食管鳞状细胞癌（ESCC）由于其侵袭性和普遍预后不良而面临重大的临床和治疗挑战。我们启动了一项 II 期临床试验 (ChiCTR1900027160)，以评估开创性的新辅助化疗免疫治疗方案的疗效，该方案包括程序性死亡 1 (PD-1) 阻断（特瑞普利单抗）、纳米颗粒白蛋白结合紫杉醇 (nab-紫杉醇) 和口服氟嘧啶衍生物 S-1，用于局部晚期 ESCC 患者。这项研究独特地将临床结果与使用成像质谱流式细胞术 (IMC) 的先进空间蛋白质组学分析相结合，以阐明肿瘤微环境 (TME) 内的动态，重点关注耐药性和反应的机械相互作用。六十名患者参与其中，在手术切除前接受联合治疗。我们的研究结果表明，62% 的患者出现主要病理缓解 (MPR)，29% 的患者出现病理完全缓解 (pCR)。 IMC 分析提供了详细的区域评估，揭示了免疫细胞的空间排列，特别是三级淋巴结构 (TLS) 内的 CD8 T 细胞和 B 细胞，以及纤维化区域中的 S100A9 炎症巨噬细胞，可以预测治疗结果。采用支持向量机 (SVM) 和随机森林 (RF) 分析等机器学习方法，我们确定了与耐药性相关的关键空间特征，并开发了药物反应预测模型，实现了 97% 的曲线下面积 (AUC) 。这些见解强调了将空间蛋白质组学整合到临床试验中以彻底剖析 TME 动态的重要作用，为 ESCC 个性化和精确的癌症治疗策略铺平道路。这种整体方法不仅增强了我们对耐药性背后机制基础的理解，还为优化 ESCC 治疗干预奠定了坚实的基础。© 2024。作者，获得 Springer Nature Limited 的独家许可。

Esophageal squamous cell carcinoma (ESCC) presents significant clinical and therapeutic challenges due to its aggressive nature and generally poor prognosis. We initiated a Phase II clinical trial (ChiCTR1900027160) to assess the efficacy of a pioneering neoadjuvant chemo-immunotherapy regimen comprising programmed death-1 (PD-1) blockade (Toripalimab), nanoparticle albumin-bound paclitaxel (nab-paclitaxel), and the oral fluoropyrimidine derivative S-1, in patients with locally advanced ESCC. This study uniquely integrates clinical outcomes with advanced spatial proteomic profiling using Imaging Mass Cytometry (IMC) to elucidate the dynamics within the tumor microenvironment (TME), focusing on the mechanistic interplay of resistance and response. Sixty patients participated, receiving the combination therapy prior to surgical resection. Our findings demonstrated a major pathological response (MPR) in 62% of patients and a pathological complete response (pCR) in 29%. The IMC analysis provided a detailed regional assessment, revealing that the spatial arrangement of immune cells, particularly CD8+ T cells and B cells within tertiary lymphoid structures (TLS), and S100A9+ inflammatory macrophages in fibrotic regions are predictive of therapeutic outcomes. Employing machine learning approaches, such as support vector machine (SVM) and random forest (RF) analysis, we identified critical spatial features linked to drug resistance and developed predictive models for drug response, achieving an area under the curve (AUC) of 97%. These insights underscore the vital role of integrating spatial proteomics into clinical trials to dissect TME dynamics thoroughly, paving the way for personalized and precise cancer treatment strategies in ESCC. This holistic approach not only enhances our understanding of the mechanistic basis behind drug resistance but also sets a robust foundation for optimizing therapeutic interventions in ESCC.© 2024. The Author(s), under exclusive licence to Springer Nature Limited.