Abstract Gastric cancer (GC) exhibits marked heterogeneity, complex molecular alterations, and limited therapeutic options. To comprehensively define its biology and vulnerabilities, we performed 15-layer multi-omics profiling of 159 gastric adenocarcinomas and 30 matched normal adjacent tissues, encompassing genomics, epigenomics, transcriptomics, proteomics, post-translational modifications, protein–protein interactions, metabolomics, and microbiome analyses, yielding more than 385,000 features. By integrating cell-state deconvolution, we defined gastric tumor ecotypes based on cellular states, providing a new framework for multi-omics integration. These ecotypes captured distinct tumor ecosystems and stromal–immune compositions and offered deeper mechanistic insight than conventional genomic or histologic classifications. Leveraging machine learning and large-scale AI models, we identified ecotype-specific molecular features and linked them to clinical outcome. To prioritize therapeutic opportunities, we applied high-outlier analysis to proteins and glycoproteins. Multiple cell-surface–associated targets showed strong high-outlier expression, including several known or emerging therapeutic candidates. Extracellular matrix (ECM) components were significantly enriched among high-outlier proteins, underscoring their central role in tumor growth, invasion, and potential therapeutic targeting. We further characterized altered cell-surface glycosylation patterns in high-outlier glycoproteins, revealing changes in immune regulation and ECM engagement. Phosphosite-resolved analysis identified key signaling signatures associated with aggressive tumor behavior. Importantly, embedding these high-outlier events within ecotype and genomic subtype frameworks revealed distinct, ecotype-specific patterns that were not apparent from genomic classification alone. Single-cell analyses localized many targets to specific stromal compartments, particularly fibroblast-rich ecosystems, suggesting that targeting fibroblast-driven niches may provide new therapeutic strategies for aggressive GC subsets. Overall, this study establishes an ecotype-guided proteogenomic approach to nominate cell-surface proteins, glycoproteins, and signaling nodes as precision therapy candidates in gastric cancer, and offers a broadly applicable model for dissecting heterogeneity in other complex malignancies. Citation Format: Yuefan Wang, Lindsey K. Olsen, Hui Zhang, Bing Zhang, Clinical Proteomic Tumor Analysis Consortium (CPTAC). Ecotype-guided multi-omics profiling identifies potential cell-surface therapeutic targets in gastric cancer abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7647.
Wang et al. (Fri,) studied this question.
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