Multi-class gene expression biomarker panel identification for the diagnosis of paediatric febrile illness
File(s)
Author(s)
Habgood-Coote, Dominic
Type
Thesis
Abstract
Febrile illness in children can result from infections by diverse viral or bacterial
pathogens as well as inflammatory conditions or cancer. The limitations of the
existing diagnostic pipeline, which relies on clinical symptoms and signs, pathogen
detection, empirical treatment and diagnoses of exclusion, contribute to missed or de-
layed diagnosis and unnecessary antibiotic use. The potential of host gene expression
biomarkers measured in blood has been demonstrated for simplified binary diagnostic
questions however, the clinical reality is that multiple potential aetiologies must be
considered and prioritised on the basis of likelihood and risks of severe disease.
In order to identify a biomarker panel which better reflects this clinical reality,
we applied a multi-class supervised learning approach to whole blood transcriptomic
datasets from children with infectious and inflammatory disease.
Three datasets were used for the analyses presented here, a single microarray
dataset, a meta-analysis of 12 publicly available microarray datasets and a newly
generated RNA-sequencing dataset. These were used for preliminary investigations
of the approach, discovery of a multi-class biomarker panel of febrile illness and valida-
tion of the biomarker panel respectively. In the merged microarray discovery dataset a
two-stage approach to feature selection and classification, based on LASSO and Ridge
penalised regression was applied to distinguish 18 disease classes. Cost-sensitivity was
incorporated in the approach as aetiologies of febrile illness vary considerably in the
risk of severe disease. The resulting 161 transcript biomarker panel could reliably
distinguish bacterial, viral, inflammatory, tuberculosis and malarial disease as well
as pathogen specific aetiologies. The panel was then validated in a newly generated
RNA-Seq dataset and compared to previously published binary biomarker panels.
The analyses presented here demonstrate that a single test for the diagnosis of
acute febrile illness in children is possible using host RNA biomarkers. A test which
could distinguish multiple aetiologies soon after presentation could be used to reduce
unnecessary antibiotic use, improve targetting of antibiotics to bacterial species and
reduce delays in the diagnosis of inflammatory diseases.
pathogens as well as inflammatory conditions or cancer. The limitations of the
existing diagnostic pipeline, which relies on clinical symptoms and signs, pathogen
detection, empirical treatment and diagnoses of exclusion, contribute to missed or de-
layed diagnosis and unnecessary antibiotic use. The potential of host gene expression
biomarkers measured in blood has been demonstrated for simplified binary diagnostic
questions however, the clinical reality is that multiple potential aetiologies must be
considered and prioritised on the basis of likelihood and risks of severe disease.
In order to identify a biomarker panel which better reflects this clinical reality,
we applied a multi-class supervised learning approach to whole blood transcriptomic
datasets from children with infectious and inflammatory disease.
Three datasets were used for the analyses presented here, a single microarray
dataset, a meta-analysis of 12 publicly available microarray datasets and a newly
generated RNA-sequencing dataset. These were used for preliminary investigations
of the approach, discovery of a multi-class biomarker panel of febrile illness and valida-
tion of the biomarker panel respectively. In the merged microarray discovery dataset a
two-stage approach to feature selection and classification, based on LASSO and Ridge
penalised regression was applied to distinguish 18 disease classes. Cost-sensitivity was
incorporated in the approach as aetiologies of febrile illness vary considerably in the
risk of severe disease. The resulting 161 transcript biomarker panel could reliably
distinguish bacterial, viral, inflammatory, tuberculosis and malarial disease as well
as pathogen specific aetiologies. The panel was then validated in a newly generated
RNA-Seq dataset and compared to previously published binary biomarker panels.
The analyses presented here demonstrate that a single test for the diagnosis of
acute febrile illness in children is possible using host RNA biomarkers. A test which
could distinguish multiple aetiologies soon after presentation could be used to reduce
unnecessary antibiotic use, improve targetting of antibiotics to bacterial species and
reduce delays in the diagnosis of inflammatory diseases.
Version
Open Access
Date Issued
2022-02
Date Awarded
2022-08
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Kaforou, Myrsini
Levin, Michael
Hoggart, Clive
Publisher Department
Department of Infectious Disease
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)