Treatment of advanced hepatocellular carcinoma: immunotherapy from checkpoint blockade to potential of cellular treatment
Bibliographic record
Abstract
The absence of potent therapeutic option accounts for the dismal prognosis of advanced hepatocellular carcinoma (HCC) with high mortality and recurrence rate. For a decade, sorafenib is the only approved systemic drug in the first-line setting and warrants as the standard-of-care for HCC in the advanced stage. Given the common failures of chemotherapies and targeted therapies in the field of HCC treatment, promising breakthroughs were eagerly needed and until recently, immunotherapies have opened a new era of anticancer treatment. The liver organ is perceived as "immunotolerant" owing to its functional role, and the hepatic immune balance is found to be deregulated during chronic liver inflammation and HCC tumorigenesis. Restoring a competent immunity by mitigation of immunosuppression signals is a contemporary approach. In this regard, novel immune checkpoint inhibitors have revolutionized cancer pharmacological treatment options with remarkable clinical outcomes in hematologic malignancy and multiple solid tumors including advanced HCC. Nivolumab, an immunotherapeutic agent to block programmed cell death protein 1 (PD-1), showed high efficacy potential for patients progressed with sorafenib and granted accelerated approval by the US Food and Drug Administration (FDA) recently. The development of this class of immunotherapeutic drug is currently based on myriad studies established on the role of T-cell mediated immunosuppression through immune checkpoints. Heterogeneous results have led to further explorations to the profile of oncogenic processes and signaling pathways associated with PD1/PD-L1 axis. Emerging evidence from preclinical studies implicate natural killer (NK) cells as a mediator to the PD-1 checkpoint signaling immunoevasion. The strategy of adopting immunomodulating ability of NK cells by immune checkpoints inhibitors is potential to additive effects in stimulating anticancer immunity. This idea is not entirely newfound but has recently gained prominence because of advances in defining phenotypic heterogeneity of NK cell populations. The physiological significance and synergistic value of NK cells await further investigation in clinical trials. In this review, an overview of the treatment paradigm shift of HCC management is presented. Current knowledge concerning immunological mechanisms of immune checkpoints attributed to T cell is further discussed and relevant ongoing clinical trials are summarized. We proposed that NK cells should be viewed as part of the network of checkpoint immunoevasion and delineate current evidence of translational clinical research in this area. It is conceivable that immune checkpoint inhibitors in combination with NK cell-based therapeutic strategies will be great promise for treatment of advanced 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".