Association des Urologues du Québec Congrès Annuel 2025 – Résumés : Session scientifique IV
Bibliographic record
Abstract
Introduction: Prostate cancer (PCa) is the most common non-skin malignancy and among the leading causes of cancer death in men.Despite available therapies, some patients inevitably fail treatments, progress, and eventually die from the disease.Predicting recurrence or progression remains highly challenging.This may reflect the underlying intra-tumoral heterogeneity, partly driven by distinct PCa cell subtypes, including androgen receptor (AR) + luminal, and AR stem and neuroendocrine cells.Their dynamic regulation and clinical relevance remain unclear, limiting our ability to stratify patients and guide therapy effectively.We aimed to characterize PCa cell subtype-specific signatures in tumors, trace them back in serial liquid biopsies, and also identify markers released by metastases.Methods: RNA was extracted and sequenced from banked fresh frozen tumors and matched benign radical prostatectomy (RP) samples from 100 lethal and 45 disease-free cases with no recurrence for >10 years after RP.Whole-blood RNA was extracted from longitudinally collected and banked blood from selected cases and sequenced, while corresponding serum was sent to proteomics platforms.An in-depth literature review was conducted to compile exhaustive lists of signatures potentially related to AR -PCa cells.Results: Bioinformatic analyses of subtype-relevant genes in tumors of lethal vs. disease-free cases revealed signatures associated with PCa lethality.Their screening in liquid biopsy transcriptomic and proteomic data showed shared AR subtype patterns appearing in late stages and not detected in disease-free cases.Moreover, exclusive AR subtype candidates were detected late in liquid biopsy of lethal cases, supporting a metastatic origin.Conclusions: These findings provide insights into tumor progression, revealing circulating markers of AR -PCa cells likely released from metastases.Patient-tailored signatures could be used for stratification and offer new therapies.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.104 | 0.021 |
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".