Abstract 4039: An isogenic CRISPR screen identifies novel MYC-driven vulnerabilities
Notice bibliographique
Résumé
Abstract c-MYC (MYC) is a central regulatory protein that is dysregulated in >50% of all human cancers and is linked to aggressive disease. Developing MYC inhibitors would revolutionize cancer treatment; however, efforts to target MYC directly using small molecular inhibitors have historically failed. A promising approach is to identify and inhibit critical MYC partner proteins to inactivate MYC and trigger cancer cell death. Inhibiting these targets therapeutically can result in synthetic lethality (MYC-SL), which can be exploited in MYC-dysregulated cancers. To identify MYC-SL targets, we performed a genome-wide CRISPR knock-out screen using an isogenic pair of non-transformed and MYC-driven breast cancer cells. In contrast to other screens, this model is both dependent on MYC and recapitulates human disease at pathological and molecular levels in vivo. Finally, these hits were cross-referenced with our MYC protein-interactome data to reveal putative MYC partner proteins that are critical for MYC activity. From our screen results, we performed gene set enrichment analysis to identify biological activities that may represent core functional dependencies in MYC dysregulated cancer cells. Using this approach, we identified and validated topoisomerase 1 (TOP1) as an actionable vulnerability that can be targeted with clinically approved inhibitors. Genetic and pharmacological inhibition of TOP1 in multiple orthogonal assays resulted in MYC-driven cell death. Finally, drug response to TOP1 inhibitors correlated with MYC levels and activity across panels of breast cancer cell lines and patient-derived organoids, highlighting TOP1 as a promising target for MYC-driven cancers. The recent accessibility of large-scale datasets detailing functional dependencies across hundreds of cancer cell lines offers an unprecedented opportunity to prioritize targets with greater translational relevance, which is a major limitation of the synthetic-lethal approach for target discovery. As a secondary analysis of our hits, we utilized DEPMAP data to classify cancer cell lines as relatively MYC-dependent or MYC-independent, enabling the in silico evaluation of each MYC-SL hit’s differential essentiality. MYC-SLs that exhibited selective essentiality in MYC-dependent cell lines were prioritized for further investigation. Results from this strategy revealed critical MYC cofactors that have been validated by us and others (e.g., CDK9), providing confidence in this approach. Excitingly, previously underexplored targets were identified with promising validation to-date. Together, this work features two successful strategies to prioritize hits from hundreds of synthetic-lethal genome-wide CRISPR screens to identify novel MYC-driven vulnerabilities in cancer. Citation Format: Peter Lin, Corey Lourenco, Jennifer Cruickshank, Luis Palomero, Jenna E. van Leeuwen, Amy H. Tong, Katherine Chan, Samah El Ghamrasni, Miquel Pujana, David W. Cescon, Jason Moffat, Linda Z. Penn. An isogenic CRISPR screen identifies novel MYC-driven vulnerabilities [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4039.
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 | 0,002 | 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 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».