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Spatial proteomic profiling elucidates immune determinants of neoadjuvant chemo-immunotherapy in esophageal squamous cell carcinoma

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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 [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
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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.

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出版当年[2023]版:
大类 | 1 区 医学
小类 | 1 区 生化与分子生物学 1 区 遗传学 2 区 细胞生物学 2 区 肿瘤学
最新[2023]版:
大类 | 1 区 医学
小类 | 1 区 生化与分子生物学 1 区 遗传学 2 区 细胞生物学 2 区 肿瘤学
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出版当年[2023]版:
Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Q1 CELL BIOLOGY Q1 GENETICS & HEREDITY Q1 ONCOLOGY
最新[2023]版:
Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Q1 CELL BIOLOGY Q1 GENETICS & HEREDITY Q1 ONCOLOGY

影响因子: 最新[2023版] 最新五年平均 出版当年[2023版] 出版当年五年平均 出版前一年[2023版]

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第一作者机构: [1]Chinese Peoples Liberat Army Gen Hosp, Med Ctr 5, Dept Med Oncol, Sr Dept Oncol, Beijing, Peoples R China
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通讯机构: [2]Nanjing Univ Aeronaut & Astronaut, Coll Artificial Intelligence, Nanjing, Peoples R China [8]Chinese Inst Med Res, Beijing, Peoples R China
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