{"title":"Spatially resolving cancer: from cell states to therapy.","authors":"Guangsheng Pei, Yang Liu, Linghua Wang","doi":"10.1016/j.trecan.2025.09.002","DOIUrl":null,"url":null,"abstract":"<p><p>Recent advances in spatial multi-omics technologies and analytical methods are transforming our understanding of how cancer cells and their microenvironments interact to drive critical processes such as lineage plasticity, immune evasion, and therapeutic resistance. By linking cancer cell states, lineage plasticity, clonal dynamics, oncogenic pathways, and cellular interactions to their spatial context, these innovations provide deep biological insights and reveal clinically relevant molecular programs and spatial biomarkers. This review highlights key breakthroughs in spatial profiling and computational approaches, including integration with computational pathology, multimodal data, and machine learning to uncover important biological insights. We discuss challenges in spatial multimodal data integration and emerging clinical applications, and we propose a roadmap to accelerate clinical translation and advance precision oncology through spatially resolved, actionable, molecular insights.</p>","PeriodicalId":23336,"journal":{"name":"Trends in cancer","volume":" ","pages":""},"PeriodicalIF":17.5000,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.trecan.2025.09.002","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Recent advances in spatial multi-omics technologies and analytical methods are transforming our understanding of how cancer cells and their microenvironments interact to drive critical processes such as lineage plasticity, immune evasion, and therapeutic resistance. By linking cancer cell states, lineage plasticity, clonal dynamics, oncogenic pathways, and cellular interactions to their spatial context, these innovations provide deep biological insights and reveal clinically relevant molecular programs and spatial biomarkers. This review highlights key breakthroughs in spatial profiling and computational approaches, including integration with computational pathology, multimodal data, and machine learning to uncover important biological insights. We discuss challenges in spatial multimodal data integration and emerging clinical applications, and we propose a roadmap to accelerate clinical translation and advance precision oncology through spatially resolved, actionable, molecular insights.
期刊介绍：
Trends in Cancer, a part of the Trends review journals, delivers concise and engaging expert commentary on key research topics and cutting-edge advances in cancer discovery and medicine.
Trends in Cancer serves as a unique platform for multidisciplinary information, fostering discussion and education for scientists, clinicians, policy makers, and patients & advocates.Covering various aspects, it presents opportunities, challenges, and impacts of basic, translational, and clinical findings, industry R&D, technology, innovation, ethics, and cancer policy and funding in an authoritative yet reader-friendly format.
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