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Open Access Publications from the University of California

Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution.

  • Author(s): Jonsson, Vanessa D
  • Blakely, Collin M
  • Lin, Luping
  • Asthana, Saurabh
  • Matni, Nikolai
  • Olivas, Victor
  • Pazarentzos, Evangelos
  • Gubens, Matthew A
  • Bastian, Boris C
  • Taylor, Barry S
  • Doyle, John C
  • Bivona, Trever G
  • et al.

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The success of targeted cancer therapy is limited by drug resistance that can result from tumor genetic heterogeneity. The current approach to address resistance typically involves initiating a new treatment after clinical/radiographic disease progression, ultimately resulting in futility in most patients. Towards a potential alternative solution, we developed a novel computational framework that uses human cancer profiling data to systematically identify dynamic, pre-emptive, and sometimes non-intuitive treatment strategies that can better control tumors in real-time. By studying lung adenocarcinoma clinical specimens and preclinical models, our computational analyses revealed that the best anti-cancer strategies addressed existing resistant subpopulations as they emerged dynamically during treatment. In some cases, the best computed treatment strategy used unconventional therapy switching while the bulk tumor was responding, a prediction we confirmed in vitro. The new framework presented here could guide the principled implementation of dynamic molecular monitoring and treatment strategies to improve cancer control.

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