Quantifying the effectiveness of different cancer therapies in patients with specific tumor mutations is critical for improving patient outcomes and advancing precision medicine. Here we perform a large-scale computational analysis of 40,903 US patients with cancer who have detailed mutation profiles, treatment sequences and outcomes derived from electronic health records. We systematically identify 458 mutations that predict the survival of patients on specific immunotherapies, chemotherapy agents or targeted therapies across eight common cancer types. We further characterize mutation–mutation interactions that impact the outcomes of targeted therapies. This work demonstrates how computational analysis of large real-world data generates insights, hypotheses and resources to enable precision oncology.

量化不同癌症疗法对具有特定肿瘤突变的患者的有效性对于改善患者预后和推进精准医疗至关重要。在这里，我们对 40,903 名美国癌症患者进行大规模计算分析，这些患者具有来自电子健康记录的详细突变谱、治疗顺序和结果。我们系统地鉴定了 458 个突变，这些突变可以预测八种常见癌症类型中特定免疫疗法、化疗药物或靶向疗法患者的生存率。我们进一步描述了影响靶向治疗结果的突变 - 突变相互作用。这项工作展示了大型现实世界数据的计算分析如何产生洞察力、假设和资源，以实现精准肿瘤学。