Denoising perturbation signatures reveal an actionable AKT-signaling gene module underlying a poor clinical outcome in endocrine-treated ER+ breast cancer.

ABSTRACT:

Background

Databases of perturbation gene expression signatures and drug sensitivity provide a powerful framework to develop personalized medicine approaches, by helping to identify actionable genomic markers and subgroups of patients who may benefit from targeted treatments.

Results

Here we use a perturbation expression signature database encompassing perturbations of over 90 cancer genes, in combination with a large breast cancer expression dataset and a novel statistical denoising algorithm, to help discern cancer perturbations driving most of the variation in breast cancer gene expression. Clustering estrogen receptor positive cancers over the perturbation activity scores recapitulates known luminal subtypes. Analysis of individual activity scores enables identification

SUBMITTER: Teschendorff AE 

PROVIDER: S-EPMC4399757 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature