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PurposeThe value of bladder cancer (BC) substaging into macroscopic (pT3b) and microscopic (pT3a) perivesical fat extension in lymph node (Ln)-negative patients is controversially discussed and limited evidence for prognostic relevance of additional histopathological factors in pT3 BC exists. We evaluated the prognostic value of pT3 substaging and established pathological and clinical parameters with focus on tumor invasive front (TIF) and tumor size.MethodsSpecimens of 52 patients treated with radical cystectomy (RC) for pT3 a/b muscle-invasive BC were reviewed and re-evaluated by a pathologist specialized in uropathology. Clinical variables and standard histopathologic characteristics were assessed including TIF and tumor size. Their value as prognosticators for overall survival (OS) and recurrence-free survival (RFS) was evaluated.ResultsMean age of patients was 67.55 years. Tumors were staged pT3a in 28 patients (53.8%) and pT3b in 24 (46.8%). Median OS was 34.51 months. Median tumor size was 3.2 cm, median TIF was 11.0 mm. Differences in OS between pT3a and pT3b were not significant (p = 0.45). Carcinoma in situ (CIS) and lymphovascular invasion (LVI) were significantly associated with pT3b tumors. Univariate analysis could not identify pathological prognosticators like TIF or tumor size for OS and RFS (p for all > 0.05).ConclusionNo significant differences in OS or RFS were observed comparing Ln-negative pT3 BC following radical cystectomy. Additional pathologic variables like TIF could not be identified as prognosticator. Relevance of pT3 BC substaging needs reevaluation in larger prospective cohorts.

Background Since the definition of different histologic subtypes of urothelial carcinomas by the World Health Organization (WHO) 2004 classification, description of molecular features and clinical behavior of these variants has gained more attention. Methods We reviewed 205 tumor samples of patients with locally advanced bladder cancer mainly treated within the randomized AUO-AB05/95 trial with radical cystectomy and adjuvant cisplatin-based chemotherapy for histologic subtypes. 178 UC, 18 plasmacytoid (PUC) and 9 micropapillary (MPC) carcinomas of the bladder were identified. Kaplan Meier analysis and backward multivariate Cox’s proportional hazards regression analysis were performed to compare overall survival between the three histologic subtypes. Results Patients suffering from PUC have the worst clinical outcome regarding overall survival compared to conventional UC and MPC of the bladder that in turn seem have to best clinical outcome (27.4 months, 62.6 months, and 64.2 months, respectively; p=0.013 by Kaplan Meier analysis). Backward multivariate Cox´s proportional hazards regression analysis (adjusted to relevant clinicopathological parameters) showed a hazard ratio of 3.2 (p=0.045) for PUC in contrast to patients suffering from MPC. Conclusions Histopathological diagnosis of rare variants of urothelial carcinoma can identify patients with poor prognosis.

The GUSTO clinical trial (Gene expression subtypes of Urothelial carcinoma: Stratified Treatment and Oncological outcomes) uses molecular subtypes to guide neoadjuvant therapies in participants with muscle‐invasive bladder cancer (MIBC). Before commencing the GUSTO trial, we needed to determine the reliability of a commercial subtyping platform (Decipher Bladder; Veracyte) when performed in an external trial laboratory as this has not been done previously. Here, we report our pre‐trial verification of the TCGA molecular subtyping model using gene expression profiling. Formalin‐fixed paraffin‐embedded tissue blocks of MIBC were used for gene expression subtyping by gene expression microarrays. Intra‐ and inter‐laboratory technical reproducibilities, together with quality control of laboratory and bioinformatics processes, were assessed. Eighteen samples underwent analysis. RNA of sufficient quality and quantity was successfully extracted from all samples. All subtypes were represented in the cohort. Each sample was subtyped twice in our laboratory and once in a separate reference laboratory. No clinically significant discordance in subtype occurred between intra‐ or inter‐laboratory replicates. Examination of sample histopathology showed variability of morphological appearances within and between subtypes. Overall, these results show that molecular subtyping by gene expression profiling is reproducible, robust and suitable for use in the GUSTO clinical trial.

We sought to define whether there are intrinsic molecular subtypes of high-grade bladder cancer. Consensus clustering performed on gene expression data from a meta-dataset of high-grade, muscle-invasive bladder tumors identified two intrinsic, molecular subsets of high-grade bladder cancer, termed \"luminal\" and \"basal-like,\" which have characteristics of different stages of urothelial differentiation, reflect the luminal and basal-like molecular subtypes of breast cancer, and have clinically meaningful differences in outcome. A gene set predictor, bladder cancer analysis of subtypes by gene expression (BASE47) was defined by prediction analysis of microarrays (PAM) and accurately classifies the subtypes. Our data demonstrate that there are at least two molecularly and clinically distinct subtypes of high-grade bladder cancer and validate the BASE47 as a subtype predictor. Future studies exploring the predictive value of the BASE47 subtypes for standard of care bladder cancer therapies, as well as novel subtype-specific therapies, will be of interest.

Genomic profiling studies have demonstrated that bladder cancer can be divided into two molecular subtypes referred to as luminal and basal with distinct clinical behaviors and sensitivities to frontline chemotherapy. We analyzed the mRNA expressions of signature luminal and basal genes in bladder cancer tumor samples from publicly available and MD Anderson Cancer Center cohorts. We developed a quantitative classifier referred to as basal to luminal transition (BLT) score which identified the molecular subtypes of bladder cancer with 80–94% sensitivity and 83–93% specificity. In order to facilitate molecular subtyping of bladder cancer in primary care centers, we analyzed the protein expressions of signature luminal (GATA3) and basal (KRT5/6) markers by immunohistochemistry, which identified molecular subtypes in over 80% of the cases. In conclusion, we provide a tool for assessment of molecular subtypes of bladder cancer in routine clinical practice.

Bladder cancer is a heterogeneous disease that is not depicted by current classification systems. It was originally classified into non-muscle invasive and muscle invasive. However, clinically and genetically variable tumors are summarized within both classes. A definition of three groups may better account for the divergence in prognosis and probably also choice of treatment. The first group represents mostly non-invasive tumors that reoccur but do not progress. Contrarily, the second group represent non-muscle invasive tumors that likely progress to the third group, the muscle invasive tumors. High throughput tumor profiling improved our understanding of the biology of bladder cancer. It allows the identification of molecular subtypes, at least three for non-muscle invasive bladder cancer (Class I, Class II and Class III) and six for muscle-invasive bladder cancer (luminal papillary, luminal non-specified, luminal unstable, stroma-rich, basal/squamous and neuroendocrine-like) with distinct clinical and molecular phenotypes. Molecular subtypes can be potentially used to predict the response to treatment (e.g., neoadjuvant chemotherapy and immune checkpoint inhibitors). Moreover, they may allow to characterize the evolution of bladder cancer through different pathways. However, to move towards precision medicine, the understanding of the biological meaning of these molecular subtypes and differences in the composition of cell subpopulations will be mandatory.

Bladder cancer is a heterogeneous group of tumours with at least 40 histological subgroups. Patients with localized disease can be cured with surgical resection or radiotherapy, but such curative options are limited in the setting of recurrent disease or distant spread, in which case systemic therapy is used to control disease and palliate symptoms. Cytotoxic chemotherapy has been the mainstay of treatment for advanced bladder cancer, but high-quality evidence is lacking to inform the management of rare subgroups that are often excluded from studies. Advances in molecular pathology, the development of targeted therapies and the resurgence of immunotherapy have led to the reclassification of bladder cancer subgroups and rigorous efforts to define predictive biomarkers for cancer therapies. In this Review, we present the current evidence for the management of conventional, variant and divergent urothelial cancer subtypes, as well as non-urothelial bladder cancers, and discuss how the integration of genomic, transcriptomic and proteomic characterization of bladder cancer could guide future therapies.

The pathologic diagnosis of pT1 substage in conventional transurethral resection of bladder tumor specimens is inaccurate and has low interobserver reproducibility owing to fragmentation and cauterization of the specimens. En bloc resection of bladder tumor is a novel surgical procedure that improves diagnostic feasibility and accuracy in the pathologic diagnosis of bladder cancer, including depth and extent of invasion. To examine the prognostic value of multiple pT1 subclassification methods, using only en bloc resection specimens. We examined 106 patients with T1 bladder cancer who underwent en bloc resection. The pT1 substages were assigned by 3 different subclassification methods by using the muscularis mucosae or stalk of the papillary lesion as diagnostic landmarks or millimetric depth of invasion. Intergroup differences in progression-free survival and recurrence-free survival rates were analyzed. The prognostic values of clinicopathologic factors for progression/recurrence were analyzed by using multivariate analysis. The pT1 substage was evaluable in all cases. Tumors with invasion into/beyond the muscularis mucosae and those beyond the stalk of the papillary lesion were associated with worse progression-free survival (P = .002 and P = .01, respectively). Notably, no patient with invasion confined to the stalk had disease progression during the 23-month median follow-up period. Only the pT1 subclassification method using the muscularis mucosae was an independent prognosticator of progression in multivariate analysis (P = .03). Precise pathologic subclassification of invasion using en bloc resection specimens may enable accurate prognosis and assessment in patients with bladder cancer with suspicious shallow invasion.

Background Bladder cancer (BLCA) exists a profound molecular diversity, with basal and luminal subtypes having different prognostic and therapeutic outcomes. Traditional methods for molecular subtyping are often time-consuming and resource-intensive. This study aims to develop machine learning models using deep learning features from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) to predict basal and luminal subtypes in BLCA. Methods RNA sequencing data and clinical outcomes were downloaded from seven public BLCA databases, including TCGA, GEO datasets, and the IMvigor210C cohort, to assess the prognostic value of BLCA molecular subtypes. WSIs from TCGA were used to construct and validate the machine learning models, while WSIs from Shanghai Tenth People’s Hospital (STPH) and The Affiliated Guangdong Second Provincial General Hospital of Jinan University (GD2H) were used as external validations. Deep learning models were trained to obtained tumor patches within WSIs. WSI level deep learning features were extracted from tumor patches based on the RetCCL model. Support vector machine (SVM), random forest (RF), and logistic regression (LR) were developed using these features to classify basal and luminal subtypes. Results Kaplan-Meier survival and prognostic meta-analyses showed that basal BLCA patients had significantly worse overall survival compared to luminal BLCA patients (hazard ratio = 1.47, 95% confidence interval: 1.25–1.73, P  < 0.001). The LR model based on tumor patch features selected by Resnet50 model demonstrated superior performance, achieving an area under the curve (AUC) of 0.88 in the internal validation set, and 0.81 and 0.64 in the external validation sets from STPH and GD2H, respectively. This model outperformed both junior and senior pathologists in the differentiation of basal and luminal subtypes (AUC: 0.85, accuracy: 74%, sensitivity: 66%, specificity: 82%). Conclusions This study showed the efficacy of machine learning models in predicting the basal and luminal subtypes of BLCA based on the extraction of deep learning features from tumor patches in H&E-stained WSIs. The performance of the LR model suggests that the integration of AI tools into the diagnostic process could significantly enhance the accuracy of molecular subtyping, thereby potentially informing personalized treatment strategies for BLCA patients.