The IL‐1/IL‐1R axis induces greater fibroblast‐derived chemokine release in human papillomavirus‐negative compared to positive oropharyngeal cancer
Bibliographic record
Abstract
Human papillomavirus (HPV) is now recognised as a major aetiological agent in the pathogenesis of oropharyngeal carcinoma (OPC). HPV-positive tumours are associated with better outcomes compared to HPV-negative tumours, possibly due to differences in their aetiology and/or the tumour microenvironment. Increased numbers of tumour-associated leukocytes have been observed in many cancers including OPC, with variable influence on prognosis depending on the leukocyte subpopulation investigated. Whether HPV status influences leukocyte recruitment to OPC remains unknown. This in-vitro study examined differences in the chemoattractant capacity of HPV-positive and HPV-negative OPC cell lines. Gene and protein expression analysis demonstrated that whilst both monocultures of HPV-positive and HPV-negative cell lines, along with normal tonsillar fibroblasts (NTF), expressed low chemokine levels, NTF cultured with conditioned medium from HPV-negative OPC cells expressed significantly higher levels of all chemokines tested compared to NTF incubated with the medium from HPV-positive OPC cell lines. HPV-negative OPC lines expressed IL-1β mRNA whereas HPV-positive cells did not, and NTF constitutively expressed IL-1R1. Pre-treatment with the IL-R antagonist, anakinra or siRNA to IL-1R1 significantly reduced chemokine secretion from NTF stimulated with conditioned medium from HPV-negative tumour cells or recombinant IL-1β (p < 0.05). These data suggest that secretion of chemokines is driven by the interaction between HPV-negative OPC cells and stromal fibroblasts through an IL-1/IL-1R-mediated mechanism that is less prominent within the HPV-positive tumour microenvironment. These observations may explain differences in leukocyte sub-populations recruited to HPV-positive versus negative OPC and indicate that HPV status is a key determinant in controlling the inflammatory tumour microenvironment.
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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) | 0.003 | 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".