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 (
janus kinase-signal transducer and activator of transcription-inhibitor) or
etanercept (
tumor necrosis factor-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 (area under the receiver operating characteristic curve [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 and
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
Psoriasis Area Severity Index scores, performance might be enhanced in future studies by measuring a wider variety of
proteins.