TRANSCRIPTOME ANALYSIS OF QUIESCENT SLE CASES UNCOVERS DYSREGULATED PATHWAYS ASSOCIATED WITH DISEASE FLARES
Notice bibliographique
Résumé
O028 / #834 Topic: AS12 - Genetics, Epigenetics, Transcriptomics Late-Breaking Abstract ABSTRACT CONCURRENT SESSION 04: ADVANCING LUPUS THERAPIES AND INSIGHTS 22-05-2025 1:40 PM - 2:40 PM Background/Purpose Unpredictability is a major challenge in systemic lupus erythematosus (SLE). Routinely used clinical and laboratory parameters fail to predict the risk of and time to flare, or the type of flare that a patient might develop. We hypothesize that molecular biosignatures may better predict flaring and might thus have merit in disease monitoring. Methods Eligible for this analysis were SLE patients from the European multicenter PRECISESADS project ( NCT02890121 ) with available transcriptome data and long-term follow-up including registration of disease flares. The analysis was restricted to patients with quiescent disease at the time of sampling, defined as a clinical SLEDAI-2K score <6. Flare was defined as any increase in disease activity resulting in a change of therapy. For each patients individualized Reactome pathways according to the Functional Analysis of Individual Microarray Expression (FAIME) algorithm, were calculated. Time-dependent analysis for interval-censored data was conducted with parametric models correcting for relevant confounding covariates associated with flares and individual regression weights. Results were deemed significant if they yielded false-discovery rate q value <0.05 and a hazard ratio (HR) >1.5 for causative pathways or <0.667 for protective ones. Results Long-term data were available in 131 patients, including 85 with a clinical SLEDAI-2K <6 at baseline. At the time of blood sampling, those patients had a mean (SD) age of 47.5 (13.9) years and a mean disease duration of 15.7 (9.7) years, and they were mostly women (n=84, 98.8%). The mean clinical SLEDAI-2K was 1.5 (1.5) and the mean total SLEDAI-2K was 3.6 (2.3). At baseline, 59 (69.4%) patients were treated with hydroxychloroquine, 25 (29%) with synthetic immunosuppressants, and 36 (42.3%) with glucocorticoids at a mean daily dose of 2.3 (0.5) mg of a prednisone equivalent. After a mean observation time of 6.9 (2.6) years, flares had occurred in 30 patients (35%). Among those 30 patients, the first flare was developed after a mean time of 3.0 (2.0) years, and the noncumulative count of those flares per domain was 18 articular, 9 cutaneous, 5 constitutional, 4 hematological, 3 vascular, and 2 renal flares. Overall, 1265 Reactome pathways were explored, of those 131 were significant according to the applied selection criteria; 83 and 48 pathways were associated with increased or reduced flaring hazards, respectively (Figure 1). Flaring patients had a reduced capability of repairing damaged DNA (especially pyrimidines), increased DNA damage due to impaired telomere function, an increased activity interferon-related pathways, an increased activity of the complement system, an increased inflammasome, reduced CTLA4 and CD28 inhibitory mechanisms, a disrupted circadian clock as well as several metabolic alterations. Figure 1. Conclusions Our findings reveal that specific pathway deregulations linked to SLE pathogenesis may herald the occurrence of flares in quiescent patients. Impaired DNA repair, increased interferon signaling, complement activation, inflammasome upregulation, and reduced CTLA4/CD28 inhibitory mechanisms suggest a predisposition to immune dysregulation preceding clinical flares. These insights highlight potential molecular predictors of flaring and suggest that targeted immunomodulation or specific interventions may be warranted in selected patients to prevent flares and mitigate the risk of long-term damage accrual, ultimately improving disease monitoring and personalized therapeutic strategies in SLE.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».