Toward precision breast cancer survival prediction utilizing combined whole genome-wide expression and somatic mutation analysis

Abstract

Breast cancer is the most common type of invasive cancer in females. It accounts for 18.2% of all cancer deaths worldwide. Although somatic mutations play important roles in cancer development and prognosis, the outcome predictions are largely based on the expression of marker genes. We submit that developing an innovative prognostic model incorporating somatic mutations with gene expression can improve survival prediction of cancer. We studied the whole genome-wide gene expression and somatic mutations of 1091 breast invasive carcinoma cases from The Cancer Genome Atlas (TCGA). We identified expression of 118 genes that could be used to build the predictor for breast cancer survival risks (log rank p < 0.0001 and c-index=0.63627122). Multiple breast cancer survival-analysis-related genes are found in this gene set, such as FOXR2, FOXD1, MTNR1B, SDC1, PF4, IGF2BP1, ZIC3, OXT2, and PID1. We then selected between different survival-risk groups of 2000 mutated genes with different mutation rates. We applied enrichment analysis to the mutated gene list and identified 25 functional annotations, 15 gene ontology, and 14 gene pathway enriched terms that were related the cancer outcomes. We built the novel predictor and our results showed that combining the different features helps improved performance of survival prediction (c-index = 0. 64033769). Thus our model can be used to facilitate the advancement of our going precision medicine research (http://americancse.org/events/csce2017/keynotes_lectures/yang_talk).