18 Aug Biosignatures Allow Single Blood Test to Identify Multiple Causes of Fever in Children
MedicalResearch.com Interview with:
Myrsini Kaforou, PhD
Senior Lecturer in Bioinformatics
Department of Infectious Disease
Imperial College London
MedicalResearch.com: What is the background for this study?
Response: Children very often present to hospital and clinics with fever, but fever is a non-specific disease symptom. The identification of the cause of fever poses a great challenge for the clinical teams worldwide. The available diagnostic tests are neither quick or accurate enough to fully base decisions on, such as withholding or administering antibiotics.
For example, cultures may take days or even weeks to provide a result.
In our research group, we are working on novel approach; instead of trying to identify the causative pathogen, which is often inaccurate or impossible, we are studying the genes in the patient’s blood that are “switched on” or “switched off” during the infection or the disease in general. Using computational/bioinformatics methods, we are able to identify out of thousands of genes, the combinations of genes, “the biosignatures” for each disease. In the past we had shown that this approach works to distinguish bacterial from viral infection, or tuberculosis disease from other conditions that mimic its symptoms. But with this work we have shown for the first time that a single set of genes, a “single gene panel” can be used to discriminate between 6 broad and/or 18 specific infectious or inflammatory conditions that cause fever in children.
MedicalResearch.com Would you explain how the technology distinguishes between different infectious agents and inflammatory ones, ie SLE?
Response: Each disease, ie each infectious or inflammatory condition has its own unique profile of genes that are “switched on” or “switched off” in the blood when the patient is ill. We have managed to look at the blood of over one thousand patients – including our well phenotyped paediatric patient cohorts, that have been recruited through international medical research consortia. Using high-throughput techniques we have measured the genes in patients’ blood, and then used machine learning methods to identify the smallest but most accurate combination of genes that would allow us to discriminate influenza from SLE from bacterial infection.
MedicalResearch.com: What are the main findings?
Response: We have shown that by using a panel of 161 genes measured in the blood of children who are unwell we can classify patients into 18 disease categories. These disease categories reflect the individual pathogen (i.e. Streptococcus pneumoniae or influenza virus) or the inflammatory condition (i.e. SLE). The results have been validated in an external dataset, where gene expression was measured by a different technology (RNA-Sequencing vs gene expression microarrays).
MedicalResearch.com: How difficult will it be to widely implement this testing?
Response: This paper describes a proof-of-concept study. We are now working on including more disease groups, and make the gene panel even smaller and more accurate. In order to translate the approach to a near point of care diagnostic test and the genes will need to be measured using simpler technology (i.e RT-PCR based). As part of our ongoing international, multi-partner DIAMONDS study, the next step is to trial the approach in thousands of patients in hospitals in Europe, Africa and Asia. This phase will assess the new approach against the current gold standard for clinical diagnosis, and how likely it is to change clinical decision making.
Habgood-Coote et al., Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature, Med (2023), https://doi.org/10.1016/j.medj.2023.06.007
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