Enhanced Type 1 Interferon Signature in Axial Spondyloarthritis Patients Unresponsive to Secukinumab Treatment
Bibliographic record
Abstract
OBJECTIVE: Axial spondyloarthritis (axSpA) is an inflammatory disease in which overactive interleukin (IL)-17A-producing cells are implicated in a central role. Therapeutically, biologics that target IL-17A, such as secukinumab, have demonstrated improved clinical outcomes. Despite this translational success, there is a gap in understanding why some patients with axSpA do not respond to IL-17A-blocking therapy. Our study aims to discriminate immune profiles between secukinumab responders (SEC-R) and nonresponders (SEC-NR). METHODS: Peripheral blood mononuclear cells were collected from 30 patients with axSpA before and 24 weeks after secukinumab treatment. Frequency of CD4+ subsets were compared between SEC-R and SEC-NR using flow cytometry. Mature CD45RO+CD45RA-CD4+ T cells were fluorescent-activated cell sorting sorted, and RNA was measured using NanoString analysis. RESULTS: SEC-NR had an increased frequency of IL-17A-producing RORγt+CD4+ T cells compared to healthy controls before secukinumab treatment (P < 0.01). SEC-NR had a significant increase of CXCR3+ CD4+ T cells before secukinumab treatment compared to SEC-R (P < 0.01). Differentially expressed gene analysis revealed up-regulation of type 1 interferon (IFN)-regulated genes in SEC-NR patients compared to SEC-R patients after receiving the biologic. SEC-R patients had an up-regulated cytotoxic CD4+ T cell gene signature before receiving secukinumab treatment compared to SEC-NR patients. CONCLUSION: The increased frequency of IL-17A-producing cells in SEC-NR patients suggests a larger inflammatory burden than SEC-R patients. With treatment, SEC-NR patients have a more pronounced type 1 IFN signature than SEC-R patients, suggesting a mechanism contributing to this larger inflammatory burden. The results point toward more immune heterogeneity in axSpA than has been recognized and highlights the need for precision therapeutics in this disease.
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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".