{"doi":"10.17615/dg0f-6e41","title":"Identification of a novel inflamed tumor microenvironment signature as a predictive biomarker of bacillus Calmette-Guérin immunotherapy in non-muscle-invasive bladder cancer","abstract":"Purpose: Improved risk stratification and predictive biomarkers of treatment response are needed for non-muscle-invasive bladder cancer (NMIBC). Here we assessed the clinical utility of targeted RNA and DNA molecular profiling in NMIBC. Experimental Design: Gene expression in NMIBC samples was profiled by NanoString nCounter, an RNA quantification platform, from two independent cohorts (n = 28, n = 50); targeted panel sequencing was performed in a subgroup (n = 50). Gene signatures were externally validated using two RNA sequencing datasets of NMIBC tumors (n = 438, n = 73). Established molecular subtype classifiers and novel gene expression signatures were assessed for associations with clinicopathologic characteristics, somatic tumor mutations, and treatment outcomes. Results: Molecular subtypes distinguished between low-grade Ta tumors with FGFR3 mutations and overexpression (UROMOL-class 1) and tumors with more aggressive clinicopathologic characteristics (UROMOL-classes 2 and 3), which were significantly enriched with TERT promoter mutations. However, UROMOL subclasses were not associated with recurrence after bacillus Calmette-Guérin (BCG) immunotherapy in two independent cohorts. In contrast, a novel expression signature of an inflamed tumor microenvironment (TME) was associated with improved recurrence-free survival after BCG. Expression of immune checkpoint genes (PD-L1/PD-1/CTLA-4) was associated with an inflamed TME, but not with higher recurrence rates after BCG. FGFR3 mutations and overexpression were both associated with low immune signatures. Conclusions: Assessment of the immune TME, rather than molecular subtypes, is a promising predictive biomarker of BCG response. Modulating the TME in an immunologically “cold” tumor warrants further investigation. Integrated transcriptomic and exome sequencing should improve treatment selection in NMIBC.","journal":"UNC Libraries","year":2022,"id":310609,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9608,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":605227,"name":"H. Furberg","orcid":null,"position":1,"is_corresponding":false},{"id":10384,"name":"X. Sun","orcid":"0000-0002-0646-2826","position":2,"is_corresponding":false},{"id":549751,"name":"A.F. Olshan","orcid":null,"position":3,"is_corresponding":false},{"id":529706,"name":"E.L. Kirk","orcid":null,"position":4,"is_corresponding":false},{"id":801426,"name":"M.A. Troester","orcid":null,"position":5,"is_corresponding":false},{"id":466451,"name":"G. Iyer","orcid":null,"position":6,"is_corresponding":false},{"id":786342,"name":"J.S. Damrauer","orcid":null,"position":7,"is_corresponding":false},{"id":1006646,"name":"B.H. Bochner","orcid":null,"position":8,"is_corresponding":false},{"id":1006647,"name":"H.A. Al-Ahmadie","orcid":null,"position":9,"is_corresponding":false},{"id":1006648,"name":"M.A. Smith","orcid":null,"position":10,"is_corresponding":false},{"id":1006649,"name":"G. Dalbagni","orcid":null,"position":11,"is_corresponding":false},{"id":1006650,"name":"K.R. Roell","orcid":null,"position":12,"is_corresponding":false},{"id":786344,"name":"K.A. Hoadley","orcid":null,"position":13,"is_corresponding":false},{"id":834743,"name":"H.C. Benefield","orcid":null,"position":14,"is_corresponding":false},{"id":803393,"name":"W.Y. Kim","orcid":null,"position":15,"is_corresponding":false},{"id":803388,"name":"S.E. Wobker","orcid":null,"position":16,"is_corresponding":false},{"id":1006651,"name":"E.J. Pietzak","orcid":null,"position":17,"is_corresponding":false},{"id":1006000,"name":"M.E. Nielsen","orcid":null,"position":18,"is_corresponding":false},{"id":835355,"name":"M.I. Milowsky","orcid":null,"position":19,"is_corresponding":false},{"id":1006645,"name":"D.B. Solit","orcid":null,"position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T00:33:19.712967Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}