γδ T 细胞调节小细胞肺癌中的抗肿瘤免疫
γδ T cells modulate anti-tumor immunity in small cell lung cancer.
我们的发现表明,活化的γδ T细胞可能是SCLC治疗的有价值靶点。
英文原题:Image analysis reveals molecularly distinct patterns of TILs in NSCLC associated with treatment outcome.
我们的结果提示,需要建立组织学亚型特异性的基于 TIL 的模型,用于生存风险分层和预测治疗应答。
尽管肺腺癌(LUAD)和鳞状细胞癌(LUSC)在组织学、生物学和临床方面存在已知差异,但人们对两者免疫环境的空间差异所知甚少。我们对 1000 多例 LUAD 和 LUSC 肿瘤进行研究,发现基于 H&E 图像计算得到的TIL(肿瘤浸润淋巴细胞)模式在 LUAD(N=421)和 LUSC(N=438)之间不同:TIL 密度可预测 LUAD 总生存期,而 TIL 的空间分布在 LUSC 中具有更强的预后意义。此外,在一组 100 例接受 6 种以上不同新辅助化疗方案的 NSCLC 外部验证队列中,LUAD 特异性 TIL 特征与 OS 相关,并可预测临床试验 CA209-057(n=303)中的治疗反应。在 LUAD 中,预后相关 TIL 特征主要由 CD4⁺ T 细胞和 CD8⁺ T 细胞组成;在 LUSC 中,免疫模式则包括 CD4⁺ T、CD8⁺ T 和 CD20⁺ B 细胞。两种亚型中,预后相关 TIL 特征均与转录组衍生的免疫评分及参与免疫识别、应答和逃逸的生物学通路相关。结果提示,需要按组织学亚型构建基于 TIL 的模型,以进行生存风险分层和治疗反应预测。本研究还提示,治疗反应预测模型应考虑 NSCLC 不同组织学亚型各自独特的形态学和分子免疫模式。
Despite known histological, biological, and clinical differences between lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), relatively little is known about the spatial differences in their corresponding immune contextures. Our study of over 1000 LUAD and LUSC tumors revealed that computationally derived patterns of tumor-infiltrating lymphocytes (TILs) on H&E images were different between LUAD (N = 421) and LUSC (N = 438), with TIL density being prognostic of overall survival in LUAD and spatial arrangement being more prognostically relevant in LUSC. In addition, the LUAD-specific TIL signature was associated with OS in an external validation set of 100 NSCLC treated with more than six different neoadjuvant chemotherapy regimens, and predictive of response to therapy in the clinical trial CA209-057 (n = 303). In LUAD, the prognostic TIL signature was primarily comprised of CD4 + T and CD8 + T cells, whereas in LUSC, the immune patterns were comprised of CD4 + T, CD8 + T, and CD20 + B cells. In both subtypes, prognostic TIL features were associated with transcriptomics-derived immune scores and biological pathways implicated in immune recognition, response, and evasion. Our results suggest the need for histologic subtype-specific TIL-based models for stratifying survival risk and predicting response to therapy. Our findings suggest that predictive models for response to therapy will need to account for the unique morphologic and molecular immune patterns as a function of histologic subtype of NSCLC.
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