Phase I clinical study of personalized peptide vaccination combined with radiotherapy for advanced hepatocellular carcinoma
Bibliographic record
Abstract
AIM: To assess the efficacy and safety of a new treatment modality, cellular immune therapy based on personalized peptide vaccination (PPV-DC-CTL) combined with radiotherapy, for treating advanced hepatocellular carcinoma (HCC). METHODS: A total of nine patients with advanced HCC were enrolled. Multidisciplinary consultation confirmed that all the patients definitely had no opportunity of surgery, because four patients had multiple liver metastases (the number of liver lesions > 3), one patient had liver metastases and portal vein tumor thrombosis, one patient had lung and bone metastases, two patients had liver and lung metastases and one patient had liver metastasis and peritoneal metastasis. Patients with metastasis were treated with precise radiotherapy combined with PPV-DC-CTL. RESULTS: Following radiotherapy and one to three cycles of PPV-DC-CTL treatment, AFP levels were significantly decreased in six patients and imaging assessment of the lesions showed a partial response (PR) in three patients and stable disease in the other three patients. The response rate was 33% and disease control rate was 66%. This regimen was found to be safe and well tolerated. None of the patients developed liver or kidney side effects. Only one patient developed grade II bone marrow suppression and the remaining patients had no significant hematological side effects. CONCLUSION: Radiotherapy combined with PPV-DC-CTL provides a new therapeutic strategy for patients with advanced HCC, which is well tolerated, safe, feasible and effective.
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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 | 0.000 |
| 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 | 0.001 |
| 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 | 0.000 |
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".