Defining subpopulations of differential drug response to reveal novel target populations

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...

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Bibliographic Details
Published inbioRxiv
Main Authors Toh, Tzen Szen, Keshava, Nirmal, Yuan, Haobin, Yang, Bingxun, Menden, Michael, Wang, Dennis
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 24.07.2019
Subjects
1-Phosphatidylinositol 3-kinase
Biomarkers
Cancer
Learning algorithms
Lung cancer
Precision medicine
Subpopulations
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Summary: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 urogenital 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. Footnotes * https://szen95.github.io/SEABED/
DOI:10.1101/435370