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To further pursue precision oncology efforts in UC, we developed a molecular classification framework applicable to transcriptomic and proteomic data from non\u2013muscle\u2010invasive bladder cancer (NMIBC), muscle\u2010invasive bladder cancer (MIBC) and urothelial cancer cell lines.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using a whole\u2010transcriptome self\u2010organised map and regularised semi\u2010supervised clustering of 4439 bulk NMIBC and MIBC transcriptomes and proteomes, and 33 UC cell lines, we identified three molecular UC clusters. Making use of both in silico and in vitro approaches, we selected promising treatment approaches for each cluster.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The three developed clusters displayed distinct signatures of mRNA, proteins, biological processes, metabolism and essential driver genes. They also differed in prognosis and machine learning\u2010predicted treatment vulnerabilities and resistance. High\u2010risk, stroma\u2010rich Cluster #1 cancers were predicted to respond to selected cytotoxic drugs, ferroptosis inducers and PARP inhibitors. For the aggressive, fast\u2010proliferating, immune\u2010infiltrated Cluster #2 tumours with basal\/squamous differentiation, cytotoxic agents and EGFR\/ERBB\u2010 and MEK\/ERK\u2010targeting therapies were proposed. Cluster #3 cancers of predominantly luminal papillary phenotype with scarce stroma and immune infiltration were enriched with NMIBC and low\u2010risk malignancies. For patients with Cluster #3 tumours, selected epigenetic drugs or EGFR\/FGFR inhibitors may represent attractive treatment options.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our novel molecular taxonomy holds promise as a practical framework for patient risk stratification and clinical trials in UC. Our molecular classification scheme may facilitate personalised transcriptome\u2010 and proteome\u2010based risk assessment and clinical trial design for the development of various therapeutics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>key points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>We developed three UC clusters, applicable for MIBC and NMIBC, which were validated using transcriptomic- and proteomic datasets.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Publically available UC cell lines were assigned to the clusters, to have in vitro models representing each cluster.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>The clusters differ in molecular and biological signatures, with distinct prognostic and therapeutic characteristics.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ctm2.70638","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T09:39:03Z","timestamp":1773135543000},"source":"Crossref","is-referenced-by-count":4,"title":["Multi\u2010omic profiling defines three distinct molecular subtypes of urothelial carcinoma with implications for precision therapy"],"prefix":"10.1002","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2208-5305","authenticated-orcid":false,"given":"Nils C. 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