PO.CL05.04 · 临床研究
转移性黑色素瘤对新辅助免疫检查点阻断应答的空间解析免疫学标志
Spatially resolved immunologic hallmarks of response to neoadjuvant immune checkpoint blockade in metastatic melanoma
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景:近期黑色素瘤中具有开创性的新辅助免疫检查点阻断(NICB)临床试验决定性地证明,在根治意向手术前给予ICB将成为转移性黑色素瘤及其他癌症的新标准治疗。从影像学到病理学应答评估的范式转变,也为获取丰富的"治疗中"组织提供了无与伦比的"机会之窗",以阐明应答的机制和生物标志物,并指导术后个体化治疗决策。迄今为止,单细胞/整体RNA研究已证明TCF7+干样CD8+ T细胞和三级淋巴结构(TLS)是ICB应答的阳性预测因素。然而,这些免疫细胞如何在新辅助肿瘤微环境的空间背景中组织和通讯仍知之甚少。
方法:我们研究了一个独特的队列,包括来自87例III期转移性黑色素瘤患者的91份FFPE生物标本,其中60份接受NICB治疗(31例完全应答,7例部分应答,22例无应答),27份为未经治疗。我们部署了最先进的技术,包括多重误差稳健荧光原位杂交(MERFISH)、FFPE蜡块的单细胞测序和多重免疫荧光,构建了这一丰富的数字资源。重要的是,为分析这些具有挑战性的高维空间数据集,我们开发了3种新颖的计算算法,包括SCIRA(一种用于空间受体-配体(R-L)相互作用的可扩展量化方法);GC-SCAN(一种基于图的聚类方法,用于在单细胞空间数据中检测生发中心(GC)/TLS结构);以及PathNet(一种用于在H&E切片上自动检测GC/TLS的端到端AI算法)。
结果:在此,我们分析了约560万个细胞,并显示在空间上不同的细胞邻域中GC/成熟TLS、TCF7+干样CD8和CD4 T细胞、耗竭CD8 T细胞、浆细胞和髓系细胞的增加与阳性应答显著相关。我们的空间(R-L)分析进一步识别出GC-B细胞与滤泡辅助T细胞之间,以及TCF7+干样T细胞与CCL19+/CCL21+成纤维细胞之间,在组织这些免疫枢纽中优先的趋化因子R-L相互作用。最后,我们的PathNet GC/TLS检测算法在特异性和敏感性上均优于近期最先进的方法,证明其在促进临床应答评估方面的潜在效用。
结论:我们利用前沿的空间组学技术和新颖的计算方法,解析了转移性黑色素瘤中NICB应答的免疫学标志。我们相信,我们的方法为如何在快速发展的标准治疗新辅助免疫治疗时代精确识别支撑治疗应答的空间上错综复杂的免疫相互作用提供了范例。
查看英文原文 English abstract
Background: The recent groundbreaking neoadjuvant immune checkpoint blockade (NICB) clinical trials in melanoma have demonstrated decisively that the administration of ICB prior to intent-to-cure surgeries will become the new standard-of-care for metastatic melanoma and beyond. The paradigm shifts from radiographic to pathologic response assessment also presents an unparalleled “window of opportunity” for the acquisition of abundant “on-treatment” tissues to elucidate the mechanisms and biomarkers of response and for guiding post-surgery personalized therapy decisions. To date, single-cell/bulk RNA studies have demonstrated that TCF7+ stem-like CD8+ T cells and tertiary lymphoid structures (TLS) are positive predictors of ICB response. However, how these immune cells organize and communicate within the spatial context of the neoadjuvant tumor microenvironment remains poorly understood.
Methods: We investigated a unique cohort of 91 FFPE biospecimens from 87 patients with stage III metastatic melanoma, including 60 treated with NICB (31 complete response, 7 partial response, 22 non-response) and 27 treatment-naïve. We deployed state-of-the-art technologies, including multiplexed error-robust fluorescent in situ hybridization (MERFISH), single cell sequencing of FFPE blocks, and multiplexed IF for this rich digital resource. Importantly, to analyze these challenging high dimensional spatial datasets, we developed 3 novel computational algorithms, including SCIRA, a scalable quantification method for spatial receptor-ligand (R-L) interactions; GC-SCAN, a graph-based clustering method to detect Germinal Center (GC)/TLS structures in single cell spatial data; and PathNet, an end-to-end AI algorithm for automated GC/TLS detection on H&E slides.
Results: Here, we interrogated ~5.6 million cells and showed that increased GC/mature TLSs, TCF7+ stem-like CD8 and CD4 T-cells, exhausted CD8 T-cells, plasma cells and myeloids in spatially distinct cellular neighborhoods are significantly associated with positive response. Our spatial (R-L) analyses further identified preferential chemokine R-L interactions between GC-B cells and follicular helper T-cell, and TCF7+ stem-like T-cells with CCL19+/CCL21+ fibroblasts in organizing these immune hubs. Lastly, Our PathNet GC/TLS detection algorithm outperforms recent state-of-the-art methods in both specificity and sensitivity, proven potential utility in facilitating clinical response assessment.
Conclusions: We leveraged cutting-edge spatial-omics technologies and novel computational methods to resolve the immunologic hallmarks of NICB response in metastatic melanoma. We believe our approach provides a model for how to precisely identify spatially intricate immune interactions that underline treatment response in the rapidly advancing era of standard of care neoadjuvant immunotherapy.
利益披露 Disclosure
Z. Liu, None.