Trial watch: immunotherapeutic strategies on the horizon for hepatocellular carcinoma
Bibliographic record
Abstract
The use of immune checkpoint inhibitors (ICIs) targeting PD-L1/PD-1 and CTLA-4 has transformed the oncology practice of hepatocellular carcinoma. However, only 25-30% of the patients with advanced HCC treated with atezolizumab-bevacizumab or tremelimumab-durvalumab (STRIDE) respond initially, and mechanistic biomarkers and novel treatment strategies are urgently needed for patients who present with or acquire resistance to first-line ICI-based therapies. The recent approval of the STRIDE regimen has also engendered new questions, such as patient selection factors (e.g. portal hypertension and history of variceal bleed) and biomarkers, and the optimal combination and sequencing of ICI-based regimens. Triumphs in the setting of advanced HCC have also galvanized considerable interest in the broader application of ICIs to early- and intermediate-stage diseases, including clinical combination of ICIs with locoregional therapies. Among these clinical contexts, the role of ICIs in liver transplantation - which is a potentially curative strategy unique to HCC management - as a bridge to liver transplant in potential candidates or in the setting of post-transplant recurrence, warrants investigation in view of the notable theoretical risk of allograft rejection. In this review, we summarize and chart the landscape of seminal immuno-oncology trials in HCC and envision future clinical developments.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".