Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Front Immunol. 2024 Sep 9;15:1379812. doi: 10.3389/fimmu.2024.1379812. eCollection 2024.
Identifying patients with non-small cell lung cancer (NSCLC) who are optimal candidates for immunotherapy is a cornerstone in clinical decision-making. The tumor immune microenvironment (TIME) is intricately linked with both the prognosis of the malignancy and the efficacy of immunotherapeutic interventions. CD8+ T cells, and more specifically, tissue-resident memory CD8+ T cells [CD8+ tissue-resident memory T (TRM) cells] are postulated to be pivotal in orchestrating the immune system's assault on tumor cells. Nevertheless, the accurate quantification of immune cell infiltration-and by extension, the prediction of immunotherapeutic efficacy-remains a significant scientific frontier.
In this study, we introduce a cutting-edge non-invasive radiomic model, grounded in TIME markers (CD3+ T, CD8+ T, and CD8+ TRM cells), to infer the levels of immune cell infiltration in NSCLC patients receiving immune checkpoint inhibitors and ultimately predict their response to immunotherapy. Data from patients who had surgical resections (cohort 1) were employed to construct a radiomic model capable of predicting the TIME. This model was then applied to forecast the TIME for patients under immunotherapy (cohort 2). Conclusively, the study delved into the association between the predicted TIME from the radiomic model and the immunotherapeutic outcomes of the patients.
For the immune cell infiltration radiomic prediction models in cohort 1, the AUC values achieved 0.765, 0.763, and 0.675 in the test set of CD3+ T, CD8+ T, and CD8+ TRM, respectively. While the AUC values for the TIME-immunotherapy predictive value were 0.651, 0.763, and 0.829 in the CD3-immunotherapy response model, CD8-immunotherapy response model, and CD8+ TRM-immunotherapy response model in cohort 2, respectively. The CD8+ TRM-immunotherapy model exhibited the highest predictive value and was significantly better than the CD3-immunotherapy model in predicting the immunotherapy response. The progression-free survival (PFS) analysis based on the predicted levels of CD3+ T, CD8+ T, and CD8+ TRM immune cell infiltration showed that the CD8+ T cell infiltration level was an independent factor (P=0.014, HR=0.218) with an AUC value of 0.938.
Our empirical evidence reveals that patients with substantial CD8+ T cell infiltration experience a markedly improved PFS compared with those with minimal infiltration, asserting the status of the CD8+ T cell as an independent prognosticator of PFS in the context of immunotherapy. Although CD8+ TRM cells demonstrated the greatest predictive accuracy for immunotherapy response, their predictive strength for PFS was marginally surpassed by that of CD8+ T cells. These insights advocate for the application of the proposed non-invasive radiomic model, which utilizes TIME analysis, as a reliable predictor for immunotherapy outcomes and PFS in NSCLC patients.
识别适合接受免疫治疗的非小细胞肺癌(NSCLC)患者是临床决策的基石。肿瘤免疫微环境(TIME)与恶性肿瘤的预后和免疫治疗干预的疗效密切相关。CD8+T 细胞,更具体地说,组织驻留记忆 CD8+T 细胞[CD8+组织驻留记忆 T(TRM)细胞]被认为在协调免疫系统攻击肿瘤细胞方面发挥着关键作用。然而,准确量化免疫细胞浸润——并由此预测免疫治疗疗效——仍然是一个重大的科学前沿。
在这项研究中,我们引入了一种前沿的非侵入性放射组学模型,该模型基于 TIME 标志物(CD3+T、CD8+T 和 CD8+TRM 细胞),用于推断接受免疫检查点抑制剂治疗的 NSCLC 患者的免疫细胞浸润水平,并最终预测他们对免疫治疗的反应。利用接受手术切除的患者(队列 1)的数据构建了一种能够预测 TIME 的放射组学模型。然后,该模型被应用于预测接受免疫治疗的患者的 TIME(队列 2)。最后,研究探讨了放射组学模型预测的 TIME 与患者免疫治疗结果之间的关联。
在队列 1 的免疫细胞浸润放射组学预测模型中,CD3+T、CD8+T 和 CD8+TRM 的测试集 AUC 值分别达到 0.765、0.763 和 0.675。而在队列 2 的 CD3-免疫治疗反应模型、CD8-免疫治疗反应模型和 CD8+TRM-免疫治疗反应模型中,TIME-免疫治疗预测值的 AUC 值分别为 0.651、0.763 和 0.829。CD8+TRM-免疫治疗模型表现出最高的预测价值,在预测免疫治疗反应方面明显优于 CD3-免疫治疗模型。基于预测的 CD3+T、CD8+T 和 CD8+TRM 免疫细胞浸润水平的无进展生存期(PFS)分析表明,CD8+T 细胞浸润水平是一个独立因素(P=0.014,HR=0.218),AUC 值为 0.938。
我们的实证证据表明,与浸润最少的患者相比,浸润大量 CD8+T 细胞的患者的 PFS 显著改善,这表明 CD8+T 细胞状态是免疫治疗中 PFS 的一个独立预后因素。尽管 CD8+TRM 细胞对免疫治疗反应的预测准确性最高,但它们对 PFS 的预测强度略逊于 CD8+T 细胞。这些研究结果支持应用基于 TIME 分析的无创放射组学模型作为 NSCLC 患者免疫治疗结果和 PFS 的可靠预测因子。