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

Alexander Ling, R. Stephanie Huang

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number5848
JournalNature communications
Volume11
Issue number1
DOIs
StatePublished - Dec 2020

Bibliographical note

Funding Information:
We thank Gary Oehlert and Emily Kurtz at the University of Minnesota Statistical Consulting Center and Dr. Hae Kyung Im at the University of Chicago for taking the time to provide feedback on our method of estimating uncertainties in the efficacy predictions produced by IDACombo. In particular, Gary made several key contributions to the design of these methods and to our understanding of their limitations. We also thank the Minnesota Supercomputing Institute (MSI, http://www.msi.umn.edu) at the University of Minnesota for providing resources that contributed to the research results reported within this paper. One of the datasets used for the analyses described in this manuscript was contributed by AstraZeneca and the Sanger Institute in collaboration with Sage Bionetworks-DREAM Challenge organizers. It was obtained as part of the AstraZeneca–Sanger Drug Combination Prediction DREAM Challenge through Synapse ID [syn4231880]. This study was supported by NIH/NCI Grants R01CA204856 and R01CA229618. R.S.H. also receives support from a research grant from the Avon Foundation for Women and an OACA Faculty Research Development grant.

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

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