The Pan-Cancer Landscape of Prognostic Germline Variants in 10,582 Patients

Abstract

AbstractWhile clinical data provides physicians with information about patient prognosis, genomic data can further improve these predictions. We analyzed sequencing data from over 10,000 cancer patients and identified hundreds of prognostic germline variants using multivariate Cox regression models. These variants provide information about patient outcomes beyond clinical information currently in use and may augment clinical decisions based on expected tumor aggressiveness. Molecularly, at least twelve of the germline variants are likely associated with patient outcome through perturbation of protein structure and at least five through association with gene expression differences. About half of these germline variants are in previously reported tumor suppressors or oncogenes, with the other half pointing to loci of previously unstudied genes in the literature that should be further investigated for roles in cancers. Our results suggest that germline variation contributes to tumor progression across most cancers and contains patient outcome information not captured by clinical factors.