Development of an objective gene expression panel as an alternative to self-reported symptom scores in human influenza challenge trials.

Abstract

BACKGROUND:

Influenza challenge trials are important for vaccine efficacy testing. Currently, disease severity is determined by self-reported scores to a list of symptoms which can be highly subjective. A more objective measure would allow for improved data analysis.

METHODS:

Twenty-one volunteers participated in an influenza challenge trial. We calculated the daily sum of scores (DSS) for a list of 16 influenza symptoms. Whole blood collected at baseline and 24, 48, 72 and 96 h post challenge was profiled on Illumina HT12v4 microarrays. Changes in gene expression most strongly correlated with DSS were selected to train a Random Forest model and tested on two independent test sets consisting of 41 individuals profiled on a different microarray platform and 33 volunteers assayed by qRT-PCR.

RESULTS:

1456 probes are significantly associated with DSS at 1% false discovery rate. We selected 19 genes with the largest fold change to train a random forest model. We observed good concordance between predicted and actual scores in the first test set (r = 0.57; RMSE = -16.1%) with the greatest agreement achieved on samples collected approximately 72 h post challenge. Therefore, we assayed samples collected at baseline and 72 h post challenge in the second test set by qRT-PCR and observed good concordance (r = 0.81; RMSE = -36.1%).

CONCLUSIONS:

We developed a 19-gene qRT-PCR panel to predict DSS, validated on two independent datasets. A transcriptomics based panel could provide a more objective measure of symptom scoring in future influenza challenge studies. Trial registration Samples were obtained from a clinical trial with the ClinicalTrials.gov Identifier: NCT02014870, first registered on December 5, 2013.

• Authors: Julius Muller, Eneida Parizotto, Richard Antrobus, James Francis, Campbell Bunce, Amanda Stranks, Marshall Nichols, Micah McClain, Adrian V S Hill, Adaikalavan Ramasamy, Sarah C Gilbert
• Journal: Journal of translational medicine (J Transl Med) Vol. 15 Issue 1 Pg. 134 (06 08 2017) ISSN: 1479-5876 [Electronic] England
• PMID: 28595644 (Publication Type: Clinical Trial, Journal Article, Research Support, Non-U.S. Gov't)

Chemical References

• Biomarkers

Topics

• Biomarkers (metabolism)
• Gene Expression Regulation
• Humans
• Influenza, Human (blood, genetics)
• Oligonucleotide Array Sequence Analysis
• Reproducibility of Results
• Self Report
• Transcriptome (genetics)