TY - JOUR
T1 - Unsupervised analysis of transcriptomics in bacterial sepsis across multiple datasets reveals three robust clusters
AU - Sweeney, Timothy E.
AU - Azad, Tej D.
AU - Donato, Michele
AU - Haynes, Winston A.
AU - Perumal, Thanneer M.
AU - Henao, Ricardo
AU - Bermejo-Martin, Jesús F.
AU - Almansa, Raquel
AU - Tamayo, Eduardo
AU - Howrylak, Judith A.
AU - Choi, Augustine
AU - Parnell, Grant P.
AU - Tang, Benjamin
AU - Nichols, Marshall
AU - Woods, Christopher W.
AU - Ginsburg, Geoffrey S.
AU - Kingsmore, Stephen F.
AU - Omberg, Larsson
AU - Mangravite, Lara M.
AU - Wong, Hector R.
AU - Tsalik, Ephraim L.
AU - Langley, Raymond J.
AU - Khatri, Purvesh
N1 - Generated from Scopus record by KAUST IRTS on 2023-02-15
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
UR - http://journals.lww.com/00003246-201806000-00011
UR - http://www.scopus.com/inward/record.url?scp=85051071963&partnerID=8YFLogxK
U2 - 10.1097/CCM.0000000000003084
DO - 10.1097/CCM.0000000000003084
M3 - Article
SN - 0090-3493
VL - 46
SP - 915
EP - 925
JO - Critical Care Medicine
JF - Critical Care Medicine
IS - 6
ER -