Notch signaling maintains a progenitor-like subclass of hepatocellular carcinoma
Bibliographic record
Abstract
Abstract Hepatocellular carcinomas (HCCs) constitute one of the few cancer indications for which mortality rates continue to rise. While Notch signaling dictates a key progenitor lineage choice during development, its role in HCC has remained controversial. Using therapeutic antibodies targeting Notch ligands and receptors to screen over 40 patient-derived xenograft models, we here identify progenitor-like HCCs that crucially depend on a tumor-intrinsic JAG1-NOTCH2 signal. Inhibiting this signal induces tumor regressions by triggering progenitor-to-hepatocyte differentiation, the same cell fate-switch that Notch controls during development. Transcriptomic analysis places the responsive tumors within the well-characterized progenitor subclass, a poor prognostic group of highly proliferative tumors, providing a diagnostic method to enrich for Notch-dependent HCCs. Furthermore, single-cell RNA sequencing uncovers a heterogeneous population of tumor cells and reveals how Notch inhibition shifts cells from a mixed cholangiocyte-hepatocyte lineage to one resembling mature hepatocytes. Analyzing the underlying transcriptional programs brings molecular detail to this process by showing that Notch inhibition de-represses expression of CEBPA, which enables the activity of HNF4α, a hepatocyte lineage factor that is otherwise quiescent. We thus describe a compelling and targetable dependency in a poor-prognosis class of HCCs.
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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) | 0.001 | 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".