Co-regulatory expression quantitative trait loci mapping: method and application to endometrial cancer

BackgroundExpression quantitative trait loci (eQTL) studies have helped identify the genetic determinants of gene expression. Understanding the potential interacting mechanisms underlying such findings, however, is challenging.MethodsWe describe a method to identify the trans-acting drivers of multiple gene co-expression, which reflects the action of regulatory molecules. This method-termed co-regulatory expression quantitative trait locus (creQTL) mapping-allows for evaluation of a more focused set of phenotypes within a clear biological context than conventional eQTL mapping.ResultsApplying this method to a study of endometrial cancer revealed regulatory mechanisms supported by the literature: a creQTL between a locus upstream of STARD13/DLC2 and a group of seven IFNβ-induced genes. This suggests that the Rho-GTPase encoded by STARD13 regulates IFNβ-induced genes and the DNA damage response.ConclusionsBecause of the importance of IFNβ in cancer, our results suggest that creQTL may provide a finer picture of gene regulation and may reveal additional molecular targets for intervention. An open source R implementation of the method is available at http://sites.google.com/site/kenkompass/.

[1]  I. Ng,et al.  Deleted in Liver Cancer (DLC) 2 Encodes a RhoGAP Protein with Growth Suppressor Function and Is Underexpressed in Hepatocellular Carcinoma* , 2003, The Journal of Biological Chemistry.

[2]  Eric P. Xing,et al.  A multivariate regression approach to association analysis of a quantitative trait network , 2008, Bioinform..

[3]  R. Shamir,et al.  Understanding Gene Sequence Variation in the Context of Transcription Regulation in Yeast , 2010, PLoS genetics.

[4]  Ting Wang,et al.  Combining phylogenetic data with co-regulated genes to identify regulatory motifs , 2003, Bioinform..

[5]  Kei Kuratomi,et al.  Growth Inhibitory Effects of IFN-β on Human Liver Cancer Cells In Vitro and In Vivo , 2007 .

[6]  John D. Storey,et al.  Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis , 2007, PLoS genetics.

[7]  Ross Ihaka,et al.  Gentleman R: R: A language for data analysis and graphics , 1996 .

[8]  A. Owen,et al.  A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae) , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[9]  J. L. Tomsig,et al.  Copines: a ubiquitous family of Ca2+-dependent phospholipid-binding proteins , 2002, Cellular and Molecular Life Sciences CMLS.

[10]  E. Xing,et al.  Statistical Estimation of Correlated Genome Associations to a Quantitative Trait Network , 2009, PLoS genetics.

[11]  D. Pe’er,et al.  Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.

[12]  Michael Foley,et al.  THE TRANSCRIPTION FACTOR SOX 11 IS A PROGNOSTIC FACTOR FOR IMPROVED RECURRENCE FREE SURVIVAL IN EPITHELIAL OVARIAN CANCER , 2008 .

[13]  Chenwei Wang,et al.  Analysis of the matrix metalloproteinase family reveals that MMP8 is often mutated in melanoma , 2009, Nature Genetics.

[14]  L. Frati,et al.  Minimal residual disease in metastatic breast cancer: treatment with IFN-beta, retinoids, and tamoxifen. , 1998, Journal of interferon & cytokine research : the official journal of the International Society for Interferon and Cytokine Research.

[15]  Hemant Ishwaran,et al.  An interferon-related gene signature for DNA damage resistance is a predictive marker for chemotherapy and radiation for breast cancer , 2008, Proceedings of the National Academy of Sciences.

[16]  I. Ng,et al.  Deleted in liver cancer 2 (DLC2) suppresses cell transformation by means of inhibition of RhoA activity. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[17]  D. Pe’er,et al.  Identifying regulatory mechanisms using individual variation reveals key role for chromatin modification , 2006, Proceedings of the National Academy of Sciences.

[18]  Elin Gustavsson,et al.  Strong lymphoid nuclear expression of SOX11 transcription factor defines lymphoblastic neoplasms, mantle cell lymphoma and Burkitt’s lymphoma , 2009, Haematologica.

[19]  Shu-Chun Lin,et al.  Copy number amplification of 3q26–27 oncogenes in microdissected oral squamous cell carcinoma and oral brushed samples from areca chewers , 2005, The Journal of pathology.

[20]  J. Mosser,et al.  Integrative genome‐wide analysis reveals a robust genomic glioblastoma signature associated with copy number driving changes in gene expression , 2009, Genes, Chromosomes and Cancer.

[21]  R. Ohno,et al.  Effect of recombinant human tumor necrosis factor on the colony growth of human leukemia progenitor cells and normal hematopoietic progenitor cells. , 1987, Blood.

[22]  M. Mayo,et al.  Copine-I represses NF-κB transcription by endoproteolysis of p65 , 2008, Oncogene.

[23]  Kwok-Kin Wong,et al.  Differential roles of telomere attrition in type I and II endometrial carcinogenesis. , 2008, The American journal of pathology.

[24]  U. Certa,et al.  Antiproliferative Activity of the Human IFN-α-Inducible Protein IFI44 , 2007 .

[25]  David A. Drubin,et al.  Learning a Prior on Regulatory Potential from eQTL Data , 2009, PLoS genetics.

[26]  R. Karlsson,et al.  Rho GTPase function in tumorigenesis. , 2009, Biochimica et biophysica acta.

[27]  Li Mao,et al.  Transcriptomic dissection of tongue squamous cell carcinoma , 2008, BMC Genomics.

[28]  Nam Huh,et al.  Prediction of Recurrence-Free Survival in Postoperative Non–Small Cell Lung Cancer Patients by Using an Integrated Model of Clinical Information and Gene Expression , 2008, Clinical Cancer Research.

[29]  S. Bogaerts,et al.  Nuclear translocation of alphaN-catenin by the novel zinc finger transcriptional repressor ZASC1. , 2005, Experimental cell research.

[30]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[31]  A. Fader,et al.  Endometrial cancer and obesity: epidemiology, biomarkers, prevention and survivorship. , 2009, Gynecologic oncology.

[32]  D. Gifford,et al.  Tissue-specific transcriptional regulation has diverged significantly between human and mouse , 2007, Nature Genetics.

[33]  James B. Brown,et al.  Developmental roles of 21 Drosophila transcription factors are determined by quantitative differences in binding to an overlapping set of thousands of genomic regions , 2009, Genome Biology.

[34]  Sangsoo Kim,et al.  Gene expression Differential coexpression analysis using microarray data and its application to human cancer , 2005 .

[35]  A. Tardón,et al.  Polymorphism +17 C/G in Matrix Metalloprotease MMP8 decreases lung cancer risk , 2008, BMC Cancer.

[36]  H. Stefánsson,et al.  Genetics of gene expression and its effect on disease , 2008, Nature.

[37]  Yang Huang,et al.  Graph theoretical approach to study eQTL: a case study of Plasmodium falciparum , 2009, Bioinform..

[38]  M. Uittenbogaard,et al.  Expression of the bHLH transcription factor Tcf12 (ME1) gene is linked to the expansion of precursor cell populations during neurogenesis. , 2002, Brain research. Gene expression patterns.

[39]  George M. Church,et al.  Biclustering of Expression Data , 2000, ISMB.

[40]  Rainer Spang,et al.  Finding disease specific alterations in the co-expression of genes , 2004, ISMB/ECCB.

[41]  Kei Kuratomi,et al.  Growth inhibitory effects of IFN-beta on human liver cancer cells in vitro and in vivo. , 2007, Journal of interferon & cytokine research : the official journal of the International Society for Interferon and Cytokine Research.

[42]  J. Castle,et al.  An integrative genomics approach to infer causal associations between gene expression and disease , 2005, Nature Genetics.

[43]  S. Möller,et al.  Time course transcriptomics of IFNB1b drug therapy in multiple sclerosis , 2010, Autoimmunity.

[44]  Jeffrey T Leek,et al.  On the design and analysis of gene expression studies in human populations , 2007, Nature Genetics.

[45]  Wojtek J. Krzanowski,et al.  Biclustering models for structured microarray data , 2005, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[46]  N. Popescu,et al.  Expression profile of the tumor suppressor genes DLC-1 and DLC-2 in solid tumors. , 2006, International journal of oncology.

[47]  J. Nap,et al.  Genetical genomics: the added value from segregation. , 2001, Trends in genetics : TIG.

[48]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[49]  A. Ziogas,et al.  Enhanced Expression of Radiation-induced Leukocyte CDKN1A mRNA in Multiple Primary Breast Cancer Patients: Potential New Marker of Cancer Susceptibility , 2009, Biomarker insights.

[50]  T. Denecke,et al.  Long-Lasting Remission of Pulmonary Metastases of Renal Cell Cancer under IFN-b Therapy in a Patient with Multiple Sclerosis , 2006, Oncology Research and Treatment.

[51]  Sami Kilpinen,et al.  The transcription factor Sox11 is a prognostic factor for improved recurrence-free survival in epithelial ovarian cancer. , 2009, European journal of cancer.

[52]  Rachel B. Brem,et al.  Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors , 2003, Nature Genetics.

[53]  A. Alcina,et al.  OAS1 gene haplotype confers susceptibility to multiple sclerosis. , 2006, Tissue antigens.

[54]  S. Horvath,et al.  Weighted gene coexpression network analysis strategies applied to mouse weight , 2007, Mammalian Genome.

[55]  John D. Storey,et al.  Mapping gene expression quantitative trait loci by singular value decomposition and independent component analysis , 2008, BMC Bioinformatics.

[56]  Arcadi Navarro,et al.  Genome-wide scan of 500,000 single-nucleotide polymorphisms among responders and nonresponders to interferon beta therapy in multiple sclerosis. , 2009, Archives of neurology.

[57]  S. Horvath,et al.  Variations in DNA elucidate molecular networks that cause disease , 2008, Nature.

[58]  V. Van Belle,et al.  Plasma MMP1 and MMP8 expression in breast cancer: Protective role of MMP8 against lymph node metastasis , 2008, BMC Cancer.

[59]  Rafael A Irizarry,et al.  Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data. , 2006, Biostatistics.

[60]  L. Kruglyak,et al.  Genetic Dissection of Transcriptional Regulation in Budding Yeast , 2002, Science.

[61]  John D. Storey,et al.  Mapping the Genetic Architecture of Gene Expression in Human Liver , 2008, PLoS biology.

[62]  I. Jonassen,et al.  Integrated genomic profiling of endometrial carcinoma associates aggressive tumors with indicators of PI3 kinase activation , 2009, Proceedings of the National Academy of Sciences.

[63]  J. Inazawa,et al.  Identification of ZASC1 encoding a Krüppel-like zinc finger protein as a novel target for 3q26 amplification in esophageal squamous cell carcinomas. , 2003, Cancer research.

[64]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[65]  A. G. de la Fuente,et al.  Gene Network Inference via Structural Equation Modeling in Genetical Genomics Experiments , 2008, Genetics.

[66]  K. Lai,et al.  IFN-Induced Protein with Tetratricopeptide Repeats 2 Inhibits Migration Activity and Increases Survival of Oral Squamous Cell Carcinoma , 2008, Molecular Cancer Research.

[67]  Gabriele Schackert,et al.  Highly specific overexpression of the transcription factor SOX11 in human malignant gliomas. , 2005, Oncology reports.

[68]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[69]  J. Hofsteenge,et al.  Copine-III interacts with ErbB2 and promotes tumor cell migration , 2010, Oncogene.

[70]  K. Syrigos,et al.  Cell Adhesion Molecules: Role and Clinical Significance in Cancer , 2009, Cancer investigation.

[71]  Hideo Nakamura,et al.  Sox11 prevents tumorigenesis of glioma-initiating cells by inducing neuronal differentiation. , 2009, Cancer research.

[72]  S. Horvath,et al.  A General Framework for Weighted Gene Co-Expression Network Analysis , 2005, Statistical applications in genetics and molecular biology.

[73]  Joshua T. Burdick,et al.  Common genetic variants account for differences in gene expression among ethnic groups , 2007, Nature Genetics.

[74]  A. Owen,et al.  A gene recommender algorithm to identify coexpressed genes in C. elegans. , 2003, Genome research.

[75]  Y. Ushkaryov,et al.  The latrophilin family: multiply spliced G protein‐coupled receptors with differential tissue distribution , 1999, FEBS letters.

[76]  M. Bartlett.  Properties of Sufficiency and Statistical Tests , 1992 .