Immune checkpoint blockades (ICBs) have revolutionized cancer therapy by inducing durable clinical responses, but only a small percentage of patients can benefit from ICB treatments. Many studies have established various biomarkers to predict ICB responses. However, different biomarkers were found with diverse performances in practice, and a timely and unbiased assessment has yet to be conducted due to the complexity of ICB-related studies and trials. In this study, we manually curated 29 published datasets with matched transcriptome and clinical data from more than 1400 patients, and uniformly preprocessed these datasets for further analyses. In addition, we collected 39 sets of transcriptomic biomarkers, and based on the nature of the corresponding computational methods, we categorized them into the gene-set-like group (with the self-contained design and the competitive design, respectively) and the deconvolution-like group. Next, we investigated the correlations and patterns of these biomarkers and utilized a standardized workflow to systematically evaluate their performance in predicting ICB responses and survival statuses across different datasets, cancer types, antibodies, biopsy times, and combinatory treatments. In our benchmark, most biomarkers showed poor performance in terms of stability and robustness across different datasets. Two scores (TIDE and CYT) had a competitive performance for ICB response prediction, and two others (PASS-ON and EIGS_ssGSEA) showed the best association with clinical outcome. Finally, we developed ICB-Portal to host the datasets, biomarkers, and benchmark results and to implement the computational methods for researchers to test their custom biomarkers. Our work provided valuable resources and a one-stop solution to facilitate ICB-related research.
免疫检查点阻断疗法通过诱导持久的临床反应，已在癌症治疗领域引发革命性突破，但仅有少数患者能从该疗法中获益。多项研究已建立多种生物标志物以预测免疫检查点阻断疗效，然而实践中发现不同标志物的预测效能存在差异，且由于相关研究与临床试验的复杂性，目前尚缺乏及时、客观的系统评估。本研究通过人工收集29个已发表数据集，涵盖1400余名患者的转录组与临床匹配数据，并对所有数据集进行统一预处理以用于后续分析。同时，我们收集了39组转录组学生物标志物，根据对应计算方法的特性将其分为基因集类（分别采用自包含设计和竞争性设计）与反卷积类两大类别。随后，我们探究了这些生物标志物间的关联性与分布规律，并采用标准化流程系统评估了它们在不同数据集、癌症类型、抗体药物、活检时间及联合治疗方案中预测免疫检查点阻断疗效和生存状态的性能。基准测试显示，大多数生物标志物在不同数据集间的稳定性与鲁棒性表现欠佳。其中TIDE和CYT两种评分在疗效预测方面表现优异，而PASS-ON与EIGS_ssGSEA则与临床结局呈现最佳关联性。最后，我们构建了ICB-Portal平台，整合数据集、生物标志物及基准测试结果，并为研究者提供计算方法以验证其定制化生物标志物。本研究成果为免疫检查点阻断相关研究提供了宝贵资源与一站式解决方案。
A Comprehensive Benchmark of Transcriptomic Biomarkers for Immune Checkpoint Blockades
肿瘤(癌症)患者之家