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.