Abstract A068 Emerging therapies for the treatment of the fusion protein driven cancer, fibrolamellar carcinoma
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".