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

• Published in: Drug resistance updates 2024-05, Vol.74, p.101080, Article 101080
• Main Authors: Che, Gang, Yin, Jie, Wang, Wankun, Luo, Yandong, Chen, Yiran, Yu, Xiongfei, Wang, Haiyong, Liu, Xiaosun, Chen, Zhendong, Wang, Xing, Chen, Yu, Wang, Xujin, Tang, Kaicheng, Tang, Jiao, Shao, Wei, Wu, Chao, Sheng, Jianpeng, Li, Qing, Liu, Jian
• Format: Article
• Language: English
• Subjects: Cell interaction; Gastric cancer; Multi-omics; Treatment response
• ISSN: 1368-7646; 1532-2084; 1532-2084
• DOI: 10.1016/j.drup.2024.101080

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.