{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T07:00:27Z","timestamp":1784962827318,"version":"3.55.0"},"reference-count":52,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":20,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100002794","name":"Cancerfonden","doi-asserted-by":"publisher","award":["21\u20131761Pj"],"award-info":[{"award-number":["21\u20131761Pj"]}],"id":[{"id":"10.13039\/501100002794","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002794","name":"Cancerfonden","doi-asserted-by":"publisher","award":["2022\u20132024"],"award-info":[{"award-number":["2022\u20132024"]}],"id":[{"id":"10.13039\/501100002794","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002794","name":"Cancerfonden","doi-asserted-by":"publisher","award":["24\u20133799Pj"],"award-info":[{"award-number":["24\u20133799Pj"]}],"id":[{"id":"10.13039\/501100002794","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002794","name":"Cancerfonden","doi-asserted-by":"publisher","award":["2025\u20132027"],"award-info":[{"award-number":["2025\u20132027"]}],"id":[{"id":"10.13039\/501100002794","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004359","name":"Vetenskapsr\u00e5det","doi-asserted-by":"publisher","award":["2023\u201001899"],"award-info":[{"award-number":["2023\u201001899"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004359","name":"Vetenskapsr\u00e5det","doi-asserted-by":"publisher","award":["2024\u20132026"],"award-info":[{"award-number":["2024\u20132026"]}],"id":[{"id":"10.13039\/501100004359","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Clinical &amp; Translational Med"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The systematic link between cellular spatial organization and its biomechanical consequences remains a critical knowledge gap in human oncology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To quantitatively map the cellular architecture of solid tumours and elucidate its mechanistic impact on mechanical signalling, immune infiltration, and patient survival.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We developed a scalable digital pathology framework, processing 7910 H&amp;E whole\u2010slide images across 21 solid tumours. A deep learning pipeline was employed to segment over 4.7 billion nuclei, enabling the calculation of cell density and\u00a0nearest neighbour distance (NND)\u00a0as key spatial metrics. To bridge morphology and function, we integrated these metrics with bulk RNA\u2010seq data from 19 TCGA cohorts (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a07401) using rigorous histological matching. Furthermore, to resolve microenvironmental heterogeneity, we performed unsupervised clustering of over 60\u00a0000 T\u2010cell transcriptomes from independent cohorts. These findings were validated through high\u2010resolution\u00a0Visium\u2010HD spatial transcriptomics\u00a0to correlate physical proximity with localized gene expression.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      While tumours exhibited significant heterogeneity, NND, but not cell density, emerged as a primary determinant of biomechanical and immune signatures. A trend\u2010level association was observed between lower NND and higher Hippo\/YAP\/TAZ pathway activity across nine matched\u00a0cancer types (Spearman's\n                      <jats:italic>\u03c1<\/jats:italic>\n                      \u00a0=\u00a0\u2010.65,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a0.058,\n                      <jats:italic>n\u00a0<\/jats:italic>\n                      =\u00a09). In addition, lower NND was significantly correlated with increased CD4\n                      <jats:sup>+<\/jats:sup>\n                      memory T\u2010cell (CD4\n                      <jats:sup>+<\/jats:sup>\n                      T\n                      <jats:sub>Mem<\/jats:sub>\n                      cell) abundance (Spearman's\n                      <jats:italic>\u03c1<\/jats:italic>\n                      \u00a0=\u00a0\u2010.86,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a0.01). Single\u2010cell analyses confirmed that CD4\n                      <jats:sup>+<\/jats:sup>\n                      T\n                      <jats:sub>Mem<\/jats:sub>\n                      cells intrinsically express mechanical stress signalling markers, which spatial transcriptomics localized to CD4\n                      <jats:sup>+<\/jats:sup>\n                      T\n                      <jats:sub>Mem<\/jats:sub>\n                      cell aggregation zones characterized by high pathway activity. Clinically, this spatial\u2010mechanical\u2010CD4\n                      <jats:sup>+<\/jats:sup>\n                      T\n                      <jats:sub>Mem<\/jats:sub>\n                      cell axis was associated with prognosis in breast, oesophageal, liver, and lung adenocarcinomas, where high mechanical signalling generally predicted poor outcomes but could be modulated by CD4\n                      <jats:sup>+<\/jats:sup>\n                      T\n                      <jats:sub>Mem<\/jats:sub>\n                      infiltration levels.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      Our study identifies low NND as a spatial correlate of biomechanical crowding that is associated with CD4\n                      <jats:sup>+<\/jats:sup>\n                      T\n                      <jats:sub>Mem<\/jats:sub>\n                      cell programming and adverse clinical outcomes. By integrating deep learning\u2010based spatial metrics with multi\u2010omics, we highlight spatial mechanics as a critical, potentially targetable dimension of the tumour microenvironment for future immunotherapies.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ctm2.70742","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T05:11:26Z","timestamp":1784697086000},"source":"Crossref","is-referenced-by-count":0,"title":["Pan\u2010cancer atlas of cellular architecture reveals a nearest\u2010neighbour distance\u2010associated biomechanical\u2010immune axis involving CD4+ memory T cells"],"prefix":"10.1002","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9709-0492","authenticated-orcid":false,"given":"Linglong","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Oncology\u2010Pathology Karolinska Institutet  Stockholm 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