Unsupervised analysis of transcriptomics in bacterial sepsis across multiple datasets reveals three robust clusters

Timothy E. Sweeney, Tej D. Azad, Michele Donato, Winston A. Haynes, Thanneer M. Perumal, Ricardo Henao, Jesús F. Bermejo-Martin, Raquel Almansa, Eduardo Tamayo, Judith A. Howrylak, Augustine Choi, Grant P. Parnell, Benjamin Tang, Marshall Nichols, Christopher W. Woods, Geoffrey S. Ginsburg, Stephen F. Kingsmore, Larsson Omberg, Lara M. Mangravite, Hector R. WongEphraim L. Tsalik, Raymond J. Langley, Purvesh Khatri

Research output: Contribution to journalArticle

54 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)915-925
Number of pages11
JournalCritical care medicine
Volume46
Issue number6
DOIs
StatePublished - 2018

All Science Journal Classification (ASJC) codes

  • Critical Care and Intensive Care Medicine

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    Sweeney, T. E., Azad, T. D., Donato, M., Haynes, W. A., Perumal, T. M., Henao, R., Bermejo-Martin, J. F., Almansa, R., Tamayo, E., Howrylak, J. A., Choi, A., Parnell, G. P., Tang, B., Nichols, M., Woods, C. W., Ginsburg, G. S., Kingsmore, S. F., Omberg, L., Mangravite, L. M., ... Khatri, P. (2018). Unsupervised analysis of transcriptomics in bacterial sepsis across multiple datasets reveals three robust clusters. Critical care medicine, 46(6), 915-925. https://doi.org/10.1097/CCM.0000000000003084