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

Area of Science:

• Genomics and Bioinformatics
• Infectious Diseases
• Precision Medicine

Background:

• Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection.
• Understanding the molecular heterogeneity of sepsis is crucial for developing effective treatments.
• Current classification systems for sepsis may not fully capture its diverse biological profiles.

Purpose of the Study:

• To identify and validate generalizable subtypes of bacterial sepsis using data-driven clustering.
• To establish a unifying framework for understanding sepsis molecular heterogeneity.
• To explore the potential for precision medicine approaches in sepsis treatment.

Main Methods:

• Advanced informatics techniques were employed to pool and analyze transcriptomic data from multiple international bacterial sepsis cohorts.
• A unified clustering analysis was performed on 14 discovery datasets (n=700).
• The identified subtypes were validated in nine independent datasets (n=600).

Main Results:

• Three distinct sepsis subtypes were identified and termed "Inflammopathic, Adaptive, and Coagulopathic."
• The Adaptive subtype was associated with lower clinical severity and mortality.
• The Coagulopathic subtype was associated with higher mortality and clinical coagulopathy, consistent across discovery and validation cohorts.

Conclusions:

• The three identified sepsis subtypes may offer a unifying framework for understanding sepsis molecular heterogeneity.
• These subtypes show statistical associations with clusters from independent sepsis cohorts.
• Further research could enable precision medicine by matching immunomodulatory therapies to specific patient subtypes.