Novel Biomarker Panel for the Early Diagnosis of Lyme Disease
Lyme disease – a systemic tick-borne bacterial infection, is the most common vector-borne disease in the United States and Europe. Disease prevalence estimates remain inaccurate primarily due to the lack of sensitive diagnostic tests. Current diagnostic tools for detecting nucleic acid or antibody responses from Borrelia burgdorferi infection have low assay sensitivity. The inability to detect the early acute phase of infection delays treatment and complete recovery for many patients. This diagnostic technology uses a novel gene expression classifier that allows for early detection of Lyme disease with high sensitivity, specificity and accuracy.
UCSF scientists used whole transcriptome data from 41 clinically diagnosed Lyme disease samples compared with 12 samples from other infections and 19 healthy controls, followed by iterative targeted RNA resequencing in conjunction with machine learning algorithms on a total of 220 unique samples. They identified a panel of 20 genes that accurately discriminated Lyme patients from healthy controls and patients with viral and other bacterial infections. This Lyme disease diagnostic exhibits 82% sensitivity in disease detection and is able to discriminate from other viral or bacterial infections with 91% specificity and 87% accuracy. Transcriptome profiling by next-generation sequencing is a promising approach to identify diagnostic host biomarkers in response to infection and the improved performance of this method makes it particularly suitable for clinical diagnosis of acute Lyme disease.
Stage of Development Diagnostic validated in 259 patient samples. Looking for Partners To develop & commercialize the technology as a diagnostic tool for Lyme disease. Data Availability Under CDA / NDA Related Materials Additional Technologies by these Inventors Tech ID/UC Case 29175/2015-177-0 Related Cases 2015-177-0
USA
