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Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution
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DOI：10.1038/srep44206.png)
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Abstract
En 中文
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.
Keywords:
INTRATUMOR HETEROGENEITY
LUNG ADENOCARCINOMAS
MIXED IMMUNOTHERAPY
ACQUIRED-RESISTANCE
MUTATIONS
CHEMOTHERAPY
DRIVEN
DNA
ACTIVATION
FRAMEWORK
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