Abstract

IntroductionSepsis is a syndromic illness that has traditionally been defined by a set of broad, highly sensitive clinical parameters. As a result, numerous distinct pathophysiologic states may meet diagnostic criteria for sepsis, leading to syndrome heterogeneity. The existence of biologically distinct sepsis subtypes may in part explain the lack of actionable evidence from clinical trials of sepsis therapies. We used microarray-based gene expression data from adult patients with sepsis in order to identify molecularly distinct sepsis subtypes.MethodsWe used partitioning around medoids (PAM) and hierarchical clustering of gene expression profiles from neutrophils taken from a cohort of septic patients in order to identify distinct subtypes. Using the medoids learned from this cohort, we then clustered a second independent cohort of septic patients, and used the resulting class labels to evaluate differences in clinical parameters, as well as the expression of relevant pharmacogenes.ResultsWe identified two sepsis subtypes based on gene expression patterns. Subtype 1 was characterized by increased expression of genes involved in inflammatory and Toll receptor mediated signaling pathways, as well as a higher prevalence of severe sepsis. There were differences between subtypes in the expression of pharmacogenes related to hydrocortisone, vasopressin, norepinephrine, and drotrecogin alpha.ConclusionsSepsis subtypes can be identified based on different gene expression patterns. These patterns may generate hypotheses about the underlying pathophysiology of sepsis and suggest new ways of classifying septic patients both in clinical practice, and in the design of clinical trials.

Highlights

  • Sepsis is a syndromic illness that has traditionally been defined by a set of broad, highly sensitive clinical parameters

  • We identified two sepsis subtypes based on gene expression patterns

  • Subtype 1 was characterized by increased expression of genes involved in inflammatory and Toll receptor mediated signaling pathways, as well as a higher prevalence of severe sepsis

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Summary

Introduction

Sepsis is a syndromic illness that has traditionally been defined by a set of broad, highly sensitive clinical parameters. The protean illnesses of the ICU are syndromic in nature, defined by a number of clinical, laboratory and radiologic criteria, rather than specific pathologic findings Examples of this include acute respiratory distress syndrome (ARDS), acute kidney injury (AKI) and sepsis. In pediatric patients, unsupervised clustering methods have been used to identify sepsis subtypes based on gene expression profiles from whole blood, and have been shown to correlate with outcomes [5,6,7]. No such analysis, has been applied to adult cases. We present an analysis of gene expression profiles from adult patients with sepsis, in which subtypes are identified using bioinformatics techniques

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