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Machine learning detects pan-cancer ras pathway activation in the cancer genome atlas

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Title: Machine learning detects pan-cancer ras pathway activation in the cancer genome atlas
Authors: Way, GP
Sanchez-Vega, F
La, K
Armenia, J
Chatila, WK
Luna, A
Sander, C
Cherniack, AD
Mina, M
Ciriello, G
Schultz, N
Sanchez, Y
Greene, CS
Item Type: Journal Article
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.
Issue Date: 3-Apr-2018
Date of Acceptance: 12-Mar-2018
URI: http://hdl.handle.net/10044/1/71261
DOI: https://doi.org/10.1016/j.celrep.2018.03.046
ISSN: 2211-1247
Publisher: Elsevier
Start Page: 172
End Page: 180
Journal / Book Title: Cell Reports
Volume: 23
Issue: 1
Copyright Statement: © 2018 The Author(s).This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Sponsor/Funder: SAIC-F-Frederick, Inc
Leidos Biomedical Research, Inc.
Funder's Grant Number: TCGA Pilot Program
15Y011ST
Keywords: Science & Technology
Life Sciences & Biomedicine
Cell Biology
PREVIOUSLY TREATED PATIENTS
PRECISION ONCOLOGY
PHASE-II
SELUMETINIB
MUTATIONS
SIGNATURES
PROTEIN
GENE
BRAF
PATHOGENESIS
Gene expression
HRAS
KRAS
NF1
NRAS
Ras
TCGA
drug sensitivity
machine learning
pan-cancer
Cancer Genome Atlas Research Network
Publication Status: Published
Online Publication Date: 2018-04-05
Appears in Collections:Department of Surgery and Cancer