Combination Neoantigen-Based Dendritic Cell Vaccination and Adoptive T-Cell Transfer Induces Antitumor Responses Against Recurrence of Hepatocellular Carcinoma
Bibliographic record
Abstract
A high rate of recurrence after curative therapy is a major challenge for the management of hepatocellular carcinoma (HCC). Currently, no effective adjuvant therapy is available to prevent HCC recurrence. We designed a personalized neoantigen-loaded dendritic cell vaccine and neoantigen-activated T-cell therapy, and used it as adjuvant therapy to treat 10 patients with HCC who had undergone curative resection or radiofrequency ablation in the first stage of a phase II trial (NCT03067493). The primary outcomes were safety and neoantigen-specific immune response. Disease-free survival (DFS) was also evaluated. The immunotherapy was successfully administered to all the patients without unexpected delay and demonstrated a reasonable safety profile with no grade ≥3 treatment-related side effects reported. Seventy percent of patients generated de novo circulating multiclonal neoantigen-specific T-cell responses. Induced neoantigen-specific immunity was maintained over time, and epitope spreading was observed. Patients who generated immune responses to treatment exhibited prolonged DFS compared with nonresponders (P = 0.012), with 71.4% experiencing no relapse for 2 years after curative treatment. High expression of an immune stimulatory signature, enhanced immune-cell infiltration (i.e., CD8+ T cells), and upregulated expression of T-cell inflammatory gene profiles were found in the primary tumors of the responders. In addition, neoantigen depletion (immunoediting) was present in the recurrent tumors compared with the primary tumors (7/9 vs. 1/17, P = 0.014), suggesting that immune evasion occurred under the pressure of immunotherapy. Our study indicates that neoantigen-based combination immunotherapy is feasible, safe, and has the potential to reduce HCC recurrence after curative treatment.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".