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Keshava, N.* ; Toh, T.S.* ; Yuan, H.* ; Yang, B.* ; Menden, M.P. ; Wang, D.*

Defining subpopulations of differential drug response to reveal novel target populations.

NPJ Syst. Biol. Appl. 5:36 (2019)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Personalised medicine has predominantly focused on genetically altered cancer genes that stratify drug responses, but there is a need to objectively evaluate differential pharmacology patterns at a subpopulation level. Here, we introduce an approach based on unsupervised machine learning to compare the pharmacological response relationships between 327 pairs of cancer therapies. This approach integrated multiple measures of response to identify subpopulations that react differently to inhibitors of the same or different targets to understand mechanisms of resistance and pathway cross-talk. MEK, BRAF, and PI3K inhibitors were shown to be effective as combination therapies for particular BRAF mutant subpopulations. A systematic analysis of preclinical data for a failed phase III trial of selumetinib combined with docetaxel in lung cancer suggests potential indications in pancreatic and colorectal cancers with KRAS mutation. This data-informed study exemplifies a method for stratified medicine to identify novel cancer subpopulations, their genetic biomarkers, and effective drug combinations.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Biomarkers ; Cancer ; Molecular Medicine ; Virtual Drug Screening; Cell Lung-cancer; Inhibitor Selumetinib Azd6244; Acquired-resistance; Antitumor-activity; Mek Inhibition; Braf; Sensitivity; Combination; Mutations; Therapy
ISSN (print) / ISBN 2056-7189
e-ISSN 2056-7189
Zeitschrift npj Systems biology and applications
Quellenangaben Band: 5, Heft: , Seiten: , Artikelnummer: 36 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)