Expression of Small Noncoding RNAs in Urinary Exosomes Classifies Prostate Cancer into Indolent and Aggressive Disease
Bibliographic record
Abstract
PURPOSE: This is the first report of the development and performance of a platform that interrogates small noncoding RNAs (sncRNA) isolated from urinary exosomes. The Sentinel™ PCa Test classifies patients with prostate cancer from subjects with no evidence of prostate cancer, the miR Sentinel CS Test stratifies patients with prostate cancer between those with low risk prostate cancer (Grade Group 1) from those with intermediate and high risk disease (Grade Group 2-5), and the miR Sentinel HG Test stratifies patients with prostate cancer between those with low and favorable intermediate risk prostate cancer (Grade Group 1 or 2) and those with high risk (Grade Group 3-5) disease. MATERIALS AND METHODS: sncRNAs were extracted from urinary exosomes of 235 participants and interrogated on miR 4.0 microarrays. Using proprietary selection and classification algorithms, informative sncRNAs were selected to customize an interrogation OpenArray™ platform that forms the basis of the tests. The tests were validated using a case-control sample of 1,436 subjects. RESULTS: The performance of the miR Sentinel PCa Test demonstrated a sensitivity of 94% and specificity of 92%. The Sentinel CS Test demonstrated a sensitivity of 93% and specificity of 90% for prediction of the presence of Grade Group 2 or greater cancer, and the Sentinel HG Test demonstrated a sensitivity of 94% and specificity of 96% for the prediction of the presence of Grade Group 3 or greater cancer. CONCLUSIONS: The Sentinel PCa, CS and HG Tests demonstrated high levels of sensitivity and specificity, highlighting the utility of interrogation of urinary exosomal sncRNAs for noninvasively diagnosing and classifying prostate cancer with high precision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".