Abstract 3778: Uncovering the role of glioma stem cells in glioblastoma chemo-resistance and recurrence
Notice bibliographique
Résumé
Abstract Glioblastoma (GBM) is the most common malignant brain tumor. Despite multimodal aggressive therapies, the median survival after diagnosis is 14 months. The failure of current therapies is due to the presence within the tumor of a subpopulation of cancer cells with stem-like properties, called glioma stem cells (GSCs), which are responsible for chemo-resistance and recurrence. The molecular mechanisms involved in chemo-resistance of this cell subpopulation and its role in recurrence are still largely unknown. In this work, we isolated GSCs from seven recurrent GBM patients and we performed single-cell RNA-sequencing (scRNA-seq) on them. The analysis showed the persistence of a conserved neurodevelopmental hierarchy in recurrent GBM with a glial-like progenitor population at the top. Furthermore, we extended this analysis by sequencing the tumor bulk of seven recurrent GBMs in order to investigate deeply cancer heterogeneity in recurrences. Our data displayed the presence of many different cell populations that recapitulate the multiple steps of the physiological neural differentiation, suggesting that this capability is not affected by standard therapies. Afterwards, we confirmed the stem-like properties of our recurrent GSCs, verifying by immunofluorescence the expression of several stem-ness markers, such as PROM1, MASH1, OLIG2 and SOX2, and assessing the tumorigenicity in vivo of these cells. Standard therapies are able to eliminate only differentiated cancer cells, but they do not eradicate the cancer stem cell subpopulation. In order to defeat GBM and avoid recurrence, GSCs have to become the goal of future therapies. Considering chemo-resistance as an intrinsic and natural property of these cells, we performed a pathways analysis comparing the scRNA-seq datasets of recurrent GBMs with scRNA-seq datasets of primary GBMs and fetal nervous tissues to identify shared molecular pathways. Intriguingly, several pathways involved in DNA repair, cell cycle control and stem cell maintenance, such as BRCA1, ATM, E2F4 and FOXM1 pathways, were uniformly overexpressed by the progenitor cells of the three groups, representing new molecular targets for therapeutic purposes. Afterwards, we tested in vitro an E2F4 inhibitor on GSCs derived from de novo and recurrent GBMs, obtaining stunning results in terms of cell growth inhibition and cell death induction. Furthermore, we pretreated recurrent GCSs with this molecule and we injected them in mice: data showed the ability of the drug to reduce tumor growth sharply and prolong the survival. Currently, we are evaluating the efficacy in vivo of this compound on a recurrent GBM mouse model, administering the drug with intracerebroventricular pumps. This project could seriously help to decipher GSCs chemo-resistance, as well as their role in GBM relapse, also providing the necessary knowledge for the development of new targeted therapies. Citation Format: Gabriele Riva, Charles Couturier, Phuong Uyen Le, Xiaohua Yan, Yu Chang Wang, Marie Christine Guiot, Ioannis Ragoussis, Kevin Petrecca. Uncovering the role of glioma stem cells in glioblastoma chemo-resistance and recurrence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3778.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,002 | 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 ».