Abstract B042: A screen to identify modifiers of BRCA1 protein level for cancer prevention and treatment
Notice bibliographique
Résumé
Abstract The breast cancer susceptibility gene 1 (BRCA1) is a nuclear phosphoprotein involved in DNA double-strand break (DSB) repair and maintenance of genome stability. A single BRCA1 mutation leads to reduced levels of functional BRCA1 protein, such that DSB repair is compromised. It has been proposed that BRCA1 is a haploinsufficient tumor suppressor gene in some contexts, which when lost accelerates the mutation rate of other critical genes thus increasing neoplastic transformation. BRCA1 expression levels are modified through exogenous exposures, and modulating BRCA1 expression may alter tumour onset, progression, and treatment options. We hypothesize that compounds that increase BRCA1 expression and function may prevent the outgrowth of BRCA1-associated breast cancer. By contrast, compounds that reduce BRCA1 protein level could be used as combination therapries in conjunction with poly-ADP ribose polymerase (PARP) inhibitors. To achieve these goals, we generated BRCA1-reporter cell lines. Using CRISPR-assisted genome editing, we endogenously tagged the BRCA1 protein with HiBiT, an 11 amino acid luminescent tag. BRCA1-targeting was verified by sequencing and immunoblotting, and functional validation was performed with known modulators of BRCA1 expression. Our tests confirmed that HiBiT-tagging allows for sensitive measurement of BRCA1 protein levels, therefore permitting screenability. BRCA1-reporter cells were subsequently used to conduct a pilot screen (64 epigenetic-modifying drugs) and a high-content screen (6,000 compounds). We identified several compounds that modified BRCA1 protein expression (B-score >3*SD). The epigenetic drug library yielded five repressors and two activators, and the high-content screen identified 163 repressors and 12 activators. Validation studies have confirmed that the activator drug, aloxistatin can elevate BRCA1-HiBiT levels in a dose-dependent manner. Additionally, aloxistatin treatment increased BRCA1 expression and function in a panel of breast cells. We have also assessed 9 down-regulators of BRCA1 from the pilot and high-content screens. Each of these nine compounds reduced the expression of BRCA1 protein and synergized with the PARP inhibitor olaparib to inhibit cancer cell growth. Collectively, our ongoing studies suggest that BRCA1 activation may reduce cancer phenotypes and BRCA1 repression can sensitize breast cancer cells to PARP-inhibition and other chemotherapies. Overall, using our reporter cell lines we have identified a number of unique compounds that effectively modify BRCA1 expression and function that may have utility in the treatment and prevention of BRCA1-associated cancers. Citation Format: Erin Sellars, Margarita Savguira, Joanne Kotsopoulos, Leonardo Salmena. A screen to identify modifiers of BRCA1 protein level for cancer prevention and treatment [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B042.
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 | 0,000 |
| 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) | 0,000 | 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 ».