Oncology drug combinations can improve therapeutic responses and increase treatment options for patients. The number of possible combinations is vast and responses can be context-specific. Systematic screens can identify clinically relevant, actionable combinations in defined patient subtypes. We present data for 109 anticancer drug combinations from AstraZeneca's oncology small molecule portfolio screened in 755 pan-cancer cell lines. Combinations were screened in a 7 × 7 concentration matrix, with more than 4 million measurements of sensitivity, producing an exceptionally data-rich resource. We implement a new approach using combination Emax (viability effect) and highest single agent (HSA) to assess combination benefit. We designed a clinical translatability workflow to identify combinations with clearly defined patient populations, rationale for tolerability based on tumor type and combination-specific “emergent” biomarkers, and exposures relevant to clinical doses. We describe three actionable combinations in defined cancer types, confirmed in vitro and in vivo, with a focus on hematologic cancers and apoptotic targets. Significance: We present the largest cancer drug combination screen published to date with 7 × 7 concentration response matrices for 109 combinations in more than 750 cell lines, complemented by multi-omics predictors of response and identification of “emergent” combination biomarkers. We prioritize hits to optimize clinical translatability, and experimentally validate novel combination hypotheses.

中文翻译：

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大规模泛癌细胞系筛选确定了可行且有效的药物组合

肿瘤药物组合可以改善治疗反应并增加患者的治疗选择。可能的组合数量非常多，并且响应可以针对具体情况。系统筛选可以识别特定患者亚型中临床相关的、可操作的组合。我们提供了阿斯利康肿瘤小分子组合中 109 种抗癌药物组合的数据，这些组合在 755 个泛癌细胞系中进行了筛选。在 7 × 7 浓度矩阵中筛选组合，进行超过 400 万次灵敏度测量，产生了异常丰富的数据资源。我们实施了一种新方法，使用组合 Emax（生存效应）和最高单一药物 (HSA) 来评估组合效益。我们设计了一个临床可转化性工作流程，以识别具有明确定义的患者群体的组合、基于肿瘤类型和组合特异性“新兴”生物标志物的耐受性原理，以及与临床剂量相关的暴露。我们描述了在定义的癌症类型中的三种可行组合，并在体外和体内得到证实，重点是血液癌症和细胞凋亡靶点。意义：我们提出了迄今为止发布的最大的癌症药物组合筛选，其中包含 750 多个细胞系中 109 种组合的 7 × 7 浓度响应矩阵，并辅以多组学响应预测因子和“新兴”组合生物标志物的识别。我们优先考虑命中以优化临床可转化性，并通过实验验证新的组合假设。