Quantitative MRI cell tracking of immune cell recruitment to tumors and draining lymph nodes in response to anti-PD-1 and a DPX-based immunotherapy
Bibliographic record
Abstract
DPX is a unique T cell activating formulation that generates robust immune responses (both clinically and preclinically) which can be tailored to various cancers via the use of tumor-specific antigens and adjuvants. While DPX-based immunotherapies may act complementary with checkpoint inhibitors, combination therapy is not always easily predictable based on individual therapeutic responses. Optimizing these combinations can be improved by understanding the mechanism of action underlying the individual therapies. Magnetic Resonance Imaging (MRI) allows tracking of cells labeled with superparamagnetic iron oxide (SPIO), which can yield valuable information about the localization of crucial immune cell subsets. In this work, we evaluated the use of a multi-echo, single point MRI pulse sequence, TurboSPI, for tracking and quantifying cytotoxic T lymphocytes (CTLs) and myeloid lineage cells (MLCs). In a subcutaneous cervical cancer model (C3) we compared untreated mice to mice treated with either a single therapy (anti-PD-1 or DPX-R9F) or a combination of both therapies. We were able to detect, using TurboSPI, significant increases in CTL recruitment dynamics in response to combination therapy. We also observed differences in MLC recruitment to therapy-draining (DPX-R9F) lymph nodes in response to treatment with DPX-R9F (alone or in combination with anti-PD-1). We demonstrated that the therapies presented herein induced time-varying changes in cell recruitment. This work establishes that these quantitative molecular MRI techniques can be expanded to study a number of cancer and immunotherapy combinations to improve our understanding of longitudinal immunological changes and mechanisms of action.
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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".