Addressing genetic tumor heterogeneity through computationally predictive combination therapy

Boyang Zhao, Justin R. Pritchard, Douglas A. Lauffenburger, Michael T. Hemann

Research output: Contribution to journalArticle

63 Scopus citations

Abstract

Recent tumor sequencing data suggest an urgent need to develop a methodology to directly address intratumoral heterogeneity in the design of anticancer treatment regimens. We use RNA interference to model heterogeneous tumors, and demonstrate successful validation of computational predictions for how optimized drug combinations can yield superior effects on these tumors both in vitro and in vivo. Importantly, we discover here that for many such tumors knowledge of the predominant subpopulation is insufficient for determining the best drug combination. Surprisingly, in some cases, the optimal drug combination does not include drugs that would treat any particular subpopulation most effectively, challenging straightforward intuition. We confirm examples of such a case with survival studies in a murine preclinical lymphoma model. Altogether, our approach provides new insights about design principles for combination therapy in the context of intratumoral diversity, data that should inform the development of drug regimens superior for complex tumors.

Original languageEnglish (US)
Pages (from-to)166-174
Number of pages9
JournalCancer Discovery
Volume4
Issue number2
DOIs
StatePublished - Feb 2014

All Science Journal Classification (ASJC) codes

  • Oncology

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