Global Meta-analysis of Urine Microbiome: Colonization of Polycyclic Aromatic Hydrocarbon-degrading Bacteria Among Bladder Cancer Patients

An exploratory study investigating the impact of the bladder tumor microbiome on Bacillus Calmette Guerin (BCG) response in non-muscle invasive bladder cancer

PURPOSE

Intravesical Bacillus Calmette-Guerin (BCG) is standard of care for intermediate- and high-risk non-muscle invasive bladder cancer (NMIBC). The effect of the bladder microbiome on response to BCG is unclear. We sought to characterize the microbiome of bladder tumors in BCG-responders and non-responders and identify potential mechanisms that drive treatment response.

MATERIALS AND METHODS

Patients with archival pre-treatment biopsy samples (2012-2018) were identified retrospectively. Prospectively, urine and fresh tumor samples were collected from individuals with high-risk NMIBC (2020-2023). BCG response was defined as tumor-free 2 years from induction therapy. Extracted DNA was sequenced for 16S rRNA and shotgun metagenomics. Primary outcomes were species richness (α-diversity) and microbial composition (β-diversity). Paired t-tests were performed for α-diversity (Observed species/Margalef). Statistical analysis for β-diversity (weighted and unweighted UniFrac distances, weighted Bray-Curtis dissimilarity) were conducted through Permanova, with 999 permutations.

RESULTS

Microbial species richness (P < 0.001) and composition (P = 0.001) differed between BCG responders and non-responders. Lactobacillus spp. were significantly enriched in BCG-responders. Shotgun metagenomics identified possible mechanistic pathways such as assimilatory sulfate reduction.

CONCLUSION

A compositional difference exists in the tumor microbiome of BCG responders and non-responders with Lactobacillus having increased abundance in BCG responders.

Understanding the microbiome as a mediator of bladder cancer progression and therapeutic response

Bladder cancer (BCa) remains a significant source of morbidity and mortality. BCa is one of the most expensive tumors to treat, in part because of a lack of nonsurgical options. The recent advent of immunotherapy, alone or in combination with other compounds, has improved therapeutic options. Resistance to immunotherapy remains common, and many patients do not have durable response. Recent advances indicate immunotherapy efficacy may be tied in part to the endogenous bacteria present in our body, more commonly referred to as the microbiome. Laboratory and clinical data now support the idea that a healthy microbiome is critical to effective response to immunotherapy. At the same time, pathogenic interactions between the microbiome and immune cells can also serve to drive formation of tumors, increasing the complexity of these interactions. Given the rising importance of immunotherapy in BCa, understanding how we might be able to alter the microbiome to improve therapeutic efficacy offers a novel route to improved patient care. The goal of this review is to examine our current understanding of microbial interactions with the immune system and cancer with an emphasis on BCa. We will further attempt to define both current gaps in knowledge and future directions that may yield beneficial results to the field.

The concentration and probabilistic health risk assessment attribute to PAHs in indoor air of Hormozgan aluminum plant, Iran

We measured PAHs concentrations to understand the potential short and long-term health risks to workers. In the anode cooking area, the lowest and highest concentrations of PAHs were found for DahA (dibenzo[a,h]anthracene) at 0.373 ± 0.326 µg/m3 and Chry (chrysene) at 1.923 ± 1.258 µg/m3, respectively. In the anode-making area, these concentrations were higher, with DahA at 0.435 ± 0.221 µg/m3 and Chry peaking at 3.841 ± 1.702 µg/m3. Risk assessment based on these concentrations indicates a hierarchical order of individual PAHs risks in the anode cooking area, led by BaP (benzo[a]pyrene), followed by other specific PAHs compounds. The total hazard quotient (THQ) for PAH exposure in both anode-making and cooking areas significantly exceeds the threshold for considered cancer risk, emphasizing the considerable danger to workers. This study underscores the urgent need to mitigate exposure to PAHs in industrial settings to protect worker health from the carcinogenic risks of such hazardous compounds.

Urinary microbiome dysbiosis is associated with an inflammatory environment and perturbed fatty acids metabolism in the pathogenesis of bladder cancer

BACKGROUND

Bladder cancer is a common malignancy with high recurrence rate. Early diagnosis and recurrence surveillance are pivotal to patients' outcomes, which require novel minimal-invasive diagnostic tools. The urinary microbiome is associated with bladder cancer and can be used as biomarkers, but the underlying mechanism is to be fully illustrated and diagnostic performance to be improved.

METHODS

A total of 23 treatment-naïve bladder cancer patients and 9 non-cancerous subjects were enrolled into the Before group and Control group. After surgery, 10 patients from the Before group were further assigned into After group. Void mid-stream urine samples were collected and sent for 16S rDNA sequencing, targeted metabolomic profiling, and flow cytometry. Next, correlations were analyzed between microbiota, metabolites, and cytokines. Finally, receiver operating characteristic (ROC) curves of the urinary biomarkers were plotted and compared.

RESULTS

Comparing to the Control group, levels of IL-6 (p < 0.01), IL-8 (p < 0.05), and IL-10 (p < 0.05) were remarkably elevated in the Before group. The α diversity of urine microbiome was also significantly higher, with the feature microbiota positively correlated to the level of IL-6 (r = 0.58, p < 0.01). Significant differences in metabolic composition were also observed between the Before and Control groups, with fatty acids and fatty acylcarnitines enriched in the Before group. After tumor resection, cytokine levels and the overall microbiome structure in the After group remained similar to that of the Before group, but fatty acylcarnitines were significantly reduced (p < 0.05). Pathway enrichment analysis revealed beta-oxidation of fatty acids was significantly involved (p < 0.001). ROC curves showed that the biomarker panel of Actinomycetaceae + arachidonic acid + IL-6 had superior diagnostic performance, with sensitivity of 0.94 and specificity of 1.00.

CONCLUSIONS

Microbiome dysbiosis, proinflammatory environment and altered fatty acids metabolism are involved in the pathogenesis of bladder cancer, which may throw light on novel noninvasive diagnostic tool development.

Targeting gut and intratumoral microbiota: a novel strategy to improve therapy resistance in cancer with a focus on urologic tumors

INTRODUCTION

Growing attention has been drawn to urologic tumors due to their rising incidence and suboptimal clinical treatment outcomes. Cancer therapy resistance poses a significant challenge in clinical oncology, limiting the efficacy of conventional treatments and contributing to disease progression. Recent research has unveiled a complex interplay between the host microbiota and cancer cells, highlighting the role of the microbiota in modulating therapeutic responses.

AREAS COVERED

We used the PubMed and Web of Science search engines to identify key publications in the fields of tumor progression and urologic tumor treatment, specifically focusing on the role of the microbiota. In this review, we summarize the current literature on how microbiota influence the tumor microenvironment and anti-tumor immunity, as well as their impact on treatments for urinary system malignancies, highlighting promising future applications.

EXPERT OPINION

We explore how the composition and function of the gut microbiota influence the tumor microenvironment and immune response, ultimately impacting treatment outcomes. Additionally, we discuss emerging strategies targeting the microbiota to enhance therapeutic efficacy and overcome resistance. The application of antibiotics, fecal microbiota transplantation, and oncolytic bacteria has improved tumor treatment outcomes, which provides a novel insight into developing therapeutic strategies for urologic cancer.

A review of machine learning methods for cancer characterization from microbiome data

Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.

Current Trends and Challenges of Microbiome Research in Bladder Cancer

PURPOSE OF THE REVIEW

Microbiome research has provided valuable insights into the associations between microbial communities and bladder cancer. However, this field faces significant challenges that hinder the interpretation, generalization, and translation of findings into clinical practice. This review aims to elucidate these challenges and highlight the importance of addressing them for the advancement of microbiome research in bladder cancer.

RECENT FINDINGS

Recent findings underscore the complexities involved in microbiome research, particularly in the context of bladder cancer. Challenges include low microbial biomass in urine samples, potential contamination issues during collection and processing, variability in sequencing methods and primer selection, and the difficulty of establishing causality between microbiota and bladder cancer. Studies have shown the impact of sample storage conditions and DNA isolation kits on microbiome analysis, emphasizing the need for standardization. Additionally, variations in urine collection methods can introduce contamination and affect results. The choice of 16S rRNA gene amplicon sequencing or shotgun metagenomic sequencing introduces technical challenges, including primer selection and sequencing read length. Establishing causality between the microbiota and bladder cancer requires experimental methods like fecal microbiota transplantation and human microbiota-associated murine models, which face their own set of challenges. Translating microbiome research into therapeutic applications is hindered by methodological variability, incomplete understanding of bioactive molecules, imperfect animal models, and the inherent heterogeneity of microbiome communities among individuals. Microbiome research in bladder cancer presents significant challenges stemming from technical and conceptual complexities. Addressing these challenges through standardization, improved experimental models, and advanced analytical approaches is essential for advancing our understanding of the microbiome's role in bladder cancer and its potential clinical applications. Achieving this goal can lead to improved patient outcomes and novel therapeutic strategies in the future.

Microbiome Profiling in Bladder Cancer Patients Using the First-morning Urine Sample

Background

Several studies support the interplay between the urinary microbiome (ie, urobiome) and bladder cancer (BCa). Specific urinary bacteria may be responsible for chronic inflammation, which in turn promotes carcinogenesis. Different signatures of urobiome in BCa patients were identified depending on tumor type, geographical area, age, and sex.

Objective

We explored the urobiome in BCa patients undergoing transurethral resection of bladder tumor (TURBT), to identify possible predictive biomarkers of cancer.

Design setting and participants

The urobiome analysis was conducted in 48 patients (13 females) undergoing TURBT, of whom 30 with BCa (five females) and 18 with benign bladder tumor, analyzing bacterial 16S rRNA by next-generation sequencing in first-morning (FM) urine samples. Forty-three cancer-free individuals and 17 prostate cancer patients were used as controls.

Outcome measurements and statistical analysis

First, we identified the better urine collection procedure to perform the urobiome analysis, comparing bacterial composition between catheterized (CAT) and FM urine samples in TURBT patients. Successively, we observed a specific urobiome in BCa patients rather than controls. A combined pipeline including the DESeq2 and linear discriminant analysis effect size tests was used to identify differential urinary taxa, strictly associated with BCa patients.

Results and limitations

The bacterial composition of CAT and FM urine samples was comparable, so the latter was used for the following analyses. An increased abundance of Porphyromonas and Porphyromonas somerae was found in BCa patients compared with controls. This signature seems to be more related (p <0.05) to male BCa patients over 50 yr old. Owing to the low biomass of urinary microbiota, several samples were excluded from the study, reducing the number of BCa patients considered.

Conclusions

FM urine samples represent a manageable specimen for a urobiome analysis; P. somerae is a specific biomarker of BCa risk.

Patient summary

Our study showed an increased abundance of Porphyromonas and Porphyromonas somerae in male bladder cancer (BCa) patients, supporting the use of a first-morning urine sample, a less invasive and low-cost collection method, for the urobiome analysis of patients at risk of BCa.

Epidemiology of Bladder Cancer in 2023: A Systematic Review of Risk Factors

CONTEXT

Bladder cancer (BC) is common worldwide and poses a significant public health challenge. External risk factors and the wider exposome (totality of exposure from external and internal factors) contribute significantly to the development of BC. Therefore, establishing a clear understanding of these risk factors is the key to prevention.

OBJECTIVE

To perform an up-to-date systematic review of BC's epidemiology and external risk factors.

EVIDENCE ACQUISITION

Two reviewers (I.J. and S.O.) performed a systematic review using PubMed and Embase in January 2022 and updated it in September 2022. The search was restricted to 4 yr since our previous review in 2018.

EVIDENCE SYNTHESIS

Our search identified 5177 articles and a total of 349 full-text manuscripts. GLOBOCAN data from 2020 revealed an incidence of 573 000 new BC cases and 213 000 deaths worldwide in 2020. The 5-yr prevalence worldwide in 2020 was 1 721 000. Tobacco smoking and occupational exposures (aromatic amines and polycyclic aromatic hydrocarbons) are the most substantial risk factors. In addition, correlative evidence exists for several risk factors, including specific dietary factors, imbalanced microbiome, gene-environment risk factor interactions, diesel exhaust emission exposure, and pelvic radiotherapy.

CONCLUSIONS

We present a contemporary overview of the epidemiology of BC and the current evidence for BC risk factors. Smoking and specific occupational exposures are the most established risk factors. There is emerging evidence for specific dietary factors, imbalanced microbiome, gene-external risk factor interactions, diesel exhaust emission exposure, and pelvic radiotherapy. Further high-quality evidence is required to confirm initial findings and further understand cancer prevention.

PATIENT SUMMARY

Bladder cancer is common, and the most substantial risk factors are smoking and workplace exposure to suspected carcinogens. On-going research to identify avoidable risk factors could reduce the number of people who get bladder cancer.

The bladder microbiome of NMIBC and MIBC patients revealed by 2bRAD-M

Background

Bladder cancer (BCa) is the most common malignancy of the urinary tract which can be divided into non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), and their microbial differences are not fully understood. This study was conducted by performing 2bRAD sequencing for Microbiome (2bRAD-M) on NMIBC and MIBC tissue samples to investigate the microbiota differences between NMIBC and MIBC individuals.

Methods

A total of 22 patients with BCa, including 7 NMIBC and 15 MIBC, were recruited. Tumor tissues were surgically removed as samples and DNA was extracted. Type IIB restriction endonucleases were used to enzymatically cleave the microbial genome for each microbe's tag and map it to species-specific 2bRAD markers to enable qualitative and quantitative studies of microbes between MIBC and NMIBC tissues.

Results

A total of 527 species were detected. The microbial diversity of NMIBC tissues was significantly higher than that of MIBC tissues. Microbial composition of the two tumor tissues was similar, where Ralstonia_sp000620465 was the most dominant species. 4 species (Acinetobacter_guillouiae, Anoxybacillus_A_rupiensis, Brevibacillus_agri and Staphylococcus_lugdunensis) were enriched in NMIBC, while Ralstonia_mannitolilytica, Ralstonia_pickettii, and Ralstonia_sp000620465 were overrepresented in MIBC. 252 discriminatory character taxa were also revealed by linear discriminant analysis effect sizea (LEfSe). Species importance point plots identified Ralstonia_sp000620465, Cutibacterium_acnes and Ralstonia_pickettii as the three most important species between the two groups. Meanwhile, functional annotation analysis showed 3011 different COGs and 344 related signaling pathways between MIBC and NMIBC microbiome.

Conclusion

This first 2bRAD-M microbiome study on MIBC and NMIBC tissues revealed significant differences in the microbial environment between the two groups, which implies a potential association between tumor microbial dysbiosis and BCa, and provides a possible target and basis for subsequent studies on the mechanisms of BCa development and progression.

Current Viewpoint on Female Urogenital Microbiome-The Cause or the Consequence?

An increasing amount of evidence implies that native microbiota is a constituent part of a healthy urinary tract (UT), making it an ecosystem on its own. What is still not clear is whether the origin of the urinary microbial community is the indirect consequence of the more abundant gut microbiota or a more distinct separation exists between these two systems. Another area of uncertainty is the existence of a link between the shifts in UT microbial composition and both the onset and persistence of cystitis symptoms. Cystitis is one of the most common reasons for antimicrobial drugs prescriptions in primary and secondary care and an important contributor to the problem of antimicrobial resistance. Despite this fact, we still have trouble distinguishing whether the primary cause of the majority of cystitis cases is a single pathogen overgrowth or a systemic disorder affecting the entire urinary microbiota. There is an increasing trend in studies monitoring changes and dynamics of UT microbiota, but this field of research is still in its infancy. Using NGS and bioinformatics, it is possible to obtain microbiota taxonomic profiles directly from urine samples, which can provide a window into microbial diversity (or the lack of) underlying each patient's cystitis symptoms. However, while microbiota refers to the living collection of microorganisms, an interchangeably used term microbiome referring to the genetic material of the microbiota is more often used in conjunction with sequencing data. It is this vast amount of sequences, which are truly "Big Data", that allow us to create models that describe interactions between different species contributing to an UT ecosystem, when coupled with machine-learning techniques. Although in a simplified predator-prey form these multi-species interaction models have the potential to further validate or disprove current beliefs; whether it is the presence or the absence of particular key players in a UT microbial ecosystem, the exact cause or consequence of the otherwise unknown etiology in the majority of cystitis cases. These insights might prove to be vital in our ongoing struggle against pathogen resistance and offer us new and promising clinical markers.