Combined exome and transcriptome sequencing of non-muscle-invasive bladder cancer: associations between genomic changes, expression subtypes, and clinical outcomes

A Systematic Review of Novel Intravesical Approaches for the Treatment of Patients with Non-muscle-invasive Bladder Cancer

BACKGROUND AND OBJECTIVE

Intravesical therapy is central to managing non-muscle-invasive bladder cancer (NMIBC); yet, recurrence and progression remain common, underscoring the need for new treatments. This systematic review evaluates clinical trials of novel intravesical therapies for all risk categories of NMIBC.

METHODS

A comprehensive literature search was conducted to identify the clinical trials assessing the effectiveness, safety, and tolerability of intravesical therapies for NMIBC. The search focused on studies published from 2020 to 2024, including trials on bacillus Calmette-Guérin (BCG)-unresponsive/refractory disease as well as on BCG-naïve and intermediate-risk patients. Mechanisms of action and drug delivery methods were summarized. No statistical syntheses were performed due to limited comparative data.

KEY FINDINGS AND LIMITATIONS

Out of 2998 studies identified, 36 reported on efficacy and safety, and six provided patient-reported outcomes (PROs). Intravesical therapies included BCG-based therapies, chemotherapy combinations, chemical-drug conjugates, thermogels, hyperthermic chemotherapy, osmotic pumps, and gene therapy. Initial response rates ranged from 42% to 85% for BCG-unresponsive/refractory patients and from 65% to 100% for treatment-naïve patients. The 12-mo recurrence-free survival rates ranged from 22% to 83% and 39% to 92%, respectively. Progression and severe toxicity (grade ≥3) were rare (0-17% and 0-20%, respectively). PROs were stable. The limitations included early-phase studies, heterogeneous outcome assessments, and a need for research on long-term durability, comparative effectiveness, quality of life, and cost.

CONCLUSIONS AND CLINICAL IMPLICATIONS

This systematic review highlights the promising efficacy and tolerability of novel intravesical therapies for NMIBC. However, further research is needed to refine treatment strategies and assess long-term outcomes, quality of life, and economic factors. Future studies should include multiarm, multistage designs with a focus on patient-centered outcomes.

Exploring the utility of a NGS multigene panel to predict BCG response in patients with non-muscle invasive bladder cancer

Objectives

Intravesical Bacillus Calmette-Guérin (BCG) therapy is a gold standard for patients with high-risk non-muscle invasive bladder cancer (NMIBC). Although a long-lasting therapeutic response is observed in most patients, BCG failure occurs in 30%-50% of patients and a progression to muscle-invasive disease is found in 10%-15%. Therefore, predicting high-risk patients who might not benefit from BCG treatment is critical. The purpose of this study was to identify, whether the presence of specific oncogenic mutations might be indicative of BCG treatment response.

Methods

Nineteen high-grade NMIBC patients who received intravesical BCG were retrospectively enrolled and divided into "responders" and "non-responders" groups. Tissue samples from transurethral resection of bladder cancer were performed before starting therapy and were examined using a multigene sequencing panel.

Results

Mutations in TP53, FGFR3, PIK3CA, KRAS, CTNNB1, ALK and DDR2 genes were detected. TP53 and FGFR3 were found to be the most frequently mutated genes in our cohort (31.6% and 26.3%, respectively), followed by PIK3CA (15.8%). In the BCG-responsive patient group, 90% of samples were found to have mutated genes, with almost 50% of them showing mutations in tyrosine kinase receptors and CTNNB1 genes. On the other hand, in the BCG-unresponsive group, we found mutations in 44.4% of samples, mainly in TP53 gene.

Conclusions

Our findings suggest that a Next-Generation Sequencing (NGS) multigene panel is useful in predicting BCG response in patients with NMIBC.

Molecular biomarkers of progression in non-muscle-invasive bladder cancer - beyond conventional risk stratification

The global incidence of bladder cancer is more than half a million diagnoses each year. Bladder cancer can be categorized into non-muscle-invasive bladder cancer (NMIBC), which accounts for ~75% of diagnoses, and muscle-invasive bladder cancer (MIBC). Up to 45% of patients with NMIBC develop disease progression to MIBC, which is associated with a poor outcome, highlighting a clinical need to identify these patients. Current risk stratification has a prognostic value, but relies solely on clinicopathological parameters that might not fully capture the complexity of disease progression. Molecular research has led to identification of multiple crucial players involved in NMIBC progression. Identified biomarkers of progression are related to cell cycle, MAPK pathways, apoptosis, tumour microenvironment, chromatin stability and DNA-damage response. However, none of these biomarkers has been prospectively validated. Reported gene signatures of progression do not improve NMIBC risk stratification. Molecular subtypes of NMIBC have improved our understanding of NMIBC progression, but these subtypes are currently unsuitable for clinical implementation owing to a lack of prospective validation, limited predictive value as a result of intratumour subtype heterogeneity, technical challenges, costs and turnaround time. Future steps include the development of consensus molecular NMIBC subtypes that might improve conventional clinicopathological risk stratification. Prospective implementation studies of biomarkers and the design of biomarker-guided clinical trials are required for the integration of molecular biomarkers into clinical practice.

Comprehensive genomic characterization of early-stage bladder cancer

Understanding the molecular landscape of nonmuscle-invasive bladder cancer (NMIBC) is essential to improve risk assessment and treatment regimens. We performed a comprehensive genomic analysis of patients with NMIBC using whole-exome sequencing (n = 438), shallow whole-genome sequencing (n = 362) and total RNA sequencing (n = 414). A large genomic variation within NMIBC was observed and correlated with different molecular subtypes. Frequent loss of heterozygosity in FGFR3 and 17p (affecting TP53) was found in tumors with mutations in FGFR3 and TP53, respectively. Whole-genome doubling (WGD) was observed in 15% of the tumors and was associated with worse outcomes. Tumors with WGD were genomically unstable, with alterations in cell-cycle-related genes and an altered immune composition. Finally, integrative clustering of multi-omics data highlighted the important role of genomic instability and immune cell exhaustion in disease aggressiveness. These findings advance our understanding of genomic differences associated with disease aggressiveness in NMIBC and may ultimately improve patient stratification.

Prototol for the Prospective Sample Collection for Cancer of Bladder (ProCaB) Trial by the Cancer of the Bladder Leuven (CaBLe) Consortium

Non-muscle-invasive bladder cancer (NMIBC) is a heterogeneous disease categorized as low, intermediate, high, or very high risk, for which recurrence and progression rates and thus management strategies differ. Current molecular subclassification of bladder cancer (BC) is mainly based on data for muscle-invasive disease, with very few data for NMIBC. A more accurate classification system is needed for better stratification of NMIBC using multiomics and immunohistopathological molecular data alongside clinical data collected in a prospective cohort. ProCaB (Prospective Sample Collection for Cancer of Bladder) is a single-center non-interventional, prospective study recruiting all eligible patients diagnosed with BC in a tertiary center in the Flanders region of Belgium. Clinical data have been collected in a prospective registry since August 2013. Biosamples (blood, urine, and BC tissue) are collected from each patient at diagnosis and are stored at -80°C at BioBank UZ Leuven after appropriate processing according to the protocol. Multiomics (genomics, epigenetics, transcriptomics, proteomics, lipidomics, metabolomics) and immunohistopathology analyses will be performed on appropriate samples. The target is to enroll 300 patients over a 5-yr period, and all patients will be followed for 5 yr. The objective is to create a biobank of samples from patients diagnosed with BC for use in multiomics and immunohistopathological analyses. Results from these analyses, together with long-term clinical data, can be used for comprehensive multilayered molecular characterization of disease recurrence and progression in intermediate- and (very) high-risk NMIBC, identification of multibiomarker panels for better stratification, and identification of a patient subgroup that does not respond to bacillus Calmette-Guérin treatment. This trial is registered on ClinicalTrials.gov as NCT04167332.

Development and validation of a recurrence risk assessment model for high-grade bladder cancer based on TCGA and GEO

Background

Bladder cancer is one of the most commonly diagnosed urinary cancers worldwide. Although muscle-invasive bladder cancer (MIBC) accounts for only 25% of bladder cancer cases, it has a high recurrence rate and poor prognosis, especially among high-grade cases. Despite the existence of some molecular markers, there is a clear clinical need for a robust recurrence prediction model that can assist in patient management and therapeutic decision-making. Therefore, we aimed to use public databases to develop such an effective assessment model.

Methods

We developed a recurrence risk assessment model for high-grade bladder cancer based on the clinical information of 217 cases from The Cancer Genome Atlas (TCGA) and profiles of 87 samples from GSE31684 in the Gene Expression Omnibus (GEO) database. Edge R was used to analyze differences between RNAs of bladder cancer in the TCGA database, with thresholds of P<0.05 and |log2(fold change)| >1; least absolute shrinkage and selection operator (LASSO) Cox regression models were used to screen the RNAs significantly related to recurrence with minimum λ. Survival receiver operating characteristic (ROC) and area under the curve (AUC) was used to assess the predictive accuracy of the model in the training and validation sets of GSE31684.

Results

There were 2,876 differential RNAs obtained from TCGA data. Among a total of 284 RNAs identified as significantly related to recurrence of bladder cancer, 49 were obtained by LASSO regression, and 30 were finally obtained by multifactor risk regression to construct a risk assessment model. The model was found to predict the prognosis of bladder cancer recurrence well, with an AUC of 0.911 in the TCGA training set and an adjusted AUC value of 0.839 in the GEO validation set.

Conclusions

The recurrence assessment model is a relatively accurate recurrence prediction tool for high-grade bladder cancer and could provide a guidance for the treatment of bladder cancer.

The sex gap in bladder cancer survival - a missing link in bladder cancer care?

The differences in bladder cancer outcomes between the sexes has again been highlighted. Uncommon among cancers, bladder cancer outcomes are notably worse for women than for men. Furthermore, bladder cancer is three to four times more common among men than among women. Factors that might explain these sex differences include understanding the importance of haematuria as a symptom of bladder cancer by both clinicians and patients, the resultant delays in diagnosis and referral of women with haematuria, and health-care access. Notably, these factors seem to have geographical variation and are not consistent across all health-care systems. Likewise, data relating to sex-specific treatment responses for patients with non-muscle-invasive or muscle-invasive bladder cancer are inconsistent. The influence of differences in the microbiome, bladder wall thickness and urine dwell times remain to be elucidated. The interplay of hormone signalling, gene expression, immunology and the tumour microenvironment remains complex but probably underpins the sexual dimorphism in disease incidence and stage and histology at presentation. The contribution of these biological phenomena to sex-specific outcome differences is probable, albeit potentially treatment-specific, and further understanding is required. Notwithstanding these aspects, we identify opportunities to harness biological differences to improve treatment outcomes, as well as areas of fundamental and translational research to pursue. At the level of policy and health-care delivery, improvements can be made across the domains of patient awareness, clinician education, referral pathways and guideline-based care. Together, we aim to highlight opportunities to close the sex gap in bladder cancer outcomes.

A Prognostic Index Derived From LASSO-Selected Preoperative Inflammation and Nutritional Markers for Non-Muscle-Invasive Bladder Cancer

BACKGROUND

There is an urgent need to identify a robust predictor for BCG response in patients with non-muscle-invasive bladder cancer (NMIBC). We aimed to employ the Lasso regression model for the selection and construction of an index (BCGI) utilizing inflammation and nutrition indicators to predict the response to BCG therapy.

METHODS

After acquiring the ethics approval, we searched the electric medical records in our institution and performed data screening. Then, we developed the BCGI using a Lasso regression model and subsequently evaluated its performance in both the train and internal test datasets through Kaplan-Meier survival curves and Cox regression analysis. Then, we also evaluated the prognostic value of BCGI alongside the EAU2021 model.

RESULTS

The training dataset and internal test dataset contained 295 and 196 patients, respectively. Referring to the Lasso results, BCGI consisted of hemoglobin, albumin, and platelet count, which could significantly predict the recurrence of NMIBC patients who accepted BCG in train (P = .012) and test (P = .004) datasets. The BCGI also exhibited statistically prognostic value in no smoking history, World Health Organization high grade, and T1 subgroups, both in train and test datasets. In multivariable analysis, BCGI exhibited independent prognostic value in train (P = .012) and test (P = .012) datasets. Finally, we constructed a nomogram that consisted of smoking history, T stage, World Health Organization grade, tumor size, and BCGI. Then, BCGI demonstrated significant independent prognostic value in NMIBC patients treated with BCG, a result not observed with the EAU2021 score or classification.

CONCLUSION

Based on the results, we reasonably suggest that BCGI may be a useful predictor for NMIBC patients who accepted BCG. Furthermore, we have demonstrated the efficacy of constructing a prognostic index using clinical factors and a Lasso regression model, a versatile approach applicable to various medical conditions.

Patterns of mutations in nine cancer-related genes and PAF development among smoking male patients diagnosed with bladder cancer

BACKGROUND

Smoking is one of the most popular risk factors provoking bladder cancer (BC). This research intended to estimate cigarette smoking effect involving PAF signs between smoking patients with BC and non-smoking patients with same diagnosis to define relations with pathological characteristics and their prognosis on zero-relapse and disease-associated recovery.

METHODS

Two groups of smokers (n = 54) and non-smokers (n = 62) were selected. Both cohorts of patients had BC. They were evaluated utilizing NGS on 9 cancer-related genes and confirmed through the Sanger DNA sequencing and histopathological tests based on H&E staining. The factor of smoking and impact of PAF development by ELISA assay and PAF-R manifestation in terms of immunochemical evaluation on BC areas comparing to a control group (n = 30) was examined involving healthy contributors, including the use of well-designed statistical trials.

RESULTS

The multivariate evaluation showed considerable rise in mutation patterns related to smoking among BC patients (group 3), increase in PAF development (***P<0.001) and vivid signs of PAF-R contrasted to non-smokers with BC (group 2) and control group (group 1). All the identified biological changes (gains/losses) were recorded at the same locations in both groups. Patients from group 3 held 3-4 various mutations, while patients from group 2 held 1-3 various mutations. Mutations were not identified in 30 respondents from control group. The most repeated mutations were identified in 3 of 9 examined genes, namely TP53, PIK3CA and PTEN, with highest rates of increase in Group 3. Moreover, histopathological tests revealed barely identifiable and abnormal traits in BC tissues, i.e. were without essential histopathological changes between groups 2 and 3.

CONCLUSION

Smoking of cigarettes provokes PAF development due to urothelial inflammation and rise of mutations in 9 cancer-related genes. These are indicative factors of inducing BC.

Construction of noninvasive prognostic model of bladder cancer patients based on urine proteomics and screening of natural compounds

BACKGROUND

Bladder cancer (BCa) has a high incidence and recurrence rate worldwide. So far, there is no noninvasive detection of BCa therapy and prognosis based on urine multi-omics. Therefore, it is necessary to explore noninvasive predictive models and novel treatment modalities for BCa.

METHODS

First, we performed protein analysis of urine from five BCa patients and five healthy individuals using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Combining multi-omics data to mine particular and sensitive molecules to predict BCa prognosis. Second, urine proteomics data were combined with TCGA transcriptome data to select differential genes that were specifically highly expressed in urine and tissues. Further, the Lasso equation was used to screen specific molecules to construct a noninvasive prediction model of BCa. Finally, natural compounds of specific molecules were selected by combined network pharmacology and molecular docking to complete molecular structure docking.

RESULTS

A noninvasive predictive model was constructed using PSMB5, P4HB, S100A16, GET3, CNP, TFRC, DCXR, and MPZL1, specific molecules screened by multi-omics, and clinical features, which had good predictive value at 1, 3, and 5 years of prediction. High expression of these target genes suggests a poor prognosis in patients with BCa, and they were mainly involved in cell adhesion molecules and the IGF pathway. In addition, the corresponding drugs and natural compounds were selected by network pharmacology, and the molecular structure 7NHT of PSMB5 was found to be well docked to Ellagic acid, a natural compound in Hetaoren that we found. The 3D structure 6I7S of P4HB was able to bind to Stigmasterol in Shanzha stably, and the structure 6WRV of TFRC as an iron transport carrier was also able to bind to Stigmasterol in Shanzha stably. The structures 1WOJ, 3D3W, and 6IGW of CNP, DCXR, and MPZL1 can also play an important role in combination with the natural compounds (S)-Stylopine, Kryptoxanthin, and Sitosterol in Maqianzi, Yumixu, and Laoguancao.

CONCLUSION

The noninvasive prediction model based on urinomics had excellent potential in predicting the prognosis of patients with BCa. The multi-omics screening of specific molecules combined with pharmacology and compound molecular docking can promote the research and development of novel drugs.