- 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
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.