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. Since the time the work was completed, Dr. Sweeney's affiliation has changed to Inflammatix. The views expressed are those of the authors and do not reflect the official policy or position of the Department of Veterans Affairs, the Department of Defense, or the U.S. Government. Drs. Sweeney and Khatri contributed to study conception and design; Drs. Bermejo-Martin, Almansa, Tamayo, Howrylak, Choi, Parnell, Tang, Nichols, Woods, Ginsburg, Kingsmore, Wong, Tsalik, and Langley contributed materials; Drs. Sweeney, Azad, Donato, Haynes, Perumal, Henao, Omberg, Mangravite, Wong, and Khatri contributed methods; Dr. Sweeney performed the analyses; Dr. Sweeney drafted the article; all authors critically revised the article. Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's website (http://ift.tt/29S62lw). Supported, in part, by Defense Advanced Research Projects Agency and the Army Research Office through Grant W911NF-15-1-0107. The CAPSOD study was supported, in part, by the National Institutes of Health grants U01AI066569, P20RR016480, HHSN266200400064C. The views expressed are those of the authors and do not reflect the official policy or position of the Department of Veterans Affairs, the Department of Defense, or the U.S. Government. Drs. Sweeney, Donato, Howrylak, Choi, Nichols, Kingsmore, Wong, Langley, and Khatri received support for article research from the National Institutes of Health (NIH). Drs. Bermejo-Martin, Almansa, Tamayo, and Khatri were supported by Instituto de Salud Carlos III (grants EMER07/050, PI13/02110, PI16/01156). Dr. Wong was supported by National Institute of General Medical Sciences grants R01GM099773 and R01GM108025. Drs. Sweeney, Nichols, and Khatri received support from the Bill & Melinda Gates Foundation. Drs. Sweeney and Khatri are cofounders of Inflammatix, which has a commercial interest in sepsis diagnostics, but played no role in this study. Drs. Donato's and Wongs' institutions received funding from the NIH. Drs. Perumal, Mangravite, and Langley received support for article research from Defense Advanced Research Projects Agency (DARPA). Dr. Henao received funding from OncocellMDx and InfiniaML. Dr. Nichols also received support for article research from the Defense Advanced Research Projects Agency Army Research Office. Dr. Howrylak also received support for article research from the National Center for Advancing Translational Sciences (UL1 TR000127 and KL2 TR000126). Dr. Ginsburg received other support as a founder of Host Response. Drs. Omberg's and Mangravite's institutions received funding from DARPA. Dr. Tsalik's institution received funding from Novartis Vaccines and Diagnostics, and he disclosed other support (unrelated to this work) from grants from the NIH, DARPA, Defense Threat Reduction Agency, and the Henry M. Jackson Foundation; research support from bioMerieux and BioFire Diagnostics; consulting for Immunexpress, and bioMerieux; employment by Duke University and the Durham VA Health Care System; patents (or patents pending) for the Molecular Classification of Bacterial Infection, and Methods to Diagnose and Treat Acute Respiratory Infections; and equity in Host Response. Dr. Langley was supported by the National Center for Advancing Translational Sciences of the NIH under award number UL1TR001417. Dr. Khatri's institution received funding from the NIH and the Bill & Melinda Gates Foundation and he received funding from Inflammatix and he is supported by grants from the National Institute for Allergy and Infectious Diseases (grants 1U19AI109662, U19AI057229 and U54I117925). The 33-gene set has been disclosed to the Stanford Office of Technology Licensing for possible patent protection. The remaining authors have disclosed that they do not have any potential conflicts of interest. For information regarding this article, E-mail: tes17@alumni.stanford.edu; pkhatri@stanford.edu Copyright © by 2018 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.

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