Salsalate improves the anti-tumor efficacy of lenvatinib in MASH-driven hepatocellular carcinoma
Bibliographic record
Abstract
Background & Aims Metabolic dysfunction-associated steatohepatitis (MASH) is a growing cause of hepatocellular carcinoma (HCC) worldwide. The complex microenvironment of these tumors, characterized by metabolic dysfunction, hypoxia, steatosis, and fibrosis, limits the effectiveness of standard-of-care therapies, such as the multi-tyrosine kinase inhibitor lenvatinib (LEN). Salsalate (SAL), is a rheumatoid arthritis therapy that enhances fatty acid oxidation and reduces de novo lipogenesis, fibrosis and cell proliferation pathways. We hypothesize that addition of SAL could improve the efficacy of LEN in MASH-HCC. Methods We assessed the efficacy of combination therapy using clinically relevant concentrations of LEN and SAL in human HCC cell models, orthotopic xenograft and MASH-HCC mouse models. In addition, assays assessing fatty acid oxidation and lipogenesis, protein immunoblotting and RNA-sequencing were used to understand mechanisms involved. Results LEN + SAL synergistically suppressed the proliferation and clonogenic survival of cells ( p ≤0.0001), prolonged survival in an orthotopic xenograft model ( p = 0.02), and reduced angiogenesis, fibrosis, and steatosis ( p ≤0.05) in a MASH-HCC model. These effects were associated with activation of AMPK and inhibition of the mTOR-HIF1α and Erk1/2 signaling pathways. RNA-sequencing analysis in both Hep3B cells and livers of the MASH-HCC mouse model revealed that SAL enhanced fatty acid oxidation and suppressed fibrosis and cell cycle progression, while LEN reduced angiogenesis with regulatory network analysis, suggesting a potential role for activating transcription factor 3 (ATF3) and ETS-proto-oncogene-1 (ETS-1). Conclusions These data indicate that combining LEN and SAL, which exert distinct effects leading to improvements in the liver microenvironment (steatosis, angiogenesis, and fibrosis) and inhibition of tumor proliferation, may have therapeutic potential for MASH-driven HCC. Impact and implications Although rates of MASH-HCC are on the rise globally, standard-of-care multi-tyrosine kinase inhibitors and immunotherapy have limited efficacy in this HCC etiology. Metabolic targeting with SAL inhibits cancer growth kinetics while also alleviating drivers of MASH by increasing fatty acid oxidation and reducing de novo lipogenesis and fibrosis. Combined LEN and SAL improved survival and MASH-HCC pathology in mouse models without adverse effects. Given that SAL is a safe, economical, and approved medication, this concept holds great translational potential that could provide a new treatment avenue for patients with unresected MASH-HCC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".