Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

The Cancer Genome Atlas Research Network

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

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 tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders. Way et al. develop a machine-learning approach using PanCanAtlas data to detect Ras activation in cancer. Integrating mutation, copy number, and expression data, the authors show that their method detects Ras-activating variants in tumors and sensitivity to MEK inhibitors in cell lines.

Original languageEnglish (US)
Pages (from-to)172-180.e3
JournalCell Reports
Volume23
Issue number1
DOIs
StatePublished - Apr 3 2018

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Atlases
Learning systems
Tumors
Genes
Chemical activation
Genome
Oncology
Mitogen-Activated Protein Kinase Kinases
Neoplasms
Cells
Transcriptome
Identification (control systems)
RNA
Cell Line
Mutation
Machine Learning

Keywords

  • drug sensitivity
  • Gene expression
  • HRAS
  • KRAS
  • machine learning
  • NF1
  • NRAS
  • pan-cancer
  • Ras
  • TCGA

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas. / The Cancer Genome Atlas Research Network.

In: Cell Reports, Vol. 23, No. 1, 03.04.2018, p. 172-180.e3.

Research output: Contribution to journalArticle

The Cancer Genome Atlas Research Network. / Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas. In: Cell Reports. 2018 ; Vol. 23, No. 1. pp. 172-180.e3.
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