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

Gregory P Way; Francisco Sanchez-Vega; Konnor La; Joshua Armenia; Walid K Chatila; Augustin Luna; Chris Sander; Andrew D Cherniack; Marco Mina; Giovanni Ciriello; Nikolaus Schultz; Yolanda Sanchez; Casey S Greene

doi:10.1016/j.celrep.2018.03.046

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

Journal article

Open access

Peer reviewed

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

Gregory P Way, Francisco Sanchez-Vega, Konnor La, Joshua Armenia, Walid K Chatila, Augustin Luna, Chris Sander, Andrew D Cherniack, Marco Mina, Giovanni Ciriello, …

Cell reports (Cambridge), Vol.23(1), pp.172-180.e3

04/03/2018

DOI: 10.1016/j.celrep.2018.03.046

PMCID: PMC5918694

PMID: 29617658

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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.

Machine Learning

Signal Transduction

Cell Line, Tumor

Gene Expression Regulation, Neoplastic

Genome, Human

Humans

Neoplasms - genetics

Neoplasms - metabolism

ras Proteins - genetics

ras Proteins - metabolism

Details

Title: Subtitle: Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas
Creators: Gregory P Way - University of Pennsylvania
Francisco Sanchez-Vega - Memorial Sloan Kettering Cancer Center
Konnor La - Memorial Sloan Kettering Cancer Center
Joshua Armenia - Memorial Sloan Kettering Cancer Center
Walid K Chatila - Memorial Sloan Kettering Cancer Center
Augustin Luna - Harvard University
Chris Sander - Harvard University
Andrew D Cherniack - Massachusetts Institute of Technology
Marco Mina - University of Lausanne
Giovanni Ciriello - University of Lausanne
Nikolaus Schultz - Memorial Sloan Kettering Cancer Center
Yolanda Sanchez - Dartmouth College
Casey S Greene - University of Pennsylvania
Contributors: Cancer Genome Atlas Research Network (Contributor)
Deqin Ma (Contributor) - University of Iowa, Pathology
Mohammed M Milhem (Contributor) - University of Iowa, Internal Medicine
Aaron D Bossler (Contributor) - University of Iowa, Pathology
Resource Type: Journal article
Publication Details: Cell reports (Cambridge), Vol.23(1), pp.172-180.e3
DOI: 10.1016/j.celrep.2018.03.046
PMID: 29617658
PMCID: PMC5918694
NLM abbreviation: Cell Rep
ISSN: 2211-1247
eISSN: 2211-1247
Grant note: U24 CA143843 / NCI NIH HHS U24 CA210957 / NCI NIH HHS U54 HG003079 / NHGRI NIH HHS P30 CA016672 / NCI NIH HHS U24 CA143883 / NCI NIH HHS U24 CA210990 / NCI NIH HHS U24 CA143799 / NCI NIH HHS T32 HG000046 / NHGRI NIH HHS R50 CA221675 / NCI NIH HHS U24 CA143867 / NCI NIH HHS U24 CA143858 / NCI NIH HHS U24 CA143882 / NCI NIH HHS P30 CA008748 / NCI NIH HHS U54 HG003067 / NHGRI NIH HHS U24 CA143845 / NCI NIH HHS R01 NS095411 / NINDS NIH HHS U24 CA143835 / NCI NIH HHS U54 HG003273 / NHGRI NIH HHS U24 CA143840 / NCI NIH HHS U24 CA144025 / NCI NIH HHS U24 CA143866 / NCI NIH HHS P30 ES013508 / NIEHS NIH HHS U24 CA210949 / NCI NIH HHS U24 CA143848 / NCI NIH HHS R01 CA163722 / NCI NIH HHS
Language: English
Date published: 04/03/2018
Academic Unit: Hematology, Oncology, and Blood & Marrow Transplantation; Pathology; Internal Medicine
Record Identifier: 9984183983902771

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