Network-guided modeling allows tumor-type independent prediction of sensitivity to all-trans-retinoic acid.

ABSTRACT:

Background

All-trans-retinoic acid (ATRA) is a differentiating agent used in the treatment of acute-promyelocytic-leukemia (APL) and it is under-exploited in other malignancies despite its low systemic toxicity. A rational/personalized use of ATRA requires the development of predictive tools allowing identification of sensitive cancer types and responsive individuals.

Materials and methods

RNA-sequencing data for 10 080 patients and 33 different tumor types were derived from the TCGA and Leucegene datasets and completely re-processed. The study was carried out using machine learning methods and network analysis.

Results

We profiled a large panel of breast-cancer cell-lines for in vitro sensitivity to ATRA and exploited the associated basal gene-expression data to init

SUBMITTER: Bolis M 

PROVIDER: S-EPMC5834014 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature