Abstract IA022: Arginine, serine, xCT and ME, the therapeutic metabolism of different sarcomas
Notice bibliographique
Résumé
Abstract The absolute diversity of the biology of the sarcomas makes therapeutic development challenging. As most of tumor metabolism is composed of transporters and enzymes, finding metabolic dependencies should allow for small molecule targeting. Due to the rapid metabolic evolution that tumors undergo in response to the targeting of any one metabolic pathway, a deep understanding of sarcoma metabolism is needed. First, the most common metabolic adaptation that sarcomas undergo is loss of expression of argininosuccinate synthetase 1 (ASS1), which is silenced in ~90% of cases by methylation. This results in sarcomas being arginine auxotrophic and sensitive to arginine starvation therapies, such as with arginine deiminase (ADI-PEG20). Not only does arginine starvation alter the Warburgian biology of sarcomas making them dependent of glutamine, but it can be used to upregulate the expression of cell surface transports such as hENT, which allows to gemcitabine internalization. In addition, mouse modeling has demonstrated that vesicular trafficking is key to overcoming initial arginine starvation in vivo, a process that can be blocked with chloroquine. This arginine dependency is likely related to the mesenchymal origin of sarcomas, which would explain its high recurrence rate of ASS1 silencing across histologies. Next, due to the upregulation of 3-phosphoglycerate dehydrogenase (PHGDH), osteosarcoma preferentially utilizes glucose to make the serine that enters the folate cycle, as opposed to completing lower glycolysis. In addition, as osteosarcoma does not seem to depend on extracellular serine import, inhibition of PHGDH leads to pro-survival compensation by the mTORC pathway. This can be targeted on both the AMP Kinase and the AKT dependent parts of the MTORC pathway that converge at FOXO3. When both are inhibited approach demonstrates unique triple synergy and therapeutic strategy for osteosarcoma due to its unique serine biology. Finally, synovial sarcoma lack the expression of malic enzyme 1 (ME1), also likely due to its cell of origin. This leads to reduced glucose oxidation, enhanced glycolysis, and compensatory increased flux through the pentose phosphate pathway for cytoplasmic NADPH production. Additionally, absence of ME1 in SS results in significant reductions in the GSH/GSSG ratio as well as a reduced glutathione synthesis. Sensitivity to GSH pathway inhibition is also reduced while sensitivity to inhibition of the thioredoxin system is significantly increased. ME1 absence results in increases in the labile iron pool that sensitizes synovial sarcoma to ferroptosis. Therefore, ME1 null SS is exquisitely sensitive to induction of ferroptosis with xCT inhibition. This can be accomplished by erastin analogs in vitro and ACXT-3102 (a tumor targeted erastin) in vivo. As we learn more about each sarcoma, we must understand the underlying metabolism of each subtype and their cell of origin. Given the targetable nature of metabolic enzymes and transporters, an ever deeper understanding of sarcoma metabolism is warranted. Citation Format: Brian A. Van Tine. Arginine, serine, xCT and ME, the therapeutic metabolism of different sarcomas [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr IA022.
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 | 0,000 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 ».