Tumor Eradication by Hepatitis B Virus X Antigen-Specific CD8+ T Cells in Xenografted Nude Mice
Bibliographic record
Abstract
We have previously reported several CTL epitopes derived from the hepatitis B viral X Ag (HBx). In this study, we evaluated whether HBx-specific CTLs can be effectively used in adoptive cancer immunotherapy. To validate the possibility, four peptides containing a HLA-A2.1-restricted binding consensus motif were identified from the HBx protein and tested for their ability to activate CTL from PBMCs isolated from chronic carriers of HBV (n = 12). We selected two highly potent epitopes, HBx 52-60 (HLSLRGLFV) and HBx 115-123 (CLFKDWEEL), that are capable of inducing Ag-specific cytotoxic T cells in patient PBMCs. For adoptive immunotherapy using HBx-specific CTLs, we generated CTL clones restricted to the HBx 52-60 or HBx 115-123 peptide using a limiting dilution technique. LC-46, an HBx 52-60-specific clone, is CD62L(-)CD69(+)CD45RO(+)CD45RA(-)CD25(dim) and is stained by IFN-gamma (approximately 92%), IL-2 (30%), and TNF-alpha (56%), but not by IL-5, IL-10, IL-12, or TNF-beta, indicating that the cells are fully activated T cytotoxic 1-type cells. When LC-46 cells were adoptively transferred into xenografted nude mice bearing human hepatomas expressing HLA-A2.1 molecules and intracellular HBx proteins, the tumors were eradicated. Taken together, our data provide solid evidence for the feasibility of adoptive immunotherapy with HBx-sensitized CTLs in hepatitis disease, including hepatocellular carcinoma (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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".