Supporting data for “Deep learning-Driven Multimodal Immunomics: Spatial Profiling of Tumor Microenvironment in Esophageal Squamous Cell Carcinoma Undergoing Neoadjuvant Immunochemotherapy"

This dataset is relative to a research project investigating immune-related mechanisms underlying long-term prognosis in esophageal squamous cell carcinoma patients receiving neoadjuvant immunochemotherapy. The study included a retrospective cohort of 72 patients, integrating multiple data modalities to characterize the tumor immune microenvironment. The dataset comprises multiplex immunohistochemistry images, spatial proteomics profiles, and related deep learning model. In addition, selected validation results from single-cell RNA sequencing and in vitro/in vivo functional assays are included to support the biological relevance of candidate immune cell subtypes. These files collectively support a multi-dimensional analysis pipeline combining spatial imaging, deep learning, and immunological profiling.

本数据集关联一项研究项目，旨在探究接受新辅助免疫化疗（neoadjuvant immunochemotherapy）的食管鳞状细胞癌（esophageal squamous cell carcinoma）患者长期预后背后的免疫相关机制。该研究纳入72例患者的回顾性队列（retrospective cohort），通过整合多模态数据对肿瘤免疫微环境（tumor immune microenvironment）进行表征。本数据集包含多重免疫组化图像、空间蛋白质组学（spatial proteomics）图谱以及配套深度学习模型。此外，数据集还收录了单细胞RNA测序（single-cell RNA sequencing）与体外/体内功能实验（in vitro/in vivo functional assays）的精选验证结果，以佐证候选免疫细胞亚型（immune cell subtypes）的生物学相关性。上述文件共同支撑起一套融合空间成像、深度学习与免疫组学分析的多维度分析流程。