A gene signature based method for identifying subtypes and subtype-specific drivers in cancer with an application to medulloblastoma.

ABSTRACT:

Background

Subtypes are widely found in cancer. They are characterized with different behaviors in clinical and molecular profiles, such as survival rates, gene signature and copy number aberrations (CNAs). While cancer is generally believed to have been caused by genetic aberrations, the number of such events is tremendous in the cancer tissue and only a small subset of them may be tumorigenic. On the other hand, gene expression signature of a subtype represents residuals of the subtype-specific cancer mechanisms. Using high-throughput data to link these factors to define subtype boundaries and identify subtype-specific drivers, is a promising yet largely unexplored topic.

Results

We report a systematic method to automate the identification of cancer subtypes and candidate

SUBMITTER: Chen P 

PROVIDER: S-EPMC3820164 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature