Comprehensive genomic profiling of urine DNA for urothelial carcinoma detection and risk prediction.
Authors: Keyan Salari, Jason J. Lee, Vincent T. Bicocca, Vincent Caruso, Brian C. Mazzarella, Kevin G. Phillips, Trevor G. Levin, Adam S. Feldman
Journal: Journal of Clinical Oncology (2022)

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.