Nasal brushing molecular endotyping distinguishes patients with chronic rhinosinusitis with nasal polyps with better response to dupilumab
Bibliographic record
Abstract
BACKGROUND: There is evidence of pathophysiologic diversity in chronic rhinosinusitis with nasal polyps (CRSwNP), but data characterizing the molecular endotypes of CRSwNP and their association with treatment are lacking. OBJECTIVE: This study aimed to identify gene signatures associated with CRSwNP endotypes, clinical features, and dupilumab treatment response. METHODS: Nasal brushing samples were collected from 89 patients randomized to dupilumab 300 mg every 2 weeks or placebo in the SINUS-52 trial (NCT02898454). Microarrays were used to identify transcriptional clusters and assess the relationship between gene expression and baseline clinical features and clinical response to dupilumab. Endotype signatures were determined using differential expression analysis. RESULTS: Two distinct transcriptional clusters (C1 and C2) were identified, both with elevated type 2 biomarkers. At baseline, C2 patients had higher mean Nasal Polyp Score and higher type 2 biomarker levels than C1 patients. At week 24, significant improvements in clinical outcomes (dupilumab vs placebo) were observed in both clusters, although the magnitude of improvements was significantly greater in C2 than in C1, and more C2 patients demonstrated clinically meaningful responses. Gene set enrichment analysis supported the existence of 2 molecular endotypes: C2 was enriched in genes associated with type 2 inflammation (including periostin, cadherin-26, and type 2 cysteine protease inhibitors), while C1 was enriched in genes associated with T cell activation and IL-12 production. CONCLUSIONS: Two distinct gene signatures associated with CRSwNP clinical features were identified; the endotype signatures were associated with clinical outcome measures and magnitude of dupilumab response.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".