Inhibition of Wnt/β-catenin increases anti-tumor activity by synergizing with sorafenib in hepatocellular carcinoma
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) poses a major global health challenge owing to limited treatment efficacy and drug resistance to therapies such as the tyrosine kinase inhibitor (TKI) sorafenib. We utilized a microfluidic three-dimensional (3D) drug testing system to assess drug responses in 37 fresh clinical samples and performed immunohistochemical analysis of 41 tumor tissue samples that received sorafenib therapy. Results revealed that Wnt/β-catenin activation was associated with sorafenib resistance, with higher nuclear β-catenin levels predicting poor response. Targeting Wnt/β-catenin via genetic intervention enhanced TKI sensitivity by promoting apoptosis and reducing clonogenicity. Through a large scale of drug and inhibitor library screening, we identified PRI-724, a potent CREB-binding protein (CBP)/β-catenin transcription antagonist, which synergistically induces apoptosis with sorafenib in vitro and in vivo by inhibiting β-catenin/CBP/c-myc, β-catenin nuclear localization and ERK/AKT signaling. The microfluidic 3D drug testing system confirmed the synergistic anti-tumor effects of this combination, underscoring its clinical application potential. Conclusively, our study provides a new combination therapy with sorafenib and PRI-724 to overcome TKI resistance and improve clinical outcomes in patients with HCC. Schematic representation of the speculative molecular mechanism model. Our study revealed that β-catenin activation drives sorafenib resistance in HCC, and disrupting β-catenin enhances sorafenib efficacy by promoting apoptosis and inhibiting proliferation. The combination of sorafenib and PRI-724, a Wnt/β-catenin inhibitor, showed synergistic anti-tumor effects in vitro across various HCC cell lines, in vivo using xenograft models, ex vivo utilizing MDT chip system to explore clinical applications, offering a novel therapeutic strategy for HCC patients.
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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".