Spondyloarthritis & psoriatic arthritis
Predicting clinical response in psoriatic arthritis through integrative analysis of transcriptomics and proteomics
Authors' conclusion
Integrated baseline gene and protein expression profiles combined with clinical variables can predict treatment response and identify differential treatment effects between individual patients.
Study context
- Population
- Psoriatic arthritis (PsA) patients in the development cohort of the TOFA-PREDICT trial; 40 DMARD-naïve and 40 who failed DMARD treatmentdevelopment cohort
- Participants
- 80 patients
- Comparison
- Tofacitinib vs methotrexate or add-on tofacitinib vs etanercept
- Primary endpoint
- Treatment response: reaching minimal disease activity; prediction by baseline CD4+ T-cell transcriptomic and proteomic markers with clinical variables (AUC-ROC)
- Follow-up
- 16 weeks
Methodological detailsfour-arm, prediction model development, cross-validated, XGBoost and sPLS-DA feature selection
- Four-arm
- Prediction model development
- Cross-validated
- XGBoost and sPLS-DA feature selection
Primary result
As reported in the abstract
Prediction of treatment response, integrated multi-omics model with treatment-predictor interactions (highest performance)
AUC (mean ± SD)
0.70 ± 0.19
Variation (SD) in treatment-effects in patients
15.2% ± 14.8%
Predictors selected: transcriptomic, proteomic, clinical
18, 10, 2
Further findings
Treatment response (MDA), all patients · 16 weeks
- Patients responding to treatment
- 50%
Interconnection of selected proteins (related to immune system processes)
- Selected proteins significantly interconnected
- P = 3.41E-5
Original publication
Predicting clinical response in psoriatic arthritis through integrative analysis of transcriptomics and proteomics. Arthritis Res Ther. Published 12 March 2026. doi:10.
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