Distinguishing aggressive versus nonaggressive prostate cancer using a novel prognostic proteomics biopsy test, ProMark.
Bibliographic record
Abstract
5090 Background: Current clinical and pathological parameters are insufficient for accurate prediction of progression risk for patients with biopsy Gleason grades 3+3 or 3+4. Reasons for this include biopsy sampling error and pathologist grading discordance, resulting in inaccurate prostate pathology assessment. Consequently, a majority of these patients are over-treated. We have established a novel proteomics-based test, ProMark for automated quantitative measurements of biomarkers from tumor epithelium of intact FFPE biopsy tissue. The test generates a personalized risk score predictive of prostate tumor pathology at the time of biopsy. Methods: A clinical biopsy simulation study using prostatectomy tissue (N=380) was designed to identify 12 biomarkers that can predict surgical Gleason score and lethal disease despite sampling error. Next, a clinical study of 381 biopsies with matched prostatectomy annotation was done to select the best marker subset predictive of prostate pathology. Subsequently, the locked model was validated in a separate blinded clinical study with centralized Gleason grading (N=274). Results: The Promark risk scores were strongly predictive of prostate tumor pathology, the primary study objective. The test was able to separate ‘favorable’ cases [surgical Gleason score 3+3 or 3+4; organ-confined (<=pT2)] from ‘non-favorable’ cases [non-organ-confined disease (≥T3a, N, or M) or surgical Gleason >=4+3] with an AUC of 0.68 (0.61-0.74;p<0.0001). At risk score <0.33 the specificity for prediction of favorable disease is 90% with a predictive value of 81%. At a risk score > 0.8, the predictive value for non-favorable disease is 77%. Importantly, ProMark provides improved personalized disease prediction relative to standard risk stratification systems, including NCCN and D’Amico. Conclusions: We have established a novel prognostic test, ProMark, for prostate cancer biopsies. The risk scores are generated independent of clinical and pathological parameters and correlate strongly with pathological outcome. The test could be useful as an aid in clinical decision making for stratification of patients into good and poor candidates for active surveillance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".