Exploiting Temporal Collateral Sensitivity in Tumor Clonal Evolution
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Tidor_Exploiting temporal.pdf
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Author(s)
Zhao, Boyang
Srinivas, Raja Ram
Creixell Morera, Pau
Tidor, Bruce
Lauffenburger, Douglas A
Hemann, Michael
Pritchard, Justin R.
Date Issued
February 2016
Journal
Cell
Publisher
Elsevier
Citation
Zhao, Boyang et al. “Exploiting Temporal Collateral Sensitivity in Tumor Clonal Evolution.” Cell 165, 1 (March 2016): 234–246 © 2016 Elsevier Inc
Version
Author's final manuscript
Abstract
The prevailing approach to addressing secondary drug resistance in cancer focuses on treating the resistance mechanisms at relapse. However, the dynamic nature of clonal evolution, along with potential fitness costs and cost compensations, may present exploitable vulnerabilities—a notion that we term “temporal collateral sensitivity.” Using a combined pharmacological screen and drug resistance selection approach in a murine model of Ph⁺ acute lymphoblastic leukemia, we indeed find that temporal and/or persistent collateral sensitivity to non-classical BCR-ABL1 drugs arises in emergent tumor subpopulations during the evolution of resistance toward initial treatment with BCR-ABL1-targeted inhibitors. We determined the sensitization mechanism via genotypic, phenotypic, signaling, and binding measurements in combination with computational models and demonstrated significant overall survival extension in mice. Additional stochastic mathematical models and small-molecule screens extended our insights, indicating the value of focusing on evolutionary trajectories and pharmacological profiles to identify new strategies to treat dynamic tumor vulnerabilities.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Biology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Koch Institute for Integrative Cancer Research at MIT
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1016/j.cell.2016.01.045
