Current Immunotherapy Strategies and Emerging Biomarkers for the Treatment of Hepatocellular Carcinoma
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Hepatocellular carcinoma (HCC), the predominant form of liver cancer, ranks as the third leading cause of cancer-related deaths worldwide. With the shift from viral hepatitis to metabolically dysfunction-associated steatosis liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH) as primary etiologies, we aimed to review ongoing clinical trials in adult HCC patients to highlight emerging treatments, particularly for nonviral HCC cases. METHODS: We searched ClinicalTrial.gov (last March 2025) for interventional trials. We included ongoing (recruiting/active/not recruiting), phase I-IV, adults (>18 years old), and HCC-focused only clinical trials. We excluded observational and interventional (biological, genetic, device, or procedure) clinical trials. RESULTS: This review highlights recent advances in HCC treatment, with a focus on the transformative role of immunotherapy. Evidence suggests that nonviral HCC, as well as HCC with MASLD/MASH background livers, may have reduced sensitivity to immunotherapy. Thus, there is a critical need for molecular insights to improve patient stratification. Moreover, we examine how new diagnostic tools, including liquid biopsies, influence treatment decisions and aid in monitoring responses. Limitations limited MASLD/MASH-specific trial data. CONCLUSIONS: We review current research and its integration into clinical practice, advancing HCC therapy toward personalized, patient-centered care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".