The impact of intratumoral heterogeneity on prognostic biomarkers in localized prostate cancer.
Bibliographic record
Abstract
46 Background: Genomic biomarkers can identify patients that harbour aggressive disease. The utility of these biomarkers is uncertain due to genomic variation between prostate biopsy specimens. To quantify the robustness of genomic biomarkers, we performed spatio-genomic characterization of distinct tumor foci. We scored three validated DNA-based biomarkers of early biochemical recurrence: percentage of genome with a copy number aberration (PGA), a 100-loci biomarker, and an optimized 31- loci biomarker derived from the previous. For each biomarker, we determined their robustness to intratumoral heterogeneity in association with predicting early biochemical recurrence (eBCR; ≤18 months) and long term control (LTC; ≥48 months). Methods: We queried a registry of 1054 patients with high-risk prostate cancer who underwent a radical prostatectomy (RP). We developed a cohort (n = 42) risk matched by clinicopathologic prognostic indices. Half of the patients had eBCR, while the other half had LTC. We profiled multiple tumor foci per patient, analyzing 119 tumor foci. For each focus, CNA profiles were generated, and three biomarker scores were calculated. For each patient and biomarker, we calculated the score of the lowest-score region, the highest-score region, or sampling of all foci and use the mean score. Results: All three biomarkers distinguished LTC from eBCR. PGA scores separated the two groups with an area under the receiver operator curves (AUC) ranging from 0.75-0.80. The 100- and 31-loci signatures, had AUCs ranging from 0.76-0.85 and 0.76-0.80 respectively. Using Cox proportional hazards modeling, we found that PGA was associated with LTC (Hazard ratio (HR) range: 2.56-6.22; p < 0.05. This was replicated for the 100-loci signature (HR range: 3.55-5.23; p < 0.05). The 31-loci detected associations with eBCR independent of how different foci were summarized (log-rank p-value range: 5.1 x 10-4- 5.9 x 10-3). Conclusions: Despite divergence in biomarker scores, all three predicted eBCR. Our study suggests that genomic biomarkers can overcome intratumoral heterogeneity, making discrete samples potentially adequate in patients with high-risk disease to determine the risk of eBCR after radical treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".