Combination treatment of radiofrequency ablation and peptide neoantigen vaccination: Promising modality for future cancer immunotherapy
Bibliographic record
Abstract
Background: The safety and immunogenicity of a personalized neoantigen-based peptide vaccine, iNeo-Vac-P01, was reported previously in patients with a variety of cancer types. The current study investigated the synergistic effects of radiofrequency ablation (RFA) and neoantigen vaccination in cancer patients and tumor-bearing mice. Methods: Twenty-eight cancer patients were enrolled in this study, including 10 patients who had received RFA treatment within 6 months before vaccination (Cohort 1), and 18 patients who had not (Cohort 2). Individualized neoantigen peptide vaccines were designed, manufactured, and subcutaneously administrated with GM-CSF as an adjuvant for all patients. Mouse models were employed to validate the synergistic efficacy of combination treatment of RFA and neoantigen vaccination. Results: IFN-γ ELISpot assay showed that patients in Cohort 1 had stronger neoantigen-specific immune responses at baseline and post vaccination. Mice receiving combination treatment of RFA and neoantigen vaccines displayed higher antitumor immune responses than mice receiving single modality. The combination of PD-1 blockage with RFA and neoantigen vaccines further enhanced the antitumor response in mice. Conclusion: Neoantigen vaccination after local RFA treatment could improve the clinical and immune response among patients of different cancer types. The synergistic antitumor potentials of these two modalities were also validated in mice, and might be further enhanced by immune checkpoint inhibition. The mechanisms of their synergies require further investigation. Clinical trial registration: https://clinicaltrials.gov/, identifier NCT03662815.
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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) | 0.002 | 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".