Anti-PD-1 increases the clonality and activity of tumor infiltrating antigen specific T cells induced by a potent immune therapy consisting of vaccine and metronomic cyclophosphamide
Bibliographic record
Abstract
Background Future cancer immunotherapies will combine multiple treatments to generate functional immune responses to cancer antigens through synergistic, multi-modal mechanisms. In this study we explored the combination of three distinct immunotherapies: a class I restricted peptide-based cancer vaccine, metronomic cyclophosphamide (mCPA) and anti-PD-1 treatment in a murine tumor model expressing HPV16 E7 (C3). Methods Mice were implanted with C3 tumors subcutaneously. Tumor bearing mice were treated with mCPA (20 mg/kg/day PO) for seven continuous days on alternating weeks, vaccinated with HPV16 E749-57 peptide antigen formulated in the DepoVax (DPX) adjuvanting platform every second week, and administered anti-PD-1 (200 μg/dose IP) after each vaccination. Efficacy was measured by following tumor growth and survival. Immunogenicity was measured by IFN-γ ELISpot of spleen, vaccine draining lymph nodes and tumor draining lymph nodes. Tumor infiltration was measured by flow cytometry for CD8α+ peptide-specific T cells and RT-qPCR for cytotoxic proteins. The clonality of tumor infiltrating T cells was measured by TCRβ sequencing using genomic DNA. Results Untreated C3 tumors had low expression of PD-L1 in vivo and anti-PD-1 therapy alone provided no protection from tumor growth. Treatment with DPX/mCPA could delay tumor growth, and tri-therapy with DPX/mCPA/anti-PD-1 provided long-term control of tumors. We found that treatment with DPX/mCPA/anti-PD-1 enhanced systemic antigen-specific immune responses detected in the spleen as determined by IFN-γ ELISpot compared to those in the DPX/mCPA group, but immune responses in tumor-draining lymph nodes were not increased. Although no increases in antigen-specific CD8α+ TILs could be detected, there was a trend for increased expression of cytotoxic genes within the tumor microenvironment as well as an increase in clonality in mice treated with DPX/mCPA/anti-PD-1 compared to those with anti-PD-1 alone or DPX/mCPA. Using a library of antigen-specific CD8α+ T cell clones, we found that antigen-specific clones were more frequently expanded in the DPX/mCPA/anti-PD-1 treated group. Conclusions These results demonstrate how the efficacy of anti-PD-1 may be improved by combination with a potent and targeted T cell activating immune therapy.
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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) | 0.000 | 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) | 0.001 | 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".