Zhao Boyang, Pritchard Justin R, Lauffenburger Douglas A, Hemann Michael T
1Computational and Systems Biology Program, 2The David H. Koch Institute for Integrative Cancer Research, Departments of 3Biology, and 4Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts.
Cancer Discov. 2014 Feb;4(2):166-74. doi: 10.1158/2159-8290.CD-13-0465. Epub 2013 Dec 6.
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.
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.
近期的肿瘤测序数据表明，迫切需要开发一种方法，以便在设计抗癌治疗方案时直接应对肿瘤内的异质性。我们利用RNA干扰对异质性肿瘤进行建模，并证明了针对优化药物组合如何在体外和体内对这些肿瘤产生更优效果的计算预测得到了成功验证。重要的是，我们在此发现，对于许多此类肿瘤而言，仅了解主要亚群的情况不足以确定最佳药物组合。令人惊讶的是，在某些情况下，最佳药物组合并不包括对任何特定亚群治疗效果最显著的药物，这挑战了直观认知。我们在小鼠临床前淋巴瘤模型中通过生存研究证实了此类情况的实例。总之，我们的方法为肿瘤内多样性背景下的联合治疗设计原则提供了新见解，这些数据应为针对复杂肿瘤的更优药物方案的开发提供参考。
本研究首次展示了如何合理设计联合用药方案，利用现有化疗药物使肿瘤细胞死亡最大化，同时使克隆亚群的生长最小化。
