Abstract PO-084: Deletion of macrophage migration inhibitory factor promotes antitumoral T cell infiltration and inhibits MDSC recruitment to the head and neck cancer microenvironment
Bibliographic record
Abstract
Abstract Head and neck squamous cell carcinoma (HNSCC) is a significant public health concern worldwide. Immunomodulatory targets in the HNSCC tumor microenvironment are crucial to enhance the efficacy of HNSCC immunotherapy. Macrophage migration inhibitory factor (MIF) is a pro-inflammatory cytokine that has been linked to worse prognosis in many cancers, but the mechanistic role of MIF in HNSCC remains unclear. Using a murine orthotopic oral cancer model in Mif+/+ or Mif− / − mice, we determined the function of host derived MIF in HNSCC tumor development, metastasis as well as localized and systemic tumor immune responses. We found that Mif− / − mice have decreased tumor growth and burden compared to their wild-type counterparts. Flow cytometric analysis of immune populations within the primary tumor site revealed increased T cell recruitment to the HNSCC tumor microenvironment, within the tumors of Mif− / − mice. MIF deletion also enhanced the effector function of anti-tumoral Th1 cells and decreased the accumulation of granulocytic MDSC in the tumor microenvironment. Furthermore, we show that isolated MDSC chemotactically respond to MIF in a dose dependent manner which is inhibited when MIF is depleted. Interestingly, MDSCs of tumor bearing Mif− / − mice expressed increased levels of PDL1 compared to Mif+/+ mice. However, Mif− / − and Mif+/+ MDSCs have comparable abilities to suppress T cell proliferation. Herein we describe a chemotactic and immunomodulatory role for MIF in the context of HNSCC. MIF targeted immunomodulation remains a potentially viable cancer therapeutic, however more remains to be understood about the cellular interaction of MIF within the tumor microenvironment before these targeted therapies can be effectively employed Citation Format: Steve Oghumu. Deletion of macrophage migration inhibitory factor promotes antitumoral T cell infiltration and inhibits MDSC recruitment to the head and neck cancer microenvironment [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-084.
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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) | 0.002 | 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".