A Pan-Cancer and Polygenic Bayesian Hierarchical Model for the Effect of Somatic Mutations on Survival

TL;DR

This paper introduces a Bayesian hierarchical model that leverages data across multiple cancer types to assess how somatic mutations influence patient survival, identifying key mutations like TP53 and FAT4.

Contribution

It presents a novel pan-cancer Bayesian survival model that borrows information across cancer types and compares multiple parametric survival models for better prediction.

Findings

Log-normal model provided the best fit for survival data.

Mutations in TP53 and FAT4 are most predictive of survival.

Model validated through simulation to ensure accurate posterior coverage.

Abstract

We built a novel Bayesian hierarchical survival model based on the somatic mutation profile of patients across 50 genes and 27 cancer types. The pan-cancer quality allows for the model to "borrow" information across cancer types, motivated by the assumption that similar mutation profiles may have similar (but not necessarily identical) effects on survival across different tissues-of-origin or tumor types. The effect of a mutation at each gene was allowed to vary by cancer type while the mean effect of each gene was shared across cancers. Within this framework we considered four parametric survival models (normal, log-normal, exponential, and Weibull), and we compared their performance via a cross-validation approach in which we fit each model on training data and estimate the log-posterior predictive likelihood on test data. The log-normal model gave the best fit, and we investigated the partial effect of each gene on survival via a forward selection procedure. Through this we determined that mutations at TP53 and FAT4 were together the most useful for predicting patient survival. We validated the model via simulation to ensure that our algorithm for posterior computation gave nominal coverage rates. The code used for this analysis can be found at http://github.com/sarahsamorodnitsky/Pan-Cancer-Survival-Modeling , and the results are at http://ericfrazerlock.com/surv_figs/SurvivalDisplay.html .