Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations

We report a meta-analysis of breast, prostate, ovarian, and endometrial cancer genome-wide association data (effective sample size: 237,483 cases/317,006 controls). This identified 465 independent lead variants (P<5×10−8) across 192 genomic regions. Four lead variants were >1Mb from previously identified risk loci for the four cancers and an additional 23 lead variant-cancer associations were novel for one of the cancers. Bayesian models supported pleiotropic effects involving at least two cancers at 222/465 lead variants in 118/192 regions. Gene-level association analysis identified 13 shared susceptibility genes (P<2.6×10−6) in 13 regions not previously implicated in any of the four cancers and not uncovered by our variant-level meta-analysis. Several lead variants had opposite effects across cancers, including a cluster of such variants in the TP53 pathway. Fifty-four lead variants were associated with blood cell traits and suggested genetic overlaps with clonal hematopoiesis. Our study highlights the remarkable pervasiveness of pleiotropy across hormone-related cancers, further illuminating their shared genetic and mechanistic origins at variant- and gene-level resolution.

[1]  J. Witte,et al.  Telomere structure and maintenance gene variants and risk of five cancer types , 2016, International journal of cancer.

[2]  Eleazar Eskin,et al.  Increasing the power of meta-analysis of genome-wide association studies to detect heterogeneous effects , 2017, Bioinform..

[3]  A. Moustakas,et al.  Sp1 Plays a Critical Role in the Transcriptional Activation of the Human Cyclin-dependent Kinase Inhibitor p21 WAF1/Cip1 Gene by the p53 Tumor Suppressor Protein* , 2001, The Journal of Biological Chemistry.

[4]  S. Cummings,et al.  Bone Mineral Density and Risk of Breast Cancer in Older Women: The Study of Osteoporotic Fractures , 1996 .

[5]  David M. Evans,et al.  Life-Course Genome-wide Association Study Meta-analysis of Total Body BMD and Assessment of Age-Specific Effects. , 2018, American journal of human genetics.

[6]  Eleazar Eskin,et al.  Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. , 2011, American journal of human genetics.

[7]  M. Pike,et al.  The dose-effect relationship between 'unopposed' oestrogens and endometrial mitotic rate: its central role in explaining and predicting endometrial cancer risk. , 1988, British Journal of Cancer.

[8]  R. Eeles,et al.  A Review of Prostate Cancer Genome-Wide Association Studies (GWAS) , 2018, Cancer Epidemiology, Biomarkers & Prevention.

[9]  N. Laird,et al.  Meta-analysis in clinical trials. , 1986, Controlled clinical trials.

[10]  H. Walczak,et al.  Exploring the TRAILs less travelled: TRAIL in cancer biology and therapy , 2017, Nature Reviews Cancer.

[11]  Eleazar Eskin,et al.  A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping. , 2013, Human molecular genetics.

[12]  Michael Jones,et al.  Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer , 2017, Nature Genetics.

[13]  P. Visscher,et al.  Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry , 2018, bioRxiv.

[14]  I. Borecki,et al.  A correlated meta-analysis strategy for data mining "OMIC" scans. , 2012, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[15]  Xiaoting Chen,et al.  Genetic Associations with Gestational Length and Spontaneous Preterm Birth , 2017, The New England journal of medicine.

[16]  Gabor T. Marth,et al.  A global reference for human genetic variation , 2015, Nature.

[17]  Erdogan Taskesen,et al.  Functional mapping and annotation of genetic associations with FUMA , 2017, Nature Communications.

[18]  R. Drapkin,et al.  It's Totally Tubular....Riding The New Wave of Ovarian Cancer Research. , 2016, Cancer research.

[19]  D. Noh,et al.  Identification of novel breast cancer susceptibility loci in meta-analyses conducted among Asian and European descendants , 2020, Nature Communications.

[20]  G. Selivanova,et al.  Integrated high-throughput analysis identifies Sp1 as a crucial determinant of p53-mediated apoptosis , 2014, Cell Death and Differentiation.

[21]  C. Vachon,et al.  Common Genetic Variation and Breast Cancer Risk—Past, Present, and Future , 2018, Cancer Epidemiology, Biomarkers & Prevention.

[22]  M. Cole MYC association with cancer risk and a new model of MYC-mediated repression. , 2014, Cold Spring Harbor perspectives in medicine.

[23]  H. Hakonarson,et al.  ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data , 2010, Nucleic acids research.

[24]  Tsutomu Ohta,et al.  PH Domain-Only Protein PHLDA3 Is a p53-Regulated Repressor of Akt , 2009, Cell.

[25]  S. Mccarroll,et al.  Monogenic and polygenic inheritance become instruments for clonal selection , 2019, bioRxiv.

[26]  Steven Gallinger,et al.  Cross-Cancer Genome-Wide Analysis of Lung, Ovary, Breast, Prostate, and Colorectal Cancer Reveals Novel Pleiotropic Associations. , 2016, Cancer research.

[27]  P. Visscher,et al.  Dissection of genetic variation and evidence for pleiotropy in male pattern baldness , 2018, Nature Communications.

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

[29]  Vincent H. Everett,et al.  Meta-analysis of Immunochip data of four autoimmune diseases reveals novel single-disease and cross-phenotype associations , 2018, Genome Medicine.

[30]  Jeremy Schwartzentruber,et al.  Whole genome sequencing and imputation in isolated populations identify genetic associations with medically-relevant complex traits , 2017, Nature Communications.

[31]  Kari Stefansson,et al.  A germline variant in the TP53 polyadenylation signal confers cancer susceptibility , 2011, Nature Genetics.

[32]  Embrace,et al.  Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus , 2016 .

[33]  M. Stephens,et al.  Bayesian multivariate reanalysis of large genetic studies identifies many new associations , 2019, bioRxiv.

[34]  A. Jemal,et al.  Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.

[35]  D. Rickman,et al.  The Expanding World of N-MYC-Driven Tumors. , 2018, Cancer discovery.

[36]  J. Weinstein,et al.  Integrated Analysis of TP53 Gene and Pathway Alterations in The Cancer Genome Atlas. , 2019, Cell reports.

[37]  A. Venkitaraman Cancer Suppression by the Chromosome Custodians, BRCA1 and BRCA2 , 2014, Science.

[38]  Nicholas Eriksson,et al.  Germ line variants predispose to both JAK2 V617F clonal hematopoiesis and myeloproliferative neoplasms. , 2016, Blood.

[39]  K. Khanna,et al.  Cdk1/Erk2- and Plk1-dependent phosphorylation of a centrosome protein, Cep55, is required for its recruitment to midbody and cytokinesis. , 2005, Developmental cell.

[40]  Sina A. Gharib,et al.  Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis , 2018, bioRxiv.

[41]  Sandra L. Halverson,et al.  Fine-scale mapping of the 5q11.2 breast cancer locus reveals at least three independent risk variants regulating MAP3K1. , 2015, American journal of human genetics.

[42]  Gary D Bader,et al.  Association analysis identifies 65 new breast cancer risk loci , 2017, Nature.

[43]  Matti Pirinen,et al.  Bayesian meta-analysis across genome-wide association studies of diverse phenotypes , 2018, bioRxiv.

[44]  Abctb Investigators,et al.  Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses. , 2020 .

[45]  K. Khanna,et al.  Beyond cytokinesis: the emerging roles of CEP55 in tumorigenesis , 2016, Oncogene.

[46]  Peter Kraft,et al.  Identification of nine new susceptibility loci for endometrial cancer , 2018, Nature Communications.

[47]  V. Beral,et al.  Endometrial cancer and hormone-replacement therapy in the Million Women Study. , 2005, Lancet.

[48]  P. Crosignani Breast cancer and hormone-replacement therapy in the Million Women Study. , 2003, Maturitas.

[49]  V. Beral Ovarian cancer and hormone replacement therapy in the Million Women Study , 2007, The Lancet.

[50]  Stephen Burgess,et al.  PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations , 2019, Bioinform..

[51]  Jing Wang,et al.  WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs , 2019, Nucleic Acids Res..

[52]  R. Scharpf,et al.  High grade serous ovarian carcinomas originate in the fallopian tube , 2017, Nature Communications.

[53]  Andrew D. Johnson,et al.  Parent-of-origin specific allelic associations among 106 genomic loci for age at menarche , 2014, Nature.

[54]  S. Cummings,et al.  Bone mineral density and risk of breast cancer in older women: the study of osteoporotic fractures. Study of Osteoporotic Fractures Research Group. , 1996, JAMA.

[55]  R. Nelson,et al.  Bone mineral density and the subsequent risk of cancer in the NHANES I follow-up cohort , 2002, BMC Cancer.

[56]  Lara E Sucheston-Campbell,et al.  Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer , 2017, Nature Genetics.

[57]  William J. Astle,et al.  The Polygenic and Monogenic Basis of Blood Traits and Diseases , 2020, Cell.

[58]  Mitchell J. Machiela,et al.  LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants , 2015, Bioinform..

[59]  J. Shendure,et al.  A general framework for estimating the relative pathogenicity of human genetic variants , 2014, Nature Genetics.

[60]  Minoru Kanehisa,et al.  New approach for understanding genome variations in KEGG , 2018, Nucleic Acids Res..

[61]  Jacob C. Ulirsch,et al.  Genetic predisposition to mosaic Y chromosome loss in blood , 2019, bioRxiv.

[62]  Peter Kraft,et al.  Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types. , 2016, Cancer discovery.

[63]  William J. Astle,et al.  Allelic Landscape of Human Blood Cell Trait Variation and Links , 2016 .

[64]  Eurie L. Hong,et al.  Annotation of functional variation in personal genomes using RegulomeDB , 2012, Genome research.

[65]  M. Daly,et al.  Genetic predisposition to myeloproliferative neoplasms implicates hematopoietic stem cell biology , 2019, bioRxiv.

[66]  Anna Zhukova,et al.  Modeling sample variables with an Experimental Factor Ontology , 2010, Bioinform..

[67]  Christopher D. Brown,et al.  The GTEx Consortium atlas of genetic regulatory effects across human tissues , 2019, Science.

[68]  H. Stefánsson,et al.  Large-scale genome-wide association meta-analysis of endometriosis reveals 13 novel loci and genetically-associated comorbidity with other pain conditions , 2018, bioRxiv.

[69]  K. Khanna,et al.  Cep55 overexpression promotes genomic instability and tumorigenesis in mice , 2019, Communications Biology.

[70]  Manolis Kellis,et al.  ChromHMM: automating chromatin-state discovery and characterization , 2012, Nature Methods.

[71]  Jack A. Taylor,et al.  Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses , 2019, bioRxiv.

[72]  Joris M. Mooij,et al.  MAGMA: Generalized Gene-Set Analysis of GWAS Data , 2015, PLoS Comput. Biol..

[73]  Scott W. Lowe,et al.  Putting p53 in Context , 2017, Cell.

[74]  J. Cauley Estrogen and bone health in men and women , 2015, Steroids.

[75]  A. Ridley,et al.  Rho GTPases in cancer cell biology , 2008, FEBS letters.

[76]  Valerie Beral,et al.  Breast cancer and hormone-replacement therapy in the Million Women Study , 2003, The Lancet.

[77]  Paul Tempst,et al.  Regulation of p53 activity through lysine methylation , 2004, Nature.

[78]  Hunna J. Watson,et al.  Genome wide meta-analysis identifies genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders , 2019, bioRxiv.

[79]  Michael Q. Zhang,et al.  Integrative analysis of 111 reference human epigenomes , 2015, Nature.

[80]  D. Kiel,et al.  Bone mass and the risk of prostate cancer: the Framingham Study. , 2002, The American journal of medicine.