{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T07:23:23Z","timestamp":1787729003600,"version":"build-2784847793"},"reference-count":70,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"content-version":"vor","delay-in-days":18,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Clinical &amp;amp; Translational Med"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Diffuse large B\u2010cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this study, we analysed a real\u2010world cohort of 178 newly diagnosed DLBCL patients homogeneously treated with R\u2010CHOP\u2010like regimens, integrating RNA sequencing, targeted mutational profiling and digital deconvolution using the EcoTyper algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Five malignant B\u2010cell states (S1\u2013S5) were identified, each reflecting distinct differentiation and transcriptional programs with prognostic relevance, S1 and S5 representing the most favourable and adverse subtypes, respectively. EcoTyper\u2010defined lymphoma ecotypes (LE1\u2013LE9), capturing co\u2010associations between malignant and microenvironmental cell states, further refined prognostic classification, with LE1\/LE2 and LE9 associated with the worst and most favourable prognosis, respectively. Both B\u2010cell states and LE retained independent prognostic value in multivariable analyses after adjusting for the cell of origin and the revised international prognostic index. Mutational profiling revealed state\u2010specific patterns: S1 harboured mutations in genes involved in epigenetic regulation, cytoskeletal organisation and tumour microenvironment (TME) interactions (\n                      <jats:italic>CREBBP<\/jats:italic>\n                      ,\n                      <jats:italic>EZH2<\/jats:italic>\n                      and\n                      <jats:italic>ACTB<\/jats:italic>\n                      ), whereas S5 was enriched for mutations promoting survival signalling, immune evasion and differentiation blockade (\n                      <jats:italic>MYD88<\/jats:italic>\n                      ,\n                      <jats:italic>CD79B<\/jats:italic>\n                      and\n                      <jats:italic>TP53<\/jats:italic>\n                      ). In vitro validation using human five DLBCL cell lines independently recapitulated the S1 and S5 transcriptomic states, supporting the existence of intrinsic, cell\u2010autonomous biological programs. Pathway analysis indicated that S1 was linked to cell adhesion, extracellular matrix remodelling and inflammatory signalling, while S5 displayed enrichment of cell\u2010cycle deregulation, metabolic reprogramming and immune evasion. Accordingly, S1 tumours appear reliant on TME interactions, whereas S5 tumours adopt a predominantly TME independent, intrinsically aggressive phenotype.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Collectively, these findings delineate a biologically coherent framework linking pathway deregulation with cell state and ecotype\u2010specific mutational landscapes, refining DLBCL risk classification and adding an additional dimension to conventional prognostic models.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ctm2.70757","type":"journal-article","created":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T07:11:29Z","timestamp":1787123489000},"source":"Crossref","is-referenced-by-count":0,"title":["Dissecting diffuse large B\u2010cell lymphoma heterogeneity: New insights from a state\u2010specific molecular characterisation"],"prefix":"10.1002","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4338-005X","authenticated-orcid":false,"given":"Robel","family":"Papotti","sequence":"first","affiliation":[{"name":"Clinical and Experimental Onco\u2010Hematology Unit Centro di Riferimento Oncologico di Aviano (CRO), I.R.C.C.S.  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