Developing a Clinically Feasible Personalized Medicine Approach to Pediatric Septic Shock.
Am J Respir Crit Care Med. 2014 Dec 9;
Authors: Wong HR, Cvijanovich NZ, Anas N, Allen GL, Thomas NJ, Bigham MT, Weiss SL, Fitzgerald J, Checchia PA, Meyer K, Shanley TP, Quasney M, Hall M, Gedeit R, Freishtat RJ, Nowak J, Shekhar RS, Gertz S, Dawson E, Howard K, Harmon K, Beckman E, Frank E, Lindsell CJ
Abstract
Rationale: Using microarray data, we previously identified gene expression-based subclasses of septic shock with important phenotypic differences. The subclass-defining genes correspond to adaptive immunity and glucocorticoid receptor signaling. Identifying the subclasses in real time has theranostic implications given the potential for immune-enhancing therapies and controversies surrounding adjunctive corticosteroids for septic shock. Objective: Develop and validate a real time subclassification method for septic shock. Methods: Gene expression data for the 100 subclass-defining genes were generated using a multiplex mRNA quantification platform (NanoString nCounter™) and visualized using gene expression mosaics. Study subjects (n = 168) were allocated to the subclasses using computer-assisted image analysis and microarray-based reference mosaics. A gene expression score (GES) was calculated to reduce the gene expression patterns to a single metric. The method was tested prospectively in a separate cohort (n = 132). Results: The NanoString-based data reproduced two septic shock subclasses. As previously, one subclass had decreased expression of the subclass-defining genes. The GES identified this subclass with an AUC of 0.98 (CI95 0.96 to 0.99). Prospective testing of the subclassification method corroborated these findings. Allocation to this subclass was independently associated with mortality (OR 2.8, CI95 1.3 to 6.3, p = 0.015), and adjunctive corticosteroids prescribed at physician discretion were independently associated with mortality in this subclass (OR 4.1, CI95 1.4 to 12.0, p = 0.011). Conclusions: We developed and tested a gene expression-based classification method for pediatric septic shock that meets the time constraints of the critical care environment, and can potentially inform therapeutic decisions.
PMID: 25489881 [PubMed - as supplied by publisher]