{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T10:12:17Z","timestamp":1790244737334,"version":"4.1.0"},"reference-count":94,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell Dev. Biol."],"abstract":"<jats:p>Spatial proteomics is reshaping tissue-based cancer research by linking protein expression to cellular identity, tissue architecture and clinical behavior. Conventional proteomics has provided powerful tools for protein identification and quantification, but most bulk workflows erase the spatial relationships that define tumor ecology. In contrast, spatial proteomics combines multiplexed imaging, mass spectrometry-based tissue analysis, region-selected profiling and computational spatial statistics to measure proteins in their native tissue context. This review summarizes the development of proteomics from early separation and mass spectrometry technologies to current spatial platforms. It then focuses on cancer applications, including tumor heterogeneity, tumor microenvironment, antitumor immunity, immunotherapy response, spatial biomarkers and treatment resistance. Across these areas, spatial proteomics is most valuable when it converts descriptive tissue patterns into testable hypotheses about cell states, neighborhood interactions and therapeutic vulnerabilities. The field now needs standardized workflows, clinically anchored cohorts and functional validation to move from high-dimensional tissue maps to robust translational tools.<\/jats:p>","DOI":"10.3389\/fcell.2026.1914317","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T09:45:46Z","timestamp":1790243146000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Spatial proteomics: technical evolution and applications in cancer research"],"prefix":"10.3389","volume":"14","author":[{"given":"Xiaoxiao","family":"Tian","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, The First Affiliated Hospital, College of Clinical Medicine, Henan University of Science and Technology","place":["Luoyang, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Child Healthcare, The Third Affiliated Hospital of Zhengzhou University","place":["Zhengzhou, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxiang","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Scientific Research, The First Affiliated Hospital of Zhengzhou University","place":["Zhengzhou, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"216985","DOI":"10.1016\/j.canlet.2024.216985","article-title":"Colorectal cancer-associated fibroblasts inhibit effector T cells via NECTIN2 signaling","volume":"595","author":"Agorku","year":"2024","journal-title":"Cancer Lett."},{"key":"B2","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nm.3488","article-title":"Multiplexed ion beam imaging of human breast tumors","volume":"20","author":"Angelo","year":"2014","journal-title":"Nat. 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