Early Quantification of Systemic Inflammatory Proteins Predicts Long-Term Treatment Response to Tofacitinib and Etanercept

The application of machine-learning to longitudinal gene-expression profiles has demonstrated potential to decrease the “assessment gap”, between biochemical determination and clinical manifestation, of a patient’s response to treatment. Although Psoriasis is a proven testing ground for treatment-response prediction using transcriptomic data from clinically accessible skin-biopsies, these biopsies are expensive, invasive and challenging to obtain from certain body areas. Response prediction from blood biochemical measurements could be a cheaper, less invasive predictive platform. Longitudinal profiles for 92 inflammatory and 65 cardiovascular disease proteins were measured from the blood of psoriasis patients at baseline, and 4-weeks, following tofacitinib (JAK-STAT-inhibitor) or etanercept (TNF-inhibitor) treatment, and predictive models were developed by applying machine-learning techniques such as bagging and ensembles. This data-driven approach developed predictive models able to accurately predict the 12-week clinical endpoint for psoriasis following tofacitinib (auROC=78%), or etanercept (auROC=71%) treatment in a validation dataset, revealing a robust predictive protein signature including well-established psoriasis markers such as IL-17A & IL-17C, highlighting potential for biologically meaningful and clinically useful response predictions using blood protein data. Although most blood classifiers were outperformed by simple models trained using PASI scores, performance might be enhanced in future studies by measuring a wider variety of proteins.