Using <scp>MRI</scp> cell tracking to monitor immune cell recruitment in response to a peptide‐based cancer vaccine
Bibliographic record
Abstract
Purpose MRI cell tracking can be used to monitor immune cells involved in the immunotherapy response, providing insight into the mechanism of action, temporal progression of tumor growth, and individual potency of therapies. To evaluate whether MRI could be used to track immune cell populations in response to immunotherapy, CD8+ cytotoxic T cells, CD4+CD25+FoxP3+ regulatory T cells, and myeloid‐derived suppressor cells were labeled with superparamagnetic iron oxide particles. Methods Superparamagnetic iron oxide‐labeled cells were injected into mice (one cell type/mouse) implanted with a human papillomavirus‐based cervical cancer model. Half of these mice were also vaccinated with DepoVaxTM (ImmunoVaccine, Inc., Halifax, Nova Scotia, Canada), a lipid‐based vaccine platform that was developed to enhance the potency of peptide‐based vaccines. Results MRI visualization of CD8+ cytotoxic T cells, regulatory T cells, and myeloid‐derived suppressor cells was apparent 24 h post‐injection, with hypointensities due to iron‐labeled cells clearing approximately 72 h post‐injection. Vaccination resulted in increased recruitment of CD8+ cytotoxic T cells, and decreased recruitment of myeloid‐derived suppressor cells and regulatory T cells to the tumor. We also found that myeloid‐derived suppressor cell and regulatory T cell recruitment were positively correlated with final tumor volume. Conclusion This type of analysis can be used to noninvasively study changes in immune cell recruitment in individual mice over time, potentially allowing improved application and combination of immunotherapies. Magn Reson Med 80:304–316, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".