Progression-associated molecular changes in basal/squamous and sarcomatoid bladder carcinogenesis

Preclinical models of bladder cancer: BBN and beyond

Preclinical modelling is a crucial component of advancing the understanding of cancer biology and therapeutic development. Several models exist for understanding the pathobiology of bladder cancer and evaluating therapeutics. N-butyl-N-(4-hydroxybutyl)-nitrosamine (BBN)-induced bladder cancer is a commonly used model that recapitulates many of the features of human disease. Particularly in mice, BBN is a preferred laboratory model owing to a high level of reproducibility, high genetic fidelity to the human condition, and its relative ease of use. However, important aspects of the model are often overlooked in laboratory studies. Moreover, the advent of new models has yielded a variety of methodologies that complement the use of BBN. Toxicokinetics, histopathology, molecular genetics and sex can differ between available models and are important factors to consider in bladder cancer modelling.

Deep learning-based analysis of EGFR mutation prevalence in lung adenocarcinoma H&E whole slide images

EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitations in that it does not sufficiently reflect tumor heterogeneity and lacks interpretability. In this study, we developed a deep learning model to predict the presence of EGFR mutations by analyzing histopathological patterns in whole slide images (WSIs). We also introduced the EGFR mutation prevalence (EMP) score, which quantifies EGFR prevalence in WSIs based on patch-level predictions, and evaluated its interpretability and utility. Our model estimates the probability of EGFR prevalence in each patch by partitioning the WSI based on multiple-instance learning and predicts the presence of EGFR mutations at the slide level. We utilized a patch-masking scheduler training strategy to enable the model to learn various histopathological patterns of EGFR. This study included 868 WSI samples from lung adenocarcinoma patients collected from three medical institutions: Hallym University Medical Center, Inha University Hospital, and Chungnam National University Hospital. For the test dataset, 197 WSIs were collected from Ajou University Medical Center to evaluate the presence of EGFR mutations. Our model demonstrated prediction performance with an area under the receiver operating characteristic curve of 0.7680 (0.7607-0.7720) and an area under the precision-recall curve of 0.8391 (0.8326-0.8430). The EMP score showed Spearman correlation coefficients of 0.4705 (p = 0.0087) for p.L858R and 0.5918 (p = 0.0037) for exon 19 deletions in 64 samples subjected to next-generation sequencing analysis. Additionally, high EMP scores were associated with papillary and acinar patterns (p = 0.0038 and p = 0.0255, respectively), whereas low EMP scores were associated with solid patterns (p = 0.0001). These results validate the reliability of our model and suggest that it can provide crucial information for rapid screening and treatment plans.

Identification and validation of SHC1 and FGFR1 as novel immune-related oxidative stress biomarkers of non-obstructive azoospermia

Background

Non-obstructive azoospermia (NOA) is a major contributor of male infertility. Herein, we used existing datasets to identify novel biomarkers for the diagnosis and prognosis of NOA, which could have great significance in the field of male infertility.

Methods

NOA datasets were obtained from the Gene Expression Omnibus (GEO) database. CIBERSORT was utilized to analyze the distributions of 22 immune cell populations. Hub genes were identified by applying weighted gene co-expression network analysis (WGCNA), machine learning methods, and protein-protein interaction (PPI) network analysis. The expression of hub genes was verified in external datasets and was assessed by receiver operating characteristic (ROC) curve analysis. Gene set enrichment analysis (GSEA) was applied to explore the important functions and pathways of hub genes. The mRNA-microRNA (miRNA)-transcription factors (TFs) regulatory network and potential drugs were predicted based on hub genes. Single-cell RNA sequencing data from the testes of patients with NOA were applied for analyzing the distribution of hub genes in single-cell clusters. Furthermore, testis tissue samples were obtained from patients with NOA and obstructive azoospermia (OA) who underwent testicular biopsy. RT-PCR and Western blot were used to validate hub gene expression.

Results

Two immune-related oxidative stress hub genes (SHC1 and FGFR1) were identified. Both hub genes were highly expressed in NOA samples compared to control samples. ROC curve analysis showed a remarkable prediction ability (AUCs > 0.8). GSEA revealed that hub genes were predominantly enriched in toll-like receptor and Wnt signaling pathways. A total of 24 TFs, 82 miRNAs, and 111 potential drugs were predicted based on two hub genes. Single-cell RNA sequencing data in NOA patients indicated that SHC1 and FGFR1 were highly expressed in endothelial cells and Leydig cells, respectively. RT-PCR and Western blot results showed that mRNA and protein levels of both hub genes were significantly upregulated in NOA testis tissue samples, which agree with the findings from analysis of the microarray data.

Conclusion

It appears that SHC1 and FGFR1 could be significant immune-related oxidative stress biomarkers for detecting and managing patients with NOA. Our findings provide a novel viewpoint for illustrating potential pathogenesis in men suffering from infertility.

Molecular profile of bladder cancer progression to clinically aggressive subtypes

Bladder cancer is a histologically and clinically heterogenous disease. Most bladder cancers are urothelial carcinomas, which frequently develop distinct histological subtypes. Several urothelial carcinoma histological subtypes, such as micropapillary, plasmacytoid, small-cell carcinoma and sarcomatoid, show highly aggressive behaviour and pose unique challenges in diagnosis and treatment. Comprehensive genomic characterizations of the urothelial carcinoma subtypes have revealed that they probably arise from a precursor subset of conventional urothelial carcinomas that belong to different molecular subtypes - micropapillary and plasmacytoid subtypes develop along the luminal pathway, whereas small-cell and sarcomatoid subtypes evolve along the basal pathway. The subtypes exhibit distinct genomic alterations, but in most cases their biological properties seem to be primarily determined by specific gene expression profiles, including epithelial-mesenchymal transition, urothelial-to-neural lineage plasticity, and immune infiltration with distinct upregulation of immune regulatory genes. These breakthrough studies have transformed our view of bladder cancer histological subtype biology, generated new hypotheses for therapy and chemoresistance, and facilitated the discovery of new therapeutic targets.

The urothelial gene regulatory network: understanding biology to improve bladder cancer management

The urothelium is a stratified epithelium composed of basal cells, one or more layers of intermediate cells, and an upper layer of differentiated umbrella cells. Most bladder cancers (BLCA) are urothelial carcinomas. Loss of urothelial lineage fidelity results in altered differentiation, highlighted by the taxonomic classification into basal and luminal tumors. There is a need to better understand the urothelial transcriptional networks. To systematically identify transcription factors (TFs) relevant for urothelial identity, we defined highly expressed TFs in normal human bladder using RNA-Seq data and inferred their genomic binding using ATAC-Seq data. To focus on epithelial TFs, we analyzed RNA-Seq data from patient-derived organoids recapitulating features of basal/luminal tumors. We classified TFs as "luminal-enriched", "basal-enriched" or "common" according to expression in organoids. We validated our classification by differential gene expression analysis in Luminal Papillary vs. Basal/Squamous tumors. Genomic analyses revealed well-known TFs associated with luminal (e.g., PPARG, GATA3, FOXA1) and basal (e.g., TP63, TFAP2) phenotypes and novel candidates to play a role in urothelial differentiation or BLCA (e.g., MECOM, TBX3). We also identified TF families (e.g., KLFs, AP1, circadian clock, sex hormone receptors) for which there is suggestive evidence of their involvement in urothelial differentiation and/or BLCA. Genomic alterations in these TFs are associated with BLCA. We uncover a TF network involved in urothelial cell identity and BLCA. We identify novel candidate TFs involved in differentiation and cancer that provide opportunities for a better understanding of the underlying biology and therapeutic intervention.

What is a Bladder Cancer Molecular Subtype? - Counterpoint

In an accompanying paper, Mattias Höglund discusses on what is a bladder cancer molecular subtype. He emphasizes the need to consider the aim of tumor classification, which is obviously critical to the approach. He also focuses on considering primarily the identity features of the neoplastic cells. Here, we provide a counterpoint. While largely agreeing with his views, we underline that other parameters that may vary in a spatial or temporal scale, and the tumor microenvironment, can also provide relevant information to render tumor classifications clinically useful. Furthermore, tumor heterogeneity and evolution during the disease course - natural or under therapeutic pressure - should be considered.

Patient-derived organoids identify tailored therapeutic options and determinants of plasticity in sarcomatoid urothelial bladder cancer

Sarcomatoid Urothelial Bladder Cancer (SARC) is a rare and aggressive histological subtype of bladder cancer for which therapeutic options are limited and experimental models are lacking. Here, we report the establishment of a long-term 3D organoid-like model derived from a SARC patient (SarBC-01). SarBC-01 emulates aggressive morphological, phenotypical, and transcriptional features of SARC and harbors somatic mutations in genes frequently altered in sarcomatoid tumors such as TP53 (p53) and RB1 (pRB). High-throughput drug screening, using a library comprising 1567 compounds in SarBC-01 and conventional urothelial carcinoma (UroCa) organoids, identified drug candidates active against SARC cells exclusively, or UroCa cells exclusively, or both. Among those, standard-of-care chemotherapeutic drugs inhibited both SARC and UroCa cells, while a subset of targeted drugs was specifically effective in SARC cells, including agents targeting the Glucocorticoid Receptor (GR) pathway. In two independent patient cohorts and in organoid models, GR and its encoding gene NR3C1 were found to be significantly more expressed in SARC as compared to UroCa, suggesting that high GR expression is a hallmark of SARC tumors. Further, glucocorticoid treatment impaired the mesenchymal morphology, abrogated the invasive ability of SARC cells, and led to transcriptomic changes associated with reversion of epithelial-to-mesenchymal transition, at single-cell level. Altogether, our study highlights the power of organoids for precision oncology and for providing key insights into factors driving rare tumor entities.

AMEND: active module identification using experimental data and network diffusion

BACKGROUND

Molecular interaction networks have become an important tool in providing context to the results of various omics experiments. For example, by integrating transcriptomic data and protein-protein interaction (PPI) networks, one can better understand how the altered expression of several genes are related with one another. The challenge then becomes how to determine, in the context of the interaction network, the subset(s) of genes that best captures the main mechanisms underlying the experimental conditions. Different algorithms have been developed to address this challenge, each with specific biological questions in mind. One emerging area of interest is to determine which genes are equivalently or inversely changed between different experiments. The equivalent change index (ECI) is a recently proposed metric that measures the extent to which a gene is equivalently or inversely regulated between two experiments. The goal of this work is to develop an algorithm that makes use of the ECI and powerful network analysis techniques to identify a connected subset of genes that are highly relevant to the experimental conditions.

RESULTS

To address the above goal, we developed a method called Active Module identification using Experimental data and Network Diffusion (AMEND). The AMEND algorithm is designed to find a subset of connected genes in a PPI network that have large experimental values. It makes use of random walk with restart to create gene weights, and a heuristic solution to the Maximum-weight Connected Subgraph problem using these weights. This is performed iteratively until an optimal subnetwork (i.e., active module) is found. AMEND was compared to two current methods, NetCore and DOMINO, using two gene expression datasets.

CONCLUSION

The AMEND algorithm is an effective, fast, and easy-to-use method for identifying network-based active modules. It returned connected subnetworks with the largest median ECI by magnitude, capturing distinct but related functional groups of genes. Code is freely available at https://github.com/samboyd0/AMEND .