Abstract PR003: Mechanisms of therapeutic vulnerability of mismatch repair defective cancers to RNA polymerase I inhibitors
Notice bibliographique
Résumé
Abstract The goal of this study was to identify cancer types with synthetic vulnerability to RNA polymerase I (Pol I) inhibitors and to resolve mechanistic underpinnings of this vulnerability. Specific and selective Pol I inhibitors, pioneered by our group, block the transcription of rRNA, which is the rate-limiting step in ribosome biogenesis and protein synthesis. We conducted unbiased large-scale cancer cell line screens with the Pol I inhibitors and performed correlative analyses using DepMap and Achilles databases to identify cancer types and features that sensitize cancer cells to these inhibitors. The most sensitive lineages were colorectal, endometrial, esophageal, leukemia, myeloma, and ovarian. Microsatellite instable (MSI) cancer cell lines were highly sensitive and significantly more sensitive than microsatellite stable lines. Comparison of the cancer cell line responses to the Achilles gene essentiality database showed high correlation of the sensitivity to genetic knock-out of three Pol I subunits. This unbiased finding strongly supports identification of Pol I as the target. Correlative analyses for vulnerability identified two ribosomal proteins, RPL22 and RPL22L1, and MDM4, a p53 transcriptional repressor as the top candidates. Notably, RPL22K15Rfs hotspot mutation, the top-ranking genetic correlate, is present in up 70% MSI cancer cell lines and tumors. RPL22 and RPL22L1 are a synthetic lethal paralog pair. RPL22 is a negative regulator of RPL22L1 and consequently, haploinsufficiency or loss of RPL22 induces RPL22L1. RPL22L1 was the top-ranking expression correlate. Lastly, MDM4 was the top protein marker and is highly expressed in MSI cancers. These findings were validated using genetic and mechanistic analyses. We show that either genetic or chemical inhibition of the Pol I enzyme decreases the expression of RPL22L1 and MDM4 and alters their splicing. RPL22L1 is spliced to an alternative 3’ splice site in intron 2 leading to an early truncation. MDM4 exon 6 skipping converts the full-length p53 repressive form to a short NMD-targeted form. Mechanistically, by using GoldCLIP-seq, rMATS and splicing analyses, we show that RPL22 is a master regulator of splicing by binding to intronic sequences and splice sites of RPL22L1 and MDM4. Finally, we show the efficacy and tolerance of the Pol I inhibitors in xenograft and patient-derived MSI models expressing these markers. In summary, this synthetic vulnerability screen using new Pol I inhibitors uncovered a connection between Pol I transcription and cellular splicing programs and identified perturbation of this connection in MSI cancers. We conclude that RPL22 mutation could serve as tumor-agnostic biomarker for targeting cancers by Pol I inhibitors. Furthermore, this work identifies a new Pol I transcription-dependent ribosomal protein-governed pathway that converges on the regulation of p53. Citation Format: Marikki Laiho, Wenjun Fan, Hester Liu, Gregory C. Stachelek, Asma Begum, Catherine E. Davis, Tony Dorado, Pablo de Leon, Rajeshkumar N.V., James C. Barrow. Mechanisms of therapeutic vulnerability of mismatch repair defective cancers to RNA polymerase I inhibitors [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 PR003.
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 ».