治疗性肿瘤学面临的重大挑战之一是确定谁可能从特定治疗中获得生存益处。关于经纵向循环肿瘤DNA（ctDNA）动态预测生存的研究通常较小或非随机。我们评估了来自随机3期IMpower150研究的466名非小细胞肺癌（NSCLC）患者5个时间点的ctDNA，并使用机器学习共同模拟多个ctDNA度量来预测总生存（OS）。通过治疗前三个周期第1天的ctDNA检测，可以对稳定病情（风险比（HR）= 3.2（2.0-5.3），P＜0.001；高-中危险组与低-中危险组的中位数为7.1对22.3个月）和部分缓解（HR = 3.3（1.7-6.4），P＜0.001；中位数为8.8对28.6个月）的患者进行风险分层。该模型还在随机3期OAK研究中的外部验证队列中鉴定出高危患者；该研究比较ICI和化疗的NSCLC（OS HR = 3.73（1.83-7.60），P = 0.00012）。采用我们的ctDNA模型进行临床试验场景的模拟表明，早期ctDNA测试优于早期放射性影像学检测来预测试验结果。总体而言，治疗期间测量ctDNA动态可以改善患者风险分层，并且可能允许在临床试验期间对竞争性治疗进行早期区分。©2023年作者。

One of the great challenges in therapeutic oncology is determining who might achieve survival benefits from a particular therapy. Studies on longitudinal circulating tumor DNA (ctDNA) dynamics for the prediction of survival have generally been small or nonrandomized. We assessed ctDNA across 5 time points in 466 non-small-cell lung cancer (NSCLC) patients from the randomized phase 3 IMpower150 study comparing chemotherapy-immune checkpoint inhibitor (chemo-ICI) combinations and used machine learning to jointly model multiple ctDNA metrics to predict overall survival (OS). ctDNA assessments through cycle 3 day 1 of treatment enabled risk stratification of patients with stable disease (hazard ratio (HR) = 3.2 (2.0-5.3), P < 0.001; median 7.1 versus 22.3 months for high- versus low-intermediate risk) and with partial response (HR = 3.3 (1.7-6.4), P < 0.001; median 8.8 versus 28.6 months). The model also identified high-risk patients in an external validation cohort from the randomized phase 3 OAK study of ICI versus chemo in NSCLC (OS HR = 3.73 (1.83-7.60), P = 0.00012). Simulations of clinical trial scenarios employing our ctDNA model suggested that early ctDNA testing outperforms early radiographic imaging for predicting trial outcomes. Overall, measuring ctDNA dynamics during treatment can improve patient risk stratification and may allow early differentiation between competing therapies during clinical trials.© 2023. The Author(s).