Integrated machine learning pipeline for aberrant biomarker enrichment (i-mAB): characterizing clusters of differentiation within a compendium of systemic lupus erythematosus patients

Clusters of differentiation ( ) are cell surface biomarkers that denote key biological differences between cell types and disease state. CD-targeting therapeutic monoclonal antibodies ( ) afford rich trans-disease repositioning opportunities. Within a compendium of systemic lupus erythematous ( ) patients, we applied the Integrated machine learning pipeline for aberrant biomarker enrichment ( ) to profile gene expression features affecting CD20, CD22 and CD30 gene aberrance. First, a novel Relief-based algorithm identified interdependent features(p=681) predicting treatment-naïve SLE patients (balanced accuracy=0.822). We then compiled CD-associated expression profiles using regularized logistic regression and pathway enrichment analyses. On an independent general cell line model system data, we replicated associations ( ) of (p =1.69e-9) and (p =4.63e-8) with CD22; (p =7.00e-4), (p =1.71e-2), and (p =3.34e-2) with CD30; and , a phosphatase linked to bone mineralization, with both CD22(p =4.37e-2) and CD30(p =7.40e-3). Utilizing carefully aggregated secondary data and leveraging hypotheses, i-mAB fostered robust biomarker profiling among interdependent biological features.