MRI of combination immunotherapy in an epithelial ovarian cancer preclinical model
Bibliographic record
Abstract
<title>Abstract</title> Background Novel treatments are needed for epithelial ovarian cancer, the most lethal gynecologic malignancy due to late diagnosis, resistance to treatment, and high relapse rate. Immunotherapies such as checkpoint inhibitors (i.e, anti-PD-1) and peptide-based therapies (DPX-Survivac) have strong potential to improve responses. Magnetic resonance imaging (MRI) can be used to track tumor growth and iron-labelled immune cells longitudinally at the individual level. We studied MRI immune cell tracking in response to the combination of DPX-Survivac, anti-PD-1, and an intermittent low dose of Cyclophosphamide (CPA), which has been shown to suppress cancer growth in a preclinical model of ovarian cancer. Methods HHD-DR1 mice were orthotopically implanted with mouse ovarian surface epithelial (MOSE) cancer cells. Myeloid and activated CD8 <sup>+</sup> cells were isolated from disease- and treatment-matched donor mice, labelled with superparamagnetic iron oxide (SPIO) and intravenously injected on 41, 48, and 55 days post-implant with either type of cells. Mice were scanned using MRI approximately 24h after SPIO-labeled cell injections. Results Tumor volumes in the treatment group were significantly lower than in the control group as measured by MRI ( <italic>p</italic> < 0.01). The density of SPIO-labelled myeloid and CD8 <sup>+</sup> T cells in tumors was higher in the treatment group than in the control group. Furthermore, ascitic fluid in treated mice has a significantly higher frequency of CD45 <sup>+</sup> leukocytes. Conclusion Using MRI, our study has shown that this combination treatment can slow down ovarian tumor growth and increase the recruitment of myeloid and CD8 + cells to tumors. This study provides insights into how MRI can be used in concert with biological assays to study how immunotherapy and chemotherapy combinations exert their antitumor effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".