Addressing Genetic Tumor Heterogeneity through Computationally Predictive Combination Therapy

1Computational and Systems Biology Program, Massachusetts Institute of Technology.
Cancer Discovery (Impact Factor: 19.45). 12/2013; 4(2). DOI: 10.1158/2159-8290.CD-13-0465
Source: PubMed

ABSTRACT 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.

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    • "These limitations can be addressed using the systems biology framework incorporating qualitative and quantitative models, since these models consider the topology of the network and the quantitative measure of molecular activities (such as protein activity, mRNA † expression and metabolite concentration). Thus in silico systems biology is regarded as a promising avenue to discover a combination of targets and modulators to produce synergistic effects or avoid antagonist effects.[53] [54] "
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    • "Thereby, there is a need to design adaptive drug combinations to achieve tumor response, reduce chances of relapse, and prolong patient survival. In line with this, bioinformatics and systems biology approaches interrogating the huge amount of patient datasets produced until now may provide models and signatures that are more comprehensive and predictive than the mutation status alone [21, 141, 142]. Rational combination therapies guided by real-time monitoring of tumor evolution along treatment, coupled with integrated omics approaches, will ultimately inform trial design to improve patients care in the coming years. "
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