Multi-omic profiling defines three distinct molecular subtypes of urothelial carcinoma with implications for precision therapy

Abstract

Background: Urothelial carcinoma (UC) is a biologically heterogeneous disease, and current molecular classifications have limited integration into clinical decision-making. To further pursue precision oncology efforts in UC, we developed a molecular classification framework applicable to transcriptomic and proteomic data from non–muscle-invasive bladder cancer (NMIBC), muscle-invasive bladder cancer (MIBC) and urothelial cancer cell lines.

Methods: Using a whole-transcriptome self-organised map and regularised semi-supervised 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.

Results: 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-predicted treatment vulnerabilities and resistance. High-risk, stroma-rich Cluster #1 cancers were predicted to respond to selected cytotoxic drugs, ferroptosis inducers and PARP inhibitors. For the aggressive, fast-proliferating, immune-infiltrated Cluster #2 tumours with basal/squamous differentiation, cytotoxic agents and EGFR/ERBB- and MEK/ERK-targeting therapies were proposed. Cluster #3 cancers of predominantly luminal papillary phenotype with scarce stroma and immune infiltration were enriched with NMIBC and low-risk malignancies. For patients with Cluster #3 tumours, selected epigenetic drugs or EGFR/FGFR inhibitors may represent attractive treatment options.

Conclusions: 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- and proteome-based risk assessment and clinical trial design for the development of various therapeutics.

key points:

Keywords

bladder cancer / drug response / molecular classification / proteomics / transcriptomics

References

[1] Ferlay J, Ervik M, Lam F, et al. Cancer today (powered by GLOBOCAN 2018): IARC CancerBase No. 15. International Agency for Research on Cancer. 2018.

[2] Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. 2021; 71(3): 209-249.

[3] Matulewicz RS, Steinberg GD. Non-muscle-invasive bladder cancer: overview and contemporary treatment landscape of neoadjuvant chemoablative therapies. Rev Urol. 2020; 22(2): 43-51.

[4] Roviello G, Santoni M, Sonpavde GP, Catalano M. The evolving treatment landscape of metastatic urothelial cancer. Nat Rev Urol. 2024; 21(10): 580-592.

[5] Powles T, Valderrama BP, Gupta S, et al. Enfortumab vedotin and pembrolizumab in untreated advanced urothelial cancer. N Engl J Med. 2024; 390(10): 875-888.

[6] van der Heijden MS, Sonpavde G, Powles T, et al. Nivolumab plus gemcitabine-cisplatin in advanced urothelial carcinoma. N Engl J Med. 2023; 389(19): 1778-1789.

[7] Loriot Y, Matsubara N, Park SH, et al. Erdafitinib or chemotherapy in advanced or metastatic urothelial carcinoma. N Engl J Med. 2023; 389(21): 1961-1971.

[8] van der Heijden AG, Bruins HM, Carrion A, et al. European Association of Urology guidelines on muscle-invasive and metastatic bladder cancer: summary of the 2025 Guidelines. Eur Urol. 2025; 87(5): 582-600.

[9] Hedegaard J, Lamy P, Nordentoft I, et al. Comprehensive transcriptional analysis of early-stage urothelial carcinoma. Cancer cell. 2016; 30(1): 27-42.

[10] Hurst CD, Alder O, Platt FM, et al. Genomic subtypes of non-invasive bladder cancer with distinct metabolic profile and female gender bias in KDM6A mutation frequency. Cancer Cell. 2017; 32(5): 701-715. e7.

[11] Lindskrog SV, Prip F, Lamy P, et al. An integrated multi-omics analysis identifies prognostic molecular subtypes of non-muscle-invasive bladder cancer. Nat Commun. 2021; 12(1): 2301.

[12] Robertson AG, Kim J, Al-Ahmadie H, et al. Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell. 2017; 171(3): 540-556.e25.

[13] Damrauer JS, Hoadley KA, Chism DD, et al. Intrinsic subtypes of high-grade bladder cancer reflect the hallmarks of breast cancer biology. Proc Natl Acad Sci. 2014; 111(8): 3110-3115.

[14] Rebouissou S, Bernard-Pierrot I, de Reyniès A, et al. EGFR as a potential therapeutic target for a subset of muscle-invasive bladder cancers presenting a basal-like phenotype. Sci Transl Med. 2014; 6(244):244ra91.

[15] Blaveri E, Simko JP, Korkola JE, et al. Bladder cancer outcome and subtype classification by gene expression. Clin Cancer Res. 2005; 11(11): 4044-4055.

[16] Dyrskjøt L, Thykjaer T, Kruhøffer M, et al. Identifying distinct classes of bladder carcinoma using microarrays. Nat Genet. 2003; 33(1): 90-96.

[17] Lindgren D, Frigyesi A, Gudjonsson S, et al. Combined gene expression and genomic profiling define two intrinsic molecular subtypes of urothelial carcinoma and gene signatures for molecular grading and outcome. Cancer Res. 2010; 70(9): 3463-3472.

[18] Sjödahl G, Lauss M, Lövgren K, et al. A molecular taxonomy for urothelial carcinoma. Clin Cancer Res. 2012; 18(12): 3377-3386.

[19] Tan TZ, Rouanne M, Tan KT, Huang RY-J, Thiery J-P. Molecular subtypes of urothelial bladder cancer: results from a meta-cohort analysis of 2411 tumors. Eur Urol. 2019; 75(3): 423-432.

[20] Volkmer J-P, Sahoo D, Chin RK, et al. Three differentiation states risk-stratify bladder cancer into distinct subtypes. Proc Natl Acad Sci. 2012; 109(6): 2078-2083.

[21] Fontugne J, Xylinas E, Krucker C, et al. Transcriptomic profiling of upper tract urothelial carcinoma: bladder cancer consensus classification relevance, molecular heterogeneity, and differential immune signatures. Mod Pathol. 2023; 36(11):100300.

[22] Kamoun A, de Reyniès A, Allory Y, et al. A consensus molecular classification of muscle-invasive bladder cancer. Eur Urol. 2020; 77(4): 420-433.

[23] Liu J, Lichtenberg T, Hoadley KA, et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell. 2018; 173(2): 400-416.e11.

[24] Balar AV, Galsky MD, Rosenberg JE, et al. Atezolizumab as first-line treatment in cisplatin-ineligible patients with locally advanced and metastatic urothelial carcinoma: a single-arm, multicentre, phase 2 trial. Lancet. 2017; 389(10064): 67-76.

[25] Mariathasan S, Turley SJ, Nickles D, et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature. 2018; 554(7693): 544-548.

[26] Kim W-J, Kim E-J, Kim S-K, et al. Predictive value of progression-related gene classifier in primary non-muscle invasive bladder cancer. Mol Cancer. 2010; 9(1): 3.

[27] Lindgren D, Sjödahl G, Lauss M, et al. Integrated genomic and gene expression profiling identifies two major genomic circuits in urothelial carcinoma. PLoS ONE. 2012; 7(6):e38863.

[28] Choi W, Porten S, Kim S, et al. Identification of distinct basal and luminal subtypes of muscle-invasive bladder cancer with different sensitivities to frontline chemotherapy. Cancer Cell. 2014; 25(2): 152-165.

[29] Sjödahl G, Eriksson P, Liedberg F, Höglund M. Molecular classification of urothelial carcinoma: global mRNA classification versus tumour-cell phenotype classification. J Pathol. 2017; 242(1): 113-125.

[30] Guo CC, Bondaruk J, Yao H, et al. Assessment of luminal and basal phenotypes in bladder cancer. Sci Rep. 2020; 10(1): 9743.

[31] Seiler R, Ashab HAD, Erho N, et al. Impact of molecular subtypes in muscle-invasive bladder cancer on predicting response and survival after neoadjuvant chemotherapy. Eur Urol. 2017; 72(4): 544-554.

[32] Song B-N, Kim S-K, Mun J-Y, Choi Y-D, Leem S-H, Chu I-S. Identification of an immunotherapy-responsive molecular subtype of bladder cancer. EBioMedicine. 2019; 50: 238-245.

[33] Seiler R, Gibb EA, Wang NQ, et al. Divergent biological response to neoadjuvant chemotherapy in muscle-invasive bladder cancer. Clin Cancer Res. 2019; 25(16): 5082-5093.

[34] Guo CC, Majewski T, Zhang L, et al. Dysregulation of EMT drives the progression to clinically aggressive sarcomatoid bladder cancer. Cell Rep. 2019; 27(6): 1781-1793.e4.

[35] Efstathiou JA, Mouw KW, Gibb EA, et al. Impact of immune and stromal infiltration on outcomes following bladder-sparing trimodality therapy for muscle-invasive bladder cancer. Eur Urol. 2019; 76(1): 59-68.

[36] Sjödahl G, Eriksson P, Patschan O, et al. Molecular changes during progression from nonmuscle invasive to advanced urothelial carcinoma. Int J Cancer. 2020; 146(9): 2636-2647.

[37] van der Heijden MS, Powles T, Petrylak D, et al. Predictive biomarkers for survival benefit with ramucirumab in urothelial cancer in the RANGE trial. Nat Commun. 2022; 13(1): 1878.

[38] Smith TAD, Lane B, More E, et al. Comparison of multiple gene expression platforms for measuring a bladder cancer hypoxia signature. Mol Med Rep. 2022; 26(2): 12777.

[39] Groeneveld CS, Sanchez-Quiles V, Dufour F, et al. Proteogenomic characterization of bladder cancer reveals sensitivity to apoptosis induced by tumor necrosis factor–related apoptosis-inducing ligand in FGFR3-mutated tumors. Eur Urol. 2024; 85(5): 483-494.

[40] Damrauer JS, Beckabir W, Klomp J, et al. Collaborative study from the Bladder Cancer Advocacy Network for the genomic analysis of metastatic urothelial cancer. Nat Commun. 2022; 13(1): 6658.

[41] Dressler FF, Diedrichs F, Sabtan D, et al. Proteomic analysis of the urothelial cancer landscape. Nat Commun. 2024; 15(1): 4513.

[42] Stroggilos R, Mokou M, Latosinska A, et al. Proteome-based classification of nonmuscle invasive bladder cancer. Int J Cancer. 2020; 146(1): 281-294.

[43] DepMap B. Current DepMap release data, including CRISPR screens, PRISM drug screens, copy number, mutation, expression, and fusions. DepMap 21Q2 Public. 2024.

[44] Trevor Hastie RT, B Narasimhan, G Chu. Imputation for microarray data (currently KNN only).

[45] Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012; 28(6): 882-883.

[46] Ramaker RC, Lasseigne BN, Hardigan AA, et al. RNA sequencing-based cell proliferation analysis across 19 cancers identifies a subset of proliferation-informative cancers with a common survival signature. Oncotarget. 2017; 8(24): 38668-38681.

[47] Li X, Luo S, Fu W, et al. Discovery of a proliferation essential gene signature and actin-like 6A as potential biomarkers for predicting prognosis and neoadjuvant chemotherapy response in triple-positive breast cancer. Cancer. 2024; 130(S8): 1435-1448.

[48] Altorki NK, Bhinder B, Borczuk AC, Elemento O, Mittal V, McGraw TE. A signature of enhanced proliferation associated with response and survival to anti-PD-L1 therapy in early-stage non-small cell lung cancer. Cell Rep Med. 2024; 5(3):101438.

[49] Locard-Paulet M, Palasca O, Jensen LJ. Identifying the genes impacted by cell proliferation in proteomics and transcriptomics studies. PLOS Comput Biol. 2022; 18(10):e1010604.

[50] Pang KL, Li P, Yao XR, Xiao WT, Ren X, He JY. Deciphering a proliferation-essential gene signature based on CRISPR-Cas9 screening to predict prognosis and characterize the immune microenvironment in HNSCC. BMC Cancer. 2025; 25(1): 756.

[51] Venet D, Dumont JE, Detours V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLOS Comput Biol. 2011; 7(10):e1002240.

[52] Ingebriktsen LM, Finne K, Akslen LA, Wik E. A novel age-related gene expression signature associates with proliferation and disease progression in breast cancer. Br J Cancer. 2022; 127(10): 1865-1875.

[53] Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinform. 2013; 14(1): 7.

[54] Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017; 18(1): 220.

[55] Finotello F, Mayer C, Plattner C, et al. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Med. 2019; 11(1): 34.

[56] Yang W, Soares J, Greninger P, et al. Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013; 41(D1): D955-D961.

[57] Seashore-Ludlow B, Rees MG, Cheah JH, et al. Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov. 2015; 5(11): 1210-1223.

[58] Corsello SM, Nagari RT, Spangler RD, et al. Discovering the anticancer potential of non-oncology drugs by systematic viability profiling. Nat Cancer. 2020; 1(2): 235-248.

[59] Geeleher P, Cox N, Huang RS. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS ONE. 2014; 9(9):e107468.

[60] Müller-Dott S, Tsirvouli E, Vazquez M, et al. Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities. Nucleic Acids Res. 2023; 51(20): 10934-10949.

[61] Schubert M, Klinger B, Klünemann M, et al. Perturbation-response genes reveal signaling footprints in cancer gene expression. Nat Commun. 2018; 9(1): 20.

[62] Badia IMP, Vélez Santiago J, Braunger J, et al. decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinform Adv. 2022; 2(1):vbac016.

[63] Therneau TM. Extending the Cox Model. Springer; 1997: 51-84.

[64] Kassambara A, Kosinski M, Biecek P. Survminer: Drawing Survival Curves Using 'ggplot2'. CRAN: Contributed Packages. 2016.

[65] Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc: Ser B (Methodol). 1995; 57(1): 289-300.

[66] Kohonen T. Self-Organizing Maps. Springer Science & Business Media; 2012.

[67] Wehrens R, Kruisselbrink J. Flexible self-organizing maps in kohonen 3.0. J Stat Softw. 2018; 87(7): 1-18.

[68] Raymaekers J, Zamar RH. Regularized k-means through hard-thresholding. J Mach Learn Res. 2022; 23(93): 1-48.

[69] Leng M, Wang J, Cheng J, Zhou H, Chen X. Adaptive semi-supervised clustering algorithm with label propagation. J Softw Eng. 2014; 8: 14-22.

[70] Venna J, Kaski S. Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study. Springer; 2001: 485-491.

[71] Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987; 20: 53-65.

[72] McInnes L, Healy J, Melville J. 2018. Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:180203426.

[73] Pichler R, van Creij NCH, Mertens LS, et al. FGFR1/3 signaling as an Achilles’ heel of phenotypic diversity in urothelial carcinoma. Eur Urol Oncol. 2025; S2588-9311(25): 00197-X.

[74] Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005; 4:Article17.

[75] Csardi G, Nepusz T. The igraph software package for complex network research. InterJ Complex Syst. 2006; 1695: 1-9.

[76] Traag VA, Waltman L, van Eck NJ. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep. 2019; 9(1): 5233.

[77] Thiele I, Swainston N, Fleming RMT, et al. A community-driven global reconstruction of human metabolism. Nat Biotechnol. 2013; 31(5): 419-425.

[78] Gavai AK, Supandi F, Hettling H, Murrell P, Leunissen JAM, van Beek JHGM. Using bioconductor package BiGGR for metabolic flux estimation based on gene expression changes in brain. PLoS ONE. 2015; 10(3):e0119016.

[79] King ZA, Lu J, Dräger A, et al. BiGG models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res. 2016; 44(D1): D515-D522.

[80] Dempster JM, Pacini C, Pantel S, et al. Agreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets. Nat Commun. 2019; 10(1): 5817.

[81] Dempster JM, Boyle I, Vazquez F, et al. Chronos: a cell population dynamics model of CRISPR experiments that improves inference of gene fitness effects. Genome Biol. 2021; 22(1): 343.

[82] Canty A, Ripley B, Brazzale A. 2024 boot: Bootstrap Functions (Originally by Angelo Canty for S). Version: 1.3–30.

[83] Chamie K, Litwin MS, Bassett JC, et al. Recurrence of high-risk bladder cancer: a population-based analysis. Cancer. 2013; 119(17): 3219-3227.

[84] Hof JP, Kiemeney LALM, Aben KKH, van der Heijden AG, Vrieling A, Vermeulen SH. Recurrence patterns in a large contemporary cohort of patients with non-muscle invasive bladder cancer. Clin Genitourin Cancer. 2026; 24(1):102464.

[85] Parker JS, Mullins M, Cheang MC, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009; 27(8): 1160-1167.

[86] Zhao SG, Chen WS, Das R, et al. Clinical and genomic implications of luminal and basal subtypes across carcinomas. Clin Cancer Res. 2019; 25(8): 2450-2457.

[87] Rice ME, Harris GT. Comparing effect sizes in follow-up studies: ROC area, Cohen's d, and r. Law Hum Behav. 2005; 29(5): 615-620.

[88] Naba A, Clauser KR, Hoersch S, Liu H, Carr SA, Hynes RO. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Mol Cell Proteomics. 2012; 11(4):M111.014647.

[89] Pichler R, Siska PJ, Tymoszuk P, et al. A chemokine network of T cell exhaustion and metabolic reprogramming in renal cell carcinoma. Front Immunol. 2023; 14:1095195.

[90] Törzsök P, Santer FR, Kunz Y, et al. Biological and therapeutic implications of sex hormone-related gene clustering in testicular cancer. Basic Clin Androl. 2025; 35(1): 8.

[91] Moiso E. Manual curation of TCGA treatment data and identification of potential markers of therapy response. MedRxiv. 2021:2021.04. 30.21251941.

[92] Wright CJM, McCormack PL. Trametinib: first Global Approval. Drugs. 2013; 73(11): 1245-1254.

[93] Yoshida T, Kakegawa J, Yamaguchi T, et al. Identification and characterization of a novel chemotype MEK inhibitor able to alter the phosphorylation state of MEK1/2. Oncotarget. 2012; 3(12): 1533-1545.

[94] Tomlinson DC, Lamont FR, Shnyder SD, Knowles MA. Fibroblast growth factor receptor 1 promotes proliferation and survival via activation of the mitogen-activated protein kinase pathway in bladder cancer. Cancer Res. 2009; 69(11): 4613-4620.

[95] Williams SV, Hurst CD, Knowles MA. Oncogenic FGFR3 gene fusions in bladder cancer. Hum Mol Genet. 2013; 22(4): 795-803.

[96] Yu H, Sfakianos JP, Wang L, et al. Tumor-infiltrating myeloid cells confer de novo resistance to PD-L1 blockade through EMT–stromal and Tgfβ-dependent mechanisms. Mol Cancer Ther. 2022; 21(11): 1729-1741.

[97] Rouanne M, Betari R, Radulescu C, et al. Stromal lymphocyte infiltration is associated with tumour invasion depth but is not prognostic in high-grade T1 bladder cancer. Eur J Cancer. 2019; 108: 111-119.

[98] Thorsson V, Gibbs DL, Brown SD, et al. The immune landscape of cancer. Immunity. 2018; 48(4): 812-830.e14.

[99] Venning FA, Wullkopf L, Erler JT. Targeting ECM disrupts cancer progression. Front Oncol. 2015; 5: 224.

[100] Zhao H, Chen Z, Fang Y, et al. Prediction of prognosis and recurrence of bladder cancer by ECM-related genes. J Immunol Res. 2022; 2022:1793005.

[101] Lodewijk I, Dueñas M, Rubio C, et al. Liquid biopsy biomarkers in bladder cancer: a current need for patient diagnosis and monitoring. Int J Mol Sci. 2018; 19(9): 2514.

[102] Wu S, Li R, Jiang Y, et al. Liquid biopsy in urothelial carcinoma: detection techniques and clinical applications. Biomed Pharmacother. 2023; 165:115027.

[103] Chen CK, Liao J, Li MS, Khoo BL. Urine biopsy technologies: cancer and beyond. Theranostics. 2020; 10(17): 7872-7888.

[104] Mengual L, Frantzi M, Mokou M, et al. Multicentric validation of diagnostic tests based on BC-116 and BC-106 urine peptide biomarkers for bladder cancer in two prospective cohorts of patients. Br J Cancer. 2022; 127(11): 2043-2051.

[105] Frantzi M, van Kessel KE, Zwarthoff EC, et al. Development and validation of urine-based peptide biomarker panels for detecting bladder cancer in a multi-center study. Clin Cancer Res. 2016; 22(16): 4077-4086.

[106] Mo Q, Nikolos F, Chen F, et al. Prognostic power of a tumor differentiation gene signature for bladder urothelial carcinomas. J Natl Cancer Inst. 2018; 110(5): 448-459.

[107] Eriksson P, Aine M, Veerla S, Liedberg F, Sjödahl G, Höglund M. Molecular subtypes of urothelial carcinoma are defined by specific gene regulatory systems. BMC Med Genomics. 2015; 8: 25.

[108] Sadhukhan P, Feng M, Illingworth E, et al. YAP1 induces bladder cancer progression and promotes immune evasion through IL-6/STAT3 pathway and CXCL deregulation. J Clin Invest. 2024; 135(2): 171164.

[109] Ando K, Kurashina R, Motoi N, et al. Positive regulatory loop of platelet-derived growth factor DD-induced STAT3 activation is associated with poor prognosis in advanced urothelial carcinoma. Biochem Biophys Res Commun. 2023; 676: 165-170.

[110] Peixoto A, Fernandes E, Gaiteiro C, et al. Hypoxia enhances the malignant nature of bladder cancer cells and concomitantly antagonizes protein O-glycosylation extension. Oncotarget. 2016; 7(39): 63138-63157.

[111] Smith V, Lee D, Reardon M, et al. Hypoxia is associated with increased immune infiltrates and both anti-tumour and immune suppressive signalling in muscle-invasive bladder cancer. Int J Mol Sci. 2023; 24(10): 8956.

[112] Theodoropoulos VE, Lazaris AC, Sofras F, et al. Hypoxia-inducible factor 1α expression correlates with angiogenesis and unfavorable prognosis in bladder cancer. Eur Urol. 2004; 46(2): 200-208.

[113] Lodhi T, Song YP, West C, Hoskin P, Choudhury A. Hypoxia and its modification in bladder cancer: current and future perspectives. Clin Oncol. 2021; 33(6): 376-390.

[114] Wu Z, Zhu K, Liu Q, et al. Profiles of immune infiltration in bladder cancer and its clinical significance: an integrative genomic analysis. Int J Med Sci. 2020; 17(6): 762-772.

[115] Eich ML, Chaux A, Guner G, et al. Tumor immune microenvironment in non-muscle-invasive urothelial carcinoma of the bladder. Hum Pathol. 2019; 89: 24-32.

[116] Guo CC, Lee S, Lee JG, et al. Molecular profile of bladder cancer progression to clinically aggressive subtypes. Nat Rev Urol. 2024; 21(7): 391-405.

[117] Fontugne J, Wong J, Cabel L, et al. Progression-associated molecular changes in basal/squamous and sarcomatoid bladder carcinogenesis. J Pathol. 2023; 259(4): 455-467.

[118] Olislagers M, de Jong FC, Rutten VC, Boormans JL, Mahmoudi T, Zuiverloon TCM. Molecular biomarkers of progression in non-muscle-invasive bladder cancer—beyond conventional risk stratification. Nat Rev Urol. 2025; 22(2): 75-91.

[119] Fujii Y. Prediction models for progression of non-muscle-invasive bladder cancer: a review. Int J Urol. 2018; 25(3): 212-218.

[120] Cox A, Klümper N, Stein J, et al. Molecular urothelial tumor cell subtypes remain stable during metastatic evolution. Eur Urol. 2024; 85(4): 328-332.

[121] Chu CE, Chen Z, Whiting K, et al. Clinical outcomes, genomic heterogeneity, and therapeutic considerations across histologic subtypes of urothelial carcinoma. Eur Urol. 2025; 88(5): 472-481.

[122] Powles T, Catto JWF, Galsky MD, et al. Perioperative durvalumab with neoadjuvant chemotherapy in operable bladder cancer. N Engl J Med. 2024; 391(19): 1773-1786.

[123] Shan Z, Tang W, Shi Z, Shan T. Ferroptosis: an emerging target for bladder cancer therapy. Curr Issues Mol Biol. 2023; 45(10): 8201-8214.

[124] Fan L, Guo D, Zhu C, et al. LRRC45 accelerates bladder cancer development and ferroptosis inhibition via stabilizing NRF2 by competitively KEAP1 interaction. Free Radic Biol Med. 2025; 226: 29-42.

[125] Zhou Q, Meng Y, Li D, et al. Ferroptosis in cancer: from molecular mechanisms to therapeutic strategies. Signal Transd Targeted Ther. 2024; 9(1): 55.

[126] Hong T, Lei G, Chen X, et al. PARP inhibition promotes ferroptosis via repressing SLC7A11 and synergizes with ferroptosis inducers in BRCA-proficient ovarian cancer. Redox Biol. 2021; 42:101928.

[127] Zhang J, Ouyang D, Liu M, Xiang Y, Li Z. Research progress on ferroptosis and PARP inhibitors in ovarian cancer: action mechanisms and resistance mechanisms. Front Pharmacol. 2025; 16:1598279.

[128] Xie X, Chen C, Wang C, et al. Targeting GPX4-mediated ferroptosis protection sensitizes BRCA1-deficient cancer cells to PARP inhibitors. Redox Biol. 2024; 76:103350.

[129] Guo J, Xu B, Han Q, et al. Ferroptosis: a novel anti-tumor action for cisplatin. Cancer Res Treat. 2018; 50(2): 445-460.

[130] Zhao Y, Ren P, Yang Z, Wang L, Hu C. Inhibition of SND1 overcomes chemoresistance in bladder cancer cells by promoting ferroptosis. Oncol Rep. 2023; 49(1): 16.

[131] Wu M, Sheng L, Cheng M, et al. Low doses of decitabine improve the chemotherapy efficacy against basal-like bladder cancer by targeting cancer stem cells. Oncogene. 2019; 38(27): 5425-5439.

[132] Rose M, Maurer A, Wirtz J, et al. EGFR activity addiction facilitates anti-ERBB based combination treatment of squamous bladder cancer. Oncogene. 2020; 39(44): 6856-6870.

[133] Merrill NM, Vandecan NM, Day KC, et al. MEK is a promising target in the basal subtype of bladder cancer. Oncotarget. 2020; 11(44): 3921-3932.

[134] Tate T, Plumber SA, Al-Ahmadie H, et al. Combined Mek inhibition and Pparg activation eradicates muscle invasive bladder cancer in a mouse model of BBN-induced carcinogenesis. bioRxiv. 2023.

[135] Plumber SA, Tate T, Al-Ahmadie H, et al. Rosiglitazone and trametinib exhibit potent anti-tumor activity in a mouse model of muscle invasive bladder cancer. Nat Commun. 2024; 15(1): 6538.

[136] Jung M, Rose M, Knuechel R, et al. Characterisation of tumour-immune phenotypes and PD-L1 positivity in squamous bladder cancer. BMC Cancer. 2023; 23(1): 113.

[137] Kim B, Lee C, Kim YA, Moon KC. PD-L1 expression in muscle-invasive urinary bladder urothelial carcinoma according to basal/squamous-like phenotype. Front Oncol. 2020; 10:527385.

[138] Manzano RG, Catalan-Latorre A, Brugarolas A. RB1 and TP53 co-mutations correlate strongly with genomic biomarkers of response to immunity checkpoint inhibitors in urothelial bladder cancer. BMC Cancer. 2021; 21(1): 432.

[139] Ozsagir YO, Ozsagir E, Dil E, Eren H, Aydin Mungan S, Bedir R. Prognostic impact of EGFR expression and immunohistochemistry-based "molecular classification" in bladder cancer. Ann Diagn Pathol. 2024; 73:152380.

[140] Lang H, Béraud C, Cabel L, et al. Integrated molecular and pharmacological characterization of patient-derived xenografts from bladder and ureteral cancers identifies new potential therapies. Front Oncol. 2022; 12:930731.

[141] Grivas PD, Day KC, Karatsinides A, et al. Evaluation of the antitumor activity of dacomitinib in models of human bladder cancer. Mol Med. 2013; 19(1): 367-376.

[142] Weickhardt AJ, Price TJ, Chong G, et al. Dual targeting of the epidermal growth factor receptor using the combination of cetuximab and erlotinib: preclinical evaluation and results of the phase II DUX study in chemotherapy-refractory, advanced colorectal cancer. J Clin Oncol. 2012; 30(13): 1505-1512.

[143] Seres M, Spacayova K, Sulova Z, Spaldova J, Breier A, Pavlikova L. Dynamic multilevel regulation of EGFR, KRAS, and MYC oncogenes: driving cancer cell proliferation through (epi)genetic and post-transcriptional/translational pathways. Cancers (Basel). 2025; 17(2): 248.

[144] Solca F, Dahl G, Zoephel A, et al. Target binding properties and cellular activity of afatinib (BIBW 2992), an irreversible ErbB family blocker. J Pharmacol Exp Ther. 2012; 343(2): 342-350.

[145] Nagpal A, Redvers RP, Ling X, et al. Neoadjuvant neratinib promotes ferroptosis and inhibits brain metastasis in a novel syngeneic model of spontaneous HER2+ve breast cancer metastasis. Breast Cancer Res. 2019; 21(1): 94.

[146] Feldinger K, Kong A. Profile of neratinib and its potential in the treatment of breast cancer. Breast Cancer (Dove Med Press). 2015; 7: 147-162.

[147] Nini A, Hoffmann MJ, Lampignano R, et al. Evaluation of HER2 expression in urothelial carcinoma cells as a biomarker for circulating tumor cells. Cytometry Part B: Clin Cytometry. 2020; 98(4): 355-367.

[148] Karkera JD, Cardona GM, Bell K, et al. Oncogenic characterization and pharmacologic sensitivity of activating fibroblast growth factor receptor (FGFR) genetic alterations to the selective FGFR inhibitor erdafitinib. Mol Cancer Ther. 2017; 16(8): 1717-1726.

[149] Nogova L, Sequist LV, Perez Garcia JM, et al. Evaluation of BGJ398, a fibroblast growth factor receptor 1-3 kinase inhibitor, in patients with advanced solid tumors harboring genetic alterations in fibroblast growth factor receptors: results of a global phase I, dose-escalation and dose-expansion study. J Clin Oncol. 2017; 35(2): 157-165.

[150] Catto JWF, Tran B, Rouprêt M, et al. Erdafitinib in BCG-treated high-risk non-muscle-invasive bladder cancer. Ann Oncol. 2024; 35(1): 98-106.

[151] Eisner JR, de Jong FC, Shibata Y, et al. Characterization of FGFR alterations and activation in patients with high-risk non–muscle-invasive bladder cancer. Clin Cancer Res. 2024; 30(23): 5374-5384.

[152] Ross JS, Wang K, Gay LM, et al. A high frequency of activating extracellular domain ERBB2 (HER2) mutation in micropapillary urothelial carcinoma. Clin Cancer Res. 2014; 20(1): 68-75.

[153] Choi W, Czerniak B, Ochoa A, et al. Intrinsic basal and luminal subtypes of muscle-invasive bladder cancer. Nat Rev Urol. 2014; 11(7): 400-410.

[154] Leary JB, Enright T, Bakaloudi DR, et al. Frequency and nature of genomic alterations in ERBB2-altered urothelial bladder cancer. Target Oncol. 2024; 19(3): 447-458.

[155] Chaves Medina MJ, Villada Florez D, Castañeda Millán DA, García-Perdomo HA. Using epigenetics as an alternative treatment for bladder cancer: a literature review. Urologia. 2026; 93(1): 3-9.

[156] Neyret-Kahn H, Fontugne J, Meng XY, et al. Epigenomic mapping identifies an enhancer repertoire that regulates cell identity in bladder cancer through distinct transcription factor networks. Oncogene. 2023; 42(19): 1524-1542.

[157] Li W, Luo P, Chen Q, et al. Epigenetic modifications in bladder cancer: crosstalk between DNA methylation and miRNAs. Front Immunol. 2025; 16:1518144.

[158] Nickerson ML, Witte N, Im KM, et al. Molecular analysis of urothelial cancer cell lines for modeling tumor biology and drug response. Oncogene. 2017; 36(1): 35-46.

[159] Eray A, SE Özhan. Classification of bladder cancer cell lines according to regulon activity. Turk J Biol. 2021; 45(6): 656-666.

[160] Lee Y-C, Lam H-M, Rosser C, Theodorescu D, Parks WC, Chan KS. The dynamic roles of the bladder tumour microenvironment. Nat Rev Urol. 2022; 19(9): 515-533.

[161] Chen H, Pan Y, Jin X, Chen G. An immune cell infiltration-related gene signature predicts prognosis for bladder cancer. Sci Rep. 2021; 11(1):16679.

[162] Zhao H, Lin N, Ho VWS, et al. Patient-derived bladder cancer organoids as a valuable tool for understanding tumor biology and developing personalized treatment. Adv Sci. 2025; 12(13):2414558.

[163] Zhao H, Ho VWS, Liu K, et al. Organoid models in bladder cancer: from bench to bedside? Bladder Cancer. 2025; 11(2):23523735251330404.

[164] Sjödahl G, Jackson CL, Bartlett JM, Siemens DR, Berman DM. Molecular profiling in muscle-invasive bladder cancer: more than the sum of its parts. J Pathol. 2019; 247(5): 563-573.

RIGHTS & PERMISSIONS

2026 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.