Classification of patients with sepsis according to blood genomic endotype: a prospective cohort study.

BACKGROUND Host responses during sepsis are highly heterogeneous, which hampers the identification of patients at high risk of mortality and their selection for targeted therapies. In this study, we aimed to identify biologically relevant molecular endotypes in patients with sepsis. METHODS This was a prospective observational cohort study that included consecutive patients admitted for sepsis to two intensive care units (ICUs) in the Netherlands between Jan 1, 2011, and July 20, 2012 (discovery and first validation cohorts) and patients admitted with sepsis due to community-acquired pneumonia to 29 ICUs in the UK (second validation cohort). We generated genome-wide blood gene expression profiles from admission samples and analysed them by unsupervised consensus clustering and machine learning. The primary objective of this study was to establish endotypes for patients with sepsis, and assess the association of these endotypes with clinical traits and survival outcomes. We also established candidate biomarkers for the endotypes to allow identification of patient endotypes in clinical practice. FINDINGS The discovery cohort had 306 patients, the first validation cohort had 216, and the second validation cohort had 265 patients. Four molecular endotypes for sepsis, designated Mars1-4, were identified in the discovery cohort, and were associated with 28-day mortality (log-rank p=0·022). In the discovery cohort, the worst outcome was found for patients classified as having a Mars1 endotype, and at 28 days, 35 (39%) of 90 people with a Mars1 endotype had died (hazard ratio [HR] vs all other endotypes 1·86 [95% CI 1·21-2·86]; p=0·0045), compared with 23 (22%) of 105 people with a Mars2 endotype (HR 0·64 [0·40-1·04]; p=0·061), 16 (23%) of 71 people with a Mars3 endotype (HR 0·71 [0·41-1·22]; p=0·19), and 13 (33%) of 40 patients with a Mars4 endotype (HR 1·13 [0·63-2·04]; p=0·69). Analysis of the net reclassification improvement using a combined clinical and endotype model significantly improved risk prediction to 0·33 (0·09-0·58; p=0·008). A 140-gene expression signature reliably stratified patients with sepsis to the four endotypes in both the first and second validation cohorts. Only Mars1 was consistently significantly associated with 28-day mortality across the cohorts. To facilitate possible clinical use, a biomarker was derived for each endotype; BPGM and TAP2 reliably identified patients with a Mars1 endotype. INTERPRETATION This study provides a method for the molecular classification of patients with sepsis to four different endotypes upon ICU admission. Detection of sepsis endotypes might assist in providing personalised patient management and in selection for trials. FUNDING Center for Translational Molecular Medicine, Netherlands.

[1]  S. Opal,et al.  Sepsis: a roadmap for future research. , 2015, The Lancet. Infectious diseases.

[2]  T. Elliott,et al.  Assembly and function of the two ABC transporter proteins encoded in the human major histocompatibility complex , 1992, Nature.

[3]  O. Ramilo,et al.  Challenges in infant immunity: implications for responses to infection and vaccines , 2011, Nature Immunology.

[4]  T. Sweeney,et al.  Risk Stratification and Prognosis in Sepsis: What Have We Learned from Microarrays? , 2016, Clinics in chest medicine.

[5]  Thomas P. Shanley,et al.  Genomic expression profiling across the pediatric systemic inflammatory response syndrome, sepsis, and septic shock spectrum* , 2009, Critical care medicine.

[6]  Matthew D. Wilkerson,et al.  ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking , 2010, Bioinform..

[7]  Pablo Tamayo,et al.  Metagenes and molecular pattern discovery using matrix factorization , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[8]  M. Pencina,et al.  Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond , 2008, Statistics in medicine.

[9]  Purvesh Khatri,et al.  A comprehensive time-course–based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set , 2015, Science Translational Medicine.

[10]  T. van der Poll,et al.  Interobserver Agreement of Centers for Disease Control and Prevention Criteria for Classifying Infections in Critically Ill Patients* , 2013, Critical care medicine.

[11]  J. Marshall Why have clinical trials in sepsis failed? , 2014, Trends in molecular medicine.

[12]  H. Wong,et al.  Pediatric Sepsis - Part I: "Children are not small adults!" , 2011, The open inflammation journal.

[13]  Anna Rautanen,et al.  Genomic landscape of the individual host response and outcomes in sepsis: a prospective cohort study , 2016, The Lancet. Respiratory medicine.

[14]  Mitchell M. Levy,et al.  2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference , 2003, Intensive Care Medicine.

[15]  Jonathan Cohen,et al.  The International Sepsis Forum Consensus Conference on Definitions of Infection in the Intensive Care Unit , 2005, Critical care medicine.

[16]  U. Ruttimann,et al.  Pediatric risk of mortality (PRISM) score. , 1988, Critical care medicine.

[17]  Anne-Laure Boulesteix,et al.  CMA – a comprehensive Bioconductor package for supervised classification with high dimensional data , 2008, BMC Bioinformatics.

[18]  J M Hughes,et al.  CDC definitions for nosocomial infections, 1988. , 1988, American journal of infection control.

[19]  H. Wong,et al.  Gene expression profiling in sepsis: timing, tissue, and translational considerations. , 2014, Trends in molecular medicine.

[20]  T. van der Poll Future of sepsis therapies , 2016, Critical Care.

[21]  R. Hotchkiss,et al.  Sepsis-induced immunosuppression: from cellular dysfunctions to immunotherapy , 2013, Nature Reviews Immunology.

[22]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[23]  Y. Blouquit,et al.  Isolation, characterization, and structure of a mutant 89 Arg----Cys bisphosphoglycerate mutase. Implication of the active site in the mutation. , 1989, The Journal of biological chemistry.

[24]  Derek C Angus,et al.  The search for effective therapy for sepsis: back to the drawing board? , 2011, JAMA.

[25]  Yurii S. Aulchenko,et al.  PredictABEL: an R package for the assessment of risk prediction models , 2011, European Journal of Epidemiology.

[26]  J. Vincent,et al.  The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure , 1996, Intensive Care Medicine.

[27]  Florian Markowetz,et al.  Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions , 2013, Nature Medicine.

[28]  L. Joosten,et al.  Broad defects in the energy metabolism of leukocytes underlie immunoparalysis in sepsis , 2016, Nature Immunology.

[29]  G. Decavalas,et al.  Severe Sepsis and Septic Shock , 2018 .

[30]  S. Gabriel,et al.  Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. , 2010, Cancer cell.

[31]  F. Markowetz,et al.  The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups , 2012, Nature.

[32]  P. Rousseeuw Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[33]  T. van der Poll,et al.  Incidence, Risk Factors, and Attributable Mortality of Secondary Infections in the Intensive Care Unit After Admission for Sepsis. , 2016, JAMA.

[34]  B. Yangco,et al.  CDC definitions for nosocomial infections. , 1989, American journal of infection control.

[35]  G. Escobar,et al.  Hospital deaths in patients with sepsis from 2 independent cohorts. , 2014, JAMA.

[36]  J. Zimmerman,et al.  Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today’s critically ill patients* , 2006, Critical care medicine.

[37]  J. Cavaillon,et al.  Host response biomarkers in the diagnosis of sepsis: a general overview. , 2015, Methods in molecular biology.

[38]  T. van der Poll,et al.  A Molecular Host Response Assay to Discriminate Between Sepsis and Infection-Negative Systemic Inflammation in Critically Ill Patients: Discovery and Validation in Independent Cohorts , 2015, PLoS medicine.

[39]  D. Maslove,et al.  Identification of sepsis subtypes in critically ill adults using gene expression profiling , 2012, Critical Care.

[40]  I. Amelio,et al.  Serine and glycine metabolism in cancer☆ , 2014, Trends in biochemical sciences.

[41]  Jeffrey S. Morris,et al.  The Consensus Molecular Subtypes of Colorectal Cancer , 2015, Nature Medicine.

[42]  A. Zwinderman,et al.  A molecular biomarker to diagnose community-acquired pneumonia on intensive care unit admission. , 2015, American journal of respiratory and critical care medicine.