Insights Into a "Negative" ICU Trial Derived From Gene Expression Profiling.

Department of Critical Care Medicine, Queen's University, Kingston, ON, Canada. Department of Medicine, Queen's University, Kingston, ON, Canada. Critical Illness and Injury Research Centre, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada. Department of Surgery, University of Toronto, Toronto, ON, Canada. Kingston Health Sciences Center, Queen's University, Kingston, ON, Canada.

Critical care medicine. 2019;(12):e941-e947

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Abstract

OBJECTIVES Randomized controlled trials in the ICU often fail to show differences in endpoints between groups. We sought to explore reasons for this at a molecular level by analyzing transcriptomic data from a recent negative trial. Our objectives were to determine if randomization successfully balanced transcriptomic features between groups, to assess transcriptomic heterogeneity among the study subjects included, and to determine if the study drug had any effect at the gene expression level. DESIGN Bioinformatics analysis of transcriptomic and clinical data collected in the course of a randomized controlled trial. SETTING Tertiary academic mixed medical-surgical ICU. PATIENTS Adult, critically ill patients expected to require invasive mechanical ventilation more than 48 hours. INTERVENTIONS Lactoferrin or placebo delivered enterally and via an oral swab for up to 28 days. MEASUREMENTS AND MAIN RESULTS We found no major imbalances in transcriptomic features between groups. Unsupervised analysis did not reveal distinct clusters among patients at the time of enrollment. There were marked differences in gene expression between early and later time points. Patients in the lactoferrin group showed changes in the expression of genes associated with immune pathways known to be associated with lactoferrin. CONCLUSIONS In this clinical trial, transcriptomic data provided a useful complement to clinical data, suggesting that the reasons for the negative result were less likely related to the biological efficacy of the study drug, and may instead have been related to poor sensitivity of the clinical outcomes. In larger studies, transcriptomics may also prove useful in predicting response to treatment.

Methodological quality

Publication Type : Randomized Controlled Trial

Metadata

MeSH terms : Gene Expression