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Computationally predicting clinical drug combination efficacy with cancer cell line screens and independent drug action

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  • Alexander Ling

    (University of Minnesota
    University of Chicago)

  • R. Stephanie Huang

    (University of Minnesota)

Abstract

Evidence has recently emerged that many clinical cancer drug combinations may derive their efficacy from independent drug action (IDA), where patients only receive benefit from the single most effective drug in a drug combination. Here we present IDACombo, an IDA based method to predict the efficacy of drug combinations using monotherapy data from high-throughput cancer cell line screens. We show that IDACombo predictions closely agree with measured drug combination efficacies both in vitro (Pearson’s correlation = 0.93 when comparing predicted efficacies to measured efficacies for >5000 combinations) and in a systematically selected set of clinical trials (accuracy > 84% for predicting statistically significant improvements in patient outcomes for 26 first line therapy trials). Finally, we demonstrate how IDACombo can be used to systematically prioritize combinations for development in specific cancer settings, providing a framework for quickly translating existing monotherapy cell line data into clinically meaningful predictions of drug combination efficacy.

Suggested Citation

  • Alexander Ling & R. Stephanie Huang, 2020. "Computationally predicting clinical drug combination efficacy with cancer cell line screens and independent drug action," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19563-6
    DOI: 10.1038/s41467-020-19563-6
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