Navigating second-line therapy after immunotherapy in advanced HCC☆
Bibliographic record
Abstract
In recent years, systemic treatment options for hepatocellular carcinoma (HCC) have expanded significantly, with pivotal phase 3 trials reporting unprecedented survival and response outcomes. However, these advancements have largely focused on first-line strategies, thereby challenging existing treatment sequences and raising questions about the optimal approach following disease progression. Several therapeutic options are available in second and further line — including immunotherapies, tyrosine kinase inhibitors, and local therapies — but so far comprehensive head-to-head comparisons to establish the ideal treatment sequence are lacking. Consequently, most evidence guiding second-line therapy has been derived from post-hoc analyses, real-world data, and select prospective studies, primarily based on phase 2 designs. In clinical practice, treatment decisions integrate this evolving evidence base with patient-specific factors such as prior treatment response, liver function, and performance status, often within the constraints of regulatory and accessibility considerations. Ongoing research is investigating novel approaches, including the continuation of immunotherapy post-progression, the application of CAR T-cells, and targeted treatments against specific markers like FGF19 and glypican-3. This review provides an overview of the current evidence for second-line and subsequent therapies, explores key considerations in treatment sequencing, and highlights emerging strategies that may further refine the therapeutic landscape for HCC.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".