Addressing genetic tumor heterogeneity through computationally predictive combination therapy.

TitleAddressing genetic tumor heterogeneity through computationally predictive combination therapy.
Publication TypeJournal Article
Year of Publication2014
AuthorsZhao, B, Pritchard, JR, Lauffenburger, DA, Hemann, MT
JournalCancer Discov
Date Published2014 Feb

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. Significance: This study provides the first example of how combination drug regimens, using existing chemotherapies, can be rationally designed to maximize tumor cell death, while minimizing the outgrowth of clonal subpopulations.

Alternate JournalCancer Discov
PubMed ID24318931
PubMed Central IDPMC3975231
Grant List5T32GM008334 / GM / NIGMS NIH HHS / United States
P30 CA014051 / CA / NCI NIH HHS / United States
P30-CA14051 / CA / NCI NIH HHS / United States
U54 CA112967 / CA / NCI NIH HHS / United States
U54-CA112967-06 / CA / NCI NIH HHS / United States