Gayvert Kaitlyn M, Aly Omar, Platt James, Bosenberg Marcus W, Stern David F, Elemento Olivier
Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, United States of America.
Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, United States of America.
PLoS Comput Biol. 2017 Jan 13;13(1):e1005308. doi: 10.1371/journal.pcbi.1005308. eCollection 2017 Jan.
A promising alternative to address the problem of acquired drug resistance is to rely on combination therapies. Identification of the right combinations is often accomplished through trial and error, a labor and resource intensive process whose scale quickly escalates as more drugs can be combined. To address this problem, we present a broad computational approach for predicting synergistic combinations using easily obtainable single drug efficacy, no detailed mechanistic understanding of drug function, and limited drug combination testing. When applied to mutant BRAF melanoma, we found that our approach exhibited significant predictive power. Additionally, we validated previously untested synergy predictions involving anticancer molecules. As additional large combinatorial screens become available, this methodology could prove to be impactful for identification of drug synergy in context of other types of cancers.
解决获得性耐药问题的一个有前景的替代方法是依靠联合疗法。正确组合的确定通常是通过反复试验来完成的，这是一个劳动和资源密集型的过程，随着可组合药物的增加，其规模会迅速扩大。为了解决这个问题，我们提出了一种广泛的计算方法，用于使用易于获得的单一药物疗效来预测协同组合，无需对药物功能有详细的机制理解，并且只需进行有限的药物组合测试。当应用于突变型BRAF黑色素瘤时，我们发现我们的方法具有显著的预测能力。此外，我们验证了以前未经测试的涉及抗癌分子的协同作用预测。随着更多大型组合筛选数据的出现，这种方法可能被证明对识别其他类型癌症中的药物协同作用具有重要意义。
