Gastric Cancer (GC) characteristically exhibits heterogeneous responses to treatment, particularly in relation to immuno plus chemo therapy, necessitating a precision medicine approach. This study is centered around delineating the cellular and molecular underpinnings of drug resistance in this context. We undertook a comprehensive multi-omics exploration of postoperative tissues from GC patients undergoing the chemo and immuno-treatment regimen. Concurrently, an image deep learning model was developed to predict treatment responsiveness. Our initial findings associate apical membrane cells with resistance to fluorouracil and oxaliplatin, critical constituents of the therapy. Further investigation into this cell population shed light on substantial interactions with resident macrophages, underscoring the role of intercellular communication in shaping treatment resistance. Subsequent ligand-receptor analysis unveiled specific molecular dialogues, most notably TGFB1-HSPB1 and LTF-S100A14, offering insights into potential signaling pathways implicated in resistance. Our SVM model, incorporating these multi-omics and spatial data, demonstrated significant predictive power, with AUC values of 0.93 and 0.84 in the exploration and validation cohorts respectively. Hence, our results underscore the utility of multi-omics and spatial data in modeling treatment response. Our integrative approach, amalgamating mIHC assays, feature extraction, and machine learning, successfully unraveled the complex cellular interplay underlying drug resistance. This robust predictive model may serve as a valuable tool for personalizing therapeutic strategies and enhancing treatment outcomes in gastric cancer.

中文翻译：

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规避胃癌耐药性：化疗和免疫治疗反应动力学的空间多组学探索


胃癌 (GC) 的特点是对治疗表现出异质性反应，特别是与免疫加化疗相关的反应，因此需要精准医疗方法。这项研究的重点是描绘这种情况下耐药性的细胞和分子基础。我们对接受化疗和免疫治疗方案的胃癌患者的术后组织进行了全面的多组学探索。同时，开发了图像深度学习模型来预测治疗反应。我们的初步发现将顶膜细胞与氟尿嘧啶和奥沙利铂（治疗的关键成分）的耐药性联系起来。对这个细胞群的进一步研究揭示了与常驻巨噬细胞的实质性相互作用，强调了细胞间通讯在形成治疗抵抗中的作用。随后的配体-受体分析揭示了特定的分子对话，最引人注目的是 TGFB1-HSPB1 和 LTF-S100A14，为与耐药性相关的潜在信号通路提供了见解。我们的 SVM 模型结合了这些多组学和空间数据，表现出显着的预测能力，在探索和验证队列中 AUC 值分别为 0.93 和 0.84。因此，我们的结果强调了多组学和空间数据在治疗反应建模中的效用。我们的综合方法结合了 mIHC 检测、特征提取和机器学习，成功地揭示了耐药性背后复杂的细胞相互作用。这种强大的预测模型可以作为个性化治疗策略和增强胃癌治疗效果的宝贵工具。