The pro-tumoral and anti-tumoral roles of EphA4 on T regulatory cells and tumor associated macrophages during HNSCC tumor progression
Bibliographic record
Abstract
Head and Neck Squamous Cell Carcinoma (HNSCC) is a deadly cancer with poor response to targeted therapy, largely driven by an immunosuppressive tumor microenvironment (TME). Here we examine the immune-modulatory role of the receptor tyrosine kinase EphA4 in HNSCC progression. Within the TME, EphA4 is primarily expressed on regulatory T cells (Tregs) and macrophages. In contrast ephrinB2, an activating ligand of EphA4, is expressed in tumor blood vessels. Using genetically engineered mouse models, we show that EphA4 expressed in Tregs promotes tumor growth, whereas EphA4 expressed in monocytes inhibits tumor growth. In contrast, ephrinB2 knockout in blood vessels reduces both intratumoral Tregs and macrophages. A novel specific EphA4 inhibitor, APY-d3-PEG4, reverses the accelerated tumor growth we had previously reported with EphB4 cancer cell knockout. EphA4 knockout in macrophages not only enhanced their differentiation into M2 macrophage but also increased Treg suppressive activity. APY-d3-PEG4 reversed the accelerated growth seen in the EphA4 knockout of monocytes but conferred no additional benefit when EphA4 was knocked out on Tregs. Underscoring an EphA4-mediated interplay between Tregs and macrophages, we found that knockout of EphA4 in Tregs not only decreases their activation but also reduces tumor infiltration of pro-tumoral M2 macrophages. These data identify Tregs as a primary target of APY-d3-PEG4 and suggest a role for Tregs in regulating macrophage conversion. These data also support the possible anti-cancer therapeutic value of bispecific peptides or antibodies capable of promoting EphA4 blockade in Tregs but not macrophages. Significance: EphA4 in regulatory T cells has a pro-tumoral effect while EphA4 in macrophages plays an anti-tumoral role underscoring the necessity of developing biologically rational therapeutics.
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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".