Abstract
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.
Highlights
Drug developers face a conundrum in predicting the efficacy of their investigational compound compared to existing drugs used as the standard of care treatment
segmentation algorithm coupled with biomarker detection (SEABED) analysed >800 cancer cells for each pair of drugs, and we evaluated two established drug response measures: the drug concentration required to reduce cell viability by half (IC50) and the area under the dose–response curve (AUC; Fig. 1a)
We show that the drug response of 802 cell lines treated with either SB590885 (BRAF inhibitor) or CI-1040 (MEK inhibitor) could be segmented into seven distinct subpopulations with a median size of 40 cell lines by integrating the two metrics of drug response, AUC and IC50
Summary
Drug developers face a conundrum in predicting the efficacy of their investigational compound compared to existing drugs used as the standard of care treatment. We present results from our platform, SEABED (SEgmentation And Biomarker Enrichment of Differential treatment response), to demonstrate how unsupervised machine learning can discover intrinsic partitions in the drug response measurements of two or more drugs that directly correspond to distinct pharmacological patterns of response with therapeutic biomarkers. The subpopulation enriched for independent action rather than probable synergy by examining subpopulations uniquely sensitive to a single drug,[21] which may be precisely targeted by identified biomarkers This is evident in subpopulations of cell lines with divergent response, where there is sensitivity to either drug but not both. The difference in response between afatinib and selumetinib was significantly greater (t-test P < 0.01) between the subpopulations identified and the total population of PIK3CA an investigational therapy may be more effective than the or BRAF mutant cell lines (Fig. 1f, g)
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