Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas
Precision oncology uses genomic evidence to match patients with treatment but often fails to
identify all patients who may respond. The transcriptome of these “hidden responders” may reveal
responsive molecular states. We describe and evaluate a machine-learning approach to classify
aberrant pathway activity in tumors, which may aid in hidden responder identification. The
algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across
The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in