Despite refinement of the staging of nonmuscle-invasive bladder cancer, there remain no biomarkers to guide therapy and decision-making for the urologist and patients. To further understand the molecular features of nonmuscle-invasive bladder cancers, researchers have employed unsupervised machine learning tools such as clustering to pinpoint molecular features in tumors from patients with similar clinical diagnoses but different behaviors in clinical measurements, such as response to treatment. However, the usefulness of this approach beyond academic pursuits, particularly in personalizing bladder cancer clinical care, remains uncertain.

In the early stages of subtyping, the efforts were comprehensive, involving tumors without specific distinctions based on stage or grade. Despite their simplicity, these initial endeavors played a crucial role in identifying gene expression programs active in bladder cancer at different stages. However, in predicting clinical outcomes, these models did not offer more information than what could be gleaned from a histopathologic analysis of the tumor.^1,2

As sequencing technologies evolved, larger cohorts were sequenced, offering finer resolution into the heterogeneous gene expression programs driving disease states at different stages/grades. At the forefront was the Lund 7-subtype classification system generated using data from 308 tumors spanning stages and grades. Three of the 7 subtypes exhibited an unbalanced enrichment of nonmuscle-invasive tumors, marking early identification of heterogeneous gene expression programs found in nonmuscle-invasive disease. Recent work from the Dyrskjøt group³ has expanded on this by exclusively sequencing early-stage tumors (Ta, T1, and carcinoma in situ). This work identified a unique transcriptomic profile of early-stage cancer associated with a higher probability of progression to muscle-invasive disease. Our research and a recent study by de Jong et al have added to this narrative by revealing distinct gene expression programs within clinically observed T1 staged tumors, influencing response or resistance to bacillus Calmette-Guérin (BCG).^4,5

While subtyping based on transcriptomics seems to be the most widely used approach, Hurst et al clustered Ta-stage tumors based on their copy number profiles, revealing 2 subtypes.⁶ The first GS1, with no or rare copy number alterations, and a second subtype GS2 with extensive chromosomal aberrations and a higher tumor mutation burden. While neither of the subtypes were associated with recurrence or progression, tumors with GS1 features were common in female patients.

Several other independent cohorts have identified genomic markers linked to clinical outcomes such as BCG response in early-stage tumors. Bellmunt et al identified ERCC2 and BRCA2 mutations, and a high tumor mutation burden to be associated with good outcomes after BCG in T1 stage high-grade tumors, findings that remain to be validated in larger cohorts.

Over the past decade, addressing the persistent BCG shortage has been a major concern for urologists and their patients and remains a challenge without an immediate solution. Despite BCG being the gold standard for decades, considering the advances made since its introduction, it is time to allocate significant clinical and scientific resources to identify therapeutic alternatives that can precisely target each tumor, a task that can be achieved by translating subtyping efforts into clinical practice.

For these efforts to be successful, several things need to be considered: First, studies like the one led by Kamoun et al for muscle-invasive disease are needed to aggregate the different nonmuscle-invasive subtypes into 1 consensus profile.⁸ Second, many of the existing classifications are based on bulk RNA expression profiles collected at a single time point. The extent of interpatient heterogeneity in these subtype profiles remains uncertain. Further investigations are required to address this uncertainty, particularly through single-cell or spatial analysis. Third, as has been demonstrated in breast cancer, basal-luminal states are transitional and can change over time, being influenced by therapy or disease progression.^9,10 More work needs to be done in preclinical models and longitudinal patient studies to understand the stability of these subtypes throughout disease progression. Fourth, we need to focus on developing classification models informed by incorporating multimodal data—transcriptomic, genomic, histopathology, and clinical attributes such as sex, smoking status, and UTI diagnosis, that can provide higher predictive performance. While commercial tests like Decipher can currently predict the transcriptomic subtypes of a given bladder tumor, more work in preclinical and clinical models needs to be conducted to validate the current therapy recommendations provided for each unique subtype.

• 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.
• Dyrskjøt L, Thykjaer T, Kruhøffer M, et al. Identifying distinct classes of bladder carcinoma using microarrays. Nat Genet. 2003;33(1):90-96.
• 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.
• Robertson AG, Groeneveld CS, Jordan B, et al. Identification of differential tumor subtypes of T1 bladder cancer. Eur Urol. 2020;78(4):533-537.
• de Jong FC, Laajala TD, Hoedemaeker RF, et al. Non-muscle-invasive bladder cancer molecular subtypes predict differential response to intravesical bacillus Calmette-Guérin. Sci Transl Med. 2023;15(697):eabn4118.
• 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.
• Bellmunt J, Kim J, Reardon B, et al. Genomic predictors of good outcome, recurrence, or progression in high-grade T1 non–muscle-invasive bladder cancer. Cancer Res. 2020;80(20):4476-4486.
• 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.
• Priedigkeit N, Hartmaier RJ, Chen Y, et al. Intrinsic subtype switching and acquired ERBB2/HER2 amplifications and mutations in breast cancer brain metastases. JAMA Oncol. 2017;3(5):666-671.
• Garcia-Recio S, Thennavan A, East MP, et al. FGFR4 regulates tumor subtype differentiation in luminal breast cancer and metastatic disease. J Clin Invest. 130(9):4871-4887.