Clinical validation of an ultra-deep next generation DNA sequencing approach for the detection of bladder cancer in the urine.
Bibliographic record
Abstract
e16515 Background: Currently available bladder cancer (BCa) urine tests suffer from poor sensitivity, specificity and limited insight on disease classification. We have developed an assay to identify bladder tumor associated mutations in urine using broad and ultra-deep next generation DNA sequencing of over 100,000 genomic loci. In a cohort of patients from a variety of urologic practice settings, we aimed to describe the sensitivity and specificity of this assay in the detection of BCa. Methods: Urine samples and clinical data for 114 patients were collected from three outpatient urology practices and two academic urology centers. Of the 114 patients, 48 patients had active BCa, and 66 did not. Tumor grade distribution was 33 high grade, 13 low grade, and 2 unknown. Stage distribution among the 48 BCa patients was Ta (n = 19), TIS (n = 1), T1 (n = 5), > T2 (n = 20), Tx (n = 3). Of the 66 patients without BCa, most had GU diagnoses and previous cancer histories (non-BCa), including BPH, hematuria, urinary tract symptoms, kidney stones and prostatitis. Twenty patients had previous or concurrent cancer diagnoses (non-BCa). DNA was extracted and sequenced on a HiSeq 2500. Sequencing data was run through a previously validated algorithm, which classified samples as positive or negative. Results: In this patient cohort, our assay achieved a sensitivity of 93%, specificity of 89%, and an incidence adjusted negative predictive value of 99% for detection of BCa. Interestingly, false positives were most common in patients with concurrent or previous cancer histories. Mutational profiles predicted tumor grade with 80% accuracy. In a select cohort of longitudinally tracked patients, 9 of whom ultimately developed BCa, our test demonstrated a 3-9 month lead time over cystoscopy in 5 patients with minimally residual or occult disease. Conclusions: We describe the clinical validation of a urine-based, next generation DNA sequencing assay in the detection of bladder BCa. Our data further demonstrate potential utility in molecular grading, as well as the prediction of future BCa recurrence. Multiple retrospective and prospective studies are underway to further validate clinical validity and utility in other GU cancers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".