Translational Medicine and Oncologists Meet

Autoimmune diseases (ADs) are a family of complex heterogeneous disorders with similar underlying mechanisms characterized by immune responses against self. The link that might exert reliable control over morbidity, mortality and disabling rates and significantly optimize the cost of treatment for the latter is just Predictive, Preventive and Personalized Medicine (PPPM). And a combination of genomic and proteomic biomarkers are becoming of great significance to predict risks of the chronification. Although a single biomarker is a tangible entity that can be easily understood, aggregate measures comprised of multiple genes are also informative as the combinatorial biomarkers. The simultaneous addition of combinatorial biomarkers of autoimmune disorders to the score index panel substantially improves he risk stratification fordeath and disabling. And there is urgent clinical need to identify predictive combinatorial biomarkers for subclinical PIFAS (post-infectious autoimmune syndrome) to allow preventive/treatment strategies to be instituted early in the disease running. Thus, the need for biomarkers of newer generations, newer algorithms and software applications to support integrative data analysis and to secure the databanks becomes critical to the discovery of linkages and concordance between different data types such as genomics-, proteomics-, immunomics-, metabolomics- and clinically-rooted. PPPM is thus a model of healthcare services being tailored to the individual and dictates a construction of PPPM-based algorithms to diagnose, predict, and to prevent autoimmune conditions in time!