Syngeneic mouse model of YES-driven metastatic and proliferative hepatocellular carcinoma
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is a disease of high unmet medical need that has become a global health problem. The development of targeted therapies for HCC has been hindered by the incomplete understanding of HCC pathogenesis and the limited number of relevant preclinical animal models. We recently unveiled a previously uncharacterized YES kinase (encoded by YES1)-dependent oncogenic signaling pathway in HCC. To model this subset of HCC, we established a series of syngeneic cell lines from liver tumors of transgenic mice expressing activated human YES. The resulting cell lines (referred to as HepYF) were enriched for expression of stem cell and progenitor markers, proliferated rapidly, and were characterized by high SRC family kinase (SFK) activity and activated mitogenic signaling pathways. Transcriptomic analysis indicated that HepYF cells are representative of the most aggressive proliferation class G3 subgroup of HCC. HepYF cells formed rapidly growing metastatic tumors upon orthotopic implantation into syngeneic hosts. Treatment with sorafenib or the SFK inhibitor dasatinib markedly inhibited the growth of HepYF tumors. The new HepYF HCC cell lines provide relevant preclinical models to study the pathogenesis of HCC and test novel small-molecule inhibitor and immunotherapy approaches.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".