Validation of a gene expression-based subclassification strategy for pediatric septic shock

Abstract

Objective: Septic shock heterogeneity has important implications for clinical trial implementation and patient management. We previously addressed this heterogeneity by identifying three putative subclasses of children with septic shock based exclusively on a 100-gene expression signature. Here we attempted to prospectively validate the existence of these gene expression-based subclasses in a validation cohort.

Design: Prospective observational study involving microarray-based bioinformatics.

Setting: Multiple pediatric intensive care units in the United States.

Patients: Separate derivation (n = 98) and validation (n = 82) cohorts of children with septic shock.

Interventions: None other than standard care.

Measurements and main results: Gene expression mosaics of the 100 class-defining genes were generated for 82 individual patients in the validation cohort. Using computer-based image analysis, patients were classified into one of three subclasses ("A," "B," or "C") based on color and pattern similarity relative to reference mosaics generated from the original derivation cohort. After subclassification, the clinical database was mined for phenotyping. Subclass A patients had higher illness severity relative to subclasses B and C as measured by maximal organ failure, fewer intensive care unit-free days, and a higher Pediatric Risk of Mortality score. Patients in subclass A were characterized by repression of genes corresponding to adaptive immunity and glucocorticoid receptor signaling. Separate subclass assignments were conducted by 21 individual clinicians using visual inspection. The consensus classification of the clinicians had modest agreement with the computer algorithm.

Conclusions: We have validated the existence of subclasses of children with septic shock based on a biologically relevant, 100-gene expression signature. The subclasses have relevant clinical differences.

Figure 1

GEDI-generated reference mosaics (top panel) and examples of GEDI-generated individual patient mosaics from the validation cohort (bottom panel). The reference mosaics are derived from the previously published derivation cohort (Refs. and 7) and represent the mean expression values for patients in the respective subclasses. Both the reference mosaics and the individual validation cohort examples depict the expression levels of the same 100 class-defining genes. The color bar on the right provides the relative gene expression based on the respective color intensities.

Figure 2

The distribution of potential majority calls (maximum = 21; minimum = 8) among the 21 clinical evaluators. These majority calls led to the final consensus classification for the 82 patients in the validation cohort.

Figure 3

The individual expression mosaics of the four patients in the validation cohort that received < 11 majority calls by the clinical evaluators. Patient 1 (non-survivor) and Patient 2 (survivor) were allocated to subclass C by the computer algorithm. Patient 3 (non-survivor) and Patient 4 (survivor) were allocated to subclass A by the computer algorithm.