Liposomal Doxorubicin, but Not Platinum-Taxane, Supports MHC-II Expression and Immune Maturation in the Ovarian Tumor Microenvironment
Bibliographic record
Abstract
BACKGROUND: Ovarian cancer is an immunologically cold tumor that is treated with surgery and a chemotherapy regimen of platinum agents with taxanes. Paradoxically, elevated levels of several immune markers are effective at predicting prognosis for patients with ovarian cancer, though it is not clear how chemotherapy might influence this. Chemotherapy elicits immunogenic cell death, yet tumor-controlling doses of chemotherapy are also immunotoxic. OBJECTIVES: To evaluate interactions of chemotherapy with the immune system, we studied the impact of chemotherapy in an aggressive mouse model of ovarian cancer developed within our lab. METHODS: Using a single-cell transcriptomics sequencing approach, supported by flow cytometry, we evaluated the influence of a first-line therapy, cisplatin and docetaxel, and a second-line therapy, pegylated liposomal doxorubicin (PLD), on control of tumor growth and on tumor-associated immune populations of cells. RESULTS: Both chemotherapy approaches were effective at controlling tumor growth and selectively depleted tumor cells from distinct transcriptional clusters. Both chemotherapies also resulted in relative increases in immune populations compared to untreated tumor-bearing mice, but immune populations from PLD-treated mice were more abundant and expressed a greater fraction of maturity-associated transcripts and increased proportions of tumor resident macrophage populations. PLD treatment selectively upregulated MHC class II on tumor cells, and this could be replicated in vitro across ovarian cancer cell lines and in patient tumor cells ex vivo. CONCLUSIONS: Altogether, the results support the notion that PLD has a greater capacity for immunopotentiation, which may be important to consider if immunotherapy approaches are adapted for ovarian tumors in the future.
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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 | 0.000 |
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".