A developmental biliary lineage program cooperates with Wnt activation to promote cell proliferation in hepatoblastoma
Bibliographic record
Abstract
Cancers evolve not only through the acquisition and clonal transmission of somatic mutations but also by epigenetic mechanisms that modify cell phenotype. Here, we use histology-guided and spatial transcriptomics to characterize hepatoblastoma, a childhood liver cancer that exhibits significant histologic and proliferative heterogeneity despite clonal activating mutations in the Wnt/β-catenin pathway. Highly proliferative regions with embryonal histology show high expression of Wnt target genes, the embryonic biliary transcription factor SOX4, and striking focal expression of the growth factor FGF19. In patient-derived tumoroids with constitutive Wnt activation, FGF19 is a required growth signal for FGF19-negative cells. Indeed, some tumoroids contain subsets of cells that endogenously express FGF19, downstream of Wnt/β-catenin and SOX4. Thus, the embryonic biliary lineage program cooperates with stabilized nuclear β-catenin, inducing FGF19 as a paracrine growth signal that promotes tumor cell proliferation, together with active Wnt signaling. In this pediatric cancer presumed to originate from a multipotent hepatobiliary progenitor, lineage-driven heterogeneity results in a functional growth advantage, a non-genetic mechanism whereby developmental lineage programs influence tumor evolution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".