Developing a Highly Specific Biomarker for Germ Cell Malignancies: Plasma miR371 Expression Across the Germ Cell Malignancy Spectrum
Bibliographic record
Abstract
PURPOSE: Our objective was to evaluate operating characteristics, particularly specificity and positive predictive value (PPV), by mapping plasma miR371 expression to actual clinical events in patients with a history of germ cell tumor. PATIENTS AND METHODS: One hundred eleven male patients with a history of or newly diagnosed germ cell tumors were evaluable. Biospecimens obtained before confirmed clinical events were analyzed for miR371 expression with blinding of providers and laboratory personnel to analytic results or clinical status, respectively. Cases (patients with clinically confirmed active germ cell malignancy [aGCM]) and controls (patients with no clinically confirmed aGCM) were assigned over the course of the management. Patients were assigned risk status (high, low, or moderate) based on the composite clinical picture at time points in management. RESULTS: Considering all cases and controls and results of prospectively obtained biosamples analyzed for miR371 expression, 46 (35%) of 132 samples had clinically confirmed aGCM over the course of management; 44 (96%) of these 46 patients had plasma miR371 expression (true positives) with no false positives. Two (4%) of 46 patients had no miRNA expression despite pathologic confirmation of aGCM (false negatives). Plasma miR371 expression in confirmed aGCM had a specificity, sensitivity, positive predictive value, and negative predictive value of 100%, 96%, 100%, and 98%, respectively. Interpretation of sensitivity and negative predictive value is limited by modest follow-up. Specificity and sensitivity were 100% and 98%, 100% and 92%, and 100% and 97% in the low-, moderate-, and high-risk groups, respectively, with a median follow-up time of 15 months. CONCLUSION: Plasma miR371 expression predicts aGCM with high specificity and positive predictive value. Although other operating characteristics of miR371 await longer follow-up for more complete definition, the findings of a highly specific liquid biopsy strongly support moving forward with large-scale, real-world clinical trials to further define full operating characteristics and to identify clinical utility and areas of patient benefit.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".