The urinary microbiome associated with bladder cancer

Bučević-Popović, Viljemka; Šitum, Marijan; Chow, Cheryl-Emiliane T.; Chan, Luisa; Roje, Blanka; Terzić, Janoš

doi:10.1038/s41598-018-29054-w

Cited by 216 publications

(197 citation statements)

References 57 publications

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“…20,21 In bladder cancer research, using urine specimens, one study observed increased richness of microbiota diversity in bladder cancer patients, whereas no significant differences in microbial diversity were observed in a different evaluation. 10,22 Based on our findings, we hypothesize that the loss of microbiota diversity may be related to the incidence of bladder cancer. As previously shown in the gut, the balance between specific microbial groups may be more relevant than overall urine composition.…”

Section: Discussionmentioning

confidence: 72%

Dysbiosis signatures of the microbial profile in tissue from bladder cancer

Liu

Lü

et al. 2019

Cancer Medicine

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BackgroundTo examine the microbial profiles in parenchyma tissues in bladder cancer.MethodsTissue samples of cancerous bladder mucosa were collected from patients diagnosed with bladder cancer (22 carcinoma tissues and 12 adjacent normal tissues). The V3‐V4 region of the bacterial 16S rRNA gene was PCR amplified, followed by sequencing on an Illumina MiSeq platform. Bioinformatics analysis for microbial classification and functional assessment was performed to assess bladder microbiome diversity and variations.ResultsThe predominant phylum in both tissues was Proteobacteria. The cancerous tissues exhibited lower species richness and diversity. Beta diversity significantly differed between the cancerous and normal tissues. Lower relative abundances of the microbial genera Lactobacillus, Prevotella_9, as well as Ruminococcaceae were observed, whereas those of Cupriavidus spp., an unknown genus of family Brucellaceae, and Acinetobacter, Anoxybacillus, Escherichia‐Shigella, Geobacillus, Pelomonas, Ralstonia, and Sphingomonas were higher in the cancerous tissues. These findings indicate that these genera may be potentially utilized as biomarkers for bladder cancer. PICRUSt analysis revealed that several pathways involved in the metabolism of harmful chemical compounds were enriched in the cancer tissues, thereby providing evidence that environmental factors are strongly associated with bladder cancer etiology.ConclusionThis is the first study that has described and analyzed the dysbiotic motifs of urinary microbiota in the parenchymatous tissues of bladder cancer via 16S rRNA gene sequencing. Our results suggest that changes in the bladder microbiome may serve as biomarkers for bladder cancer, possibly assisting in disease screening and monitoring.

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Section: Discussionmentioning

confidence: 72%

Dysbiosis signatures of the microbial profile in tissue from bladder cancer

Liu

Lü

et al. 2019

Cancer Medicine

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“…While these techniques have been used extensively to study the urinary microbiome as it pertains to benign urological disease states, only a small number of studies to date have investigated the role of the urinary microbiome in bladder cancer [4,5]. Unfortunately, all published studies in this domain have been limited by their use of voided urine, which increases the potential for introduction of urethral and peri-urethral contamination into patient samples.…”

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confidence: 99%

Surgical Metastasectomy in Renal Cell Carcinoma: A Systematic Review

Ouzaïd

Capitanio

Staehler

et al. 2019

European Urology Oncology

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Given these considerations, CN in mRCC should not be disregarded, at least not in selected cases of low metastatic burden or in symptomatic cases. Conflicts of InterestNone declared. References 1 Mejean A, Ravaud A, Thezenas S et al. Sunitinib alone or after nephrectomy in metastatic renal-cell carcinoma. N Engl J Med 2018; 379: 417-27 2 Hanna N, Sun M, Meyer CP et al. Survival analyses of patients with metastatic renal cancer treated with targeted therapy with or without cytoreductive nephrectomy: a national cancer data base study. J Clin Oncol 2016; 34: 3267-75 3 Arora S, Sood A, Dalela D et al. Cytoreductive nephrectomy: assessing the generalizability of the CARMENA trial to real-world national cancer data base cases. Eur Urol 2019; 75: 352-3 4 Choueiri TK, Motzer RJ. Systemic therapy for metastatic renal-cell carcinoma. N Engl J Med 2017; 376: 354-66 5 Capitanio U, Montorsi F. Renal cancer. Lancet 2016; 387: 894-906 6 Cella D, Escudier B, Tannir NM et al. Quality of life outcomes for cabozantinib versus everolimus in patients with metastatic renal cell carcinoma: METEOR phase III randomized trial. J Clin Oncol 2018; 36: 757-64 7 Turajlic S, Xu H, Litchfield K et al. Tracking cancer evolution reveals constrained routes to metastases: TRACERx renal. Cell 2018; 173: 581-94.e12 8 Ouzaid I, Capitanio U, Staehler M et al. Surgical metastasectomy in renal cell carcinoma: a systematic review. Eur Urol 2018 [Epub ahead of print].

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“…Another urine sample contained a high Lactobacillus abundance, which has previously been shown to be abundant in urine samples (40). In addition, presence of Atopobium , Gardnerella , Campylobacter , Prevotella and Anaerococcus point towards an existing urinary microbiota (41). However, Pseudomonas , a common Zymo kit contaminant, was still found in this urine sample, and for Magna more than 25% of reads could not be classified (Fig S3C).…”

Section: Resultsmentioning

confidence: 99%

Toward standards in clinical microbiome studies: comparison of three DNA extraction methods and two bioinformatic pipelines

Ducarmon

Hornung

Geelen

et al. 2019

Preprint

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11When studying the microbiome using next generation sequencing, DNA extraction method, 12 sequencing procedures and bioinformatic processing are crucial to obtain reliable data. 13 Method choice has been demonstrated to strongly affect the final biological interpretation. 14 We assessed the performance of three DNA extraction methods and two bioinformatic 15 pipelines for bacterial microbiota profiling through 16S rRNA gene amplicon sequencing, 16 using positive and negative controls for DNA extraction and sequencing, and eight different 17 types of high-or low-biomass samples. Performance was evaluated based on quality control 18 passing, DNA yield, richness, diversity and compositional profiles. All DNA extraction 19 methods retrieved the theoretical relative bacterial abundance with maximum three-fold 20 change, although differences were seen between methods, and library preparation and 21 sequencing induced little variation. Bioinformatic pipelines showed different results for 22 estimating richness, but diversity and compositional profiles were comparable. DNA 23 extraction methods were successful for feces and oral swabs and variation induced by DNA 24 extraction methods was lower than inter-subject (biological) variation. For low-biomass 25 samples, a mixture of genera present in negative controls and sample-specific genera, 26possibly representing biological signal, were observed. We conclude that the tested 27 bioinformatic pipelines perform equally with pipeline-specific advantages and disadvantages. 28Two out of three extraction methods performed equally well, while one method was less 29 accurate regarding retrieval of compositional profiles. Lastly, we demonstrate the importance 30 of including negative controls when analyzing low bacterial biomass samples. 31 IMPORTANCE 32Method choice throughout the workflow of a microbiome study, from sample collection to 33 DNA extraction and sequencing procedures, can greatly affect results. This study evaluated 34 3 three different DNA extraction methods and two bioinformatic pipelines by including 35 positive and negative controls, and various biological specimens. By identifying an optimal 36 combination of DNA extraction method and bioinformatic pipeline use, we hope to 37 contribute to increased methodological consistency in microbiome studies. Our methods were 38 not only applied to commonly studied samples for microbiota analysis, e.g. feces, but also for 39 more rarely studied, low-biomass samples. Microbiota composition profiles of low-biomass 40 samples (e.g. urine and tumor biopsies) were not always distinguishable from negative 41 controls, or showed partial overlap, confirming the importance of including negative controls 42 in microbiome studies, especially when low bacterial biomass is expected. 43

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The urinary microbiome associated with bladder cancer

Cited by 216 publications

References 57 publications

Dysbiosis signatures of the microbial profile in tissue from bladder cancer

Dysbiosis signatures of the microbial profile in tissue from bladder cancer

Surgical Metastasectomy in Renal Cell Carcinoma: A Systematic Review

Toward standards in clinical microbiome studies: comparison of three DNA extraction methods and two bioinformatic pipelines

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