Abstract A21: Enhancing chemotherapeutic responses in CNS malignancy through suppression of hyperactive DNA damage repair pathways
Notice bibliographique
Résumé
Abstract Introduction: We are targeting DNA repair pathways to enhance existing chemoradiotherapeutic strategies against medulloblastoma (MB) and malignant glioma (MG), two highly invasive tumors of the central nervous system (CNS). Current methods to treat childhood medulloblastoma are highly intrusive and lead to poor quality of life while the three-year survival rate of patients afflicted with malignant glioma remains abysmal (<5%). Recurrence of these exceedingly malignant tumors is pervasive as they can adopt several mechanisms to resist anti-cancer therapeutics including activation and up-regulation of DNA repair pathways that act to resolve DNA damage elicited by radiation and chemotherapeutic agents (chemo-radiotherapy). DNA repair inhibitors like Poly (ADP-Ribose) Polymerase (PARPi), DNA-dependent protein kinase (DNA-PKi) and Ataxia Telengiectasia Mutated (ATMi) have shown promise to sensitize tumors to DNA damaging chemo-radiotherapeutics as these inhibitors specifically target single yet highly critical DNA repair response pathway enzymes. In combination with anti-tumor agents, these sensitizers can significantly augment anti-cancer therapeutic success. However, differing tumors have variable expression/activity of these enzymes and their corresponding repair pathway(s), therefore; their identification, characterization and the development of strategies to modulate their expression can enhance current anti-tumor treatment efficacy. Methods: I will identify specific differentially-regulated DNA repair enzymes/pathways by gene expression methodology. From these findings, I will inhibit these DNA repair enzymes via shRNA or enzyme-specific inhibitors (if available), to chemosensitize MB and MG cells to DNA damaging therapeutics in an effort to reduce DNA repair in these cells thereby boosting tumor genotoxicity and cell death. Extent of DNA damage will be measured using novel high-throughput DNA repair assays combined with unique cellular DNA damage reporters/sensors to facilitate these studies. Results: In comparing subsets of MB and MG cell lines with existing DNA repair inhibitors, I have found differing DNA repair pathways as potential targets to sensitize these CNS tumors to chemo-radiotherapeutics. MB cells rely on DNA-PK/SSBR (single strand break repair) dependent pathways to resolve induced genotoxicity, while MG utilizes the ATM/SSBR pathways. My focus is now to expand this dataset in additional pertinent tumors and to perform a detailed comparative analysis to identify specific highly active tumor-specific DNA repair enzymes with which to target in order to enhance tumor cell death. These include the use of DNA damage repair response PCR arrays to compare the expression levels of individual DNA repair enzyme/pathway members amongst these CNS tumors. Conclusion: I have identified unique DNA repair enzymes, which may mediate specific differential chemo-radioresistant phenotypes in MB and MG. An expanded analysis is currently underway to further differentiate DNA repair mechanisms and therapeutic responses between these two brain tumor types. I hope to translate these findings into pre-clinical models whereby my data may lead to identifying next-generation brain cancer treatments with improved patient survival and quality-of-life. Citation Format: Marina Mostafizar, Sachin Katyal. Enhancing chemotherapeutic responses in CNS malignancy through suppression of hyperactive DNA damage repair pathways [abstract]. In: Proceedings of the AACR Special Conference on DNA Repair: Tumor Development and Therapeutic Response; 2016 Nov 2-5; Montreal, QC, Canada. Philadelphia (PA): AACR; Mol Cancer Res 2017;15(4_Suppl):Abstract nr A21.
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 | 0,001 |
| 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 | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».