Circumventing drug resistance in gastric cancer: A spatial multi-omics exploration of chemo and immuno-therapeutic response dynamics

IF 15.8 1区 医学 Q1 PHARMACOLOGY & PHARMACY Drug Resistance Updates Pub Date : 2024-03-19 DOI:10.1016/j.drup.2024.101080
Gang Che , Jie Yin , Wankun Wang , Yandong Luo , Yiran Chen , Xiongfei Yu , Haiyong Wang , Xiaosun Liu , Zhendong Chen , Xing Wang , Yu Chen , Xujin Wang , Kaicheng Tang , Jiao Tang , Wei Shao , Chao Wu , Jianpeng Sheng , Qing Li , Jian Liu
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Abstract

Background

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.

Methods

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.

Results

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.

Conclusion

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.

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规避胃癌的耐药性:化疗和免疫治疗反应动态的空间多组学探索
胃癌(GC)对治疗的反应各不相同,尤其是对免疫加化疗的反应,这就需要采用精准医疗方法。本研究的核心是阐明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的 GC 患者的术后组织进行了全面的多组学研究。同时,我们还开发了一个图像深度学习模型来预测治疗反应性。我们的初步研究结果表明,根尖膜细胞与氟尿嘧啶和奥沙利铂的耐药性有关,而氟尿嘧啶和奥沙利铂是治疗的关键成分。对这一细胞群的进一步研究揭示了其与常驻巨噬细胞之间的实质性相互作用,强调了细胞间交流在形成耐药性方面的作用。随后的配体-受体分析揭示了特定的分子对话,其中最引人注目的是 TGFB1-HSPB1 和 LTF-S100A14,为了解与耐药性有关的潜在信号通路提供了线索。我们的 SVM 模型结合了这些多组学和空间数据,显示出显著的预测能力,在探索组和验证组中的 AUC 值分别为 0.93 和 0.84。因此,我们的研究结果凸显了多组学和空间数据在治疗反应建模中的实用性。我们的综合方法融合了 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。这一强大的预测模型可作为个性化治疗策略和提高胃癌治疗效果的重要工具。
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来源期刊
Drug Resistance Updates
Drug Resistance Updates 医学-药学
CiteScore
26.20
自引率
11.90%
发文量
32
审稿时长
29 days
期刊介绍: Drug Resistance Updates serves as a platform for publishing original research, commentary, and expert reviews on significant advancements in drug resistance related to infectious diseases and cancer. It encompasses diverse disciplines such as molecular biology, biochemistry, cell biology, pharmacology, microbiology, preclinical therapeutics, oncology, and clinical medicine. The journal addresses both basic research and clinical aspects of drug resistance, providing insights into novel drugs and strategies to overcome resistance. Original research articles are welcomed, and review articles are authored by leaders in the field by invitation. Articles are written by leaders in the field, in response to an invitation from the Editors, and are peer-reviewed prior to publication. Articles are clear, readable, and up-to-date, suitable for a multidisciplinary readership and include schematic diagrams and other illustrations conveying the major points of the article. The goal is to highlight recent areas of growth and put them in perspective. *Expert reviews in clinical and basic drug resistance research in oncology and infectious disease *Describes emerging technologies and therapies, particularly those that overcome drug resistance *Emphasises common themes in microbial and cancer research
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