Only a fraction of patients with cancer respond to immune checkpoint blockade (ICB) treatment, but current decision-making procedures have limited accuracy. In this study, we developed a machine learning model to predict ICB response by integrating genomic, molecular, demographic and clinical data from a comprehensively curated cohort (MSK-IMPACT) with 1,479 patients treated with ICB across 16 different cancer types. In a retrospective analysis, the model achieved high sensitivity and specificity in predicting clinical response to immunotherapy and predicted both overall survival and progression-free survival in the test data across different cancer types. Our model significantly outperformed predictions based on tumor mutational burden, which was recently approved by the U.S. Food and Drug Administration for this purpose¹. Additionally, the model provides quantitative assessments of the model features that are most salient for the predictions. We anticipate that this approach will substantially improve clinical decision-making in immunotherapy and inform future interventions.

只有一小部分癌症患者对免疫检查点阻断（ICB）治疗有反应，但目前的决策程序准确性有限。在这项研究中，我们开发了一种机器学习模型，通过整合来自全面策划的队列 (MSK-IMPACT) 的基因组、分子、人口统计和临床数据来预测 ICB 反应，该队列包含 1,479 名接受 ICB 治疗的 16 种不同癌症类型的患者。在回顾性分析中，该模型在预测免疫治疗的临床反应方面实现了高灵敏度和特异性，并在不同癌症类型的测试数据中预测了总生存期和无进展生存期。我们的模型显着优于基于肿瘤突变负荷的预测，该模型最近为此目的获得了美国食品和药物管理局的批准¹。此外，该模型还提供对预测最重要的模型特征的定量评估。我们预计这种方法将大大改善免疫治疗的临床决策并为未来的干预措施提供信息。