Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
Nat Med. 2022 Aug;28(8):1656-1661. doi: 10.1038/s41591-022-01873-5. Epub 2022 Jun 30.
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.
量化特定肿瘤突变患者的不同癌症疗法的疗效对于改善患者预后和推进精准医学至关重要。在这里，我们对 40903 名美国癌症患者进行了大规模的计算分析，这些患者具有详细的突变谱、从电子健康记录中获得的治疗方案和结果。我们系统地确定了 458 个突变，这些突变可以预测 8 种常见癌症类型中特定免疫疗法、化疗药物或靶向治疗的患者的生存率。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。这项工作展示了如何通过对大型真实世界数据进行计算分析来产生见解、假设和资源，从而实现精准肿瘤学。
