Abstract A068 Emerging therapies for the treatment of the fusion protein driven cancer, fibrolamellar carcinoma
Notice bibliographique
Résumé
Abstract Fibrolamellar carcinoma (FLC) is characterized by a single genomic alteration, a 400 kB deletion resulting in the fusion transcript DNAJB1::PRKACA, which encodes a fusion oncoprotein essential for tumor initiation and maintenance. This study aims to find therapeutics that can kill FLC cells. We use three model systems: Patient tumors, fresh from resection, that we have made into organoids, that we have implanted into immune compromised mice (PDX), or we have screened directly, immediately after resection. We found lack of efficacy of agents current in the clinic. We have previously characterized the transcriptome of FLC and identified a number of oncogenic genes and pathways that are upregulated including the wnt pathway, EGF, and the wnt pathway. However, agents that blocked these had no effect on tumor survival. We switched to using three different approaches for therapy: i) A functional precision medicine screen using a drug-repurposing library; ii) antisense oligonucleotides against the RNA junction of DNAJB1::PRKACA transcript; iii) Degrader of the DNAJB1::PRKACA fusion protein. Functional precision medicine: We found a number of agents that were extremely efficacious. What they shared in common was pathways of metabolism of these drugs that were down-regulated in FLC. For example, irinotecan, a topoisomerase I inhibitor, was extremely effective. It is removed from liver cells through the addition of a sugar group by UGT1A1 which is decreased at the transcript and protein level. There were some variations in the extent to which patient tumors responded to irinotecan, but the variations could be eliminated by blocking the anti-apoptotic pathway Bcl-xL. We are currently preparing the combination of irinotecan and a PROTAC (proteolysis targeting chimeras) against Bcl-xL for a clinical trial. Antisense oligos: We created shRNA that tiled across the DNAJB1::PRKACA junction and identified some that eliminated DNAJB1::PRKACA at the RNA and protein level with no effect on DNAJB1 and no effect on PRKACA. When these were induced in FLC tumors cells grown as PDX, the tumors not only stopped growing, but shrank. This demonstrates that DNAJB1::PRKACA not only triggers FLC, but also continues to drive FLC and the FLC tumors are oncogenically addicted to DNAJB1::PRKACA. The same shRNA had no detectable effects on non-FLC liver tumors. We next tested siRNA against FLC grown as PDX. The efficacy of the siRNA were increased by conjugation to the sugar GalNAc which binds to the asialyoglycoprotein receptor on FLC cells. Degraders of the oncoprotein: We developed a degrader that selectively degraded the DNAJB1::PRKACA with no detectable effects on the wt PRKACA. This degrader effectively killed FLC tumors growing as PDX. Each of these three approaches represent emerging technologies for pediatric tumors. For each we now have a proof of principle, and our efforts are now focused on improving delivery and studies of efficacy and safety in the hope of moving these into the clinic to provide respite for this usually lethal childhood tumor. Citation Format: Mahsa Shirani, Michael Tomasini, Christoph Neumayer, Denise Ng, Gadi Lalazar, Bassem Shebl, Philip Coffino, Barbara A. Lyons, Sanford Simon. Emerging therapies for the treatment of the fusion protein driven cancer, fibrolamellar carcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A068.
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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) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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) | 0,007 | 0,002 |
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 ».