Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters

Objectives: To find and validate generalizable sepsis subtypes using data-driven clustering. Design: We used advanced informatics techniques to pool data from 14 bacterial sepsis transcriptomic datasets from eight different countries (n = 700). Setting: Retrospective analysis. Subjects: Persons admitted to the hospital with bacterial sepsis. Interventions: None. Measurements and Main Results: A unified clustering analysis across 14 discovery datasets revealed three subtypes, which, based on functional analysis, we termed “Inflammopathic, Adaptive, and Coagulopathic.” We then validated these subtypes in nine independent datasets from five different countries (n = 600). In both discovery and validation data, the Adaptive subtype is associated with a lower clinical severity and lower mortality rate, and the Coagulopathic subtype is associated with higher mortality and clinical coagulopathy. Further, these clusters are statistically associated with clusters derived by others in independent single sepsis cohorts. Conclusions: The three sepsis subtypes may represent a unifying framework for understanding the molecular heterogeneity of the sepsis syndrome. Further study could potentially enable a precision medicine approach of matching novel immunomodulatory therapies with septic patients most likely to benefit.

[1]  Purvesh Khatri,et al.  Benchmarking Sepsis Gene Expression Diagnostics Using Public Data* , 2017, Critical care medicine.

[2]  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.

[3]  Yaniv Dotan,et al.  A Novel Host-Proteome Signature for Distinguishing between Acute Bacterial and Viral Infections , 2015, PloS one.

[4]  S. Kingsmore,et al.  Gene Expression-Based Classifiers Identify Staphylococcus aureus Infection in Mice and Humans , 2013, PloS one.

[5]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[6]  Derek C Angus,et al.  Toward Smarter Lumping and Smarter Splitting: Rethinking Strategies for Sepsis and Acute Respiratory Distress Syndrome Clinical Trial Design. , 2016, American journal of respiratory and critical care medicine.

[7]  S. Ryter,et al.  Inflammasome-regulated cytokines are critical mediators of acute lung injury. , 2012, American journal of respiratory and critical care medicine.

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

[9]  E. Xing,et al.  Discovery of the gene signature for acute lung injury in patients with sepsis. , 2009, Physiological genomics.

[10]  Stephen J. Huang,et al.  A distinct influenza infection signature in the blood transcriptome of patients with severe community-acquired pneumonia , 2012, Critical Care.

[11]  David R. Booth,et al.  Identifying Key Regulatory Genes in the Whole Blood of Septic Patients to Monitor Underlying Immune Dysfunctions , 2013, Shock.

[12]  M. Paye,et al.  Early and dynamic changes in gene expression in septic shock patients: a genome-wide approach , 2014, Intensive Care Medicine Experimental.

[13]  Peter Humburg,et al.  Shared and Distinct Aspects of the Sepsis Transcriptomic Response to Fecal Peritonitis and Pneumonia , 2017, American journal of respiratory and critical care medicine.

[14]  K. Famous,et al.  Acute Respiratory Distress Syndrome Subphenotypes Respond Differently to Randomized Fluid Management Strategy , 2016, American journal of respiratory and critical care medicine.

[15]  Michael G. Barnes,et al.  Genome-level expression profiles in pediatric septic shock indicate a role for altered zinc homeostasis in poor outcome. , 2007, Physiological genomics.

[16]  R. Hotchkiss,et al.  Anti-PD-L1 peptide improves survival in sepsis. , 2017, The Journal of surgical research.

[17]  Stephanie J. Reisinger,et al.  An Integrated Clinico-Metabolomic Model Improves Prediction of Death in Sepsis , 2013, Science Translational Medicine.

[18]  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.

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

[20]  J. Eiros,et al.  Transcriptomic correlates of organ failure extent in sepsis. , 2015, The Journal of infection.

[21]  P. Khatri,et al.  Robust classification of bacterial and viral infections via integrated host gene expression diagnostics , 2016, Science Translational Medicine.

[22]  T. van der Poll,et al.  Severe sepsis and septic shock. , 2013, The New England journal of medicine.

[23]  T. Miyakawa,et al.  Genomic responses in mouse models poorly mimic human inflammatory diseases , 2013 .

[24]  Damien Chaussabel,et al.  Genomic transcriptional profiling identifies a candidate blood biomarker signature for the diagnosis of septicemic melioidosis , 2009, Genome Biology.

[25]  R. Bellomo,et al.  The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). , 2016, JAMA.

[26]  Lawrence Carin,et al.  Host gene expression classifiers diagnose acute respiratory illness etiology , 2016, Science Translational Medicine.

[27]  Mike Thomas,et al.  Cluster analysis and clinical asthma phenotypes. , 2008, American journal of respiratory and critical care medicine.

[28]  J. Wetterslev,et al.  Antithrombin III for critically ill patients: a systematic review with meta-analysis and trial sequential analysis , 2016, Intensive Care Medicine.

[29]  Purvesh Khatri,et al.  Integrated, Multi-cohort Analysis Identifies Conserved Transcriptional Signatures across Multiple Respiratory Viruses , 2015, Immunity.

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

[31]  S. Opal,et al.  The Next Generation of Sepsis Clinical Trial Designs: What Is Next After the Demise of Recombinant Human Activated Protein C?* , 2014, Critical care medicine.

[32]  Robert J Freishtat,et al.  BMC Medicine BioMed Central , 2009 .

[33]  Jing Chen,et al.  ToppGene Suite for gene list enrichment analysis and candidate gene prioritization , 2009, Nucleic Acids Res..

[34]  M. Hubank,et al.  Transcriptional Instability during Evolving Sepsis May Limit Biomarker Based Risk Stratification , 2013, PloS one.

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

[36]  J Ean,et al.  Efficacy and safety of recombinant human activated protein C for severe sepsis. , 2001, The New England journal of medicine.

[37]  Olivier Gevaert,et al.  Combined Mapping of Multiple clUsteriNg ALgorithms (COMMUNAL): A Robust Method for Selection of Cluster Number, K , 2015, Scientific Reports.

[38]  C. Planey,et al.  CoINcIDE: A framework for discovery of patient subtypes across multiple datasets , 2016, Genome Medicine.

[39]  Purvesh Khatri,et al.  A community approach to mortality prediction in sepsis via gene expression analysis , 2018, Nature Communications.

[40]  Benjamin Tang,et al.  Development and validation of a novel molecular biomarker diagnostic test for the early detection of sepsis , 2011, Critical care.

[41]  R. Kolamunnage-Dona,et al.  Novel biomarker combination improves the diagnosis of serious bacterial infections in Malawian children , 2012, BMC Medical Genomics.

[42]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[43]  C. Lindsell,et al.  Developing a clinically feasible personalized medicine approach to pediatric septic shock. , 2015, American journal of respiratory and critical care medicine.

[44]  E. Seeley,et al.  Increased expression of neutrophil-related genes in patients with early sepsis-induced ARDS. , 2015, American journal of physiology. Lung cellular and molecular physiology.

[45]  Sue E. Poynter,et al.  Validation of a gene expression-based subclassification strategy for pediatric septic shock* , 2011, Critical care medicine.

[46]  S. Khoo,et al.  Host response transcriptional profiling reveals extracellular components and ABC (ATP-binding cassette) transporters gene enrichment in typhoid fever-infected Nigerian children , 2011, BMC infectious diseases.

[47]  Richard L. Amdur,et al.  Interleukin-1 Receptor Blockade Is Associated With Reduced Mortality in Sepsis Patients With Features of Macrophage Activation Syndrome: Reanalysis of a Prior Phase III Trial* , 2016, Critical care medicine.

[48]  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.