Sepsis in transit: from clinical to molecular classification

In the previous issue of Critical Care, Maslove and colleagues studied circulating neutrophil transcriptional expression to discover and validate a molecular subclassification of adult patients with sepsis. The authors divided patients into small derivation (n = 55) and validation (n = 71) cohorts. Their complex methodology included partitioning around medoid and hierarchical clustering methods to define two transcriptionally distinct subtypes of sepsis. Pathway analysis found that chemokine and cytokine pathways as well as Toll-like receptor signaling were enhanced. Investigation of specific drug target genes relevant to sepsis found significantly different expression levels between the two molecular subtypes. Interestingly, most patient characteristics did not differ between groups, except for an increase in the proportion of severe sepsis in molecular subtype 1. Possible confounders of this study were the small sample size, population stratification, and lack of information regarding drug interventions, all of which support the need for more studies with larger cohorts that include transcriptional profiles. This thought-provoking hypothesis-generating study could lead to a new neutrophil expression-based molecular classification of adult sepsis.

preclinical research, yet randomized controlled trials (RCTs) generally have not demonstrated drug effi cacy in sepsis. In the search for a novel approach that parallels very successful discoveries in cancer, subclassifi cation of septic patients on the basis of molecular signatures combined with relevant clinical features is a promising strategy in sepsis.
In the previous issue of Critical Care, Maslove and colleagues [1] combined neutrophil gene expression micro array data of septic patients from two previous prospective studies of critically ill and septic patients [2,3] and then divided patients randomly into derivation (n = 55) and validation (n = 71) cohorts. Th e authors' statistical and mathematical methodology is complex. With the derivation cohort, they began using partitioning around medoids (PAM) clustering based on Euclidean distance on all data to create two clusters, from which they derived a list of diff erentially expressed genes. In parallel, a GenBank search was used to create a list of candidate genes related to sepsis. A series of enrichment steps, including PAM and signifi cance analysis of microarrays, were applied to the cross-reference of the two lists in order to fi nd the most discriminatory genes, the optimal k value, and silhouette value. K is the number of clusters, and silhouette values describe how well defi ned the clusters are. Th ey settled on a k value of 2 and a silhouette value of 0.3, which allowed successful class labeling of the derivation patients.
To increase the robustness of the cluster identifi cation, the original data were also analyzed by using hierarchical clustering based on Manhattan distance, resulting in the same clustering of the patients with clear bimodal distribution after principal component analysis.
Th ese complex methods were then repeated in the validation cohort. Th e authors report a fi nal silhouette width of 0.26, suggesting that the methods applied well to the validation cohort. Satisfi ed with the clusters, Maslove and colleagues used hierarchical clustering with pathway analysis for both derivation and validation cohorts to determine co-expressed genes distinct to the clusters. Perhaps not surprisingly, chemokine and cytokine pathways as well as Toll-like receptor signaling were at

Abstract
In the previous issue of Critical Care, Maslove and colleagues studied circulating neutrophil transcriptional expression to discover and validate a molecular subclassifi cation of adult patients with sepsis. The authors divided patients into small derivation (n = 55) and validation (n = 71) cohorts. Their complex methodology included partitioning around medoid and hierarchical clustering methods to defi ne two transcriptionally distinct subtypes of sepsis. Pathway analysis found that chemokine and cytokine pathways as well as Toll-like receptor signaling were enhanced. Investigation of specifi c drug target genes relevant to sepsis found signifi cantly diff erent expression levels between the two molecular subtypes. Interestingly, most patient characteristics did not diff er between groups, except for an increase in the proportion of severe sepsis in molecular subtype 1. Possible confounders of this study were the small sample size, population stratifi cation, and lack of information regarding drug interventions, all of which support the need for more studies with larger cohorts that include transcriptional profi les. This thoughtprovoking hypothesis-generating study could lead to a new neutrophil expression-based molecular classifi cation of adult sepsis. the top of the pathway analysis. Interestingly, some pathways considered novel in sepsis (cell cycle, cancer (p53), and Parkinson's disease pathways) were also identifi ed.
Of note, there were no signifi cant diff erences in patient characteristics between the two clusters, except for an increased proportion of patients who had severe sepsis in molecular subtype 1. Despite diff erences in severe sepsis rates, the proportions of septic shock and mortality rates were not diff erent between groups, thus highlighting the potential for new classes of patients with sepsis.
Maslove and colleagues also used the Pharmacogenomics Knowledge Base and GeneMania to identify sepsis drug gene targets for drotrecogin alpha (activated protein C), vasopressin, hydrocortisone, and norepinephrine. Th ey found signifi cant diff erences in fold changes of expression of many of these target genes between sepsis subtypes 1 and 2. Th is is encouraging for the design of future clinical trials that could include circulating neutrophil expression to classify patients according to predicted response to drugs (that is, a predictive biomarker).
Th e work of Maslove and colleagues is similar to recent advances in transcriptional subclassifi cation of cancer [4], pediatric sepsis [5], and myocardial dysfunction in septic shock [6]. Overall, the authors found two distinct sepsis subtypes based on a molecular signature that was otherwise unidentifi ed based on classic clinical characteristics.
Points to be considered for improvement in future studies are the lack of information about patient ethnicity and the specifi c interventions the patients received in the study by Maslove and colleagues. Genetic variation alters inter-individual expression of infl ammatory mediators [7][8][9][10][11]; hence, caution should be exercised to reduce the risk of false-positive, spurious associations due to population stratifi cation. We suggest that future studies report ethnicity and relevant patient genotypes and evaluate larger sample sizes to optimize statistical power and clinical external validity. To improve the understanding of drug effi cacy and safety, more information is needed regarding drug treatments because, for example, glucocorticoids alter expression of many targets of infl am matory pathways [12,13], yet data on glucocorticoid treatment of patients were not included in the analyses by Maslove and colleagues. Indeed, stratifi cation by drug treatment to examine the interaction of the gene expression profi les of the relevant pharmacogenomic genes and response to these drugs would be very interesting and could explain the low signal-to-noise ratio in many RCTs in sepsis.
In summary, Maslove and colleagues draw attention to the existence of transcriptionally based clusters of patients which could lead to a very useful novel approach to clinical trial design and ultimately treatment of sepsis [14,15]. Th e lack of predictive biomarkers in previous clinical trials may indeed be contributing to their limited success [15]. Replication of fi ndings such as those of Maslove and colleagues in larger cohorts with genotypic and pharmacologic intervention data is imperative to further the fi eld and perhaps increase our ability to discover and validate eff ective treatments for sepsis.

Authors' contributions
SAT executed the literature review, contributed to the critical analysis, and composed the manuscript. JAR developed the initial critical analysis and edited the manuscript.

Competing interests
SAT declares that she has no competing interests. JAR holds stock in and is on the Board (June 2012 to the present) of Sirius Genomics Inc. (Vancouver, BC, Canada), which has submitted patents that are owned by the University of British Columbia (UBC) and licensed to Sirius Genomics Inc. and that are related to the genetics of sepsis and its treatment; UBC has also submitted a patent related to the use of vasopressin in septic shock. He is an inventor on these patents. He has received consulting fees from Ferring Pharmaceuticals (Parsippany, NJ, USA), which manufactures vasopressin and is developing selepressin; from AstraZeneca (London, UK), which is developing anti-tumor necrosis factor-alpha; from BioCritica (Indianapolis, IN, USA), which used to sell activated protein C in the US; from MedImmune (Gaithersburg, MD, USA); from Grifols (Barcelona, Spain), which sells albumin; and from Sirius Genomics Inc. He has received grant support from Sirius Genomics, Ferring Pharmaceuticals, AstraZeneca, and Eli Lilly and Company (Indianapolis, IN, USA) that is provided to and administered by UBC. He has received speaking honoraria from Pfi zer Inc (New York, NY, USA) and Eli Lilly and Company.