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.

Many recent studies have now shown that even under healthy conditions, the bladder and urinary tract harbors its own microbial community, collectively known as the urinary microbiota. This contradicts the long held notion that urine is a sterile environment in the absence of an acute infection. Given this relatively new discovery, many basic questions which are critical for our understanding of the role that the urinary microbiota plays in human health and disease remain unanswered. As this is an emerging area of study, optimized techniques and protocols to identify microorganisms in the urinary tract are still being established. This is made more challenging for the urinary microbiota given its low microbial biomass. A clear understanding of the unique technical considerations of low microbial biomass samples, as well the impact of key elements of experimental design and computational analysis on downstream interpretation will improve the interpretability and comparability of results across methods and studies both for the urinary microbiota as well as other sites of low microbial abundance.

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450 Background: Clinical diagnosis and risk stratification of patients with urothelial carcinoma (UC) remains a challenge, with high rates of recurrence and disease progression following treatment. Urinary comprehensive genomic profiling (uCGP) has significant potential to aid in both diagnosis and prognostication of non-muscle-invasive and muscle-invasive disease. Methods: uCGP was performed on urine specimens collected at 9 centers across the US from 577 subjects prior to cystoscopy. 152 subjects were UC tumor positive (de novo and recurrence), 191 had a history of UC but negative by surveillance cystoscopy at time of collection, and 234 were urology control subjects undergoing cystoscopy without evidence of UC. Urine DNA was sequenced and comprehensively profiled across 60 genes for 6 classes of mutations using the CLIA-validated UroAmplitude test. Disease detection and molecular grade (high grade vs. low grade) algorithms were trained (n=345) and validated (n=232) in independent cohorts. Results: Among UC tumor positives, grade distribution was 53% high grade, 41% low grade, and 6% unknown. Stage distribution was Tis (5%), Ta (57%), T1 (16%), ≥T2 (15%), Tx (7%). 99% of tumor positive patients had one or more mutation identified. Interestingly, 69% of UC surveillance negative and 49% of urology controls also had at least one high impact mutation. The prevalence of mutations among controls necessitates machine learning algorithms to classify disease status. In validation, de novo tumor diagnosis demonstrated sensitivity of 93.8% and specificity of 89.4% and a NPV of 98.8% in urology controls. Recurrent tumors were detected with a PPV of 73.5%, sensitivity of 62.5% and specificity of 89.0% in patients with a history of UC. Molecular grading predicted high-grade with a PPV of 90.9% and a specificity of 96.7% compared to pathology. Urinary TP53 mutations were enriched in ≥T2 tumors relative to Ta (OR=14.8 [95%CI 4.6-47.5], P=0.00001). Copy number alterations were also associated with increased risk of muscle invasion, metastasis, and enriched for CIS relative to Ta tumors (≥T2: OR=6.4 [95%CI 1.8-22.9], P=0.019; CIS: OR=10.5 [95%CI 1.9-58.9], P=0.04). Conclusions: We developed and validated a uCGP test that provides robust noninvasive detection of UC across a diverse group of patients and clinical contexts, including non-muscle-invasive and muscle-invasive UC. Mutations with actionable or prognostic value are found in most subjects. These data suggest that uCGP classifies tumor presence with better performance than traditional urinary biomarkers. Importantly, uCGP identifies genomic markers of muscle invasion, metastasis, and CIS. With longer term follow-up, uCGP mutational profiles may reveal important prognostic information regarding risk of disease recurrence and progression. Additional studies are underway to further support the generalizability of these findings.

The growing popularity of minibikes among adolescents is evident by the number of advertisements and participants. Potentially fatal injuries to the neck were observed among several minibike enthusiasts. Typically, a minibike rider with his neck in extension unexpectedly collides with a hidden wire or cable. With only abrasions or contusions on his neck, the subject arrives at the emergency room in extreme airway distress. Attempts to manipulate his neck for mouth-to-mouth resuscitation or intubation produce greater air hunger. Only a low tracheotomy performed rapidly can save his life. The basic pathology in all cases encountered has been a laryngotracheal separation. Other associated injuries have been a fractured larynx, damaged recurrent laryngeal nerves, esophageal rupture and pneumothorax.