Monitoring Immune Checkpoint Regulators as Predictive Biomarkers in Hepatocellular Carcinoma
Bibliographic record
Abstract
The global burden of Hepatocellular carcinoma (HCC), one of the frequent causes of cancer-related deaths worldwide, is rapidly increasing partly due to the limited treatment options available for this disease and recurrence due to therapy resistance. Immune checkpoint inhibitors that are proved to be beneficial in the treatment of advanced melanoma and other cancer types are currently in clinical trials in HCC. These ongoing trials are testing the efficacy and safety of a few select checkpoints in HCC. Similar to observations in other cancers, these immune checkpoint blockade treatments as monotherapy may benefit only a fraction of HCC patients. Studies that assess the prevalence and distribution of other immune checkpoints/modulatory molecules in HCC have been limited. Moreover, robust predictors to identify which HCC patients will respond to immunotherapy are currently lacking. The objective of this study is to perform a comprehensive evaluation on different immune modulators as predictive biomarkers to monitor HCC patients at high risk for poor prognosis. We screened publically available HCC patient databases for the expression of previously well described immune checkpoint regulators and evaluated the usefulness of these immune modulators to predict high risk, patient overall survival and recurrence. We also identified the immune modulators that synergized with known immune evasion molecules PD-L1, PD-1 and CTLA-4 and correlated with worse patient outcomes. We evaluated the association between the expression of epithelial-to-mesenchymal (EMT) markers and PD-L1 in HCC patient tumors. We also examined the relationship of tumor mutational burden with HCC patient survival. Notably, expression of immune modulators B7-H4, PD-L2, TIM-3, and VISTA were independently associated with worse prognosis, while B7-H4, CD73 and VISTA predicted low recurrence free survival. Moreover, the prognosis of patients expressing high PD-L1 with high B7-H4, TIM-3, VISTA, CD73 and PD-L2 expression was significantly worse. Interestingly, PD-L1 expression in HCC patients in the high risk group was closely associated with EMT marker expression and prognosticates poor survival. In HCC patients, high TMB predicted worse patient outcomes than those with low TMB.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".