Abstract
BackgroundWhile clinical factors such as age, grade, stage, and histological subtype provide physicians with information about patient prognosis, genomic data can further improve these predictions. Previous studies have shown that germline variants in known cancer driver genes are predictive of patient outcome, but no study has systematically analyzed multiple cancers in an unbiased way to identify genetic loci that can improve patient outcome predictions made using clinical factors.MethodsWe analyzed sequencing data from the over 10,000 cancer patients available through The Cancer Genome Atlas to identify germline variants associated with patient outcome using multivariate Cox regression models.ResultsWe identified 79 prognostic germline variants in individual cancers and 112 prognostic germline variants in groups of cancers. The germline variants identified in individual cancers provide additional predictive power about patient outcomes beyond clinical information currently in use and may therefore augment clinical decisions based on expected tumor aggressiveness. Molecularly, at least 12 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. Almost half of these germline variants are in previously reported tumor suppressors, oncogenes or cancer driver genes with the other half pointing to genomic loci that should be further investigated for their roles in cancers.ConclusionsGermline variants are predictive of outcome in cancer patients and specific germline variants can improve patient outcome predictions beyond predictions made using clinical factors alone. The germline variants also implicate new means by which known oncogenes, tumor suppressor genes, and driver genes are perturbed in cancer and suggest roles in cancer for other genes that have not been extensively studied in oncology. Further studies in other cancer cohorts are necessary to confirm that germline variation is associated with outcome in cancer patients as this is a proof-of-principle study.
Highlights
While clinical factors such as age, grade, stage, and histological subtype provide physicians with information about patient prognosis, genomic data can further improve these predictions
Because the final variant call set was created by merging variant calls from whole-exome sequenced (WXS) normal tissue samples, WXS tumor samples, and RNA sequenced tumor samples, we evaluated our variant calls for contamination by somatic mutations or RNA editing
Determination of prognostic clinical models for each cancer To identify prognostic germline variants that provide additional outcome information not already captured by clinical variables, we created clinical models predictive of patient outcome for each cancer using the clinical information previously collected by the The Cancer Genome Atlas (TCGA) research network along with the components of calculated race from The Cancer Genome Ancestry Atlas
Summary
While clinical factors such as age, grade, stage, and histological subtype provide physicians with information about patient prognosis, genomic data can further improve these predictions. Somatic mutations in cancers have received substantial attention in oncology as they can be used to individualize drug selection [2, 3]. While much effort has been directed towards characterizing somatic mutations in cancer, recent studies suggest that germline variants have significant clinical utility. Germline variation can affect drug sensitivity, predict drug toxicity, and could help select therapy to minimize side-effects [14,15,16,17,18,19,20,21,22,23,24,25,26]. Some germline variants increase patient risk for specific somatic aberrations, suggesting that germline variation may impact disease course [27]
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