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Open Access Highly Accessed Research

Identification of sepsis subtypes in critically ill adults using gene expression profiling

David M Maslove12*, Benjamin M Tang34 and Anthony S McLean3

Author Affiliations

1 Center for Clinical Informatics, Stanford University School of Medicine, Stanford, CA, USA

2 Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA, USA

3 Department of Intensive Care Medicine, Nepean Hospital and Nepean Clinical School, University of Sydney, Penrith, NSW 2750, Australia

4 School of Public Health, Faculty of Medicine, University of Sydney, NSW 2006, Australia

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Critical Care 2012, 16:R183  doi:10.1186/cc11667


See related commentary by Thair and Russell, http://ccforum.com/content/16/6/173

Published: 4 October 2012

Abstract

Introduction

Sepsis 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.

Methods

We 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.

Results

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. There were differences between subtypes in the expression of pharmacogenes related to hydrocortisone, vasopressin, norepinephrine, and drotrecogin alpha.

Conclusions

Sepsis 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.

Keywords:
Sepsis; severe sepsis; septic shock; gene expression profiling; microarray analysis; biomedical informatics; critical care; intensive care