  Clusters of differentiation (CD) are cell surface biomarkers that denote key
biological differences between cell types and disease state. CD-targeting
therapeutic monoclonal antibodies (mAB) afford rich trans-disease repositioning
opportunities. Within a compendium of systemic lupus erythematous (SLE)
patients, we applied the Integrated machine learning pipeline for aberrant
biomarker enrichment (i-mAB) to profile de novo gene expression features
affecting CD20, CD22 and CD30 gene aberrance. First, a novel relief-based
algorithm identified interdependent features(p=681) predicting
treatment-na\"ive 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 (in silico) of BCL7A(padj=1.69e-9) and
STRBP(padj=4.63e-8) with CD22; NCOA2(padj=7.00e-4), ATN1(padj=1.71e-2), and
HOXC4(padj=3.34e-2) with CD30; and PHOSPHO1, a phosphatase linked to bone
mineralization, with both CD22(padj=4.37e-2) and CD30(padj=7.40e-3). Utilizing
carefully aggregated secondary data and leveraging a priori hypotheses, i-mAB
fostered robust biomarker profiling among interdependent biological features.
