Abstract B10: Modulation of mRNA translation regulation in highly metastatic ssteosarcoma cells increases their sensitivity to redox stress
Notice bibliographique
Résumé
Abstract Improvement in outcomes for pediatric patients with metastatic osteosarcoma still remains elusive despite the development of new multiagent combinations. This unmet clinical need underscores the need for novel approaches that examine the metastatic process itself in order to identify new molecular targets whose modulation may have antimetastatic activity. During lung metastasis progression, osteosarcoma cells (OS) have to quickly adapt to the hostile microenvironment of the lung. Elucidating exactly how highly aggressive metastatic OS cells adapt to the lung microenvironment is the subject of the current work. Our research utilizes a pair of clonally related human OS cell lines, MG63.3 and MG63, with highly or poorly metastatic in vivo phenotypes, respectively. These OS cells experience redox stress in the lung microenvironment, as evident by in situ immunoreactivity with 3-nitrotyrosine (3-NT)---a marker of oxidative damage. In cell culture studies where high and poorly metastatic OS cells are exposed to PABA/NO, a chemical inducer of redox stress which also causes 3-NT accumulation, highly metastatic MG63.3 cells show functional differences in their response to redox stress compared to poorly metastatic MG63 cells. MG63.3 cells show lower levels of 3-NT staining, lower levels of caspase 3/7 activity, and have higher growth rates compared to MG63 cells in the presence of PABA/NO. These results suggest that MG63.3 cells show a greater adaptability to redox stress compared to MG63 cells. It is known that regulation of mRNA translation is a mechanism by which cancer cells can quickly adapt to the changing extracellular milieu. Furthermore, it has been shown that MG63.3 cells are able to translate weak mRNAs more efficiently than MG63 cells under stressful conditions. Such weak mRNAs encode for proteins involved in growth and proliferation. When we examine the expression levels of eukaryotic initiation factors such as eIF4E, eIF4G, and eIF4A, all of which are part of the eIF4F cap-initiation complex, we find that certain factors (such as eIF4G1) are differentially upregulated (both at the transcript and protein level) in MG63.3 cells compared to MG63 cells. We hypothesize that inhibition of eIF4G binding to the eIF4F complex will reduce the ability of MG63.3 cell to translate mRNAs important for survival and proliferation during redox stress. To address this hypothesis, we tested whether a small-molecule inhibitor of eIF4G/eIF4E interactions, called 4EGI-1, can sensitize highly metastatic MG63.3 cells to PABA/NO. When exposed to noncytotoxic levels of 4EGI-1 (≤ 20 μM), we find that 4EGI-1 can sensitize MG63.3 cells with lower concentrations of PABA/NO compared to control groups. 4EGI-1 treatment, in the presence of PABA/NO, can cause MG63.3 cells to accumulate higher levels of 3-NT compared to control groups. Furthermore, using an Incucyte machine to assess cell proliferation over 5 days, we find that combination treatment (4EGI-1 and PABA/NO) can greatly inhibit the growth of MG63.3 cells. These results suggest that certain mRNA transcripts that are important in adapting to redox stress are dependent on the eIF4F cap-initiation complex. Efforts are currently under way to further characterize which gene transcripts are important to redox stress adaptation. In addition, by using the pulmonary metastasis assay, we will be assessing whether 4EGI-1 has antimetastatic activity. Collectively, the data presented in the current work suggest that the adaptive mechanisms that metastatic OS cells use to manage redox stress may be an attractive therapeutic target in the development of novel antimetastatic therapeutics. Citation Format: Michael M. Lizardo, Poul Sorensen. Modulation of mRNA translation regulation in highly metastatic ssteosarcoma cells increases their sensitivity to redox stress [abstract]. In: Proceedings of the AACR Conference on Advances in Sarcomas: From Basic Science to Clinical Translation; May 16-19, 2017; Philadelphia, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(2_Suppl):Abstract nr B10.
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 | 0,000 |
| 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) | 0,003 | 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 ».