Advances in Immunotherapy for Hepatocellular Carcinoma (HCC)
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is the second most common cause of cancer-related deaths in the world. More than half of patients with HCC present with advanced stage, and highly active systemic therapies are crucial for improving outcomes. Immune checkpoint inhibitor (ICI)-based therapies have emerged as novel therapy options for advanced HCC. Only one third of patients achieve an objective response with ICI-based therapies due to primary resistance or acquired resistance. The liver tumor microenvironment is naturally immunosuppressive, and specific mutations in cell signaling pathways allow the tumor to evade the immune response. Next, gene sequencing of the tumor tissue or circulating tumor DNA may delineate resistance mechanisms to ICI-based therapy and provide a rationale for novel combination therapies. In this review, we discuss the results of key clinical trials that have led to approval of ICI-based therapy options in advanced HCC and summarize the ongoing clinical trials. We review resistance mechanisms to ICIs and discuss how immunotherapies may be optimized based on the emerging research of tumor biomarkers and genomic alterations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".