > 2 in three different cohorts. Conclusion M-Sig is strongly prognostic for metastatic progression, and may provide clinical utility in combination with treatment prediction tools to better guide patient care. In addition, the platform-independent nature of the signature makes it an excellent research tool as it can be directly applied onto existing, and future, datasets. PMID:25974184

DeSigN: connecting gene expression with therapeutics for drug repurposing and development.

PubMed

Lee, Bernard Kok Bang; Tiong, Kai Hung; Chang, Jit Kang; Liew, Chee Sun; Abdul Rahman, Zainal Ariff; Tan, Aik Choon; Khang, Tsung Fei; Cheong, Sok Ching

2017-01-25

The drug discovery and development pipeline is a long and arduous process that inevitably hampers rapid drug development. Therefore, strategies to improve the efficiency of drug development are urgently needed to enable effective drugs to enter the clinic. Precision medicine has demonstrated that genetic features of cancer cells can be used for predicting drug response, and emerging evidence suggest that gene-drug connections could be predicted more accurately by exploring the cumulative effects of many genes simultaneously. We developed DeSigN, a web-based tool for predicting drug efficacy against cancer cell lines using gene expression patterns. The algorithm correlates phenotype-specific gene signatures derived from differentially expressed genes with pre-defined gene expression profiles associated with drug response data (IC 50 ) from 140 drugs. DeSigN successfully predicted the right drug sensitivity outcome in four published GEO studies. Additionally, it predicted bosutinib, a Src/Abl kinase inhibitor, as a sensitive inhibitor for oral squamous cell carcinoma (OSCC) cell lines. In vitro validation of bosutinib in OSCC cell lines demonstrated that indeed, these cell lines were sensitive to bosutinib with IC 50 of 0.8-1.2 μM. As further confirmation, we demonstrated experimentally that bosutinib has anti-proliferative activity in OSCC cell lines, demonstrating that DeSigN was able to robustly predict drug that could be beneficial for tumour control. DeSigN is a robust method that is useful for the identification of candidate drugs using an input gene signature obtained from gene expression analysis. This user-friendly platform could be used to identify drugs with unanticipated efficacy against cancer cell lines of interest, and therefore could be used for the repurposing of drugs, thus improving the efficiency of drug development.

KEA-71 Smart Current Signature Sensor (SCSS)

NASA Technical Reports Server (NTRS)

Perotti, Jose M.

2010-01-01

This slide presentation reviews the development and uses of the Smart Current Signature Sensor (SCSS), also known as the Valve Health Monitor (VHM) system. SCSS provides a way to not only monitor real-time the valve's operation in a non invasive manner, but also to monitor its health (Fault Detection and Isolation) and identify potential faults and/or degradation in the near future (Prediction/Prognosis). This technology approach is not only applicable for solenoid valves, and it could be extrapolated to other electrical components with repeatable electrical current signatures such as motors.

Theoretical Characterizaiton of Visual Signatures

NASA Astrophysics Data System (ADS)

Kashinski, D. O.; Chase, G. M.; di Nallo, O. E.; Scales, A. N.; Vanderley, D. L.; Byrd, E. F. C.

2015-05-01

We are investigating the accuracy of theoretical models used to predict the visible, ultraviolet, and infrared spectra, as well as other properties, of product materials ejected from the muzzle of currently fielded systems. Recent advances in solid propellants has made the management of muzzle signature (flash) a principle issue in weapons development across the calibers. A priori prediction of the electromagnetic spectra of formulations will allow researchers to tailor blends that yield desired signatures and determine spectrographic detection ranges. Quantum chemistry methods at various levels of sophistication have been employed to optimize molecular geometries, compute unscaled vibrational frequencies, and determine the optical spectra of specific gas-phase species. Electronic excitations are being computed using Time Dependent Density Functional Theory (TD-DFT). A full statistical analysis and reliability assessment of computational results is currently underway. A comparison of theoretical results to experimental values found in the literature is used to assess any affects of functional choice and basis set on calculation accuracy. The status of this work will be presented at the conference. Work supported by the ARL, DoD HPCMP, and USMA.

A mesenchymal-like phenotype and expression of CD44 predict lack of apoptotic response to sorafenib in liver tumor cells.

PubMed

Fernando, Joan; Malfettone, Andrea; Cepeda, Edgar B; Vilarrasa-Blasi, Roser; Bertran, Esther; Raimondi, Giulia; Fabra, Àngels; Alvarez-Barrientos, Alberto; Fernández-Salguero, Pedro; Fernández-Rodríguez, Conrado M; Giannelli, Gianluigi; Sancho, Patricia; Fabregat, Isabel

2015-02-15

The multikinase inhibitor sorafenib is the only effective drug in advanced cases of hepatocellular carcinoma (HCC). However, response differs among patients and effectiveness only implies a delay. We have recently described that sorafenib sensitizes HCC cells to apoptosis. In this work, we have explored the response to this drug of six different liver tumor cell lines to define a phenotypic signature that may predict lack of response in HCC patients. Results have indicated that liver tumor cells that show a mesenchymal-like phenotype, resistance to the suppressor effects of transforming growth factor beta (TGF-β) and high expression of the stem cell marker CD44 were refractory to sorafenib-induced cell death in in vitro studies, which correlated with lack of response to sorafenib in nude mice xenograft models of human HCC. In contrast, epithelial-like cells expressing the stem-related proteins EpCAM or CD133 were sensitive to sorafenib-induced apoptosis both in vitro and in vivo. A cross-talk between the TGF-β pathway and the acquisition of a mesenchymal-like phenotype with up-regulation of CD44 expression was found in the HCC cell lines. Targeted CD44 knock-down in the mesenchymal-like cells indicated that CD44 plays an active role in protecting HCC cells from sorafenib-induced apoptosis. However, CD44 effect requires a TGF-β-induced mesenchymal background, since the only overexpression of CD44 in epithelial-like HCC cells is not sufficient to impair sorafenib-induced cell death. In conclusion, a mesenchymal profile and expression of CD44, linked to activation of the TGF-β pathway, may predict lack of response to sorafenib in HCC patients. © 2014 UICC.

Peripheral Blood Gene Expression as a Novel Genomic Biomarker in Complicated Sarcoidosis

PubMed Central

Sweiss, Nadera J.; Chen, Edward S.; Moller, David R.; Knox, Kenneth S.; Ma, Shwu-Fan; Wade, Michael S.; Noth, Imre; Machado, Roberto F.; Garcia, Joe G. N.

2012-01-01

Sarcoidosis, a systemic granulomatous syndrome invariably affecting the lung, typically spontaneously remits but in ∼20% of cases progresses with severe lung dysfunction or cardiac and neurologic involvement (complicated sarcoidosis). Unfortunately, current biomarkers fail to distinguish patients with remitting (uncomplicated) sarcoidosis from other fibrotic lung disorders, and fail to identify individuals at risk for complicated sarcoidosis. We utilized genome-wide peripheral blood gene expression analysis to identify a 20-gene sarcoidosis biomarker signature distinguishing sarcoidosis (n = 39) from healthy controls (n = 35, 86% classification accuracy) and which served as a molecular signature for complicated sarcoidosis (n = 17). As aberrancies in T cell receptor (TCR) signaling, JAK-STAT (JS) signaling, and cytokine-cytokine receptor (CCR) signaling are implicated in sarcoidosis pathogenesis, a 31-gene signature comprised of T cell signaling pathway genes associated with sarcoidosis (TCR/JS/CCR) was compared to the unbiased 20-gene biomarker signature but proved inferior in prediction accuracy in distinguishing complicated from uncomplicated sarcoidosis. Additional validation strategies included significant association of single nucleotide polymorphisms (SNPs) in signature genes with sarcoidosis susceptibility and severity (unbiased signature genes - CX3CR1, FKBP1A, NOG, RBM12B, SENS3, TSHZ2; T cell/JAK-STAT pathway genes such as AKT3, CBLB, DLG1, IFNG, IL2RA, IL7R, ITK, JUN, MALT1, NFATC2, PLCG1, SPRED1). In summary, this validated peripheral blood molecular gene signature appears to be a valuable biomarker in identifying cases with sarcoidoisis and predicting risk for complicated sarcoidosis. PMID:22984568

DNA methylation analysis of paediatric low-grade astrocytomas identifies a tumour-specific hypomethylation signature in pilocytic astrocytomas.

PubMed

Jeyapalan, Jennie N; Doctor, Gabriel T; Jones, Tania A; Alberman, Samuel N; Tep, Alexander; Haria, Chirag M; Schwalbe, Edward C; Morley, Isabel C F; Hill, Alfred A; LeCain, Magdalena; Ottaviani, Diego; Clifford, Steven C; Qaddoumi, Ibrahim; Tatevossian, Ruth G; Ellison, David W; Sheer, Denise

2016-05-27

Low-grade gliomas (LGGs) account for about a third of all brain tumours in children. We conducted a detailed study of DNA methylation and gene expression to improve our understanding of the biology of pilocytic and diffuse astrocytomas. Pilocytic astrocytomas were found to have a distinctive signature at 315 CpG sites, of which 312 were hypomethylated and 3 were hypermethylated. Genomic analysis revealed that 182 of these sites are within annotated enhancers. The signature was not present in diffuse astrocytomas, or in published profiles of other brain tumours and normal brain tissue. The AP-1 transcription factor was predicted to bind within 200 bp of a subset of the 315 differentially methylated CpG sites; the AP-1 factors, FOS and FOSL1 were found to be up-regulated in pilocytic astrocytomas. We also analysed splice variants of the AP-1 target gene, CCND1, which encodes cell cycle regulator cyclin D1. CCND1a was found to be highly expressed in both pilocytic and diffuse astrocytomas, but diffuse astrocytomas have far higher expression of the oncogenic variant, CCND1b. These findings highlight novel genetic and epigenetic differences between pilocytic and diffuse astrocytoma, in addition to well-described alterations involving BRAF, MYB and FGFR1.

Hi-C Chromatin Interaction Networks Predict Co-expression in the Mouse Cortex

PubMed Central

Hulsman, Marc; Lelieveldt, Boudewijn P. F.; de Ridder, Jeroen; Reinders, Marcel

2015-01-01

The three dimensional conformation of the genome in the cell nucleus influences important biological processes such as gene expression regulation. Recent studies have shown a strong correlation between chromatin interactions and gene co-expression. However, predicting gene co-expression from frequent long-range chromatin interactions remains challenging. We address this by characterizing the topology of the cortical chromatin interaction network using scale-aware topological measures. We demonstrate that based on these characterizations it is possible to accurately predict spatial co-expression between genes in the mouse cortex. Consistent with previous findings, we find that the chromatin interaction profile of a gene-pair is a good predictor of their spatial co-expression. However, the accuracy of the prediction can be substantially improved when chromatin interactions are described using scale-aware topological measures of the multi-resolution chromatin interaction network. We conclude that, for co-expression prediction, it is necessary to take into account different levels of chromatin interactions ranging from direct interaction between genes (i.e. small-scale) to chromatin compartment interactions (i.e. large-scale). PMID:25965262

Proteomics assisted profiling of antimicrobial peptide signatures from black pepper (Piper nigrum L.).

PubMed

Umadevi, P; Soumya, M; George, Johnson K; Anandaraj, M

2018-05-01

Plant antimicrobial peptides are the interesting source of studies in defense response as they are essential components of innate immunity which exert rapid defense response. In spite of abundant reports on the isolation of antimicrobial peptides (AMPs) from many sources, the profile of AMPs expressed/identified from single crop species under certain stress/physiological condition is still unknown. This work describes the AMP signature profile of black pepper and their expression upon Phytophthora infection using label-free quantitative proteomics strategy. The differential expression of 24 AMPs suggests that a combinatorial strategy is working in the defense network. The 24 AMP signatures belonged to the cationic, anionic, cysteine-rich and cysteine-free group. As the first report on the possible involvement of AMP signature in Phytophthora infection, our results offer a platform for further study on regulation, evolutionary importance and exploitation of theses AMPs as next generation molecules against pathogens.

Prognostic Power of a Tumor Differentiation Gene Signature for Bladder Urothelial Carcinomas.

PubMed

Mo, Qianxing; Nikolos, Fotis; Chen, Fengju; Tramel, Zoe; Lee, Yu-Cheng; Hayashi, Kazukuni; Xiao, Jing; Shen, Jianjun; Chan, Keith Syson

2018-05-01

Muscle-invasive bladder cancers (MIBCs) cause approximately 150 000 deaths per year worldwide. Survival for MIBC patients is heterogeneous, with no clinically validated molecular markers that predict clinical outcome. Non-MIBCs (NMIBCs) generally have favorable outcome; however, a portion progress to MIBC. Hence, development of a prognostic tool that can guide decision-making is crucial for improving clinical management of bladder urothelial carcinomas. Tumor grade is defined by pathologic evaluation of tumor cell differentiation, and it often associates with clinical outcome. The current study extrapolates this conventional wisdom and combines it with molecular profiling. We developed an 18-gene signature that molecularly defines urothelial cellular differentiation, thus classifying MIBCs and NMIBCs into two subgroups: basal and differentiated. We evaluated the prognostic capability of this "tumor differentiation signature" and three other existing gene signatures including the The Cancer Genome Atlas (TCGA; 2707 genes), MD Anderson Cancer Center (MDA; 2252 genes/2697 probes), and University of North Carolina at Chapel Hill (UNC; 47 genes) using five gene expression data sets derived from MIBC and NMIBC patients. All statistical tests were two-sided. The tumor differentiation signature demonstrated consistency and statistical robustness toward stratifying MIBC patients into different overall survival outcomes (TCGA cohort 1, P = .03; MDA discovery, P = .009; MDA validation, P = .01), while the other signatures were not as consistent. In addition, we analyzed the progression (Ta/T1 progressing to ≥T2) probability of NMIBCs. NMIBC patients with a basal tumor differentiation signature associated with worse progression outcome (P = .008). Gene functional term enrichment and gene set enrichment analyses revealed that genes involved in the biologic process of immune response and inflammatory response are among the most elevated within basal bladder cancers

Detection of Tuberculosis in HIV-Infected and -Uninfected African Adults Using Whole Blood RNA Expression Signatures: A Case-Control Study

PubMed Central

Anderson, Suzanne T.; Bangani, Nonzwakazi; Banwell, Claire M.; Brent, Andrew J.; Crampin, Amelia C.; Dockrell, Hazel M.; Eley, Brian; Heyderman, Robert S.; Hibberd, Martin L.; Kern, Florian; Langford, Paul R.; Ling, Ling; Mendelson, Marc; Ottenhoff, Tom H.; Zgambo, Femia; Wilkinson, Robert J.; Coin, Lachlan J.; Levin, Michael

2013-01-01

Background A major impediment to tuberculosis control in Africa is the difficulty in diagnosing active tuberculosis (TB), particularly in the context of HIV infection. We hypothesized that a unique host blood RNA transcriptional signature would distinguish TB from other diseases (OD) in HIV-infected and -uninfected patients, and that this could be the basis of a simple diagnostic test. Methods and Findings Adult case-control cohorts were established in South Africa and Malawi of HIV-infected or -uninfected individuals consisting of 584 patients with either TB (confirmed by culture of Mycobacterium tuberculosis [M.TB] from sputum or tissue sample in a patient under investigation for TB), OD (i.e., TB was considered in the differential diagnosis but then excluded), or healthy individuals with latent TB infection (LTBI). Individuals were randomized into training (80%) and test (20%) cohorts. Blood transcriptional profiles were assessed and minimal sets of significantly differentially expressed transcripts distinguishing TB from LTBI and OD were identified in the training cohort. A 27 transcript signature distinguished TB from LTBI and a 44 transcript signature distinguished TB from OD. To evaluate our signatures, we used a novel computational method to calculate a disease risk score (DRS) for each patient. The classification based on this score was first evaluated in the test cohort, and then validated in an independent publically available dataset (GSE19491). In our test cohort, the DRS classified TB from LTBI (sensitivity 95%, 95% CI [87–100]; specificity 90%, 95% CI [80–97]) and TB from OD (sensitivity 93%, 95% CI [83–100]; specificity 88%, 95% CI [74–97]). In the independent validation cohort, TB patients were distinguished both from LTBI individuals (sensitivity 95%, 95% CI [85–100]; specificity 94%, 95% CI [84–100]) and OD patients (sensitivity 100%, 95% CI [100–100]; specificity 96%, 95% CI [93–100]). Limitations of our study include the use of

Unique signatures of long noncoding RNA expression in response to virus infection and altered innate immune signaling.

PubMed

Peng, Xinxia; Gralinski, Lisa; Armour, Christopher D; Ferris, Martin T; Thomas, Matthew J; Proll, Sean; Bradel-Tretheway, Birgit G; Korth, Marcus J; Castle, John C; Biery, Matthew C; Bouzek, Heather K; Haynor, David R; Frieman, Matthew B; Heise, Mark; Raymond, Christopher K; Baric, Ralph S; Katze, Michael G

2010-10-26

Studies of the host response to virus infection typically focus on protein-coding genes. However, non-protein-coding RNAs (ncRNAs) are transcribed in mammalian cells, and the roles of many of these ncRNAs remain enigmas. Using next-generation sequencing, we performed a whole-transcriptome analysis of the host response to severe acute respiratory syndrome coronavirus (SARS-CoV) infection across four founder mouse strains of the Collaborative Cross. We observed differential expression of approximately 500 annotated, long ncRNAs and 1,000 nonannotated genomic regions during infection. Moreover, studies of a subset of these ncRNAs and genomic regions showed the following. (i) Most were similarly regulated in response to influenza virus infection. (ii) They had distinctive kinetic expression profiles in type I interferon receptor and STAT1 knockout mice during SARS-CoV infection, including unique signatures of ncRNA expression associated with lethal infection. (iii) Over 40% were similarly regulated in vitro in response to both influenza virus infection and interferon treatment. These findings represent the first discovery of the widespread differential expression of long ncRNAs in response to virus infection and suggest that ncRNAs are involved in regulating the host response, including innate immunity. At the same time, virus infection models provide a unique platform for studying the biology and regulation of ncRNAs.

Unique Signatures of Long Noncoding RNA Expression in Response to Virus Infection and Altered Innate Immune Signaling

PubMed Central

Peng, Xinxia; Gralinski, Lisa; Armour, Christopher D.; Ferris, Martin T.; Thomas, Matthew J.; Proll, Sean; Bradel-Tretheway, Birgit G.; Korth, Marcus J.; Castle, John C.; Biery, Matthew C.; Bouzek, Heather K.; Haynor, David R.; Frieman, Matthew B.; Heise, Mark; Raymond, Christopher K.; Baric, Ralph S.; Katze, Michael G.

2010-01-01

Studies of the host response to virus infection typically focus on protein-coding genes. However, non-protein-coding RNAs (ncRNAs) are transcribed in mammalian cells, and the roles of many of these ncRNAs remain enigmas. Using next-generation sequencing, we performed a whole-transcriptome analysis of the host response to severe acute respiratory syndrome coronavirus (SARS-CoV) infection across four founder mouse strains of the Collaborative Cross. We observed differential expression of approximately 500 annotated, long ncRNAs and 1,000 nonannotated genomic regions during infection. Moreover, studies of a subset of these ncRNAs and genomic regions showed the following. (i) Most were similarly regulated in response to influenza virus infection. (ii) They had distinctive kinetic expression profiles in type I interferon receptor and STAT1 knockout mice during SARS-CoV infection, including unique signatures of ncRNA expression associated with lethal infection. (iii) Over 40% were similarly regulated in vitro in response to both influenza virus infection and interferon treatment. These findings represent the first discovery of the widespread differential expression of long ncRNAs in response to virus infection and suggest that ncRNAs are involved in regulating the host response, including innate immunity. At the same time, virus infection models provide a unique platform for studying the biology and regulation of ncRNAs. PMID:20978541

HA117 endows HL60 cells with a stem-like signature by inhibiting the degradation of DNMT1 via its ability to down-regulate expression of the GGL domain of RGS6

PubMed Central

Li, Shuangshuang; Wu, Huan; Wang, Yi; Li, Xiaoqing; Guo, Yuxia; Liang, Shaoyan

2017-01-01

All-trans retinoic acid (ATRA) induces complete remission in almost all patients with acute promyelocytic leukemia (APL) via its ability to induce the in vivo differentiation of APL blasts. However, prolonged ATRA treatment can result in drug resistance. In previous studies, we generated a multi-drug-resistant HL60/ATRA cell line and found it to contain a new drug resistance-related gene segment, HA117. In this study, we demonstrate that ATRA induces multi-drug-resistant subpopulations of HL60 cells with a putative stem-like signature by up-regulating the expression of the new gene segment HA117. Western blot analysis and quantitative real-time PCR demonstrated that HA117 causes alternative splicing of regulator of G-protein signaling 6 (RGS6) and down-regulation of the expression of the GGL domain of RGS6, which plays an important role in DNA methyltransferase 1 (DNMT1) degradation. Moreover, DNMT1 expression was increased in multi-drug resistance HL60/ATRA cells. Knockdown of HA117 restored expression of the GGL domain and blocked DNMT1 expression. Moreover, resistant cells displayed a putative stem-like signature with increased expression of cancer steam cell markers CD133 and CD123. The stem cell marker, Nanog, was significantly up-regulated. In conclusion, our study shows that HA117 potentially promotes the stem-like signature of the HL60/ATRA cell line by inhibiting by the ubiquitination and degradation of DNMT1 and by down-regulating the expression of the GGL domain of RGS6. These results throw light on the cellular events associated with the ATRA-induced multi-drug resistance phenotype in acute leukemia. PMID:28665981

Emotional facial expressions differentially influence predictions and performance for face recognition.

PubMed

Nomi, Jason S; Rhodes, Matthew G; Cleary, Anne M

2013-01-01

This study examined how participants' predictions of future memory performance are influenced by emotional facial expressions. Participants made judgements of learning (JOLs) predicting the likelihood that they would correctly identify a face displaying a happy, angry, or neutral emotional expression in a future two-alternative forced-choice recognition test of identity (i.e., recognition that a person's face was seen before). JOLs were higher for studied faces with happy and angry emotional expressions than for neutral faces. However, neutral test faces with studied neutral expressions had significantly higher identity recognition rates than neutral test faces studied with happy or angry expressions. Thus, these data are the first to demonstrate that people believe happy and angry emotional expressions will lead to better identity recognition in the future relative to neutral expressions. This occurred despite the fact that neutral expressions elicited better identity recognition than happy and angry expressions. These findings contribute to the growing literature examining the interaction of cognition and emotion.

Prediction of gene expression in embryonic structures of Drosophila melanogaster.

PubMed

Samsonova, Anastasia A; Niranjan, Mahesan; Russell, Steven; Brazma, Alvis

2007-07-01

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms.

Prediction of Gene Expression in Embryonic Structures of Drosophila melanogaster

PubMed Central

Samsonova, Anastasia A; Niranjan, Mahesan; Russell, Steven; Brazma, Alvis

2007-01-01

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms. PMID:17658945

Human relevance of an in vitro gene signature in HaCaT for skin sensitization.

PubMed

van der Veen, Jochem W; Hodemaekers, Henny; Reus, Astrid A; Maas, Wilfred J M; van Loveren, Henk; Ezendam, Janine

2015-02-01

The skin sensitizing potential of chemicals is mainly assessed using animal methods, such as the murine local lymph node assay. Recently, an in vitro assay based on a gene expression signature in the HaCaT keratinocyte cell line was proposed as an alternative to these animal methods. Here, the human relevance of this gene signature is assessed through exposure of freshly isolated human skin to the chemical allergens dinitrochlorobenzene (DNCB) and diphenylcyclopropenone (DCP). In human skin, the gene signature shows similar direction of regulation as was previously observed in vitro, suggesting that the molecular processes that drive expression of these genes are similar between the HaCaT cell line and freshly isolated skin, providing evidence for the human relevance of the gene signature. Copyright © 2014 Elsevier Ltd. All rights reserved.

Epigenetic signatures of invasive status in populations of marine invertebrates

NASA Astrophysics Data System (ADS)

Ardura, Alba; Zaiko, Anastasija; Morán, Paloma; Planes, Serge; Garcia-Vazquez, Eva

2017-02-01

Epigenetics, as a DNA signature that affects gene expression and enables rapid reaction of an organism to environmental changes, is likely involved in the process of biological invasions. DNA methylation is an epigenetic mechanism common to plants and animals for regulating gene expression. In this study we show, for the first time in any marine species, significant reduction of global methylation levels during the expansive phase of a pygmy mussel (Xenostrobus securis) recent invasion in Europe (two-year old), while in older introductions such epigenetic signature of invasion was progressively reduced. Decreased methylation was interpreted as a rapid way of increasing phenotypic plasticity that would help invasive populations to thrive. This epigenetic signature of early invasion was stronger than the expected environmental signature of environmental stress in younger populations sampled from ports, otherwise detected in a much older population (>90 year old) of the also invasive tubeworm Ficopomatus enigmaticus established in similar locations. Higher epigenetic than genetic diversity found in X. securis was confirmed from F. enigmaticus samples. As reported for introduced plants and vertebrates, epigenetic variation could compensate for relatively lower genetic variation caused by founder effects. These phenomena were compared with epigenetic mechanisms involved in metastasis, as parallel processes of community (biological invasion) and organism (cancer) invasions.

Identification of a B cell signature associated with renal transplant tolerance in humans

PubMed Central

Newell, Kenneth A.; Asare, Adam; Kirk, Allan D.; Gisler, Trang D.; Bourcier, Kasia; Suthanthiran, Manikkam; Burlingham, William J.; Marks, William H.; Sanz, Ignacio; Lechler, Robert I.; Hernandez-Fuentes, Maria P.; Turka, Laurence A.; Seyfert-Margolis, Vicki L.

2010-01-01

Establishing long-term allograft acceptance without the requirement for continuous immunosuppression, a condition known as allograft tolerance, is a highly desirable therapeutic goal in solid organ transplantation. Determining which recipients would benefit from withdrawal or minimization of immunosuppression would be greatly facilitated by biomarkers predictive of tolerance. In this study, we identified the largest reported cohort to our knowledge of tolerant renal transplant recipients, as defined by stable graft function and receiving no immunosuppression for more than 1 year, and compared their gene expression profiles and peripheral blood lymphocyte subsets with those of subjects with stable graft function who are receiving immunosuppressive drugs as well as healthy controls. In addition to being associated with clinical and phenotypic parameters, renal allograft tolerance was strongly associated with a B cell signature using several assays. Tolerant subjects showed increased expression of multiple B cell differentiation genes, and a set of just 3 of these genes distinguished tolerant from nontolerant recipients in a unique test set of samples. This B cell signature was associated with upregulation of CD20 mRNA in urine sediment cells and elevated numbers of peripheral blood naive and transitional B cells in tolerant participants compared with those receiving immunosuppression. These results point to a critical role for B cells in regulating alloimmunity and provide a candidate set of genes for wider-scale screening of renal transplant recipients. PMID:20501946

Expression profiling feline peripheral blood monocytes identifies a transcriptional signature associated with type two diabetes mellitus.

PubMed

O'Leary, Caroline A; Sedhom, Mamdouh; Reeve-Johnson, Mia; Mallyon, John; Irvine, Katharine M

2017-04-01

Diabetes mellitus is a common disease of cats and is similar to type 2 diabetes (T2D) in humans, especially with respect to the role of obesity-induced insulin resistance, glucose toxicity, decreased number of pancreatic β-cells and pancreatic amyloid deposition. Cats have thus been proposed as a valuable translational model of T2D. In humans, inflammation associated with adipose tissue is believed to be central to T2D development, and peripheral blood monocytes (PBM) are important in the inflammatory cascade which leads to insulin resistance and β-cell failure. PBM may thus provide a useful window to study the pathogenesis of diabetes mellitus in cats, however feline monocytes are poorly characterised. In this study, we used the Affymetrix Feline 1.0ST array to profile peripheral blood monocytes from 3 domestic cats with T2D and 3 cats with normal glucose tolerance. Feline monocytes were enriched for genes expressed in human monocytes, and, despite heterogeneous gene expression, we identified a T2D-associated expression signature associated with cell cycle perturbations, DNA repair and the unfolded protein response, oxidative phosphorylation and inflammatory responses. Our data provide novel insights into the feline monocyte transcriptome, and support the hypothesis that inflammatory monocytes contribute to T2D pathogenesis in cats as well as in humans. Copyright © 2017 Elsevier B.V. All rights reserved.

Mycobacterium tuberculosis strains exhibit differential and strain-specific molecular signatures in pulmonary epithelial cells.

PubMed

Mvubu, Nontobeko Eunice; Pillay, Balakrishna; Gamieldien, Junaid; Bishai, William; Pillay, Manormoney

2016-12-01

Although pulmonary epithelial cells are integral to innate and adaptive immune responses during Mycobacterium tuberculosis infection, global transcriptomic changes in these cells remain largely unknown. Changes in gene expression induced in pulmonary epithelial cells infected with M. tuberculosis F15/LAM4/KZN, F11, F28, Beijing and Unique genotypes were investigated by RNA sequencing (RNA-Seq). The Illumina HiSeq 2000 platform generated 50 bp reads that were mapped to the human genome (Hg19) using Tophat (2.0.10). Differential gene expression induced by the different strains in infected relative to the uninfected cells was quantified and compared using Cufflinks (2.1.0) and MeV (4.0.9), respectively. Gene expression varied among the strains with the total number of genes as follows: F15/LAM4/KZN (1187), Beijing (1252), F11 (1639), F28 (870), Unique (886) and H37Rv (1179). A subset of 292 genes was commonly induced by all strains, where 52 genes were down-regulated while 240 genes were up-regulated. Differentially expressed genes were compared among the strains and the number of induced strain-specific gene signatures were as follows: F15/LAM4/KZN (138), Beijing (52), F11 (255), F28 (55), Unique (186) and H37Rv (125). Strain-specific molecular gene signatures associated with functional pathways were observed only for the Unique and H37Rv strains while certain biological functions may be associated with other strain signatures. This study demonstrated that strains of M. tuberculosis induce differential gene expression and strain-specific molecular signatures in pulmonary epithelial cells. Specific signatures induced by clinical strains of M. tuberculosis can be further explored for novel host-associated biomarkers and adjunctive immunotherapies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Biomarker Signatures of Mitochondrial NDUFS3 in Invasive Breast Carcinoma

PubMed Central

Suhane, Sonal; Berel, Dror; Ramanujan, V Krishnan

2011-01-01

We present evidence for potential biomarker utility of a mitochondrial complex I subunit, (NDUFS3) in discriminating normal and highly invasive breast carcinoma specimens obtained from clinical patients. Besides being a robust indicator of breast cancer aggressiveness, NDUFS3 also shows clear signatures of a hypoxia/necrosis marker in invasive ductal carcinoma specimens. Statistically significant positive correlation was observed between nuclear grade and NDUFS3 expression level in the tumor specimens analyzed. We support these findings with a plausible mechanism involving mitochondrial complex I assembly defects and/or redox buffering induced mitochondrial dysfunction during the process of cancer cell transformation. From a clinical standpoint, this novel observation adds value in augmenting the current receptor-based biomarkers for better accuracy in diagnosis and predicting survival rate in patients with breast carcinoma. PMID:21867691

An 8-gene qRT-PCR-based gene expression score that has prognostic value in early breast cancer

PubMed Central

2010-01-01

Background Gene expression profiling may improve prognostic accuracy in patients with early breast cancer. Our objective was to demonstrate that it is possible to develop a simple molecular signature to predict distant relapse. Methods We included 153 patients with stage I-II hormonal receptor-positive breast cancer. RNA was isolated from formalin-fixed paraffin-embedded samples and qRT-PCR amplification of 83 genes was performed with gene expression assays. The genes we analyzed were those included in the 70-Gene Signature, the Recurrence Score and the Two-Gene Index. The association among gene expression, clinical variables and distant metastasis-free survival was analyzed using Cox regression models. Results An 8-gene prognostic score was defined. Distant metastasis-free survival at 5 years was 97% for patients defined as low-risk by the prognostic score versus 60% for patients defined as high-risk. The 8-gene score remained a significant factor in multivariate analysis and its performance was similar to that of two validated gene profiles: the 70-Gene Signature and the Recurrence Score. The validity of the signature was verified in independent cohorts obtained from the GEO database. Conclusions This study identifies a simple gene expression score that complements histopathological prognostic factors in breast cancer, and can be determined in paraffin-embedded samples. PMID:20584321

Genome-wide methylomic and transcriptomic analyses identify subtype-specific epigenetic signatures commonly dysregulated in glioma stem cells and glioblastoma.

PubMed

Pangeni, Rajendra P; Zhang, Zhou; Alvarez, Angel A; Wan, Xuechao; Sastry, Namratha; Lu, Songjian; Shi, Taiping; Huang, Tianzhi; Lei, Charles X; James, C David; Kessler, John A; Brennan, Cameron W; Nakano, Ichiro; Lu, Xinghua; Hu, Bo; Zhang, Wei; Cheng, Shi-Yuan

2018-06-21

Glioma stem cells (GSCs), a subpopulation of tumor cells, contribute to tumor heterogeneity and therapy resistance. Gene expression profiling classified glioblastoma (GBM) and GSCs into four transcriptomically-defined subtypes. Here, we determined the DNA methylation signatures in transcriptomically pre-classified GSC and GBM bulk tumors subtypes. We hypothesized that these DNA methylation signatures correlate with gene expression and are uniquely associated either with only GSCs or only GBM bulk tumors. Additional methylation signatures may be commonly associated with both GSCs and GBM bulk tumors, i.e., common to non-stem-like and stem-like tumor cell populations and correlating with the clinical prognosis of glioma patients. We analyzed Illumina 450K methylation array and expression data from a panel of 23 patient-derived GSCs. We referenced these results with The Cancer Genome Atlas (TCGA) GBM datasets to generate methylomic and transcriptomic signatures for GSCs and GBM bulk tumors of each transcriptomically pre-defined tumor subtype. Survival analyses were carried out for these signature genes using publicly available datasets, including from TCGA. We report that DNA methylation signatures in proneural and mesenchymal tumor subtypes are either unique to GSCs, unique to GBM bulk tumors, or common to both. Further, dysregulated DNA methylation correlates with gene expression and clinical prognoses. Additionally, many previously identified transcriptionally-regulated markers are also dysregulated due to DNA methylation. The subtype-specific DNA methylation signatures described in this study could be useful for refining GBM sub-classification, improving prognostic accuracy, and making therapeutic decisions.

Signatures support program

NASA Astrophysics Data System (ADS)

Hawley, Chadwick T.

2009-05-01

The Signatures Support Program (SSP) leverages the full spectrum of signature-related activities (collections, processing, development, storage, maintenance, and dissemination) within the Department of Defense (DOD), the intelligence community (IC), other Federal agencies, and civil institutions. The Enterprise encompasses acoustic, seismic, radio frequency, infrared, radar, nuclear radiation, and electro-optical signatures. The SSP serves the war fighter, the IC, and civil institutions by supporting military operations, intelligence operations, homeland defense, disaster relief, acquisitions, and research and development. Data centers host and maintain signature holdings, collectively forming the national signatures pool. The geographically distributed organizations are the authoritative sources and repositories for signature data; the centers are responsible for data content and quality. The SSP proactively engages DOD, IC, other Federal entities, academia, and industry to locate signatures for inclusion in the distributed national signatures pool and provides world-wide 24/7 access via the SSP application.

Real Traceable Signatures

NASA Astrophysics Data System (ADS)

Chow, Sherman S. M.

Traceable signature scheme extends a group signature scheme with an enhanced anonymity management mechanism. The group manager can compute a tracing trapdoor which enables anyone to test if a signature is signed by a given misbehaving user, while the only way to do so for group signatures requires revealing the signer of all signatures. Nevertheless, it is not tracing in a strict sense. For all existing schemes, T tracing agents need to recollect all N' signatures ever produced and perform RN' “checks” for R revoked users. This involves a high volume of transfer and computations. Increasing T increases the degree of parallelism for tracing but also the probability of “missing” some signatures in case some of the agents are dishonest.

Microarray Meta-Analysis Identifies Acute Lung Injury Biomarkers in Donor Lungs That Predict Development of Primary Graft Failure in Recipients

PubMed Central

Haitsma, Jack J.; Furmli, Suleiman; Masoom, Hussain; Liu, Mingyao; Imai, Yumiko; Slutsky, Arthur S.; Beyene, Joseph; Greenwood, Celia M. T.; dos Santos, Claudia

2012-01-01

Objectives To perform a meta-analysis of gene expression microarray data from animal studies of lung injury, and to identify an injury-specific gene expression signature capable of predicting the development of lung injury in humans. Methods We performed a microarray meta-analysis using 77 microarray chips across six platforms, two species and different animal lung injury models exposed to lung injury with or/and without mechanical ventilation. Individual gene chips were classified and grouped based on the strategy used to induce lung injury. Effect size (change in gene expression) was calculated between non-injurious and injurious conditions comparing two main strategies to pool chips: (1) one-hit and (2) two-hit lung injury models. A random effects model was used to integrate individual effect sizes calculated from each experiment. Classification models were built using the gene expression signatures generated by the meta-analysis to predict the development of lung injury in human lung transplant recipients. Results Two injury-specific lists of differentially expressed genes generated from our meta-analysis of lung injury models were validated using external data sets and prospective data from animal models of ventilator-induced lung injury (VILI). Pathway analysis of gene sets revealed that both new and previously implicated VILI-related pathways are enriched with differentially regulated genes. Classification model based on gene expression signatures identified in animal models of lung injury predicted development of primary graft failure (PGF) in lung transplant recipients with larger than 80% accuracy based upon injury profiles from transplant donors. We also found that better classifier performance can be achieved by using meta-analysis to identify differentially-expressed genes than using single study-based differential analysis. Conclusion Taken together, our data suggests that microarray analysis of gene expression data allows for the detection of �

Study of Dynamic Characteristics of Aeroelastic Systems Utilizing Randomdec Signatures

NASA Technical Reports Server (NTRS)

Chang, C. S.

1975-01-01

The feasibility of utilizing the random decrement method in conjunction with a signature analysis procedure to determine the dynamic characteristics of an aeroelastic system for the purpose of on-line prediction of potential on-set of flutter was examined. Digital computer programs were developed to simulate sampled response signals of a two-mode aeroelastic system. Simulated response data were used to test the random decrement method. A special curve-fit approach was developed for analyzing the resulting signatures. A number of numerical 'experiments' were conducted on the combined processes. The method is capable of determining frequency and damping values accurately from randomdec signatures of carefully selected lengths.

Theoretical Characterizaiton of Visual Signatures (Muzzle Flash)

NASA Astrophysics Data System (ADS)

Kashinski, D. O.; Scales, A. N.; Vanderley, D. L.; Chase, G. M.; di Nallo, O. E.; Byrd, E. F. C.

2014-05-01

We are investigating the accuracy of theoretical models used to predict the visible, ultraviolet and infrared spectra of product materials ejected from the muzzle of currently fielded systems. Recent advances in solid propellants has made the management of muzzle signature (flash) a principle issue in weapons development across the calibers. A priori prediction of the electromagnetic spectra of formulations will allow researchers to tailor blends that yield desired signatures and determine spectrographic detection ranges. We are currently employing quantum chemistry methods at various levels of sophistication to optimize molecular geometries, compute vibrational frequencies, and determine the optical spectra of specific gas-phase molecules and radicals of interest. Electronic excitations are being computed using Time Dependent Density Functional Theory (TD-DFT). A comparison of computational results to experimental values found in the literature is used to assess the affect of basis set and functional choice on calculation accuracy. The current status of this work will be presented at the conference. Work supported by the ARL, and USMA.

Gene expression analysis predicts insect venom anaphylaxis in indolent systemic mastocytosis.

PubMed

Niedoszytko, M; Bruinenberg, M; van Doormaal, J J; de Monchy, J G R; Nedoszytko, B; Koppelman, G H; Nawijn, M C; Wijmenga, C; Jassem, E; Elberink, J N G Oude

2011-05-01

Anaphylaxis to insect venom (Hymenoptera) is most severe in patients with mastocytosis and may even lead to death. However, not all patients with mastocytosis suffer from anaphylaxis. The aim of the study was to analyze differences in gene expression between patients with indolent systemic mastocytosis (ISM) and a history of insect venom anaphylaxis (IVA) compared to those patients without a history of anaphylaxis, and to determine the predictive use of gene expression profiling. Whole-genome gene expression analysis was performed in peripheral blood cells. Twenty-two adults with ISM were included: 12 with a history of IVA and 10 without a history of anaphylaxis of any kind. Significant differences in single gene expression corrected for multiple testing were found for 104 transcripts (P < 0.05). Gene ontology analysis revealed that the differentially expressed genes were involved in pathways responsible for the development of cancer and focal and cell adhesion suggesting that the expression of genes related to the differentiation state of cells is higher in patients with a history of anaphylaxis. Based on the gene expression profiles, a naïve Bayes prediction model was built identifying patients with IVA. In ISM, gene expression profiles are different between patients with a history of IVA and those without. These findings might reflect a more pronounced mast cells dysfunction in patients without a history of anaphylaxis. Gene expression profiling might be a useful tool to predict the risk of anaphylaxis on insect venom in patients with ISM. Prospective studies are needed to substantiate any conclusions. © 2010 John Wiley & Sons A/S.

Gene Signature for Predicting Solid Tumors Patient Prognosis | NCI Technology Transfer Center | TTC

Cancer.gov

The National Cancer Institute’s Laboratory of Human Carcinogenesis seeks parties to license or co-develop a method of predicting the prognosis of a patient diagnosed with hepatocellular carcinoma (HCC) or breast cancer by detecting expression of one or more cancer-associated genes, and a method of identifying an agent for use in treating HCC.

Trabecular morphometry by fractal signature analysis is a novel marker of osteoarthritis progression.

PubMed

Kraus, Virginia Byers; Feng, Sheng; Wang, ShengChu; White, Scott; Ainslie, Maureen; Brett, Alan; Holmes, Anthony; Charles, H Cecil

2009-12-01

To evaluate the effectiveness of using subchondral bone texture observed on a radiograph taken at baseline to predict progression of knee osteoarthritis (OA) over a 3-year period. A total of 138 participants in the Prediction of Osteoarthritis Progression study were evaluated at baseline and after 3 years. Fractal signature analysis (FSA) of the medial subchondral tibial plateau was performed on fixed flexion radiographs of 248 nonreplaced knees, using a commercially available software tool. OA progression was defined as a change in joint space narrowing (JSN) or osteophyte formation of 1 grade according to a standardized knee atlas. Statistical analysis of fractal signatures was performed using a new model based on correlating the overall shape of a fractal dimension curve with radius. Fractal signature of the medial tibial plateau at baseline was predictive of medial knee JSN progression (area under the curve [AUC] 0.75, of a receiver operating characteristic curve) but was not predictive of osteophyte formation or progression of JSN in the lateral compartment. Traditional covariates (age, sex, body mass index, knee pain), general bone mineral content, and joint space width at baseline were no more effective than random variables for predicting OA progression (AUC 0.52-0.58). The predictive model with maximum effectiveness combined fractal signature at baseline, knee alignment, traditional covariates, and bone mineral content (AUC 0.79). We identified a prognostic marker of OA that is readily extracted from a plain radiograph using FSA. Although the method needs to be validated in a second cohort, our results indicate that the global shape approach to analyzing these data is a potentially efficient means of identifying individuals at risk of knee OA progression.

Integrated Cox's model for predicting survival time of glioblastoma multiforme.

PubMed

Ai, Zhibing; Li, Longti; Fu, Rui; Lu, Jing-Min; He, Jing-Dong; Li, Sen

2017-04-01

Glioblastoma multiforme is the most common primary brain tumor and is highly lethal. This study aims to figure out signatures for predicting the survival time of patients with glioblastoma multiforme. Clinical information, messenger RNA expression, microRNA expression, and single-nucleotide polymorphism array data of patients with glioblastoma multiforme were retrieved from The Cancer Genome Atlas. Patients were separated into two groups by using 1 year as a cutoff, and a logistic regression model was used to figure out any variables that can predict whether the patient was able to live longer than 1 year. Furthermore, Cox's model was used to find out features that were correlated with the survival time. Finally, a Cox model integrated the significant clinical variables, messenger RNA expression, microRNA expression, and single-nucleotide polymorphism was built. Although the classification method failed, signatures of clinical features, messenger RNA expression levels, and microRNA expression levels were figured out by using Cox's model. However, no single-nucleotide polymorphisms related to prognosis were found. The selected clinical features were age at initial diagnosis, Karnofsky score, and race, all of which had been suggested to correlate with survival time. Both of the two significant microRNAs, microRNA-221 and microRNA-222, were targeted to p27 Kip1 protein, which implied the important role of p27 Kip1 on the prognosis of glioblastoma multiforme patients. Our results suggested that survival modeling was more suitable than classification to figure out prognostic biomarkers for patients with glioblastoma multiforme. An integrated model containing clinical features, messenger RNA levels, and microRNA expression levels was built, which has the potential to be used in clinics and thus to improve the survival status of glioblastoma multiforme patients.

Early Whole Blood Transcriptional Signatures Are Associated with Severity of Lung Inflammation in Cynomolgus Macaques with Mycobacterium tuberculosis Infection.

PubMed

Gideon, Hannah P; Skinner, Jason A; Baldwin, Nicole; Flynn, JoAnne L; Lin, Philana Ling

2016-12-15

Whole blood transcriptional profiling offers great diagnostic and prognostic potential. Although studies identified signatures for pulmonary tuberculosis (TB) and transcripts that predict the risk for developing active TB in humans, the early transcriptional changes immediately following Mycobacterium tuberculosis infection have not been evaluated. We evaluated the gene expression changes in the cynomolgus macaque model of TB, which recapitulates all clinical aspects of human M. tuberculosis infection, using a human microarray and analytics platform. We performed genome-wide blood transcriptional analysis on 38 macaques at 11 postinfection time points during the first 6 mo of M. tuberculosis infection. Of 6371 differentially expressed transcripts between preinfection and postinfection, the greatest change in transcriptional activity occurred 20-56 d postinfection, during which fluctuation of innate and adaptive immune response-related transcripts was observed. Modest transcriptional differences between active TB and latent infection were observed over the time course with substantial overlap. The pattern of module activity previously published for human active TB was similar in macaques with active disease. Blood transcript activity was highly correlated with lung inflammation (lung [ 18 F]fluorodeoxyglucose [FDG] avidity) measured by positron emission tomography and computed tomography at early time points postinfection. The differential signatures between animals with high and low lung FDG were stronger than between clinical outcomes. Analysis of preinfection signatures of macaques revealed that IFN signatures could influence eventual clinical outcomes and lung FDG avidity, even before infection. Our data support that transcriptional changes in the macaque model are translatable to human M. tuberculosis infection and offer important insights into early events of M. tuberculosis infection. Copyright © 2016 by The American Association of Immunologists, Inc.

Gene expression profiles of peripheral blood mononuclear cells reveal transcriptional signatures as novel biomarkers of cardiac remodeling in rats with aldosteronism and hypertensive heart disease.

PubMed

Gerling, Ivan C; Ahokas, Robert A; Kamalov, German; Zhao, Wenyuan; Bhattacharya, Syamal K; Sun, Yao; Weber, Karl T

2013-12-01

In searching for a noninvasive surrogate tissue mimicking the pro-oxidant/proinflammatory hypertensive heart disease (HHD) phenotype, we turned to peripheral blood mononuclear cells (PBMCs). We tested whether iterations in [Ca2+]i, [Zn2+]i, and oxidative stress in cardiomyocytes and PBMCs would complement each other, eliciting similar shifts in gene expression profiles in these tissues demonstrable during the preclinical (week 1) and pathological (week 4) stages of aldosterone/salt treatment (ALDOST). Inappropriate neurohormonal activation contributes to pathological remodeling of myocardium in HHD associated with aldosteronism. In rats receiving long-term ALDOST, evidence of reparative fibrosis replacing necrotic cardiomyocytes and coronary vasculopathy appears at week 4 associated with the induction of oxidative stress by mitochondria that overwhelms endogenous, largely Zn2+-based, antioxidant defenses. Biomarker-guided prediction of risk before the appearance of cardiac pathology would prove invaluable. In PBMCs and cardiomyocytes, quantitation of cytoplasmic free Ca2+ and Zn2+, H2O2, and 8-iosprostane levels and isolation of ribonucleic acid (RNA) and gene expression together with statistical and clustering analyses and confirmation of genes by in situ hybridization and reverse-transcription polymerase chain reaction were performed. Compared with controls, at weeks 1 and 4 of ALDOST, we found comparable increments in [Ca2+]i, [Zn2+]i, and 8-isoprotane coupled with increased H2O2 production in cardiac mitochondria and PBMCs, together with the common networks of expression profiles dominated by genes involved in oxidative stress, inflammation, and repair. These included 3 central Ingenuity pathway-linked genes: p38 mitogen-activated protein kinase, a stress-responsive protein; nuclear factor-κB, a redox-sensitive transcription factor and a proinflammatory cascade that it regulates; and transforming growth factor-β1, a fibrogenic cytokine involved in tissue

Unique transcriptome signatures and GM-CSF expression in lymphocytes from patients with spondyloarthritis.

PubMed

Al-Mossawi, M H; Chen, L; Fang, H; Ridley, A; de Wit, J; Yager, N; Hammitzsch, A; Pulyakhina, I; Fairfax, B P; Simone, D; Yi, Yao; Bandyopadhyay, S; Doig, K; Gundle, R; Kendrick, B; Powrie, F; Knight, J C; Bowness, P

2017-11-15

Spondyloarthritis encompasses a group of common inflammatory diseases thought to be driven by IL-17A-secreting type-17 lymphocytes. Here we show increased numbers of GM-CSF-producing CD4 and CD8 lymphocytes in the blood and joints of patients with spondyloarthritis, and increased numbers of IL-17A + GM-CSF + double-producing CD4, CD8, γδ and NK cells. GM-CSF production in CD4 T cells occurs both independently and in combination with classical Th1 and Th17 cytokines. Type 3 innate lymphoid cells producing predominantly GM-CSF are expanded in synovial tissues from patients with spondyloarthritis. GM-CSF + CD4 + cells, isolated using a triple cytokine capture approach, have a specific transcriptional signature. Both GM-CSF + and IL-17A + GM-CSF + double-producing CD4 T cells express increased levels of GPR65, a proton-sensing receptor associated with spondyloarthritis in genome-wide association studies and pathogenicity in murine inflammatory disease models. Silencing GPR65 in primary CD4 T cells reduces GM-CSF production. GM-CSF and GPR65 may thus serve as targets for therapeutic intervention of spondyloarthritis.

The prognostic value of a seven-microRNA classifier as a novel biomarker for the prediction and detection of recurrence in glioma patients.

PubMed

Chen, Wanghao; Yu, Qiang; Chen, Bo; Lu, Xingyu; Li, Qiaoyu

2016-08-16

Glioma is often diagnosed at a later stage, and the high risk of recurrence remains a major challenge. We hypothesized that the microRNA expression profile may serve as a biomarker for the prognosis and prediction of glioblastoma recurrence. We defined microRNAs that were associated with good and poor prognosis in 300 specimens of glioblastoma from the Cancer Genome Atlas. By analyzing microarray gene expression data and clinical information from three random groups, we identified 7 microRNAs that have prognostic and prognostic accuracy: microRNA-124a, microRNA-129, microRNA-139, microRNA-15b, microRNA-21, microRNA-218 and microRNA-7. The differential expression of these miRNAs was verified using an independent set of glioma samples from the Affiliated People's Hospital of Jiangsu University. We used the log-rank test and the Kaplan-Meier method to estimate correlations between the miRNA signature and disease-free survival/overall survival. Using the LASSO model, we observed a uniform significant difference in disease-free survival and overall survival between patients with high-risk and low-risk miRNA signature scores. Furthermore, the prognostic capability of the seven-miRNA signature was demonstrated by receiver operator characteristic curve analysis. A Circos plot was generated to examine the network of genes and pathways predicted to be targeted by the seven-miRNA signature. The seven-miRNA-based classifier should be useful in the stratification and individualized management of patients with glioma.