Abstract 3665: Stratifying prostate cancer patients through circulating genes related to prostate cell subtypes, drug targets, and therapeutic resistance
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».