Urinary comprehensive genomic profiling predicts urothelial carcinoma recurrence and identifies responders to intravesical therapy.

Intravesical therapy (IVT) is the standard of care to decrease risk of recurrence and progression for high-grade nonmuscle-invasive bladder cancer. However, post-IVT recurrence remains common and the ability to risk-stratify patients before or after IVT is limited. In this prospectively designed and accrued cohort study, we examine the utility of urinary comprehensive genomic profiling (uCGP) for predicting recurrence risk following transurethral resection of bladder tumor (TURBT) and evaluating longitudinal IVT response. Urine was collected before and after IVT instillation and uCGP testing was done using the UroAmp™ platform. Baseline uCGP following TURBT identified patients with high (61%) and low (39%) recurrence risk. At 24 months, recurrence-free survival (RFS) was 100% for low-risk and 45% for high-risk patients with a hazard ratio (HR) of 9.3. Longitudinal uCGP classified patients as minimal residual disease (MRD) Negative, IVT Responder, or IVT Refractory with 24-month RFS of 100%, 50%, and 32%, respectively. Compared with MRD Negative patients, IVT Refractory patients had a HR of 10.5. Collectively, uCGP enables noninvasive risk assessment of patients following TURBT and induction IVT. uCGP could inform surveillance cystoscopy schedules and identify high-risk patients in need of additional therapy.

Development and Multicenter Case-Control Validation of Urinary Comprehensive Genomic Profiling for Urothelial Carcinoma Diagnosis, Surveillance, and Risk-Prediction.

PURPOSE: Urinary comprehensive genomic profiling (uCGP) uses next-generation sequencing to identify mutations associated with urothelial carcinoma and has the potential to improve patient outcomes by noninvasively diagnosing disease, predicting grade and stage, and estimating recurrence risk. EXPERIMENTAL DESIGN: This is a multicenter case-control study using banked urine specimens collected from patients undergoing initial diagnosis/hematuria workup or urothelial carcinoma surveillance. A total of 581 samples were analyzed by uCGP: 333 for disease classification and grading algorithm development, and 248 for blinded validation. uCGP testing was done using the UroAmp platform, which identifies five classes of mutation: single-nucleotide variants, copy-number variants, small insertion-deletions, copy-neutral loss of heterozygosity, and aneuploidy. UroAmp algorithms predicting urothelial carcinoma tumor presence, grade, and recurrence risk were compared with cytology, cystoscopy, and pathology. RESULTS: uCGP algorithms had a validation sensitivity/specificity of 95%/90% for initial cancer diagnosis in patients with hematuria and demonstrated a negative predictive value (NPV) of 99%. A positive diagnostic likelihood ratio (DLR) of 9.2 and a negative DLR of 0.05 demonstrate the ability to risk-stratify patients presenting with hematuria. In surveillance patients, binary urothelial carcinoma classification demonstrated an NPV of 91%. uCGP recurrence-risk prediction significantly prognosticated future recurrence (hazard ratio, 6.2), whereas clinical risk factors did not. uCGP demonstrated positive predictive value (PPV) comparable with cytology (45% vs. 42%) with much higher sensitivity (79% vs. 25%). Finally, molecular grade predictions had a PPV of 88% and a specificity of 95%. CONCLUSIONS: uCGP enables noninvasive, accurate urothelial carcinoma diagnosis and risk stratification in both hematuria and urothelial carcinoma surveillance patients.

Urinary Comprehensive Genomic Profiling Correlates Urothelial Carcinoma Mutations with Clinical Risk and Efficacy of Intervention.

The clinical standard of care for urothelial carcinoma (UC) relies on invasive procedures with suboptimal performance. To enhance UC treatment, we developed a urinary comprehensive genomic profiling (uCGP) test, UroAmplitude, that measures mutations from tumor DNA present in urine. In this study, we performed a blinded, prospective validation of technical sensitivity and positive predictive value (PPV) using reference standards, and found at 1% allele frequency, mutation detection performs at 97.4% sensitivity and 80.4% PPV. We then prospectively compared the mutation profiles of urine-extracted DNA to those of matched tumor tissue to validate clinical performance. Here, we found tumor single-nucleotide variants were observed in the urine with a median concordance of 91.7% and uCGP revealed distinct patterns of genomic lesions enriched in low- and high-grade disease. Finally, we retrospectively explored longitudinal case studies to quantify residual disease following bladder-sparing treatments, and found uCGP detected residual disease in patients receiving bladder-sparing treatment and predicted recurrence and disease progression. These findings demonstrate the potential of the UroAmplitude platform to reliably identify and track mutations associated with UC at each stage of disease: diagnosis, treatment, and surveillance. Multiple case studies demonstrate utility for patient risk classification to guide both surgical and therapeutic interventions.

Michigan's Gelman Site 1,4-Dioxane Groundwater Contamination: Still Spreading Decades after Detection.

Disposal practices of industrial wastewater by Gelman Sciences led to high concentrations of 1,4-dioxane in groundwater in Michigan, USA. Since discovery of off-site pollution in 1984, the contaminated groundwater prompted closure of over 124 private wells, closure of one municipal well, and prohibition of most groundwater uses in a large section of the city of Ann Arbor. Recent 1,4-dioxane detections in shallow groundwater in Ann Arbor and in township residential wells pose new exposure threats. Patterns of increased 1,4-dioxane well concentrations raise concerns for threats to Ann Arbor's municipal water intake in the Huron River. Health effects surveillance from 1,4-dioxane exposure is lacking. The community continues to seek solutions in the decades-long fight to clean up this contamination.

Performance of Microbiome Sequence Inference Methods in Environments with Varying Biomass.

Microbiome community composition plays an important role in human health, and while most research to date has focused on high-microbial-biomass communities, low-biomass communities are also important. However, contamination and technical noise make determining the true community signal difficult when biomass levels are low, and the influence of varying biomass on sequence processing methods has received little attention. Here, we benchmarked six methods that infer community composition from 16S rRNA sequence reads, using samples of varying biomass. We included two operational taxonomic unit (OTU) clustering algorithms, one entropy-based method, and three more-recent amplicon sequence variant (ASV) methods. We first compared inference results from high-biomass mock communities to assess baseline performance. We then benchmarked the methods on a dilution series made from a single mock community-samples that varied only in biomass. ASVs/OTUs inferred by each method were classified as representing expected community, technical noise, or contamination. With the high-biomass data, we found that the ASV methods had good sensitivity and precision, whereas the other methods suffered in one area or in both. Inferred contamination was present only in small proportions. With the dilution series, contamination represented an increasing proportion of the data from the inferred communities, regardless of the inference method used. However, correlation between inferred contaminants and sample biomass was strongest for the ASV methods and weakest for the OTU methods. Thus, no inference method on its own can distinguish true community sequences from contaminant sequences, but ASV methods provide the most accurate characterization of community and contaminants. IMPORTANCE Microbial communities have important ramifications for human health, but determining their impact requires accurate characterization. Current technology makes microbiome sequence data more accessible than ever. However, popular software methods for analyzing these data are based on algorithms developed alongside older sequencing technology and smaller data sets and thus may not be adequate for modern, high-throughput data sets. Additionally, samples from environments where microbes are scarce present additional challenges to community characterization relative to high-biomass environments, an issue that is often ignored. We found that a new class of microbiome sequence processing tools, called amplicon sequence variant (ASV) methods, outperformed conventional methods. In samples representing low-biomass communities, where sample contamination becomes a significant confounding factor, the improved accuracy of ASV methods may allow more-robust computational identification of contaminants.

Community profiling of the urinary microbiota: considerations for low-biomass samples.

Many studies have shown that the urinary tract harbours its own microbial community known as the urinary microbiota, which have been implicated in urinary tract disorders. This observation contradicts the long-held notion that urine is a sterile biofluid in the absence of acute infection of the urinary tract. In light of this new discovery, many basic questions that are crucial for understanding the role of the urinary microbiota in human health and disease remain unanswered. Given that the urinary microbiota is an emerging area of study, optimized techniques and protocols to identify microorganisms in the urinary tract are still being established. However, the low microbial biomass and close proximity to higher microbial biomass environments (for example, the vagina) present distinct methodological challenges for microbial community profiling of the urinary microbiota. A clear understanding of the unique technical considerations for obtaining and analysing low microbial biomass samples, as well the influence of key elements of experimental design and computational analysis on downstream interpretation, will improve our ability to interpret and compare results across methods and studies and is relevant for studies profiling the urinary microbiota and other sites of low microbial abundance.

Metastatic lung cancer involving the vulva: two new cases and literature review.

Two new cases of metastatic lung cancer involving the vulva are presented. Metastasis from primary lung cancer is a rare cause of a vulval tumour and may require special immunohistochemical stains to determine whether the vulval tumour is primary or secondary. Awareness of the possibility of metastatic disease involving the vulva is of importance as the patient's prognosis is dependent on an accurate diagnosis and palliative treatment where appropriate.