Abstract A025: Extracellular-vesicles from the peri-prostatic adipose tissue of obese, but not lean, men promote prostate cancer aggressivity
Notice bibliographique
Résumé
Abstract Prostate cancer (PC) affects 1-in-8-men and obesity, termed a global epidemic by the WHO, affects 1-in-3. Obesity is the largest modifiable cancer risk-factor: every five-point increase in body-mass index increases risk of fatal PC by almost 10% and shortens the time to development of treatment-refractory metastatic disease. Further, weight gain is a common side-effect of mainstay androgen-deprivation therapy. The peri-prostatic adipose tissue (PPAT) is an important component of the PC tumor microenvironment (TME). PPAT volume is associated with increased PC lethality/reduced therapy response. Additionally, adipose tissue (AT) is the largest human endocrine gland, showing an altered (potentially pro-tumor) secretome in obesity. PPAT can also secrete extracellular vesicles (EVs) carrying cargo including microRNAs, which are involved in melanoma, lung, ovarian and breast cancer progression. Despite accumulating evidence showing the importance of AT secretome in tumor growth, its roles in PC progression are still poorly understood. This project investigates EV-mediated mechanisms of communication between PPAT and PC epithelial cells, and their clinical implications. To date, we established a biobank from >120 patients, with matching PPAT, tumor tissue, clinical information and MRI scans. Functionally, we showed that PPAT EVs from obese but not lean patients significantly increase proliferation and migration of PC cells in vitro. Obese PPAT EVs also reduce angiogenesis, consistent with chronic hypoxia observed in obese patient adipose suggesting a switch to non-oxidative metabolism in PC, to meet increasing energy demands. We performed small RNA-seq on PPAT EVs from obese and lean PC patients, and mRNA-seq on PC cells treated with these EVs. These analyses revealed dysregulation of cellular metabolism and extracellular-matrix (ECM) remodeling by PPAT EVs. Top PPAT-EV dysregulated genes are associated with PC survival and are increased in PC vs normal tissue. Silencing of one such PPAT-upregulated gene, TBX1, repressed PC cell migration, invasion, proliferation and EMT. We also optimized in vitro adipocyte differentiation from PPAT stem cells to demonstrate that PPAT effects are specifically attributable to mature-adipocytes. Finally, we showed that RNA- seq analysis of PPAT from genetically engineered mouse models (GEMMs) modelling PC natural history, showed dramatic changes in tissue histology, immune response, lipid metabolism and ECM genes in PPAT of aggressive-versus-indolent tumors. Since genetic alterations in GEMMs are prostate-confined, such transcriptomic changes must be mediated by paracrine signaling from PC cells. Integrative analysis of these data will hopefully elucidate novel, actionable drivers of aggressive PC progression for personalized medicine. Citation Format: Nil Grunberg, Jiani Qian, Joseph Tam, Marc Lorentzen, Sila Akdogan, Nathan Lack, Moray Campbell, Cory Abate-Shen, Bijan Khoubehi, Taimur Shah, Mathias Winkler, Hashim Ahmed, Charlotte Bevan, Claire Fletcher. Extracellular-vesicles from the peri-prostatic adipose tissue of obese, but not lean, men promote prostate cancer aggressivity [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A025.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».