Department of Pulmonary Diseases, University of Groningen and University Medical Center Groningen, the Netherlands.
Department of Pathology, University of Groningen and University Medical Center Groningen, the Netherlands.
Lung Cancer. 2022 Aug;170:52-57. doi: 10.1016/j.lungcan.2022.05.013. Epub 2022 May 25.
Predicting the outcome of immunotherapy-treated non-small cell lung cancer (NSCLC) patients is challenging. Measuring circulating tumor DNA (ctDNA) in plasma is promising, but its application for outcome delineation needs further refinement. Since most information from the next-generation sequencing (NGS) panel is typically left unused, we aim to integrate more information.
Patient and ctDNA data were compiled from five published studies involving advanced NSCLC. Plasma samples collected prior (t) and early during (t) immunotherapy were selected, tracking the changes of the highest t variant per gene. Durable benefit (DB, defined as progression free survival ≥ ½ year) was predicted. Performance was quantified using the integrated receiver operating characteristic curve (ROC AUC) and compared with the traditional molecular response (MR).
A total of 365 patients were pooled. Seven recurrently mutated genes were selected which optimally predicted DB (ROC AUC: 0.77), outperforming the MR predictor (with a ROC AUC: 0.64). Inclusion of patient characteristics led to a slight further improvement (ROC AUC: 0.80). The model performed satisfactory across all ctDNA platforms despite differences in panel size and content.
Relative to a non-informative classifier (ROC AUC: 0.5), a twofold improvement in predictive value was achieved compared to MR by an integration of changes across seven selected genes in immunotherapy-treated NSCLC patients, whilst being broadly applicable across ctDNA NGS panels.
预测接受免疫治疗的非小细胞肺癌（NSCLC）患者的结局具有挑战性。检测血浆中的循环肿瘤 DNA（ctDNA）具有很大的应用前景，但要将其用于结局的划分还需要进一步改进。由于下一代测序（NGS）面板的大部分信息通常未被使用，因此我们旨在整合更多的信息。
从五项涉及晚期 NSCLC 的已发表研究中收集了患者和 ctDNA 数据。选择了在免疫治疗前（t）和早期（t）采集的血浆样本，追踪每个基因中最高 t 变体的变化。预测持久获益（DB，定义为无进展生存期≥半年）。使用综合接受者操作特征曲线（ROC AUC）对性能进行量化，并与传统的分子反应（MR）进行比较。
共纳入 365 例患者。选择了七个经常发生突变的基因，它们能够最优地预测 DB（ROC AUC：0.77），优于 MR 预测器（ROC AUC：0.64）。纳入患者特征后略有进一步改善（ROC AUC：0.80）。尽管 ctDNA NGS 面板的大小和内容存在差异，但该模型在所有 ctDNA 平台上的表现都令人满意。
与无信息分类器（ROC AUC：0.5）相比，在接受免疫治疗的 NSCLC 患者中，通过整合七个选定基因的变化，与 MR 相比，预测价值提高了一倍，同时在广泛的 ctDNA NGS 面板中具有广泛的适用性。
