Addressing genetic tumor heterogeneity through computationally predictive combination therapy
Menée notamment à l'aide d'un modèle murin de lymphome, cette étude évalue l'intérêt d'une méthode numérique pour prédire la réponse à diverses combinaisons de traitements dans le contexte d'une diversité génétique intratumorale
Recent tumor sequencing data suggests an urgent need to develop a methodology to directly address intra-tumor heterogeneity in the design of anti-cancer 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 pre-clinical lymphoma model. Altogether, our approach provides new insights concerning design principles for combination therapy in the context of intratumoral diversity, data that should inform the development of drug regimens superior for complex tumors.
Cancer Discovery , résumé, 2013