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

TLDR

Further study could potentially enable a precision medicine approach of matching novel immunomodulatory therapies with septic patients most likely to benefit. The study aimed to find and validate generalizable sepsis subtypes using data‑driven clustering. We pooled 14 bacterial sepsis transcriptomic datasets from eight countries and validated the subtypes in nine independent datasets from five countries. The Adaptive subtype is linked to lower severity and mortality, the Coagulopathic subtype to higher mortality and coagulopathy, and the three subtypes correspond with independent cohorts, supporting a unifying framework for sepsis heterogeneity.

Abstract

To find and validate generalizable sepsis subtypes using data-driven clustering.We used advanced informatics techniques to pool data from 14 bacterial sepsis transcriptomic datasets from eight different countries (n = 700).Retrospective analysis.Persons admitted to the hospital with bacterial sepsis.None.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.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.