A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma.

ABSTRACT:

Background

We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules.

Results

We developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments t

SUBMITTER: Way GP 

PROVIDER: S-EPMC5292791 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature