Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data - Rivas, Ariel L.; Hoogesteijn, Almira L.; Antoniades, Athos; Tomazou, Marios; Buranda, Tione; Perkins, Douglas J.; Fair, Jeanne M.; Durvasula, Ravi; Fasina, Folorunso O.; Tegos, George P.; van Regenmortel, Marc H. V. (Frontiers in Immunology, 2019)
Investigating disease pathogenesis and personalized prognostics are critical biomedical priorities. Because patients sharing the same diagnosis can experience different outcomes, such as survival or death, physicians need to prognosticate specific outcomes, at personalized bases. Similarly, the rapid differentiation of several inflammatory phases matters in several syndromes. To address these topics, a pattern recognition-based method (PRM) that follows an inverse problem approach was designed to assess, in less than 10 minutes, eight concepts: synergy, pleiotropy, complexity, dynamics, ambiguity, circularity, personalized outcomes, and explanatory prognostics (pathogenesis). By creating thousands of secondary combinations derived from blood leukocyte data, the PRM measures synergic, pleiotropic, complex and dynamic data interactions, which provide personalized prognostics while some undesirable features –such as the ambiguity associated with data circularity and false results– are prevented. Here, this method is compared to Principal Component Analysis (PCA) and evaluated with data collected from hantavirus-infected humans and birds that appeared to be healthy. When human data were examined, the PRM predicted 96.9 % of all surviving patients while PCA did not distinguish outcomes. Demonstrating applications in personalized prognosis, eight PRM data structures sufficed to identify all but one of the survivors. Dynamic data patterns also distinguished survivors from non-survivors, as well as one subset of non-survivors, which exhibited chronic inflammation. When the PRM explored avian data, it differentiated immune profiles consistent with no, early, or late inflammation. Yet, PCA did not recognize patterns in avian data. Findings support the notion that immune responses, while variable, are rather deterministic: a low number of complex and dynamic data combinations may be enough to, rapidly, unmask conditions that are neither directly observable nor reliably forecasted.