Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution
Vanessa D Jonsson, Colin M Blakely, Luping Lin, Saurabh Asthana, Victor Olivas, Matthew A Gubens, Nikolai Matni, Boris C Bastian, Barry S Taylor, John C Doyle, Trever G Bivona
Abstract
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.
