Editors' ChoiceCancer

Two Drugs Are Better than One—Modeling Drug Combinations in Cancer Therapy

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Science Translational Medicine  17 Jul 2013:
Vol. 5, Issue 194, pp. 194ec116
DOI: 10.1126/scitranslmed.3006923

Therapies that target specific mutations in tumor cells have transformed the standard of care for cancer: imatinib for chronic myeloid leukemia, erlotinib and gefitinib for EGFR mutant lung cancer, and vemurafenib and dabrafenib for BRAF mutant melanoma. Targeted inhibitors deliver dramatic responses, but resistance inevitably emerges. Strategies to generate more durable responses are needed. Bozic and colleagues tackled the question of how combinations of targeted agents might affect disease control.

By following the responses of 21 melanoma patients treated with vemurafenib and calculating the changes in 68 individual tumors during therapy, Bozic et al. developed a mathematical model that predicts responses to combinations of targeted inhibitors. Using this model, the authors make several predictions that could guide how cancer therapies are developed. Their model predicts that, for targeted inhibitors, concurrent combinations of two (or three) drugs will be far more effective than sequential treatment with the same agents—and may in fact be the only strategy that can offer a cure. The model also highlights the liability of cross-resistance (in which resistance to one drug confers resistance to another) and supports the use of drug combinations that target distinct pathways. Both of these observations are supported by clinical experiences previously reported in the literature.

Although there are inherent limitations to mathematical models, this study offers a new lens through which to view the advantages and limitations of targeted therapy. As the authors highlight, one type of cancer therapy that breaks the mold for their model is immunotherapy. Because the immune system is dynamic, it can evolve in response to changes in the tumor. Ways to incorporate immunotherapy into drug-combination cancer treatments, both in mathematical models and in the clinic, will be needed for the future.

I. Bozic et al., Evolutionary dynamics of cancer in response to targeted combination therapy. eLife 2, e00747 (2013).[Full Text]

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