Integrative analysis of somatic mutations and transcriptomic data to functionally stratify breast cancer patients.

ABSTRACT:

Background

Somatic mutations can be used as potential biomarkers for subtyping and predicting outcomes for cancer patients. However, cancer patients often carry many somatic mutations, which do not always concentrate on specific genomic loci, suggesting that the mutations may affect common pathways or gene interaction networks instead of common genes. The challenge is thus to identify the functional relationships among the mutations using multi-modal data. We developed a novel approach for integrating patient somatic mutation, transcriptome and clinical data to mine underlying functional gene groups that can be used to stratify cancer patients into groups with different clinical outcomes. Specifically, we use distance correlation metric to mine the correlations between expression p

SUBMITTER: Zhang J 

PROVIDER: S-EPMC5001235 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature