Background: We aimed to determine the prognostic value of an immunoscore reflecting CD3+ and CD8+ T cell density estimated from real-world transcriptomic data of a patient cohort with advanced malignancies treated with immune checkpoint inhibitors (ICIs) in an effort to validate a reference for future machine learning-based biomarker development. Methods: Transcriptomic data was collected under the Total Cancer Care Protocol (NCT03977402) Avatar®project. The real-world immunoscore for each patient was calculated based on the estimated densities of tumor CD3+ and CD8+ T cells utilizing CIBERSORTx and the LM22 gene signature matrix. Then, the immunoscore association with overall survival (OS) was estimated using Cox regression and analyzed using Kaplan–Meier curves. The OS predictions were assessed using Harrell’s concordance index (C-index). The Youden index was used to identify the optimal cut-off point. Statistical significance was assessed using the log-rank test. Results: Our study encompassed 522 patients with four cancer types. The median duration to death was 10.5 months for the 275 participants who encountered an event. For the entire cohort, the results demonstrated that transcriptomics-based immunoscore could significantly predict patients at risk of death (p-value < 0.001). Notably, patients with an intermediate–high immunoscore achieved better OS than those with a low immunoscore. In subgroup analysis, the prediction of OS was significant for melanoma and head and neck cancer patients but did not reach significance in the non-small cell lung cancer or renal cell carcinoma cohorts. Conclusions: Calculating CD3+ and CD8+ T cell immunoscore using real-world transcriptomic data represents a promising signature for estimating OS with ICIs and can be used as a reference for future machine learning-based biomarker development.
背景：本研究旨在通过分析接受免疫检查点抑制剂（ICIs）治疗的晚期恶性肿瘤患者队列的真实世界转录组数据，评估基于CD3+和CD8+ T细胞密度构建的免疫评分预后价值，以期为未来基于机器学习的生物标志物开发提供验证依据。 方法：转录组数据采集自"全癌症诊疗方案"（NCT03977402）Avatar®项目。采用CIBERSORTx算法及LM22基因特征矩阵估算肿瘤CD3+和CD8+ T细胞密度，据此计算每位患者的真实世界免疫评分。通过Cox回归模型评估免疫评分与总生存期（OS）的关联性，并采用Kaplan-Meier曲线进行分析。使用Harrell一致性指数（C-index）评估OS预测效能，Youden指数确定最佳截断值，时序检验判定统计学显著性。 结果：研究纳入522例涵盖四种癌症类型的患者。275例发生终点事件的患者中位生存期为10.5个月。整体队列分析显示，基于转录组学的免疫评分能显著预测患者死亡风险（p值<0.001）。值得注意的是，中高免疫评分患者较低评分患者获得更优OS。亚组分析表明，该评分对黑色素瘤和头颈癌患者的OS预测具有显著性，但在非小细胞肺癌和肾细胞癌队列中未达统计学意义。 结论：利用真实世界转录组数据计算CD3+和CD8+ T细胞免疫评分，可作为预测ICIs治疗患者OS的有效标志物，并为未来基于机器学习的生物标志物开发提供重要参考依据。
肿瘤(癌症)患者之家