TY - JOUR T1 - Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution JF - bioRxiv DO - 10.1101/086553 SP - 086553 AU - Vanessa D. Jonsson AU - Collin M. Blakely AU - Luping Lin AU - Saurabh Asthana AU - Victor Olivas AU - Matthew A. Gubens AU - Nikolai Matni AU - Boris C. Bastian AU - Barry S. Taylor AU - John C. Doyle AU - Trever G. Bivona Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/11/08/086553.abstract N2 - 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. ER -