Abstract B004: 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, developing small molecules that directly target MYC is challenging. An alternative 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 normal and MYC-driven breast cancer cells. In contrast to other screens, this model is dependent on MYC and recapitulates human disease at both pathological and molecular levels in vivo. We identified high-priority hits to validate from the screen using two independent approaches: 1) a traditional gene-set enrichment analysis to identify highly represented biological pathways; and 2) analyzing the Cancer Dependency Map (DEPMAP) to select hits that are likely to be robust beyond the context of our screening conditions. Using a traditional gene-set enrichment analysis approach, we identified topoisomerase 1 (TOP1) as an actionable vulnerability that can be targeted with clinically approved inhibitors. Genetic and pharmacological inhibition of TOP1 resulted in MYC-driven cell death compared to that in control cells. Finally, drug response to TOP1 inhibitors significantly 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. As a secondary approach to interpreting our CRISPR screen hits, we analyzed DEPMAP to identify MYC-SLs that are differentially essential in MYC-dependent cancer cells. Specifically, data from RNA interference screens in hundreds of cancer cell lines were used to describe the response of these cells to MYC knockdown. These data were used to define MYC-dependent and MYC-independent cell lines within the context of this analysis. These two groups were then investigated for their in silico response to the knockdown of each of our MYC-SL hits. MYC-SLs, which were also differentially essential in MYC-dependent cancer cells from DEPMAP, were prioritized for further investigation. Critical MYC cofactors that have been validated by us and others (e.g., CDK9) were identified, providing confidence in this approach, and rationalizing ongoing investigations. 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, Linda Penn. An isogenic CRISPR screen identifies novel MYC-driven vulnerabilities [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B004.
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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 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 | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».