Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types

Abstract

Breast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identifi ed at P < 10 −8 seven new cross-cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/ BCL2L11 ; rs7937840/11q12/ INCENP ; rs1469713/19p13/ GATAD2A ), two breast and ovarian cancer risk loci (rs200182588/9q31/ SMC2 ; rs8037137/15q26/ RCCD1 ), and two breast and prostate cancer risk loci (rs5013329/1p34/ NSUN4 ; rs9375701/6q23/ L3MBTL3 ). Index variants in fi ve additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type-specifi c expression quantitative trait locus and enhancer-gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed signifi cant enrichment of death receptor signaling genes near loci with P < 10 −5 in the three-cancer meta-analysis. SIGNIFICANCE:We demonstrate that combining large-scale GWA meta-analysis fi ndings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identifi cation of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052-67.

Epidemiology of ovarian cancer: a review

“…Eighteen of these risk loci are associated with all and/or serous OC, five are associated with MOC risk, one is associated with ENOC, and one is associated with CCOC, exemplifying the genetic heterogeneity by histotype. In addition, a large-scale pooled analysis of genome-wide association studies of ovarian, breast, and prostate cancers identified five novel loci 81 . The identified common risk alleles account for approximately 4% of the polygenic risk in the European population and, taken together with high risk alleles, explain 40% of the heritability 82 .…”

omicsNPC: Applying the Non-Parametric Combination Methodology to the Integrative Analysis of Heterogeneous Omics Data

“…This indicates how integrating several data sources with OmicsNPC allows to retrieve findings that would not be identified by analyzing each omics dataset in isolation. Interestingly, an enrichment analysis performed on these 24 genes over the Disease Ontology of the OBO Foundry [46] shows that six out of the ten most enriched diseases are ovarian-related cancers (Table 1), a class of malignancies known to share similar hormonal [47] and genetic bases [48] with breast cancer.…”

Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls

“…From the 92 published BC-associated SNPs in 24 , only 51 SNPs existed in our SNP discovery set in both KBCP and OBCS sample sets. Recently, Michailidou et al .…”

“…We compared the proposed approach with a system trained on 51 known BC-associated SNPs 24,25 , a PRS-derived model and a number of conventional machine learning methods already used in GWAS to identify disease-associated SNPs. We then investigated the predictive potential of the identified SNPs in classifying ER status.…”