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|a Zhao, Boyang
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|a Massachusetts Institute of Technology. Computational and Systems Biology Program
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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|a Massachusetts Institute of Technology. Department of Biological Engineering
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|a Massachusetts Institute of Technology. Department of Biological Engineering
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|a Massachusetts Institute of Technology. Department of Biology
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Koch Institute for Integrative Cancer Research at MIT
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|a Zhao, Boyang
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|a Srinivas, Raja Ram
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|a Creixell Morera, Pau
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|a Pritchard, Justin Robert
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|a Tidor, Bruce
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|a Lauffenburger, Douglas A
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|a Hemann, Michael
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|a Srinivas, Raja Ram
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|a Creixell Morera, Pau
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|a Tidor, Bruce
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|a Lauffenburger, Douglas A
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|a Hemann, Michael
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|a Pritchard, Justin R.
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|a Exploiting Temporal Collateral Sensitivity in Tumor Clonal Evolution
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|b Elsevier,
|c 2017-09-05T19:40:58Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/111131
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|a 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.
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|a National Cancer Institute (U.S.) (Grant P30-CA14051)
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|a National Institute of General Medical Sciences (U.S.) (GM082209)
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|a National Institute of General Medical Sciences (U.S.) (5T32GM008334)
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|a National Institutes of Health (U.S.) (5T32GM008334)
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|a National Science Foundation (U.S.) (Grant 1122374)
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