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

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, 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

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.

Keywords: Biomarker; Challenge trial; Influenza; Symptom scores; Transcriptomics.

Figures

Fig. 1
Fig. 1
a Heatmap of summed symptom scores, live viral shedding assay, RT-PCR Influenza A and RT-PCR H1N1. Each row represents one subject and each column one time point in days post challenge. b Schematic overview of the three cohorts used and the supervised learning approach used for the regression analysis
Fig. 2
Fig. 2
a Volcano plot showing all 21 probes (19 genes) associated to significant changes in DSS. The threshold was set to 15% change in DSS per unit in gene expression and a FDR of 1%. b Heatmap of scaled expression values of the 19 genes significantly associated to changes in DSS
Fig. 3
Fig. 3
Intensities from Illumina ht-12 arrays of the 19 gene signature were used to train a random forest model. The model was used to predict symptom scores in an independent test set. a Actual scores were plotted against the predicted values at each day. RMSE decrease and correlation values refer to the overall model fit. b Importance of individual markers within the panel of 19 primers. X-axis shows the increase in RMSE if the respective gene is left out of the model
Fig. 4
Fig. 4
a The predicted DSS from the qRT-PCR based assay using the deltaCt values from the 19 gene signature as a test dataset were plotted against the observed DSS. b The importance of individual markers within the panel of 19 primers is shown in decreasing importance. The x-axis shows the increase in RMSE if the respective gene is left out of the model. LVS live viral shedding assay

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