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

Dokyoon Kim, Ruowang Li, Scott M. Dudek, John R. Wallace, Marylyn D. Ritchie

Research output: Contribution to journalConference articlepeer-review

12 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)96-107
Number of pages12
JournalPacific Symposium on Biocomputing
StatePublished - 2015
Event20th Pacific Symposium on Biocomputing, PSB 2015 - Big Island, United States
Duration: Jan 4 2015Jan 8 2015

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Computational Theory and Mathematics

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