Polishing the crystal ball: mining multi-omics data in dermatomyositis

Precision medicine, which recognizes and upholds the uniqueness of each individual patient and the importance of discerning these inter-individual differences on a molecular scale in order to provide truly personalized medical care, is a revolutionary approach that relies on the discovery of clinically-relevant biomarkers derived from the massive amounts of data generated by epigenomic, genomic, transcriptomic, proteomic, microbiomic, and metabolomic studies, collectively known as multi-omics. If harnessed and mined appropriately with the help of ever-evolving computational and analytic methods, the collective data from omics studies has the potential to accelerate delivery of targeted medical treatment that maximizes benefit, minimizes harm, and eliminates the "fortune-telling" inextricably linked to the prevailing trialand-error approach. For a disease such as dermatomyositis (DM), which is characterized by remarkable phenotypic heterogeneity and varying degrees of multi-organ involvement, an individualized approach that incorporates big data derived from multi-omics studies with the results of currently available serologic, histopathologic, radiologic, and electrophysiologic tests, and, most importantly, with clinical findings obtained from a thorough history and physical examination, has immense diagnostic, therapeutic, and prognostic value. In this review, we discuss omics-based research studies in DM and describe their practical applications and promising roles in guiding clinical decisions and optimizing patient outcomes.