{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T22:13:25Z","timestamp":1791843205869,"version":"4.3.4"},"reference-count":48,"publisher":"OAE Publishing Inc.","content-domain":{"domain":["oaepublish.com"],"crossmark-restriction":false},"short-container-title":["Cancer Drug Resist."],"abstract":"<jats:p>Aim: Clear cell renal cell carcinoma (ccRCC) presents an \u201cimmune paradox\u201d where high CD8&lt;sup&gt;+&lt;\/sup&gt; T cell infiltration correlates with poor survival and limited response to immune checkpoint blockade (ICB). Simple cell density metrics fail to capture the functional state of the immune microenvironment, suggesting an uncharacterized spatial mechanism underlying immune evasion and poor clinical outcomes. We aimed to develop an AI-driven spatial proteomics framework to decode this immunosuppressive phenotype.<\/jats:p>\n                  <jats:p>Methods: We developed PhenoSSP, a hierarchical deep learning framework based on a Vision Transformer backbone, designed for single-cell phenotyping from 7-channel multiplex immunofluorescence (mIF) images. It was applied to 1,633 tissue microarray (TMA) cores from 834 ccRCC patients. We defined a density-normalized Spatial Interaction Score was defined to quantify FOXP3&lt;sup&gt;+&lt;\/sup&gt; regulatory T cell (Treg) enrichment within a 30 \u03bcm radius of CD8&lt;sup&gt;+&lt;\/sup&gt; T cells. Survival analyses were performed using Kaplan\u2013Meier curves with log-rank tests and multivariable Cox regression models.<\/jats:p>\n                  <jats:p>Results: PhenoSSP achieved a balanced accuracy of 71.0% and an F1-Macro of 70.7%, outperforming conventional methods. CD8&lt;sup&gt;+&lt;\/sup&gt; T-cell density alone was not significantly associated with overall survival (OS, P = 0.057), whereas the Spatial Interaction Score was significantly associated with poor OS (log-rank test, P = 0.012) and was significantly higher in non-survivors (P = 0.032).<\/jats:p>\n                  <jats:p>Conclusion: This study reveals a spatial basis for immune evasion and poor prognosis in ccRCC: the pre-existing Treg enrichment near CD8&lt;sup&gt;+&lt;\/sup&gt; T cells, rather than the abundance of effector cells, was associated with impaired antitumor immunity. The Spatial Interaction Score serves as a candidate prognostic biomarker and provides a rationale for Treg-targeting combination strategies in patients harboring spatially defined suppressive niches.<\/jats:p>","DOI":"10.20517\/cdr.2026.27","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T10:20:54Z","timestamp":1779186054000},"update-policy":"https:\/\/doi.org\/10.20517\/oae.crossmark.policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep spatial proteomics reveals a suppressive immune niche linked to immune evasion in renal cell carcinoma"],"prefix":"10.20517","author":[{"given":"Yujia","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yicheng","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zichang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haonan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingxu","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingang","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8347-0830","authenticated-orcid":true,"given":"Hui","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"8410","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccell.2015.03.001"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1038\/nrc3245"},{"key":"B3","unstructured":"Proliferative activity of intratumoral CD8<sup>+<\/sup> T-lymphocytes as a prognostic factor in human renal cell carcinoma: clinicopathologic demonstration of antitumor immunity"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1158\/1078-0432.ccr-14-2926"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-0839-y"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1510665"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-025-03867-5"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1126\/science.1129139"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1136\/jitc-2024-010183"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1126\/science.1202947"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1016\/j.imlet.2010.06.001"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-021-01094-0"},{"key":"B13","unstructured":"Scaling vision transformers to gigapixel images via hierarchical self-supervised learning. arXiv 2022; arXiv:2206.02647. 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