CAMS Key Laboratory of Translational Research on Lung Cancer, State Key Laboratory of Molecular Oncology, Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100021, China.
Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing 100021, China.
Cell Rep Methods. 2023 Oct 23;3(10):100596. doi: 10.1016/j.crmeth.2023.100596. Epub 2023 Sep 21.
Molecular indicators of long-term survival (LTS) in response to immune-checkpoint inhibitor (ICI) treatment have the potential to provide both mechanistic and therapeutic insights. In this study, we construct predictive models of LTS following ICI therapy based on data from 158 clinical trials involving 21,023 patients of 25 cancer types with available 1-year overall survival (OS) rates. We present evidence for the use of 1-year OS rate as a surrogate for LTS. Based on these and corresponding TCGA multi-omics data, total neoantigen, metabolism score, CD8 T cell, and MHC_score were identified as predictive biomarkers. These were integrated into a Gaussian process regression model that estimates "long-term survival predictive score of immunotherapy" (iLSPS). We found that iLSPS outperformed the predictive capabilities of individual biomarkers and successfully predicted LTS of patient groups with melanoma and lung cancer. Our study explores the feasibility of modeling LTS based on multi-omics indicators and machine-learning methods.
分子标志物可预测免疫检查点抑制剂（ICI）治疗的长期生存（LTS），具有提供机制和治疗见解的潜力。本研究基于 25 种癌症类型的 158 项临床试验的数据，构建了 21023 例患者的 ICI 治疗后 LTS 预测模型，这些试验均具有 1 年总生存率（OS）数据。我们提供了使用 1 年 OS 率作为 LTS 替代指标的证据。基于这些数据和相应的 TCGA 多组学数据，总新生抗原、代谢评分、CD8 T 细胞和 MHC_score 被确定为预测生物标志物。这些标志物被整合到一个高斯过程回归模型中，该模型估计“免疫治疗的长期生存预测评分”（iLSPS）。我们发现 iLSPS 优于单个标志物的预测能力，并成功预测了黑色素瘤和肺癌患者群体的 LTS。本研究探索了基于多组学指标和机器学习方法建模 LTS 的可行性。
