Developing a Clinically Feasible Personalized Medicine Approach to Pediatric Septic Shock

• Published in: American journal of respiratory and critical care medicine Vol. 191; no. 3; pp. 309 - 315
• Main Authors: Wong, Hector R., Cvijanovich, Natalie Z., Anas, Nick, Allen, Geoffrey L., Thomas, Neal J., Bigham, Michael T., Weiss, Scott L., Fitzgerald, Julie, Checchia, Paul A., Meyer, Keith, Shanley, Thomas P., Quasney, Michael, Hall, Mark, Gedeit, Rainer, Freishtat, Robert J., Nowak, Jeffrey, Shekhar, Raj S., Gertz, Shira, Dawson, Emily, Howard, Kelli, Harmon, Kelli, Beckman, Eileen, Frank, Erin, Lindsell, Christopher J.
• Format: Journal Article
• Language: English
• Published: United States American Thoracic Society 01.02.2015
• Subjects: Child; Child, Preschool; Feasibility Studies; Female; Gene Expression Regulation; Glucocorticoids - therapeutic use; Humans; Infant; Intensive Care Units, Pediatric; Male; Mathematical Computing; Odds Ratio; Original; Phenotype; Precision Medicine; Prospective Studies; Reproducibility of Results; Severity of Illness Index; Shock, Septic - diagnosis; Shock, Septic - genetics; Shock, Septic - mortality; Shock, Septic - therapy; Signal Transduction - genetics; United States > US; glucocorticoids; subclassification; adaptive immunity; gene expression; sepsis
• DOI: 10.1164/rccm.201410-1864OC
• ISSN: 1073-449X; 1535-4970

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. To develop and validate a real-time subclassification method for septic shock. Gene expression data for the 100 subclass-defining genes were generated using a multiplex messenger RNA 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 was calculated to reduce the gene expression patterns to a single metric. The method was tested prospectively in a separate cohort (n = 132). The NanoString-based data reproduced two septic shock subclasses. As previously, one subclass had decreased expression of the subclass-defining genes. The gene expression score identified this subclass with an area under the curve of 0.98 (95% confidence interval [CI95] = 0.96-0.99). Prospective testing of the subclassification method corroborated these findings. Allocation to this subclass was independently associated with mortality (odds ratio = 2.7; CI95 = 1.2-6.0; P = 0.016), and adjunctive corticosteroids prescribed at physician discretion were independently associated with mortality in this subclass (odds ratio = 4.1; CI95 = 1.4-12.0; P = 0.011). 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.