Scientists Develop a Diagnostic Test for Gene Expression-Based Diagnosis of Early-Stage Lyme Disease
Venice Servellita, et al., published results of a study in Communications Medicine designed to address the urgent need for new diagnostic tests with improved sensitivity and specificity for Lyme disease.
The study carried out transcriptome modeling via RNA sequencing (RNA-Seq), targeted RNA-Seq, and/or machine learning-based designation of 263 peripheral blood mononuclear cell samples collected from 218 study participants. The samples included 94 patients with early-stage Lyme disease, 48 uninfected control participants, and 57 patients with other diseases such as influenza, bacteremia, or tuberculosis.
The end result of the study involved identifying a 31-gene Lyme disease classifier (LDC) panel that can differentiate early-stage Lyme patients from the control group including 23 genes (74.2%) formerly purported to be associated with clinical studies of Lyme disease patients or in vitro cell culture and rodent studies of Borrelia burgdorferi infections. The LDC was assessed using an impartial test set of samples from 63 patients yielding an overall sensitivity of 90.0%, specificity of 100%, and accuracy of 95.2%. The LDC test was positive in 85.7% of seronegative patients and was observed to endure for ≥3 weeks in 9 out of the 12 patients (75%).
The researchers conclude that the study results emphasize the prospective clinical value of a gene expression classifier for diagnosing early-stage Lyme disease, especially for patients who would otherwise be found to have negative test results based on conventional serologic testing that is currently used.