Abstract CC06-01: Identifying therapeutic options for patients with advanced prostate cancer through genes in liquid biopsies
Notice bibliographique
Résumé
Abstract Prostate cancer (PCa) is curable in most men but becomes lethal for patients with metastases at diagnosis and those who experience biochemical recurrence (BCR) after curative therapies. Androgen deprivation therapy is given upon BCR, but patients inevitably fail and become castration resistant (CRPC), dying from metastatic (m)CRPC despite receiving 2nd and 3rd line therapies (androgen receptor inhibitors/ARIs and/or taxanes). Drugs targeting other pathways have been tested in clinical trials. Few are implemented in practice due to low response rates, although some show a significant benefit in subsets of unselected patients. This clinical heterogeneity underscores the cellular and functional heterogeneity of malignant cells, highlighting the need for biomarkers of response to therapies. In line with the growing interest in using non-invasive liquid biopsies to monitor disease progression, we hypothesized that genes encoding proteins representative of prostate epithelial cell-subtypes may be detectable in blood as predictive biomarkers. Our objective was to identify such representative genes and test them in the blood of patients to determine whether they can stratify patients for optimal disease management. 14 genes pertaining to cell subtypes were chosen based on a thorough literature review. The panel was validated in transcriptomic datasets showing their predominant overexpression in metastases of mCRPC cases compared to primary tumours and benign prostate from diverse cohorts of patients. TaqMan assays were designed and optimized in serial dilutions of RNA from five prostate cancer cell lines. The panel was tested in the blood of healthy controls (n=9) and patients prior to prostatectomy (n=8), post-curative therapies (n=7), or mCRPC (n=19). In control men with no prostatic disease in their lifetime, we see low or no expression of most genes, with no correlation with age (29-71 years old). No association was seen between the proportions of different white blood cells and genes of interest expressed at varying levels in the blood of mCRPC patients. The threshold for overexpression in patients was defined as 2.58 standard deviation above the mean expression from controls (99.5% confidence interval). Phenotypic and functional diversity was seen in all categories of patients. Changes in genes patterns were significant in mCRPC cases based on current treatments (at time of blood draw) and the choice of initial curative therapy. For example, neuroendocrine genes were predominantly overexpressed in patients who underwent curative radiation therapy, whereas stem cell genes arose in cases under AR-Is at blood draw. In conclusion, we identified circulating genes that may be clinically meaningful to stratify and follow patients and predict response to therapies. Genes encoding drug targets may allow patient-tailored clinical trials for personalized treatments to impact on this unpredictable and lethal disease. Citation Format: Seta Derderian, Edouard Jarry, Arynne Santos, Mohanachary Amaravadi, Quentin Vesval, Lucie Hamel, Nathalie Cote, Marie Vanhuyse, Armen Aprikian, Simone Chevalier. Identifying therapeutic options for patients with advanced prostate cancer through genes in liquid biopsies [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr CC06-01.
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 | 0,001 | 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 | 0,001 | 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 ».