Combinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient, using cost-effective methods. Here, we developed an exact multiobjective optimization method for identifying pairwise or higher-order combinations that show maximal cancer-selectivity. The prioritization of patient-specific combinations is based on Pareto-optimization in the search space spanned by the therapeutic and nonselective effects of combinations. We demonstrate the performance of the method in the context of BRAF-V600E melanoma treatment, where the optimal solutions predicted a number of co-inhibition partners for vemurafenib, a selective BRAF-V600E inhibitor, approved for advanced melanoma. We experimentally validated many of the predictions in BRAF-V600E melanoma cell line, and the results suggest that one can improve selective inhibition of BRAF-V600E melanoma cells by combinatorial targeting of MAPK/ERK and other compensatory pathways using pairwise and third-order drug combinations. Our mechanism-agnostic optimization method is widely applicable to various cancer types, and it takes as input only measurements of a subset of pairwise drug combinations, without requiring target information or genomic profiles. Such data-driven approaches may become useful for functional precision oncology applications that go beyond the cancer genetic dependency paradigm to optimize cancer-selective combinatorial treatments.

需要组合疗法来治疗晚期癌症患者，这些患者已通过重新连接多余途径而对单一疗法产生了耐药性。由于大量潜在的药物组合，需要一种系统的方法，以使用成本有效的方法为每个患者确定安全有效的组合。在这里，我们开发了一种精确的多目标优化方法，用于识别显示出最大癌症选择性的成对或更高阶组合。特定于患者的组合的优先级基于组合的治疗效果和非选择性效果所覆盖的搜索空间中的帕累托优化。我们在BRAF-V600E黑色素瘤治疗的背景下证明了该方法的性能，最佳解决方案预测了批准用于晚期黑色素瘤的选择性BRAF-V600E抑制剂维拉非尼的多种共抑制伴侣。我们通过实验验证了BRAF-V600E黑色素瘤细胞系中的许多预测，结果表明，通过使用成对和三阶药物组合，通过联合靶向MAPK / ERK和其他补偿途径，可以改善对BRAF-V600E黑色素瘤细胞的选择性抑制。我们的与机理无关的优化方法广泛适用于各种类型的癌症，并且仅将成对药物组合的子集的测量作为输入，而无需目标信息或基因组概况。