Genetic Expression Method Allows Doctors to Rapidly Identify Subclasses of Septic Shock

For 20 years, scientific research into septic shock has tried to determine how best to identify, diagnose and treat the potentially life-threatening infection, which can quickly overwhelm the body’s immune system. But researchers have been limited by the disease’s non-specific spectrum of symptoms and treatment results that vary from patient to patient.

The editors of the American Journal of Respiratory and Critical Care Medicine describe a finding by Hector Wong, MD, director of the Division of Critical Care Medicine, as a new approach that “might help shift this impasse for children with septic shock.”

Published Feb. 1, 2015, the study reports success at identifying subclasses of septic shock in individual patients based on gene expression patterns linked to their immune system responses and glucocorticoid receptor signaling. The RNA-quantifying gene expression method also has the potential to rapidly generate clinical data, possibly within eight to 10 hours. This could become a valuable advantage for a disease that can progress from diagnosis to death in a matter of days or hours. Septic shock has a mortality rate of 40 to 60 percent in adults and 25 percent in children.

Knowing a patient’s specific disease subclass for septic shock can potentially aid therapeutic decisions. Corticosteroids – a standard protocol for septic shock treatment that works through the glucocorticoid receptor – can be life saving for many patients. However, this study shows that steroids are associated with a four-fold increase in mortality within one subclass of septic shock patients. Having a gene-based classification method for septic shock patients will help doctors quickly identify which patients should not receive steroid therapy, Wong says.

Information from gene expression also holds hope of more personalized medicine approaches for treating septic shock. Doctors may one day be able to use the patient’s own adaptive immune responses to treat the disease, or to link symptom-specific drugs to the patient’s symptom-based subclass in hopes of a greater chance of survival, Wong says.

These composite gene expression mosaics show the mean expression values for 100 subclass-defining genes based on NanoString-derived expression data. Red intensity correlates with increased gene expression, and blue intensity correlates with decreased gene expression. Examples 1 and 2 were allocated to subclass A; examples 3 and 4 were allocated to subclass B. Compared to subclass B, those in subclass A had a higher mortality rate and a more complicated course, including higher median PRISM scores, lower total white blood cell and absolute neutrophil counts, and higher absolute lymphocyte counts.
Click on image to view caption.

Citation

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. Developing a clinically feasible personalized medicine approach to pediatric septic shock. Am J Respir Crit Care Med. 2015;191(3):309-315.

Lead Researcher:

A photo of Hector Wong, MD. 
Hector Wong, MD