Comprehensive genomic profiling of urine DNA for urothelial carcinoma detection and risk prediction.

Abstract

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.

deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

“…The top four cancers are breast cancer, cervical cancer, oral cancer, and colorectal cancer. People who are addicted to tobacco, smoking, drinking alcohol, drugs like Panbarak and betel nut have chances of failure [25][26]. Just betel nut may have medicinal properties.…”

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

“…1A,B). Blinded predictions of baseline recurrence risk using the previously described UroAmp recurrence risk algorithm [23] stratified the cohort into low ( n = 13) and high ( n = 20) recurrence risk groups. A total of 13 (39%) patients experienced recurrence.…”

“…Genomic disease burden is a percentile ranking of the sum of VAF for a urine specimen. As described previously, the percentile rank underlying GDB was determined from a training cohort to generate a representative empirical distribution [23]. Each test sample was then ranked against that distribution to obtain its GDB percentile.…”

“…The independent variables of interest were the UroAmp recurrence risk algorithms for (a) baseline pre‐IVT recurrence risk and (b) longitudinal response comparing post‐IVT uCGP to pre‐IVT uGCP. Training and validation of the UroAmp recurrence risk algorithm were previously described [23]. Briefly, recurrence risk classification of ‘high’ or ‘low’ is determined according to an algorithm trained to evaluate the genes mutated and their VAF.…”

“…At the same time, FISH lacks the key benefit provided by cytology: pathologic grading. In contrast, urinary comprehensive genomic profiling (uCGP), which quantifies diverse types of somatic tumor mutations from urine‐derived DNA, detects MRD with high sensitivity and uses genomic features associated with stage and grade to support risk stratification [23]. Many genes and mutation types measured within the genomic profile are also associated with drug response or are direct targets of next‐generation drugs.…”