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Correll M, Johnson CK, Ferrari G, Brizzio M, Mak AWC, Quackenbush J, Shaw RE, Zapolanski A, Grau JB. Mutational analysis clopidogrel resistance and platelet function in patients scheduled for coronary artery bypass grafting. Genomics 2013; 101:313-7. [PMID: 23462555 DOI: 10.1016/j.ygeno.2013.01.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2012] [Revised: 01/18/2013] [Accepted: 01/19/2013] [Indexed: 11/28/2022]
Abstract
Clopidogrel is an oral antiplatelet pro-drug prescribed to 40 million patients worldwide who are at risk for thrombotic events or receiving percutaneous coronary intervention (PCI). However about a fifth of patients treated with clopidogrel do not respond adequately to the drug. From a cohort of 105 patients on whom we had functional data on clopidogrel response, we used ultra-high throughput sequencing to assay mutations in CYP2C19 and ABCB1, the two genes genetically linked to respond. Testing for mutations in CYP2C19, as recommended by the FDA, only correctly predicted if a patient would respond to clopidogrel 52.4% of the time. Similarly, testing of the ABCB1 gene only correctly foretold response in 51 (48.6%) patients. These results are clinically relevant and suggest that until additional genetic factors are discovered that predict response more completely, functional assays are more appropriate for clinical use.
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Papillon-Cavanagh S, De Jay N, Hachem N, Olsen C, Bontempi G, Aerts HJWL, Quackenbush J, Haibe-Kains B. Comparison and validation of genomic predictors for anticancer drug sensitivity. J Am Med Inform Assoc 2013; 20:597-602. [PMID: 23355484 DOI: 10.1136/amiajnl-2012-001442] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND An enduring challenge in personalized medicine lies in selecting the right drug for each individual patient. While testing of drugs on patients in large trials is the only way to assess their clinical efficacy and toxicity, we dramatically lack resources to test the hundreds of drugs currently under development. Therefore the use of preclinical model systems has been intensively investigated as this approach enables response to hundreds of drugs to be tested in multiple cell lines in parallel. METHODS Two large-scale pharmacogenomic studies recently screened multiple anticancer drugs on over 1000 cell lines. We propose to combine these datasets to build and robustly validate genomic predictors of drug response. We compared five different approaches for building predictors of increasing complexity. We assessed their performance in cross-validation and in two large validation sets, one containing the same cell lines present in the training set and another dataset composed of cell lines that have never been used during the training phase. RESULTS Sixteen drugs were found in common between the datasets. We were able to validate multivariate predictors for three out of the 16 tested drugs, namely irinotecan, PD-0325901, and PLX4720. Moreover, we observed that response to 17-AAG, an inhibitor of Hsp90, could be efficiently predicted by the expression level of a single gene, NQO1. CONCLUSION These results suggest that genomic predictors could be robustly validated for specific drugs. If successfully validated in patients' tumor cells, and subsequently in clinical trials, they could act as companion tests for the corresponding drugs and play an important role in personalized medicine.
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Kelly AD, Haibe-Kains B, Janeway KA, Hill KE, Howe E, Goldsmith J, Kurek K, Perez-Atayde AR, Francoeur N, Fan JB, April C, Schneider H, Gebhardt MC, Culhane A, Quackenbush J, Spentzos D. MicroRNA paraffin-based studies in osteosarcoma reveal reproducible independent prognostic profiles at 14q32. Genome Med 2013; 5:2. [PMID: 23339462 PMCID: PMC3706900 DOI: 10.1186/gm406] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2012] [Revised: 12/21/2012] [Accepted: 01/22/2013] [Indexed: 12/22/2022] Open
Abstract
Background Although microRNAs (miRNAs) are implicated in osteosarcoma biology and chemoresponse, miRNA prognostic models are still needed, particularly because prognosis is imperfectly correlated with chemoresponse. Formalin-fixed, paraffin-embedded tissue is a necessary resource for biomarker studies in this malignancy with limited frozen tissue availability. Methods We performed miRNA and mRNA microarray formalin-fixed, paraffin-embedded assays in 65 osteosarcoma biopsy and 26 paired post-chemotherapy resection specimens and used the only publicly available miRNA dataset, generated independently by another group, to externally validate our strongest findings (n = 29). We used supervised principal components analysis and logistic regression for survival and chemoresponse, and miRNA activity and target gene set analysis to study miRNA regulatory activity. Results Several miRNA-based models with as few as five miRNAs were prognostic independently of pathologically assessed chemoresponse (median recurrence-free survival: 59 months versus not-yet-reached; adjusted hazards ratio = 2.90; P = 0.036). The independent dataset supported the reproducibility of recurrence and survival findings. The prognostic value of the profile was independent of confounding by known prognostic variables, including chemoresponse, tumor location and metastasis at diagnosis. Model performance improved when chemoresponse was added as a covariate (median recurrence-free survival: 59 months versus not-yet-reached; hazard ratio = 3.91; P = 0.002). Most prognostic miRNAs were located at 14q32 - a locus already linked to osteosarcoma - and their gene targets display deregulation patterns associated with outcome. We also identified miRNA profiles predictive of chemoresponse (75% to 80% accuracy), which did not overlap with prognostic profiles. Conclusions Formalin-fixed, paraffin-embedded tissue-derived miRNA patterns are a powerful prognostic tool for risk-stratified osteosarcoma management strategies. Combined miRNA and mRNA analysis supports a possible role of the 14q32 locus in osteosarcoma progression and outcome. Our study creates a paradigm for formalin-fixed, paraffin-embedded-based miRNA biomarker studies in cancer.
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Schröder MS, Gusenleitner D, Quackenbush J, Culhane AC, Haibe-Kains B. RamiGO: an R/Bioconductor package providing an AmiGO visualize interface. ACTA ACUST UNITED AC 2013; 29:666-8. [PMID: 23297033 DOI: 10.1093/bioinformatics/bts708] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
SUMMARY The R/Bioconductor package RamiGO is an R interface to AmiGO that enables visualization of Gene Ontology (GO) trees. Given a list of GO terms, RamiGO uses the AmiGO visualize API to import Graphviz-DOT format files into R, and export these either as images (SVG, PNG) or into Cytoscape for extended network analyses. RamiGO provides easy customization of annotation, highlighting of specific GO terms, colouring of terms by P-value or export of a simplified summary GO tree. We illustrate RamiGO functionalities in a genome-wide gene set analysis of prognostic genes in breast cancer. AVAILABILITY AND IMPLEMENTATION RamiGO is provided in R/Bioconductor, is open source under the Artistic-2.0 License and is available with a user manual containing installation, operating instructions and tutorials. It requires R version 2.15.0 or higher. URL: http://bioconductor.org/packages/release/bioc/html/RamiGO.html
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Waldron L, Ogino S, Hoshida Y, Shima K, McCart Reed AE, Simpson PT, Baba Y, Nosho K, Segata N, Vargas AC, Cummings MC, Lakhani SR, Kirkner GJ, Giovannucci E, Quackenbush J, Golub TR, Fuchs CS, Parmigiani G, Huttenhower C. Expression profiling of archival tumors for long-term health studies. Clin Cancer Res 2012; 18:6136-46. [PMID: 23136189 DOI: 10.1158/1078-0432.ccr-12-1915] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PURPOSE More than 20 million archival tissue samples are stored annually in the United States as formalin-fixed, paraffin-embedded (FFPE) blocks, but RNA degradation during fixation and storage has prevented their use for transcriptional profiling. New and highly sensitive assays for whole-transcriptome microarray analysis of FFPE tissues are now available, but resulting data include noise and variability for which previous expression array methods are inadequate. EXPERIMENTAL DESIGN We present the two largest whole-genome expression studies from FFPE tissues to date, comprising 1,003 colorectal cancer (CRC) and 168 breast cancer samples, combined with a meta-analysis of 14 new and published FFPE microarray datasets. We develop and validate quality control (QC) methods through technical replication, independent samples, comparison to results from fresh-frozen tissue, and recovery of expected associations between gene expression and protein abundance. RESULTS Archival tissues from large, multicenter studies showed a much wider range of transcriptional data quality relative to smaller or frozen tissue studies and required stringent QC for subsequent analysis. We developed novel methods for such QC of archival tissue expression profiles based on sample dynamic range and per-study median profile. This enabled validated identification of gene signatures of microsatellite instability and additional features of CRC, and improved recovery of associations between gene expression and protein abundance of MLH1, FASN, CDX2, MGMT, and SIRT1 in CRC tumors. CONCLUSIONS These methods for large-scale QC of FFPE expression profiles enable study of the cancer transcriptome in relation to extensive clinicopathological information, tumor molecular biomarkers, and long-term lifestyle and outcome data.
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Quackenbush J. Abstract IA11: Extracting predictive networks from high-dimensional data. Cancer Epidemiol Biomarkers Prev 2012. [DOI: 10.1158/1055-9965.gwas-ia11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Abstract
Two trends are driving innovation and discovery in biological sciences: technologies that allow holistic surveys of genes, proteins, and metabolites and the growing realization that analysis and interpretation of the resulting requires an understanding of the complex phenotypic and environmental data regarding the samples from which they were derived. The growing body of biological and biomedical information in the public domain provides outstanding opportunities for leveraging what we already “know” in a systematic way to understand the problems we are studying. Here, I will provide an overview of some of the methods we are using to investigate the complexities of human cancers and to explore how we can use biological data to uncover the cellular networks and pathways that underlie human disease, building predictive models of those networks that may help to direct therapies.
Citation Format: John Quackenbush. Extracting predictive networks from high-dimensional data. [abstract]. In: Proceedings of the AACR Special Conference on Post-GWAS Horizons in Molecular Epidemiology: Digging Deeper into the Environment; 2012 Nov 11-14; Hollywood, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2012;21(11 Suppl):Abstract nr IA11.
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Wang ZC, Birkbak NJ, Culhane AC, Drapkin R, Fatima A, Tian R, Schwede M, Alsop K, Daniels KE, Piao H, Liu J, Etemadmoghadam D, Miron A, Salvesen HB, Mitchell G, DeFazio A, Quackenbush J, Berkowitz RS, Iglehart JD, Bowtell DD, Matulonis UA. Profiles of genomic instability in high-grade serous ovarian cancer predict treatment outcome. Clin Cancer Res 2012; 18:5806-15. [PMID: 22912389 PMCID: PMC4205235 DOI: 10.1158/1078-0432.ccr-12-0857] [Citation(s) in RCA: 125] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
PURPOSE High-grade serous cancer (HGSC) is the most common cancer of the ovary and is characterized by chromosomal instability. Defects in homologous recombination repair (HRR) are associated with genomic instability in HGSC, and are exploited by therapy targeting DNA repair. Defective HRR causes uniparental deletions and loss of heterozygosity (LOH). Our purpose is to profile LOH in HGSC and correlate our findings to clinical outcome, and compare HGSC and high-grade breast cancers. EXPERIMENTAL DESIGN We examined LOH and copy number changes using single nucleotide polymorphism array data from three HGSC cohorts and compared results to a cohort of high-grade breast cancers. The LOH profiles in HGSC were matched to chemotherapy resistance and progression-free survival (PFS). RESULTS LOH-based clustering divided HGSC into two clusters. The major group displayed extensive LOH and was further divided into two subgroups. The second group contained remarkably less LOH. BRCA1 promoter methylation was associated with the major group. LOH clusters were reproducible when validated in two independent HGSC datasets. LOH burden in the major cluster of HGSC was similar to triple-negative, and distinct from other high-grade breast cancers. Our analysis revealed an LOH cluster with lower treatment resistance and a significant correlation between LOH burden and PFS. CONCLUSIONS Separating HGSC by LOH-based clustering produces remarkably stable subgroups in three different cohorts. Patients in the various LOH clusters differed with respect to chemotherapy resistance, and the extent of LOH correlated with PFS. LOH burden may indicate vulnerability to treatment targeting DNA repair, such as PARP1 inhibitors.
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MESH Headings
- DNA Copy Number Variations/genetics
- Disease-Free Survival
- Female
- Gene Expression Regulation, Neoplastic
- Genomic Instability
- Humans
- Loss of Heterozygosity/genetics
- Neoplasm Grading
- Neoplasms, Cystic, Mucinous, and Serous/genetics
- Neoplasms, Cystic, Mucinous, and Serous/pathology
- Neoplasms, Cystic, Mucinous, and Serous/therapy
- Ovarian Neoplasms/genetics
- Ovarian Neoplasms/pathology
- Ovarian Neoplasms/therapy
- Polymorphism, Single Nucleotide
- Precision Medicine
- Prognosis
- Treatment Outcome
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Kusko RL, Brothers J, Liu G, Luo L, Guardela BJ, Tedrow J, Aleksyev Y, Yang IV, Correll M, Geraci M, Quackenbush J, Sciurba F, Lenburg M, Schwartz DA, Beane J, Kaminski N, Spira A. Comprehensive genomic profiling of the lung transcriptome in emphysema and idiopathic pulmonary fibrosis using RNA-Seq. BMC Proc 2012. [PMCID: PMC3467575 DOI: 10.1186/1753-6561-6-s6-p21] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Brothers J, Kusko R, Liu G, Luo L, Guardela BJ, Tedrow J, Alekesyev Y, Yang IV, Correll M, Geraci M, Quackenbush J, Sciurba F, Sebastiani P, Lenburg M, Kaminski N, Schwartz DA, Spira A, Beane J. Moving beyond gene expression: identification of lung-disease-associated novel transcripts and alternative splicing by RNA sequencing. BMC Proc 2012. [PMCID: PMC3467663 DOI: 10.1186/1753-6561-6-s6-p4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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Rozenblatt-Rosen O, Deo RC, Padi M, Adelmant G, Calderwood MA, Rolland T, Grace M, Dricot A, Askenazi M, Tavares M, Pevzner SJ, Abderazzaq F, Byrdsong D, Carvunis AR, Chen AA, Cheng J, Correll M, Duarte M, Fan C, Feltkamp MC, Ficarro SB, Franchi R, Garg BK, Gulbahce N, Hao T, Holthaus AM, James R, Korkhin A, Litovchick L, Mar JC, Pak TR, Rabello S, Rubio R, Shen Y, Singh S, Spangle JM, Tasan M, Wanamaker S, Webber JT, Roecklein-Canfield J, Johannsen E, Barabási AL, Beroukhim R, Kieff E, Cusick ME, Hill DE, Münger K, Marto JA, Quackenbush J, Roth FP, DeCaprio JA, Vidal M. Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins. Nature 2012; 487:491-5. [PMID: 22810586 PMCID: PMC3408847 DOI: 10.1038/nature11288] [Citation(s) in RCA: 305] [Impact Index Per Article: 25.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2011] [Accepted: 06/07/2012] [Indexed: 12/20/2022]
Abstract
Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations, and large numbers of somatic genomic alterations, associated with a predisposition to cancer. However, it remains difficult to distinguish background, or 'passenger', cancer mutations from causal, or 'driver', mutations in these data sets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. Here we test the hypothesis that genomic variations and tumour viruses may cause cancer through related mechanisms, by systematically examining host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways, such as Notch signalling and apoptosis, that go awry in cancer. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches increase the specificity of cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate the prioritization of cancer-causing driver genes to advance the understanding of the genetic basis of human cancer.
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Fountzilas E, Kelly AD, Perez-Atayde AR, Goldsmith J, Konstantinopoulos PA, Francoeur N, Correll M, Rubio R, Hu L, Gebhardt MC, Quackenbush J, Spentzos D. A microRNA activity map of human mesenchymal tumors: connections to oncogenic pathways; an integrative transcriptomic study. BMC Genomics 2012; 13:332. [PMID: 22823907 PMCID: PMC3443663 DOI: 10.1186/1471-2164-13-332] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2012] [Accepted: 07/06/2012] [Indexed: 11/10/2022] Open
Abstract
Background MicroRNAs (miRNAs) are nucleic acid regulators of many human mRNAs, and are associated with many tumorigenic processes. miRNA expression levels have been used in profiling studies, but some evidence suggests that expression levels do not fully capture miRNA regulatory activity. In this study we integrate multiple gene expression datasets to determine miRNA activity patterns associated with cancer phenotypes and oncogenic pathways in mesenchymal tumors – a very heterogeneous class of malignancies. Results Using a computational method, we identified differentially activated miRNAs between 77 normal tissue specimens and 135 sarcomas and we validated many of these findings with microarray interrogation of an independent, paraffin-based cohort of 18 tumors. We also showed that miRNA activity is imperfectly correlated with miRNA expression levels. Using next-generation miRNA sequencing we identified potential base sequence alterations which may explain differential activity. We then analyzed miRNA activity changes related to the RAS-pathway and found 21 miRNAs that switch from silenced to activated status in parallel with RAS activation. Importantly, nearly half of these 21 miRNAs were predicted to regulate integral parts of the miRNA processing machinery, and our gene expression analysis revealed significant reductions of these transcripts in RAS-active tumors. These results suggest an association between RAS signaling and miRNA processing in which miRNAs may attenuate their own biogenesis. Conclusions Our study represents the first gene expression-based investigation of miRNA regulatory activity in human sarcomas, and our findings indicate that miRNA activity patterns derived from integrated transcriptomic data are reproducible and biologically informative in cancer. We identified an association between RAS signaling and miRNA processing, and demonstrated sequence alterations as plausible causes for differential miRNA activity. Finally, our study highlights the value of systems level integrative miRNA/mRNA assessment with high-throughput genomic data, and the applicability of paraffin-tissue-derived RNA for validation of novel findings.
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Gusenleitner D, Howe EA, Bentink S, Quackenbush J, Culhane AC. iBBiG: iterative binary bi-clustering of gene sets. ACTA ACUST UNITED AC 2012; 28:2484-92. [PMID: 22789589 PMCID: PMC3463116 DOI: 10.1093/bioinformatics/bts438] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Motivation: Meta-analysis of genomics data seeks to identify genes associated with a biological phenotype across multiple datasets; however, merging data from different platforms by their features (genes) is challenging. Meta-analysis using functionally or biologically characterized gene sets simplifies data integration is biologically intuitive and is seen as having great potential, but is an emerging field with few established statistical methods. Results: We transform gene expression profiles into binary gene set profiles by discretizing results of gene set enrichment analyses and apply a new iterative bi-clustering algorithm (iBBiG) to identify groups of gene sets that are coordinately associated with groups of phenotypes across multiple studies. iBBiG is optimized for meta-analysis of large numbers of diverse genomics data that may have unmatched samples. It does not require prior knowledge of the number or size of clusters. When applied to simulated data, it outperforms commonly used clustering methods, discovers overlapping clusters of diverse sizes and is robust in the presence of noise. We apply it to meta-analysis of breast cancer studies, where iBBiG extracted novel gene set—phenotype association that predicted tumor metastases within tumor subtypes. Availability: Implemented in the Bioconductor package iBBiG Contact:aedin@jimmy.harvard.edu
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Skiadas CC, Duan S, Correll M, Rubio R, Karaca N, Ginsburg ES, Quackenbush J, Racowsky C. Ovarian reserve status in young women is associated with altered gene expression in membrana granulosa cells. Mol Hum Reprod 2012; 18:362-71. [PMID: 22355044 PMCID: PMC3378309 DOI: 10.1093/molehr/gas008] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2010] [Revised: 02/02/2012] [Accepted: 02/09/2012] [Indexed: 01/13/2023] Open
Abstract
Diminished ovarian reserve (DOR) is a challenging diagnosis of infertility, as there are currently no tests to predict who may become affected with this condition, or at what age. We designed the present study to compare the gene expression profile of membrana granulosa cells from young women affected with DOR with those from egg donors of similar age and to determine if distinct genetic patterns could be identified to provide insight into the etiology of DOR. Young women with DOR were identified based on FSH level in conjunction with poor follicular development during an IVF cycle (n = 13). Egg donors with normal ovarian reserve (NOR) comprised the control group (n = 13). Granulosa cells were collected following retrieval, RNA was extracted and microarray analysis was conducted to evaluate genetic differences between the groups. Confirmatory studies were undertaken with quantitative RT-PCR (qRT-PCR). Multiple significant differences in gene expression were observed between the DOR patients and egg donors. Two genes linked with ovarian function, anti-Mullerian hormone (AMH) and luteinizing hormone receptor (LHCGR), were further analyzed with qRT-PCR in all patients. The average expression of AMH was significantly higher in egg donors (adjusted P-value = 0.01), and the average expression of LHCGR was significantly higher in DOR patients (adjusted P-value = 0.005). Expression levels for four additional genes, progesterone receptor membrane component 2 (PGRMC2), prostaglandin E receptor 3 (subtype EP3) (PTGER3), steroidogenic acute regulatory protein (StAR), and StAR-related lipid transfer domain containing 4 (StarD4), were validated in a group consisting of five NOR and five DOR patients. We conclude that gene expression analysis has substantial potential to determine which young women may be affected with DOR. More importantly, our analysis suggests that DOR patients fall into two distinct subgroups based on gene expression profiles, indicating that different mechanisms may be involved during development of this pathology.
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Gulbahce N, Yan H, Dricot A, Padi M, Byrdsong D, Franchi R, Lee DS, Rozenblatt-Rosen O, Mar JC, Calderwood MA, Baldwin A, Zhao B, Santhanam B, Braun P, Simonis N, Huh KW, Hellner K, Grace M, Chen A, Rubio R, Marto JA, Christakis NA, Kieff E, Roth FP, Roecklein-Canfield J, DeCaprio JA, Cusick ME, Quackenbush J, Hill DE, Münger K, Vidal M, Barabási AL. Viral perturbations of host networks reflect disease etiology. PLoS Comput Biol 2012; 8:e1002531. [PMID: 22761553 PMCID: PMC3386155 DOI: 10.1371/journal.pcbi.1002531] [Citation(s) in RCA: 92] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2011] [Accepted: 03/28/2012] [Indexed: 12/20/2022] Open
Abstract
Many human diseases, arising from mutations of disease susceptibility genes (genetic diseases), are also associated with viral infections (virally implicated diseases), either in a directly causal manner or by indirect associations. Here we examine whether viral perturbations of host interactome may underlie such virally implicated disease relationships. Using as models two different human viruses, Epstein-Barr virus (EBV) and human papillomavirus (HPV), we find that host targets of viral proteins reside in network proximity to products of disease susceptibility genes. Expression changes in virally implicated disease tissues and comorbidity patterns cluster significantly in the network vicinity of viral targets. The topological proximity found between cellular targets of viral proteins and disease genes was exploited to uncover a novel pathway linking HPV to Fanconi anemia.
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Doroshow J, Liu ET, Pellini M, Miller V, Palmer G, Averbuch S, Green G, Novotny J, Paoletti P, Patel K, Hoos A, Gaynor R, Melemed S, Reinhard C, Teh BT, Hong WK, Kim E, Herbst R, Papadimitrakopoulou V, Gold K, Wistuba I, Lee J, Lippman S, Jackson JR, Zitvogel L, Meisel C, Workman P, Dalton WS, Botwood N, Davis BJ, Batist G, Assouline S, Camlioglu E, Tetu B, Spatz A, Diaz Z, Aguilar-Mahecha A, Basik M, Rodon J, Dienstmann R, Cortes J, Saura C, Aura C, Hernandez-Losa J, Vivancos A, Joan J, del Campo J, Felip E, Seoane J, Tabernero JT, Friend SH, Tsimberidou AM, Hong DS, Wheler JJ, Ye Y, Fu S, Piha-Paul SA, Naing A, Falchook GS, Janku F, Luthra R, Wen S, Kurzrock R, Naley M, Johnson P, Schuerer K, Lopes M, Hood LE, Yarden Y, Quackenbush J. Lectures. Ann Oncol 2012. [DOI: 10.1093/annonc/mds160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Cassidy PB, Quackenbush J, Campbell J, Moos PJ, Leachman SA. Abstract 1593: Sulforaphane protects melanocytes and skin from individuals at increased risk for melanoma from the effects of UV radiation: Role of thioredoxin reductase 1. Cancer Res 2012. [DOI: 10.1158/1538-7445.am2012-1593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Our interest in the natural product sulforaphane (SF) began with studies of patients at high risk for melanoma. Germline loss-of-function (LOF) mutations in the highly polymorphic melanocortin-1 receptor gene (MC1R), confers a 4-fold increased risk for melanoma. Much of this increase arises from the loss of MC1R-mediated upregulation of antioxidant, pigment synthesis, DNA repair and anti-apoptotic pathways that protect epidermal melanocytes from the mutagenic effects of UV radiation. We have found that SF (likely due to its antioxidant activity) protects both normal human melanocytes, and the skin of human subjects with LOF MC1R mutations, from the harmful effects of UV light. Shave biopsies were harvested from donors with either wild-type MC1R or two LOF alleles. The epidermal tissues were treated ex vivo with UV and/or SF then analyzed histologically and by qPCR. Our results showed that SF provides significant protection from UV to both melanocytes and skin. We found also that the antioxidant protein thioredoxin reductase 1 (TR1) was significantly induced by SF. In order to explore the role of TR1 in protecting melanocytes from UV-induced damage, we have created a human melanocyte cell line that expresses a micro RNA which targets TR1. These cells display increased sensitivity to the effects of UV and differential processing of the pigment synthesis protein tyrosinase. This study demonstrates the promise of SF as an agent for protection of human skin in individuals with high-risk MC1R genotypes from the effects of UV light, and highlights the role of TR1 in this process.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1593. doi:1538-7445.AM2012-1593
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Kelly AD, Fountzilas E, Perez-Atayade AR, Goldsmith J, Konstantinopoulos PA, Francoeur N, Correll M, Rubio R, Hu L, Gebhardt MC, Quackenbush J, Spentzos D. Abstract 4941: A microRNA activity map of human mesenchymal tumors: An integrative transcriptomic study. Cancer Res 2012. [DOI: 10.1158/1538-7445.am2012-4941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background MicroRNAs (miRNAs) are nucleic acid regulators of many human mRNAs, and are associated with biological processes including cell proliferation, differentiation, and apoptosis. miRNA expression levels have been used in cancer profiling studies, but some evidence suggests that they do not fully capture miRNA regulatory activity. In this study we have integrated multiple mRNA and miRNA expression datasets to use gene expression as a surrogate for determining miRNA regulatory activity. We discovered (and validated using a paraffin-based cohort) patterns associated with cancer phenotypes and oncogenic pathways in mesenchymal tumors - a very heterogeneous class of malignancies - and we investigated possible causes for miRNA activity changes. Results Using a recently developed computational method which determines miRNA activity from changes in gene expression profiles, we identified differentially activated miRNAs between 77 normal and 135 sarcoma specimens and we validated many of these findings with microarray interrogation of an independent, paraffin-based tumor cohort of 18 samples. Among these differentially activated miRNAs are 17 with a histological subtype-specific activity pattern in comparison to normal mesenchymal tissue. We also found 81 miRNAs which exhibit a pattern common to all histological subtypes, and we identified 12 miRNAs with unique activity in osteosarcomas. In addition, we have shown that miRNA activity is imperfectly correlated with miRNA expression levels. Using next-generation miRNA sequencing we identified a specific sequence alteration which may contribute to differential activity by changing mRNA/miRNA complementarity. We then analyzed miRNA activity changes related to the RAS-pathway and found 21 miRNAs that switch from silenced to activated status in parallel with RAS activation. Importantly, nearly half of these miRNAs were predicted to regulate integral parts of the miRNA processing machinery, and our gene expression analysis revealed significant reductions of these transcripts in RAS-active tumors. This suggests an association between RAS signaling and miRNA processing in which miRNAs may attenuate their own biogenesis. Conclusions Our study represents the first gene expression-based investigation of miRNA regulatory activity in human sarcomas, and our findings indicate that miRNA activity patterns derived from integrated transcriptomic data are reproducible and biologically informative in cancer. We identified an association between RAS signaling and miRNA processing, and demonstrated a sequence alteration as a plausible cause for differential miRNA activity. Finally, our study highlights the value of systems level integrative miRNA/mRNA assessment with high-throughput genomic data, and the applicability of paraffin-tissue-derived RNA for validation of novel findings.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4941. doi:1538-7445.AM2012-4941
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Bentink S, Haibe-Kains B, Risch T, Fan JB, Hirsch MS, Holton K, Rubio R, April C, Chen J, Wickham-Garcia E, Liu J, Culhane A, Drapkin R, Quackenbush J, Matulonis UA. Angiogenic mRNA and microRNA gene expression signature predicts a novel subtype of serous ovarian cancer. PLoS One 2012; 7:e30269. [PMID: 22348002 PMCID: PMC3278409 DOI: 10.1371/journal.pone.0030269] [Citation(s) in RCA: 90] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2011] [Accepted: 12/12/2011] [Indexed: 11/18/2022] Open
Abstract
Ovarian cancer is the fifth leading cause of cancer death for women in the U.S. and the seventh most fatal worldwide. Although ovarian cancer is notable for its initial sensitivity to platinum-based therapies, the vast majority of patients eventually develop recurrent cancer and succumb to increasingly platinum-resistant disease. Modern, targeted cancer drugs intervene in cell signaling, and identifying key disease mechanisms and pathways would greatly advance our treatment abilities. In order to shed light on the molecular diversity of ovarian cancer, we performed comprehensive transcriptional profiling on 129 advanced stage, high grade serous ovarian cancers. We implemented a, re-sampling based version of the ISIS class discovery algorithm (rISIS: robust ISIS) and applied it to the entire set of ovarian cancer transcriptional profiles. rISIS identified a previously undescribed patient stratification, further supported by micro-RNA expression profiles, and gene set enrichment analysis found strong biological support for the stratification by extracellular matrix, cell adhesion, and angiogenesis genes. The corresponding "angiogenesis signature" was validated in ten published independent ovarian cancer gene expression datasets and is significantly associated with overall survival. The subtypes we have defined are of potential translational interest as they may be relevant for identifying patients who may benefit from the addition of anti-angiogenic therapies that are now being tested in clinical trials.
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Haibe-Kains B, Desmedt C, Loi S, Culhane AC, Bontempi G, Quackenbush J, Sotiriou C. A three-gene model to robustly identify breast cancer molecular subtypes. J Natl Cancer Inst 2012; 104:311-25. [PMID: 22262870 DOI: 10.1093/jnci/djr545] [Citation(s) in RCA: 226] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Single sample predictors (SSPs) and Subtype classification models (SCMs) are gene expression-based classifiers used to identify the four primary molecular subtypes of breast cancer (basal-like, HER2-enriched, luminal A, and luminal B). SSPs use hierarchical clustering, followed by nearest centroid classification, based on large sets of tumor-intrinsic genes. SCMs use a mixture of Gaussian distributions based on sets of genes with expression specifically correlated with three key breast cancer genes (estrogen receptor [ER], HER2, and aurora kinase A [AURKA]). The aim of this study was to compare the robustness, classification concordance, and prognostic value of these classifiers with those of a simplified three-gene SCM in a large compendium of microarray datasets. METHODS Thirty-six publicly available breast cancer datasets (n = 5715) were subjected to molecular subtyping using five published classifiers (three SSPs and two SCMs) and SCMGENE, the new three-gene (ER, HER2, and AURKA) SCM. We used the prediction strength statistic to estimate robustness of the classification models, defined as the capacity of a classifier to assign the same tumors to the same subtypes independently of the dataset used to fit it. We used Cohen κ and Cramer V coefficients to assess concordance between the subtype classifiers and association with clinical variables, respectively. We used Kaplan-Meier survival curves and cross-validated partial likelihood to compare prognostic value of the resulting classifications. All statistical tests were two-sided. RESULTS SCMs were statistically significantly more robust than SSPs, with SCMGENE being the most robust because of its simplicity. SCMGENE was statistically significantly concordant with published SCMs (κ = 0.65-0.70) and SSPs (κ = 0.34-0.59), statistically significantly associated with ER (V = 0.64), HER2 (V = 0.52) status, and histological grade (V = 0.55), and yielded similar strong prognostic value. CONCLUSION Our results suggest that adequate classification of the major and clinically relevant molecular subtypes of breast cancer can be robustly achieved with quantitative measurements of three key genes.
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Chittenden TW, Howe EA, Taylor JM, Mar JC, Aryee MJ, Gómez H, Sultana R, Braisted J, Nair SJ, Quackenbush J, Holmes C. nEASE: a method for gene ontology subclassification of high-throughput gene expression data. ACTA ACUST UNITED AC 2012; 28:726-8. [PMID: 22247278 DOI: 10.1093/bioinformatics/bts011] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
UNLABELLED High-throughput technologies can identify genes whose expression profiles correlate with specific phenotypes; however, placing these genes into a biological context remains challenging. To help address this issue, we developed nested Expression Analysis Systematic Explorer (nEASE). nEASE complements traditional gene ontology enrichment approaches by determining statistically enriched gene ontology subterms within a list of genes based on co-annotation. Here, we overview an open-source software version of the nEASE algorithm. nEASE can be used either stand-alone or as part of a pathway discovery pipeline. AVAILABILITY nEASE is implemented within the Multiple Experiment Viewer software package available at http://www.tm4.org/mev. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Desmedt C, Majjaj S, Kheddoumi N, Singhal SK, Haibe-Kains B, El Ouriaghli F, Chaboteaux C, Michiels S, Lallemand F, Journe F, Duvillier H, Loi S, Quackenbush J, Dekoninck S, Blanpain C, Lagneaux L, Houhou N, Delorenzi M, Larsimont D, Piccart M, Sotiriou C. Characterization and clinical evaluation of CD10+ stroma cells in the breast cancer microenvironment. Clin Cancer Res 2012; 18:1004-14. [PMID: 22235100 DOI: 10.1158/1078-0432.ccr-11-0383] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
PURPOSE There is growing evidence that interaction between stromal and tumor cells is pivotal in breast cancer progression and response to therapy. Based on earlier research suggesting that during breast cancer progression, striking changes occur in CD10(+) stromal cells, we aimed to better characterize this cell population and its clinical relevance. EXPERIMENTAL DESIGN We developed a CD10(+) stroma gene expression signature (using HG U133 Plus 2.0) on the basis of the comparison of CD10 cells isolated from tumoral (n = 28) and normal (n = 3) breast tissue. We further characterized the CD10(+) cells by coculture experiments of representative breast cancer cell lines with the different CD10(+) stromal cell types (fibroblasts, myoepithelial, and mesenchymal stem cells). We then evaluated its clinical relevance in terms of in situ to invasive progression, invasive breast cancer prognosis, and prediction of efficacy of chemotherapy using publicly available data sets. RESULTS This 12-gene CD10(+) stroma signature includes, among others, genes involved in matrix remodeling (MMP11, MMP13, and COL10A1) and genes related to osteoblast differentiation (periostin). The coculture experiments showed that all 3 CD10(+) cell types contribute to the CD10(+) stroma signature, although mesenchymal stem cells have the highest CD10(+) stroma signature score. Of interest, this signature showed an important role in differentiating in situ from invasive breast cancer, in prognosis of the HER2(+) subpopulation of breast cancer only, and potentially in nonresponse to chemotherapy for those patients. CONCLUSIONS Our results highlight the importance of CD10(+) cells in breast cancer prognosis and efficacy of chemotherapy, particularly within the HER2(+) breast cancer disease.
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Haibe-Kains B, Olsen C, Djebbari A, Bontempi G, Correll M, Bouton C, Quackenbush J. Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks. Nucleic Acids Res 2012; 40:D866-75. [PMID: 22096235 PMCID: PMC3245161 DOI: 10.1093/nar/gkr1050] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2011] [Revised: 10/09/2011] [Accepted: 10/23/2011] [Indexed: 12/03/2022] Open
Abstract
Genomics provided us with an unprecedented quantity of data on the genes that are activated or repressed in a wide range of phenotypes. We have increasingly come to recognize that defining the networks and pathways underlying these phenotypes requires both the integration of multiple data types and the development of advanced computational methods to infer relationships between the genes and to estimate the predictive power of the networks through which they interact. To address these issues we have developed Predictive Networks (PN), a flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these 'known' interactions together with gene expression data to infer robust gene networks. The PN web application is accessible from http://predictivenetworks.org. The PN code base is freely available at https://sourceforge.net/projects/predictivenets/.
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Milbury CA, Correll M, Quackenbush J, Rubio R, Makrigiorgos GM. COLD-PCR enrichment of rare cancer mutations prior to targeted amplicon resequencing. Clin Chem 2011; 58:580-9. [PMID: 22194627 DOI: 10.1373/clinchem.2011.176198] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
BACKGROUND Despite widespread interest in next-generation sequencing (NGS), the adoption of personalized clinical genomics and mutation profiling of cancer specimens is lagging, in part because of technical limitations. Tumors are genetically heterogeneous and often contain normal/stromal cells, features that lead to low-abundance somatic mutations that generate ambiguous results or reside below NGS detection limits, thus hindering the clinical sensitivity/specificity standards of mutation calling. We applied COLD-PCR (coamplification at lower denaturation temperature PCR), a PCR methodology that selectively enriches variants, to improve the detection of unknown mutations before NGS-based amplicon resequencing. METHODS We used both COLD-PCR and conventional PCR (for comparison) to amplify serially diluted mutation-containing cell-line DNA diluted into wild-type DNA, as well as DNA from lung adenocarcinoma and colorectal cancer samples. After amplification of TP53 (tumor protein p53), KRAS (v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog), IDH1 [isocitrate dehydrogenase 1 (NADP(+)), soluble], and EGFR (epidermal growth factor receptor) gene regions, PCR products were pooled for library preparation, bar-coded, and sequenced on the Illumina HiSeq 2000. RESULTS In agreement with recent findings, sequencing errors by conventional targeted-amplicon approaches dictated a mutation-detection limit of approximately 1%-2%. Conversely, COLD-PCR amplicons enriched mutations above the error-related noise, enabling reliable identification of mutation abundances of approximately 0.04%. Sequencing depth was not a large factor in the identification of COLD-PCR-enriched mutations. For the clinical samples, several missense mutations were not called with conventional amplicons, yet they were clearly detectable with COLD-PCR amplicons. Tumor heterogeneity for the TP53 gene was apparent. CONCLUSIONS As cancer care shifts toward personalized intervention based on each patient's unique genetic abnormalities and tumor genome, we anticipate that COLD-PCR combined with NGS will elucidate the role of mutations in tumor progression, enabling NGS-based analysis of diverse clinical specimens within clinical practice.
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Bontempi G, Haibe-Kains B, Desmedt C, Sotiriou C, Quackenbush J. Multiple-input multiple-output causal strategies for gene selection. BMC Bioinformatics 2011; 12:458. [PMID: 22118187 PMCID: PMC3323860 DOI: 10.1186/1471-2105-12-458] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2011] [Accepted: 11/25/2011] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Traditional strategies for selecting variables in high dimensional classification problems aim to find sets of maximally relevant variables able to explain the target variations. If these techniques may be effective in generalization accuracy they often do not reveal direct causes. The latter is essentially related to the fact that high correlation (or relevance) does not imply causation. In this study, we show how to efficiently incorporate causal information into gene selection by moving from a single-input single-output to a multiple-input multiple-output setting. RESULTS We show in synthetic case study that a better prioritization of causal variables can be obtained by considering a relevance score which incorporates a causal term. In addition we show, in a meta-analysis study of six publicly available breast cancer microarray datasets, that the improvement occurs also in terms of accuracy. The biological interpretation of the results confirms the potential of a causal approach to gene selection. CONCLUSIONS Integrating causal information into gene selection algorithms is effective both in terms of prediction accuracy and biological interpretation.
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Culhane AC, Schröder MS, Sultana R, Picard SC, Martinelli EN, Kelly C, Haibe-Kains B, Kapushesky M, St Pierre AA, Flahive W, Picard KC, Gusenleitner D, Papenhausen G, O'Connor N, Correll M, Quackenbush J. GeneSigDB: a manually curated database and resource for analysis of gene expression signatures. Nucleic Acids Res 2011; 40:D1060-6. [PMID: 22110038 PMCID: PMC3245038 DOI: 10.1093/nar/gkr901] [Citation(s) in RCA: 84] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
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.
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