Analysis of small non-coding RNAs in urinary exosomes to classify prostate cancer into low-grade (GG1) and higher-grade (GG2-5).
Bibliographic record
Abstract
277 Background: To develop a new predictive test for prostate cancer, based on the interrogation of small non-coding RNAs (sncRNA) isolated from urinary exosomes. We report the development and performance of the miR Sentinel PCa Test, that distinguishes patients with prostate cancer from those with no evidence of prostate cancer (NEPC) and the miR Sentinel CS Test, that distinguishes low grade from higher grade disease. Methods: Affymetrix miR 4.0 arrays were used to identify informative sncRNAs isolated from urinary exosomes. sncRNA from 233 subjects undergoing a prostate biopsy [89 men with benign biopsies, 88 with grade group 1 (GG1) cancer and 56 patients with grade group 2-5 (GG2-5)] were interrogated on these arrays. A custom OpenArray platform was designed to interrogate the 280 most informative sncRNAs, identified using a data-driven selection algorithm. The platform was designed to categorize patients as either no cancer or cancer using the miR Sentinel PCa Test, and subclassify the patients with cancer into GG1 or GG2-5 cancer using the miR Sentinel CS Test. The performance of the miR Sentinel PCa and CS Tests was validated in an independent cohort. Results: In 233 men, theSentinel PCa Test correctly classified 89/89 subjects with no cancer and 144/144 with cancer. The Sentinel CS Test correctly identified 55/56 patients with GG2-5 and 87/88 patients with GG1. Sensitivity was 98%, Specificity 98%, NPV 98% and PPV 93%. For validation, a prospective observational study of 329 subjects (NEPC = 139; GG1= 88; GG2-5 = 102) with elevated PSA correctly classified 134/139 as no cancer [Sensitivity 98% (195/199); Specificity 96% (134/139), PPV 98% and NPV 97%]. The Sentinel CS Test classified 87/88 as GG1 and 102/102 as GG2-5 [Sensitivity 100% (102/102), Specificity 99% (87/88), PPV 99%, and NPV=100%]. Conclusions: Initial evaluation of the miR Sentinel PCa and CS Tests demonstrated the high precision of these tests to detect prostate cancer and distinguish high grade (GG2-5) disease. Further validation is ongoing.[Table: see text]
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".