Current state of immunotherapy in hepatocellular carcinoma
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) accounts for roughly 75% of primary liver malignancies and represents the sixth most commonly diagnosed cancer worldwide, with an estimated 905,677 new cases reported in 2020 alone. For decades, treatment options for advanced HCC remained severely limited, with sorafenib standing as the sole first-line systemic therapy from 2007 until 2020. The emergence of immune checkpoint inhibitors has fundamentally altered the therapeutic landscape, offering durable responses in a subset of patients who previously faced dismal prognoses. This research investigated the current patterns, efficacy, and predictive biomarkers of immunotherapy use in HCC patients across a Canadian tertiary care setting. A retrospective cohort analysis was performed at Toronto Institute of Applied Sciences, enrolling 312 patients diagnosed with unresectable or advanced HCC (Barcelona Clinic Liver Cancer stage B or C) who received at least one cycle of immunotherapy between January 2018 and December 2021. Treatment regimens included atezolizumab plus bevacizumab (34.7%), nivolumab monotherapy (22.1%), pembrolizumab monotherapy (16.8%), durvalumab plus tremelimumab (11.3%), nivolumab plus ipilimumab (9.4%), and other combinations (5.7%). The primary endpoints were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Secondary endpoints included assessment of predictive biomarkers—PD-L1 expression, tumor mutational burden (TMB), microsatellite instability (MSI) status, and alpha-fetoprotein (AFP) levels—and their association with treatment response. The overall ORR across all regimens was 27.6%, with a median PFS of 6.8 months and median OS of 16.3 months. Atezolizumab-bevacizumab demonstrated the highest ORR (33.4%) and longest median OS (19.1 months). Patients with PD-L1 expression ≥1% had significantly better response rates compared to PD-L1-negative patients (37.0% vs. 16.4%, p
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".