Identification of novel susceptibility loci and genes for prostate cancer risk: A transcriptome-wide association study in over 140,000 European descendants.

Genome-wide association studies have identified genetic variants associated with prostate cancer risk. However, these variants explain only a small fraction of the heritable component of prostate cancer risk, and the genes responsible for many of the identified associations remain unknown. To discover novel prostate cancer genetic loci and possible causal genes at previously identified risk loci, we performed a transcriptome-wide association study in 79,194 cases and 61,112 controls of European ancestry. Using data from the Genotype-Tissue Expression Project, we established genetic models to predict gene expression across the transcriptome for both prostate models and cross-tissue models and evaluated model performance using two independent datasets. We identified significant associations for 137 genes at P < 2.61×10-6, a Bonferroni-corrected threshold, including nine genes that remained significant at P < 2.61×10-6 after adjusting for all known prostate cancer risk variants in nearby regions. Of the 128 remaining associated genes, 94 have not yet been reported as potential target genes at known loci. We silenced 14 genes and many showed a consistent effect on viability and colony-forming efficiency in three cell lines. Our study provides substantial new information to advance our understanding of prostate cancer genetics and biology.

[1]  Chris Williams,et al.  RNA-SeQC: RNA-seq metrics for quality control and process optimization , 2012, Bioinform..

[2]  Hae Kyung Im,et al.  Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues , 2016, bioRxiv.

[3]  D. Stern,et al.  Transcriptional Profiles from Paired Normal Samples Offer Complementary Information on Cancer Patient Survival – Evidence from TCGA Pan-Cancer Data , 2016, Scientific Reports.

[4]  Hai Huang,et al.  Overexpression of USP39 predicts poor prognosis and promotes tumorigenesis of prostate cancer via promoting EGFR mRNA maturation and transcription elongation , 2016, Oncotarget.

[5]  P. Visscher,et al.  Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits , 2012, Nature Genetics.

[6]  Alexander Gusev,et al.  Large-scale transcriptome-wide association study identifies new prostate cancer risk regions , 2018, Nature Communications.

[7]  ENCODEConsortium,et al.  An Integrated Encyclopedia of DNA Elements in the Human Genome , 2012, Nature.

[8]  D. Hinds,et al.  Gene‐based analysis of regulatory variants identifies 4 putative novel asthma risk genes related to nucleotide synthesis and signaling , 2017, The Journal of allergy and clinical immunology.

[9]  Alan M. Kwong,et al.  A reference panel of 64,976 haplotypes for genotype imputation , 2015, Nature Genetics.

[10]  K. Thor Serotonin and norepinephrine involvement in efferent pathways to the urethral rhabdosphincter: implications for treating stress urinary incontinence. , 2003, Urology.

[11]  菊池 章史 Identification of NUCKS1 as a colorectal cancer prognostic marker through integrated expression and copy number analysis , 2012 .

[12]  R. Durbin,et al.  Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses , 2012, Nature Protocols.

[13]  S. Barnhill,et al.  A Four-Gene Expression Signature for Prostate Cancer Cells Consisting of UAP1, PDLIM5, IMPDH2, and HSPD1 , 2009 .

[14]  M. Sherman,et al.  Tumor Intrinsic Subtype Is Reflected in Cancer-Adjacent Tissue , 2014, Cancer Epidemiology, Biomarkers & Prevention.

[15]  Jeffery M. Meyer,et al.  A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer , 2018, Nature Genetics.

[16]  Asha A. Nair,et al.  Identification of candidate genes for prostate cancer-risk SNPs utilizing a normal prostate tissue eQTL data set , 2015, Nature Communications.

[17]  O. Delaneau,et al.  A linear complexity phasing method for thousands of genomes , 2011, Nature Methods.

[18]  Kaanan P. Shah,et al.  A gene-based association method for mapping traits using reference transcriptome data , 2015, Nature Genetics.

[19]  G. Kempermann Faculty Opinions recommendation of Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. , 2015 .

[20]  Peter Kraft,et al.  A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer , 2014, Nature Genetics.

[21]  M. Peckham,et al.  Specific Myosins Control Actin Organization, Cell Morphology, and Migration in Prostate Cancer Cells , 2015, Cell reports.

[22]  James L. Gulley,et al.  New gene expressed in prostate: a potential target for T cell-mediated prostate cancer immunotherapy , 2009, Cancer Immunology, Immunotherapy.

[23]  Jinjian Yang,et al.  Chloride intracellular channel 1 regulates prostate cancer cell proliferation and migration through the MAPK/ERK pathway. , 2014, Cancer biotherapy & radiopharmaceuticals.

[24]  Wei Zheng,et al.  Large-scale transcriptome-wide association study identifies new prostate cancer risk regions , 2018 .

[25]  Byungkook Lee,et al.  NGEP, a gene encoding a membrane protein detected only in prostate cancer and normal prostate. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Matthew R. Cooperberg,et al.  Epidemiology of prostate cancer , 2017, World Journal of Urology.

[27]  Peter Kraft,et al.  Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array , 2013, Nature Genetics.

[28]  T. Lehtimäki,et al.  Integrative approaches for large-scale transcriptome-wide association studies , 2015, Nature Genetics.

[29]  Yu Shyr,et al.  RNA interference (RNAi) screening approach identifies agents that enhance paclitaxel activity in breast cancer cells , 2010, Breast Cancer Research.

[30]  F. Jiang,et al.  Small nucleolar RNA 42 acts as an oncogene in lung tumorigenesis , 2011, Oncogene.

[31]  B. Stranger,et al.  Expression QTL-based analyses reveal candidate causal genes and loci across five tumor types. , 2014, Human molecular genetics.

[32]  Henry W. Long,et al.  Atlas of prostate cancer heritability in European and African-American men pinpoints tissue-specific regulation , 2016, Nature Communications.

[33]  T. Sellers,et al.  Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans , 2015, Human molecular genetics.

[34]  Hua Shen,et al.  MicroRNA-137 inhibits tumor growth and sensitizes chemosensitivity to paclitaxel and cisplatin in lung cancer , 2016, Oncotarget.

[35]  Dorota H. Sendorek,et al.  Modulation of long noncoding RNAs by risk SNPs underlying genetic predispositions to prostate cancer , 2016, Nature Genetics.

[36]  P. Donnelly,et al.  A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies , 2009, PLoS genetics.

[37]  Kap-Seok Yang,et al.  Identification of NUCKS1 as a putative oncogene and immunodiagnostic marker of hepatocellular carcinoma. , 2016, Gene.

[38]  Hiroshi Tanaka,et al.  Identification of NUCKS1 as a colorectal cancer prognostic marker through integrated expression and copy number analysis , 2013, International journal of cancer.

[39]  Ramana V. Davuluri,et al.  Identification and validation of regulatory SNPs that modulate transcription factor chromatin binding and gene expression in prostate cancer , 2016, Oncotarget.

[40]  O. Delaneau,et al.  Estimating the causal tissues for complex traits and diseases , 2016, Nature Genetics.

[41]  Nicola J. Rinaldi,et al.  Genetic effects on gene expression across human tissues , 2017, Nature.

[42]  P. Visscher,et al.  Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets , 2016, Nature Genetics.

[43]  Henry W. Long,et al.  Integration of multiethnic fine-mapping and genomic annotation to prioritize candidate functional SNPs at prostate cancer susceptibility regions. , 2015, Human molecular genetics.

[44]  G. Lou,et al.  NUCKS1 overexpression is a novel biomarker for recurrence-free survival in cervical squamous cell carcinoma , 2014, Tumor Biology.

[45]  Todd L Edwards,et al.  Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics , 2018, Nature Communications.

[46]  Jian Yang,et al.  Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits , 2016, Genome Medicine.

[47]  A. Jemal,et al.  Cancer statistics, 2019 , 2019, CA: a cancer journal for clinicians.

[48]  D. Zheng,et al.  Characterization of Human Pseudogene-Derived Non-Coding RNAs for Functional Potential , 2014, PloS one.

[49]  Michael R. Green,et al.  Transcriptional regulatory elements in the human genome. , 2006, Annual review of genomics and human genetics.

[50]  Jesse R. Dixon,et al.  Topological Domains in Mammalian Genomes Identified by Analysis of Chromatin Interactions , 2012, Nature.

[51]  I. Pastan,et al.  NGEP, a prostate-specific plasma membrane protein that promotes the association of LNCaP cells. , 2007, Cancer research.

[52]  Peter Kraft,et al.  Association of Prostate Cancer Risk Variants with Gene Expression in Normal and Tumor Tissue , 2014, Cancer Epidemiology, Biomarkers & Prevention.

[53]  J. Stanford,et al.  Genetic predisposition to prostate cancer: Update and future perspectives. , 2015, Urologic oncology.

[54]  K. D. Sørensen,et al.  Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci , 2018, Nature Genetics.

[55]  G. Bejerano,et al.  Enhancers: five essential questions , 2013, Nature Reviews Genetics.

[56]  S. Thibodeau,et al.  Chromatin interactions and candidate genes at ten prostate cancer risk loci , 2016, Scientific Reports.