Abstract B048: Performance of diagnostic biomarkers in the Canary Prostate cancer Active Surveillance Study (PASS)
Bibliographic record
Abstract
Abstract Introduction Many biomarker assays improve sensitivity in diagnosing high grade prostate cancer (PCa), but their performance during active surveillance, after a diagnosis of PCa, is not well understood. Procedures In the multicenter Canary PASS cohort, we explored the contribution of four distinct biomarker panels (blood-based: 4Kpanel, phi; urine-based: PCA3, MDxSelect RNA) and clinical variables in predicting 4-year extreme biopsy reclassification to Gleason Grade Group 3 (GG3) or above. Biomarker assays were performed between 2014-2020 on subcohorts including 727-1,176 participants; outcomes were collected through March 2020. Models were built using a) biomarker, b) biomarker + prostate volume, c) biomarker + biopsy variables d) biomarker + prostate volume + biopsy variables, and, for urine markers e) biomarker + prostate volume + biopsy variables + serum PSA. Clinical PSA was used as a reference. Partly conditional Cox proportional hazards regression models for residual time to event were constructed based on information available at each prediction time, accounting for competing risk. The probability of extreme reclassification within 4 years was calculated after diagnosis and after first follow-up biopsy (Bx1) and the accuracy was assessed with the cross-validated receiver operating characteristic (ROC) curve analysis. Results Cross validated Areas Under the Curve (AUCs) for predictions made with biomarkers ranged from 0.648 (95% CI: 0.581, 0.716) to 0.755 (95% CI: 0.687, 0.824) after diagnosis and from 0.588 (95% CI: 0.480, 0.696) to 0.669 (95% CI: 0.558, 0.780) after Bx1, and were consistently higher than AUCs for clinical PSA at both timepoints; the difference in AUC between biomarker and PSA was statistically significant only for 4Kpanel and phi. Adding clinical variables to predictive models improved AUCs to varying degrees; AUCs for full models with all variables were 0.702 (95% CI: 0.641, 0.764) to 0.776 (95% CI: 0.715, 0.837) after diagnosis and 0.740 (95% CI: 0.656, 0.824) to 0.763 (95% CI: 0.691, 0.836) after Bx1. Adding clinical variables to blood biomarkers resulted in less incremental improvement than for urine markers. A limitation of this study is that prostate MRI was not part of the PASS protocol, although MRI data collected suggests minimal predictive ability during active surveillance. Conclusions Our results suggest that early during prostate cancer active surveillance the blood-based biomarkers of the 4Kpanel and phi may predict future reclassification to higher grade cancer as well as or better than common clinical variables. Clinical variables improve the performance of the urine-based markers of PCA3 or MDxSelect. External validation studies are needed. Citation Format: Lisa F. Newcomb, Yingye Zheng, Menghan Liu, James D. Brooks, Peter R. Carroll, Atreya Dash, William J. Ellis, Christopher J. Filson, Martin E. Gleave, Michael A. Liss, Frances M. Martin, Todd M. Morgan, Peter S. Nelson, Andrew A. Wagner, Daniel W. Lin. Performance of diagnostic biomarkers in the Canary Prostate cancer Active Surveillance Study (PASS) [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr B048.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".