Pleiotropy-guided transcriptome imputation from normal and tumor tissues identifies candidate susceptibility genes for breast and ovarian cancer

Abstract

Summary Familial, sequencing, and genome-wide association studies (GWASs) and genetic correlation analyses have progressively unraveled the shared or pleiotropic germline genetics of breast and ovarian cancer. In this study, we aimed to leverage this shared germline genetics to improve the power of transcriptome-wide association studies (TWASs) to identify candidate breast cancer and ovarian cancer susceptibility genes. We built gene expression prediction models using the PrediXcan method in 681 breast and 295 ovarian tumors from The Cancer Genome Atlas and 211 breast and 99 ovarian normal tissue samples from the Genotype-Tissue Expression project and integrated these with GWAS meta-analysis data from the Breast Cancer Association Consortium (122,977 cases/105,974 controls) and the Ovarian Cancer Association Consortium (22,406 cases/40,941 controls). The integration was achieved through application of a pleiotropy-guided conditional/conjunction false discovery rate (FDR) approach in the setting of a TWASs. This identified 14 candidate breast cancer susceptibility genes spanning 11 genomic regions and 8 candidate ovarian cancer susceptibility genes spanning 5 genomic regions at conjunction FDR < 0.05 that were >1 Mb away from known breast and/or ovarian cancer susceptibility loci. We also identified 38 candidate breast cancer susceptibility genes and 17 candidate ovarian cancer susceptibility genes at conjunction FDR < 0.05 at known breast and/or ovarian susceptibility loci. The 22 genes identified by our cross-cancer analysis represent promising candidates that further elucidate the role of the transcriptome in mediating germline breast and ovarian cancer risk.

chromMAGMA: regulatory element-centric interrogation of risk variants

“…Cis -expression quantitative trait loci (eQTL) and transcriptome-wide association studies (TWASs) leverage associations between risk SNP genotype data and gene expression to identify candidate genes associated with disease risk. To date, 26 candidate genes have been identified as HGSOC candidate risk genes using these methods ( 15 , 37 , 38 ); 16 out of 26 genes (62%) previously identified by HGSOC eQTL or TWAS analyses were also nominated by chromMAGMA ( Fig 2F ). chromMAGMA identified 105 additional genes previously not implicated in HGSOC risk.…”

Identification of a novel gene signature predicting response to first-line chemotherapy in BRCA wild-type high-grade serous ovarian cancer patients

“…Specifically, the spliceosome-associated factor CTNNBL1, has been related to proliferation and invasion in HGSOC, at least partially through regulation of FOXM1 splicing [ 60 ], Notably, the FOXM1 signaling pathway is activated in 84% high-grade serous ovarian cancer [ 61 ] and linked to platinum resistance [ 62 , 63 ]. Likewise, UQCC1, a protein involved in cytochrome b translation and/or stability, has been recently identified as a candidate ovarian cancer susceptibility gene [ 64 ]. Interestingly, it has been reported that UQCC1 physically interacts with CDR2 [ 65 ], a target antigen of naturally occurring human tumor immunity, widely expressed in the majority of ovarian cancer.…”

Cis- andtrans-eQTL TWAS of breast and ovarian cancer identify more than 100 risk associated genes in the BCAC and OCAC consortia

“…We adjusted raw transcript data for the effects of age, body mass index, top five principal components, and top probabilistic estimation of expression residuals (PEER) factors. In the gene expression data from mammary tissue, we also adjusted for Estrogen Receptor 1 ( ESR1) expression in accordance with previous studies 13,23 . This gene encodes estrogen receptor α, a transcription factor that plays a critical role in regulating gene expression and cell division in mammary glands 33 .…”

“…In light of established shared genetic etiology between breast cancer and ovarian cancer 13,[28][29][30][31][32] , here we apply BGW-TWAS to conduct TWAS of each disease (and various subtypes) that consider the regulatory activity of both distal and proximal germline variants for a target gene. We first constructed gene expression imputation models using BGW-TWAS in normal breast and ovarian tissue from the Genotype-Tissue Expression (GTEx) project and subsequently imputed GReX into large-scale GWAS summary data from the Breast Cancer Association Consortium (BCAC) and Ovarian Cancer Association Consortium (OCAC) to identify genes associated with risk of overall breast cancer, five breast cancer subtypes (luminal A-like, luminal B-like, luminal B/HER2-negative-like, HER2-enriched-like, triple-negative), nonmucinous ovarian cancer, and five ovarian cancer subtypes (high grade serous, low grade serous, endometrioid, mucinous, clear cell).…”

“…Further research has helped define risk variants that are unique to distinct subtypes of breast cancer (for example, hormone receptor positive tumors) and ovarian cancer (for example, high grade and low grade serous histotypes) 5,6,8,10,11 . While pleiotropic and subtype-specific GWAS have helped delineate the germline genetic architecture of these cancers, most GWAS-derived risk variants for complex traits lie in non-coding regions of the genome 12,13 . This suggests that considerable disease risk may stem from variation in regulatory elements that affect gene transcription 14 .…”