Abstract 3665: Stratifying prostate cancer patients through circulating genes related to prostate cell subtypes, drug targets, and therapeutic resistance
Bibliographic record
Abstract
Abstract Stratification remains an obstacle for the optimal treatment of prostate cancer (PCa) patients throughout the treatment trajectory. Targeting the androgen receptor (AR) kills the majority of luminal-like cells, which are AR-positive and express Prostate Specific Antigen (PSA). However, a subset of surviving AR-positive cells develop resistance to treatments while AR-negative cells of the neuroendocrine (NE) or stem-like phenotypes emerge through selection. We reported on the clinical relevance of cell-subtype genes in whole blood RNA of advanced PCa patients. Here, we studied an expanded panel of genes to include drug targets and therapeutic resistance as predictive biomarkers to stratify patients at diagnosis and at the advanced stage of disease.Genes were chosen based on literature review, results of clinical trials in PCa, and PCa transcriptomic data. Technical validation of TaqMan RT-qPCR assays was carried out for each gene, including rigorous testing of reproducibility. Whole blood RNA was extracted from 26 healthy controls with no prostatic disease, 16 patients prior to prostatectomy, and 43 blood samples from 28 metastatic cases. Gene overexpression was defined as the 99.5% confidence interval of expression in controls. Clinical data were retrieved from patients’ charts.A panel of 64 genes was built, showing overexpression in advanced PCa but low or no expression in normal blood (including whole blood, white and red blood cell populations and platelets). Testing in control blood showed no correlation with age. The proportions of patients’ white blood cells did not correlate with gene expression in their blood. Overall, up to 44/64 genes were overexpressed in at least one patient sample. Patients with prostatic intraductal carcinoma at prostatectomy showed more circulating genes, including more NE and stemness genes, and more PCa cell subtypes represented. Intermediate and high-risk patients showed more circulating NE genes. In metastatic patients, signatures of luminal, NE, stemness, and resistance to AR inhibitors or taxanes were associated with progression. PCa-specific luminal genes were associated with shorter overall survival. Treatment resistance genes correlated with lines of treatment and current taxanes. Two targetable NE genes were overexpressed in distinct categories of patients, suggesting that they may benefit from more specific treatments.In conclusion, phenotypic and functional differences in circulating gene patterns of PCa patients correlate with pathological features, treatments, progression. They may be clinically meaningful to stratify patients and predict therapeutic response. Circulating genes encoding drug targets may justify clinical trials to offer personalized treatments and impact on lethal PCa. Citation Format: Seta Derderian, Edouard Jarry, Arynne Santos, Mohanachary Amaravadi, Quentin Vesval, Lucie Hamel, Raphael Sanchez-Salas, Alexis Rompré-Brodeur, Wassim Kassouf, Raghu Rajan, Marie Duclos, Fadi Brimo, Armen Aprikian, Simone Chevalier. Stratifying prostate cancer patients through circulating genes related to prostate cell subtypes, drug targets, and therapeutic resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3665.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".