Data from Predicted proteome association studies of breast, prostate, ovarian, and endometrial cancers implicate plasma protein regulation in cancer susceptibility

Abstract

<div>Abstract<p>Background: Predicting protein levels from genotypes for proteome-wide association studies (PWAS) may provide insight to the mechanisms underlying cancer susceptibility. Methods: We performed PWAS of breast, endometrial, ovarian, and prostate cancers and their subtypes in several large European ancestry discovery consortia (effective sample size: 237,483 cases/317,006 controls) and tested the results for replication in an independent European ancestry GWAS (31,969 cases/410,350 controls). We performed PWAS using the cancer GWAS summary statistics and two sets of plasma protein prediction models, followed by colocalization analysis. Results: Using Atherosclerosis Risk in Communities (ARIC) models, we identified 93 protein-cancer associations (FDR < 0.05). We then performed a meta-analysis of the discovery and replication PWAS, resulting in 61 significant protein-cancer associations (FDR < 0.05). Ten of 15 protein-cancer pairs that could be tested using Trans-omics for Precision Medicine (TOPMed) protein prediction models replicated with the same directions of effect in both cancer GWAS (p<0.05). To further support our results, we applied Bayesian colocalization analysis and found colocalized SNPs for SERPINA3 protein levels and prostate cancer (Posterior Probability, PP=0.65) and SNUPN protein levels and breast cancer (PP=0.62). Conclusions: We used proteome-wide association studies (PWAS) to identify potential biomarkers of hormone-related cancer risk. SNPs in SERPINA3 and SNUPN did not reach genome-wide significance for cancer in the original GWAS, highlighting the power of PWAS for novel locus discovery, with the added advantage of providing directions of protein effect. Impact: PWAS and colocalization are promising methods to identify potential molecular mechanisms underlying complex traits.</p></div>