A simple blood test based on patterns of gene expression could radically improve and speed up the diagnosis of febrile childhood diseases, say researchers.
Proof-of-concept work by an international team has shown it is possible to detect and distinguish between 18 infections or inflammatory diseases, such as group B Streptococcus (GBS), respiratory syncytial virus, and tuberculosis, and deliver a result in under an hour compared with current diagnostic tests that take days or even weeks.
The scientists reported in Cell Press Med that a future diagnostic test based on their method and translated to near point-of-care devices could be “transformative for healthcare.” Using a single sample of blood, the test could enable clinicians to diagnose the cause of fever based on a distinctive pattern of genes being “switched on or off” by the body in response to specific illnesses.
“By distinguishing between many diseases at the same time within the same test, we have developed a more comprehensive and accurate model that aligns with the way clinicians think about diagnosis,” Dr. Myrsini Kaforou, a senior lecturer at Imperial College London and co-senior author of the paper, said in a statement.
Infectious and inflammatory diseases are the most common causes for children requiring medical care in hospitals and general practitioner or community clinics. However, with general symptoms such as fever, it can be difficult to reliably diagnose and distinguish bacterial infections, which may be potentially life-threatening, from other causes which may be less serious.
“We may not know whether a fever is bacterial, viral, or something else until hours or days after a child has been admitted, when their test results come back. Such delays can stop patients getting the right treatment early on, so there is a clear and urgent need to improve diagnostics,” Professor Michael Levin, chair in pediatrics and international child health at Imperial College London and co-senior author of the paper said.
Most diagnostic tests are focused on detecting pathogens, such as lateral flow tests (LFTs) for SARS-CoV-2, HIV, or influenza, or blood cultures to confirm the presence of bacteria or yeast. However, LFTs are limited to providing a yes or no result for one possible cause of the illness, and blood cultures may take 72 hours or more to provide reliable results.
Gene expression microarrays and, more recently, RNA sequencing (RNA-seq) have revealed an alternative approach in which infectious or inflammatory diseases are characterized by unique patterns of host gene expression in patients’ blood thus bypassing the need for direct pathogen detection.
Using data from thousands of patients (including more than 1,000 children with 18 infectious or inflammatory diseases), the researchers were first able to identify which key genes were switched on or off in response to a range of illnesses, providing a molecular signature of disease.
Machine learning was then applied to identify which patterns of gene expression corresponded to the specific disease areas and pathogens, focusing on a panel of 161 genes for 18 conditions. The panel was further validated in a cohort of 411 pediatric patients admitted to the hospital with sepsis or severe infections (representing 13 of the 18 diseases), where gene expression was captured from blood analysis and diagnoses were then made using current gold-standard clinical methods.
“There is still much work to be done to progress this test to the clinic, but we are working towards it,” Kaforou said.