{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:14:09Z","timestamp":1786115649327,"version":"build-2736575974"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"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. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Immune cell populations within the tumor microenvironment (TME) critically determine cancer prognosis and therapeutic response. Although multiplexed immunohistochemistry (mIHC) provides spatial immune profiling and bulk RNA sequencing methods offer comprehensive cellular characterization, comparative studies between these complementary methodologies remain limited across diverse tumor types.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Patient samples from various tumor types, obtained from either primary or metastatic sites, were collected. For each patient, two samples were analyzed, one for mIHC and one for RNA-seq (xCell2). Both methodologies were intended to quantify immune cell populations including CD4 and CD8 T cells, regulatory T cells (Tregs), B cells and macrophages within the TME. Samples were stratified by origin according to biopsy procedure and tissue block characteristics. The primary objective was to assess concordance between methodologies for immune cell density estimation, while exploratory analysis evaluated liver-specific TME differences and immunotherapy (IO) exposure in head and neck squamous cell carcinoma (HNSCC) patients. Method correlations were analyzed using Spearman\u2019s rank correlation coefficient.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 298 patient specimens spanning 20 tumor types, overall correlation between mIHC and xCell2 deconvolution was modest (\n                      <jats:italic>\u03c1<\/jats:italic>\n                      0.35 \u2013 0.50), due to sample heterogeneity, with CD8 T cells demonstrating the strongest correlation (\n                      <jats:italic>\u03c1<\/jats:italic>\n                      \u00a0=\u00a00.50). Tumor-specific analysis revealed enhanced correlations in HNSCC (\n                      <jats:italic>\u03c1<\/jats:italic>\n                      \u00a0=\u00a00.80) and renal clear cell carcinoma (\n                      <jats:italic>\u03c1<\/jats:italic>\n                      \u00a0=\u00a00.85) for CD8 T cells, while primary tumor samples showed improved CD8 T-cell correlation compared to metastatic sites (\n                      <jats:italic>\u03c1<\/jats:italic>\n                      \u00a0=\u00a00.60). Macrophage density was significantly elevated in primary hepatocellular carcinoma versus liver metastases (p = 0.0336). Within the HNSCC subgroup, IO-exposed samples exhibited significantly reduced CD4 T-cell (p = 0.039) and B-cell (p = 0.022) infiltration compared to treatment na\u00efve specimens.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our work highlights the value of integrating spatial (mIHC) and transcriptomic (xCell2) approaches to optimize immune profiling accuracy in complex tumor tissues. CD8 T-cell populations showed the strongest methodological concordance, establishing their priority as a robust candidate biomarker for immune activation to be used in further clinical research.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1876853","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T04:25:04Z","timestamp":1785990304000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Immune profiling by bulk RNA-seq and multiplex immunofluorescence imaging across multiple cancer types"],"prefix":"10.3389","volume":"16","author":[{"given":"Catia","family":"Gaspar","sequence":"first","affiliation":[{"name":"Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, University of Toronto","place":["Toronto, ON, 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