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Accurately classifying bladder cancer patients based on Tumor Mutational Burden (TMB) is of paramount significance for prognosis and treatment decisions. To achieve that, we present a novel approach leveraging multi-omics data to differentiate between low and high TMB classes. The model combines feature selection and predictive modeling to unveil robust biomarkers associated with TMB classification. The Genetic Algorithm is employed to perform feature selection across DNA methylation, copy number alteration, and RNA-seq datasets. This process effectively reduces the dimensionality of the input data while retaining the most informative attributes. Subsequently, these selected features are projected into a latent space using non-negative matrix factorization, capturing the underlying patterns within the multi-omics data. Convolutional neural network among other machine learning machines to predict the class of TMB. The model introduces a promising classification power, showcasing the potential of these multi-omics biomarkers in accurately distinguishing between low and high TMB classes. The survival analysis reveals a substantial disparity between the cohorts classified as low-TMB and high-TMB. We propose a robust framework for TMB classification in bladder cancer that integrates multi-omics data, advanced machine learning techniques, and survival analysis to collectively pave the way for improved prognostic insights and personalized therapeutic interventions.

To identify patients with urothelial cancer most likely to benefit from neoadjuvant chemotherapy, we evaluated germline pharmacogenomic markers for an association with response in 205 patients across 3 institutions. Stage pT0 (26%) and < pT2 (50%) rates were consistent across the respective discovery and replication cohorts. Despite the large effects for 3 polymorphisms in the discovery set, none were associated with achievement of pT0 or < pT2 on replication. Multi-institutional efforts are feasible and will be necessary to achieve advances in urothelial cancer precision medicine.

To report urinary bother, urinalysis changes, disease-free survival (DFS), and overall survival (OS) over 2 years for subjects enrolled in a phase I dose-escalation trial (NCT02324582) of intravesical Bacillus Calmette-Guérin (BCG) in combination with systemic pembrolizumab for recurrent or persistent high-grade non-muscle invasive bladder cancer (HGNMIBC).

Bladder cancer is the fifth most common cancer in the United States. About one-third of bladder tumors are invasive at presentation, and despite treatment with radical cystectomy, the disease continues [...]

Objectives: To prospectively examine the changes in microbiota within the urinary tract after transrectal prostate biopsy. Materials and methods: Data, urine, and fecal samples prospectively collected from 30 patients before and after transrectal biopsy of the prostate. DNA was extracted from urine collected after a prostate massage before and after prostate biopsy, and from fecal samples collected before the biopsy. We sequenced DNA using the bacterial 16S rRNA high-throughput next-generation sequencing and analyzed changes in microbial profiles for taxonomy comparison between samples. Results: Pre-biopsy urinary microbial profiles contained Lactobacillusand Staphylococcusbacteria. Post-biopsy urinary microbial profiles included lower levels of Lactobacillusand higher levels of Prevotellabacteria. Bacteroidesbacteria were predominant in fecal samples. We identified two clustering patterns containing both pre- and post-biopsy urine samples. Cluster 1 had a urine cluster pattern that was distinct from fecal, whereas cluster 2 was similar to fecal. We observed two different modes of microbial changes, 11 patients had both of their urine (pre and post) samples associated with a particular cluster group, whereas others (n= 15) had movement between clusters 1 and 2 following the biopsy procedure. Four patient’s post-biopsy urine microbial profiles clustered very tightly to the fecal microbial profile. Conclusions: We describe two models of change in the urinary tract microbiota after prostate biopsy using 16S RNA gene analysis. Further research to determine what controls changes in the urinary microbiota after prostate biopsy can help us understand why some patients are more susceptible to develop post-biopsy infections.

The objective of this study was to determine the expression of programmed cell death-1 (PD-1) and programmed cell death ligand-L1 (PD-L1) in high-grade prostate cancer tissues, and correlate the expression with disease and patient characteristics. Of the 25 samples, 2 (8%) scored high for PD-1 expression, 2 (8%) scored high for PD-L1 expression, and 18 (72%) scored high for CD3 expression.