Abstract B022: Precision medicine for the fusion protein driven cancer, fibrolamellar carcinoma (FLC): Beyond sequencing
Bibliographic record
Abstract
Abstract Fibrolamellar carcinoma (FLC) is a rare, usually lethal primary liver tumor that affects children, adolescents and young adults. Sequencing of FLC tumors in hepatocytes reveals that in 99% of the patients there is one recurrent genomic alteration: A deletion of 400 kB. This produces DNAJB1::PRKACA, a fusion of the first exon of DNAJB1 with the 2nd – 10th exons of PRKACA, the catalytic subunit of protein kinase A (PKA). PKA exists as a holoenzyme of two catalytic subunits and two regulatory subunits. The regulatory subunit both inhibits the catalytic and localizes it in the cell. Calibrated mass spectrometry shows that in normal liver there is always an excess of regulatory>catalytic subunits, but in the adjacent FLC tumor tissue there is an excess of catalytic>regulatory subunits. Different biochemistry assays and proximity ligation reveal both free catalytic, unbound to regulatory subunits, and an increase of kinase activity in the tumor cells. Much of the increase of catalytic subunit is in the nucleus. Transducing primary human hepatocytes (PHH) with DNAJB1::PRKACA is sufficient to recapitulate the transcriptome of FLC tumors. Transduction of PHH with just the wt PRKACA is also sufficient to recapitulate the transcriptome of FLC tumors. Thus, just increasing the level of kinase is sufficient, there is nothing special about the fusion domain. From our tissue repository we have four patients who have a tumor that looks like FLC but does not have a fusion to the catalytic subunit. The only alteration in these patients is loss of regulatory subunit. The transcriptome of these patient tumors is indistinguishable from that of classic DNAJB1::PRKACA FLC in hepatocytes. Additionally, we have a few dozen patients who have either DNAJB1::PRKACA or another fusion, ATP1B1::PRKACA in the ductal cells of their liver (producing cholangiocarcinomas) or ductal cells of their pancreas (producing IOPN, Intraductal Oncolytic Pancreatic Neoplasms). The transcriptome of these patients clusters with the transcriptome of the FLC patients with DNAJB1::PRKACA in the hepatocytes. A functional precision medicine drug repurposing screen found that the EC50 of the response of freshly resected tumors from patients to a wide panel of drugs is the same as that of FLC patients with DNAJB1::PRKACA in their hepatocytes. Based on sequencing one could conclude that patients with deletion of regulatory subunit in the hepatocytes, or expression of DNAJB1::PRKACA in the hepatocytes, or expression of DNAJB1::PRKACA or ATP1B1::PRKACA in the cholangiocytes or pancreatic ductal cells are at least three distinct diseases. An analysis of the cell biological changes reveals that they all of an increase of the ratio of catalytic:regulatory subunit, they have an increase of catalytic subunit in the nucleus, and these all result in the same changes of transcriptome and the same drug-response profile. Thus, looking beyond sequencing to the cellular changes leads to a different conclusion, that maybe they should be considered the same disease. Citation Format: Sanford Simon, Mahsa Shirani, David Requena, Denise Ng, Gadi Lalazar, Solomon Levin, Michael S. Torbenson, Aatur D. Singhi, Henrik Molina, Charles M. Rice, Philip Coffino, Barbara A. Lyons. Precision medicine for the fusion protein driven cancer, fibrolamellar carcinoma (FLC): Beyond sequencing [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 B022.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.014 |
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".