Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, Anhui, China.
Clinical Pharmacology and Quantitative Science, Genmab Inc, Princeton, NJ.
JCO Precis Oncol. 2024 Jun;8:e2300718. doi: 10.1200/PO.23.00718.
To use modern machine learning approaches to enhance and automate the feature extraction from the longitudinal circulating tumor DNA (ctDNA) data and to improve the prediction of survival and disease progression, risk stratification, and treatment strategies for patients with 1L non-small cell lung cancer (NSCLC).
Using IMpower150 trial data on patients with untreated metastatic NSCLC treated with atezolizumab and chemotherapies, we developed a machine learning algorithm to extract predictive features from ctDNA kinetics, improving survival and progression prediction. We analyzed kinetic data from 17 ctDNA summary markers, including cell-free DNA concentration, allele frequency, tumor molecules in plasma, and mutation counts.
Three hundred and ninety-eight patients with ctDNA data (206 in training and 192 in validation) were analyzed. Our models outperformed existing workflow using conventional temporal ctDNA features, raising overall survival (OS) concordance index to 0.72 and 0.71 from 0.67 and 0.63 for C3D1 and C4D1, respectively, and substantially improving progression-free survival (PFS) to approximately 0.65 from the previous 0.54-0.58, a 12%-20% increase. Additionally, they enhanced risk stratification for patients with NSCLC, achieving clear OS and PFS separation. Distinct patterns of ctDNA kinetic characteristics (eg, baseline ctDNA markers, depth of ctDNA responses, and timing of ctDNA clearance, etc) were revealed across the risk groups. Rapid and complete ctDNA clearance appears essential for long-term clinical benefit.
Our machine learning approach offers a novel tool for analyzing ctDNA kinetics, extracting critical features from longitudinal data, improving our understanding of the link between ctDNA kinetics and progression/mortality risks, and optimizing personalized immunotherapies for 1L NSCLC.
利用现代机器学习方法增强和自动化从纵向循环肿瘤 DNA（ctDNA）数据中提取特征，并改善对 1 线非小细胞肺癌（NSCLC）患者的生存和疾病进展、风险分层以及治疗策略的预测。
利用未经治疗的转移性 NSCLC 患者接受阿替利珠单抗和化疗的 IMpower150 试验数据，我们开发了一种机器学习算法，从 ctDNA 动力学中提取预测特征，以改善生存和进展预测。我们分析了来自 17 个 ctDNA 总结标志物的动力学数据，包括游离 DNA 浓度、等位基因频率、血浆中的肿瘤分子和突变计数。
对 398 名具有 ctDNA 数据的患者（206 名在训练中，192 名在验证中）进行了分析。我们的模型优于使用常规时间 ctDNA 特征的现有工作流程，使总体生存率（OS）一致性指数从 C3D1 和 C4D1 的 0.67 和 0.63 分别提高到 0.72 和 0.71，并且大大改善了无进展生存率（PFS），从之前的 0.54-0.58 提高到约 0.65，提高了 12%-20%。此外，它们增强了 NSCLC 患者的风险分层，实现了明确的 OS 和 PFS 分离。在不同的风险组中，揭示了 ctDNA 动力学特征的不同模式（例如，基线 ctDNA 标志物、ctDNA 反应的深度和 ctDNA 清除的时间等）。快速和完全的 ctDNA 清除似乎对长期临床获益至关重要。
我们的机器学习方法为分析 ctDNA 动力学提供了一种新工具，从纵向数据中提取关键特征，增进我们对 ctDNA 动力学与进展/死亡率风险之间关系的理解，并优化 1 线 NSCLC 的个体化免疫治疗。
