Binning somatic mutations based on biological knowledge for predicting survival: an application in renal cell carcinoma

Pac Symp Biocomput. 2015;96-107.

Abstract

Enormous efforts of whole exome and genome sequencing from hundreds to thousands of patients have provided the landscape of somatic genomic alterations in many cancer types to distinguish between driver mutations and passenger mutations. Driver mutations show strong associations with cancer clinical outcomes such as survival. However, due to the heterogeneity of tumors, somatic mutation profiles are exceptionally sparse whereas other types of genomic data such as miRNA or gene expression contain much more complete data for all genomic features with quantitative values measured in each patient. To overcome the extreme sparseness of somatic mutation profiles and allow for the discovery of combinations of somatic mutations that may predict cancer clinical outcomes, here we propose a new approach for binning somatic mutations based on existing biological knowledge. Through the analysis using renal cell carcinoma dataset from The Cancer Genome Atlas (TCGA), we identified combinations of somatic mutation burden based on pathways, protein families, evolutionary conversed regions, and regulatory regions associated with survival. Due to the nature of heterogeneity in cancer, using a binning strategy for somatic mutation profiles based on biological knowledge will be valuable for improved prognostic biomarkers and potentially for tailoring therapeutic strategies by identifying combinations of driver mutations.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers, Tumor / genetics
  • Carcinoma, Renal Cell / genetics*
  • Carcinoma, Renal Cell / mortality
  • Computational Biology
  • Databases, Genetic
  • Humans
  • Kidney Neoplasms / genetics*
  • Kidney Neoplasms / mortality
  • Models, Genetic
  • Mutation*
  • Neural Networks, Computer
  • Prognosis
  • Survival Analysis

Substances

  • Biomarkers, Tumor