机构:[1]Chinese Peoples Liberat Army Gen Hosp, Med Ctr 5, Dept Med Oncol, Sr Dept Oncol, Beijing, Peoples R China[2]Nanjing Univ Aeronaut & Astronaut, Coll Artificial Intelligence, Nanjing, Peoples R China[3]Zhengzhou Univ, Henan Prov Peoples Hosp, Peoples Hosp, Dept Oncol, Zhengzhou, Peoples R China[4]Chinese Peoples Liberat Army Gen Hosp, Med Ctr 1, Dept Radiat Oncol, Beijing, Peoples R China[5]Chongqing Univ, Shapingba Affiliated Hosp, Chongqing, Peoples R China[6]Chengdu Med Coll, Chengdu, Peoples R China[7]Univ Elect Sci & Technol China, Sichuan Canc Hosp & Inst, Sichuan Clin Res Ctr Canc, Sichuan Canc Ctr,Affiliated Canc Hosp,Dept Med Onc, Chengdu, Peoples R China四川省肿瘤医院[8]Chinese Inst Med Res, Beijing, Peoples R China[9]Chinese Peoples Liberat Army Gen Hosp, Med Ctr 1, Dept Thorac Surg, Beijing, Peoples R China
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.
基金:
National Key Research and Development Program of China (grant 2019YFA0803000 to JS), the Excellent Youth Foundation of Zhejiang Scientific (grant R22H1610037 to JS), the National Natural Science Foundation of China (grant 82173078 to JS), the Natural Science Foundation of Zhejiang Province (grant 2022C03037 to JS). Supported by the Henan Provincial Department of Science and Technology, No. 212102310047. Supported by the Henan Provincial Health Commission, No. SBGJ202003005.