Developing a clinically feasible personalized medicine approach to pediatric septic shock

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.

Objectives: To 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 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).

Measurements and main results: 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).

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.

Keywords: adaptive immunity; gene expression; glucocorticoids; sepsis; subclassification.

Figure 1.

Composite gene expression mosaics for the 100 class-defining genes based on previous microarray data. The composite mosaics represent the mean expression values of the 100 subclass-defining genes within each subclass. Red intensity correlates with increased gene expression, and blue intensity correlates with decreased gene expression. The composite mosaics were used as a reference to classify subjects based on NanoString-generated data for the 100 subclass-defining genes. Classification was performed using computer-assisted image analysis.

Figure 2.

(A) Composite gene expression mosaics for the 100 subclass-defining genes based on NanoString-derived expression data. The composite mosaics represent the mean expression values of the 100 subclass-defining genes within each subclass. Red intensity correlates with increased gene expression, and blue intensity correlates with decreased gene expression. (B) Examples of individual patient gene expression mosaics for subjects in the test cohort based on NanoString-derived expression data. The individual patient gene expression mosaics were compared with the reference composite mosaics (A) to prospectively allocate the test cohort subjects into subclass A or B. Image comparisons were performed using computer-assisted image analysis. Examples 1 and 2 were allocated to subclass A, whereas examples 3 and 4 were allocated to subclass B.

Figure 3.

(A) Box-and-whisker plots depicting the gene expression score (GES) values for subjects in subclasses A and B. (B) Receiver operating characteristic curve demonstrating the performance of the GES for distinguishing subclass A from subclass B. Classifications based on the gene expression mosaics (NanoString-based data) were used as the gold standard classification.