Novel diagnostic approaches in TB and MDR-TB and biomarkers of treatment response
Leading partner: Imperial College (UK)
Tuberculosis (TB) causes 10.4 million new cases globally. In order to comprehensively improve the clinical and epidemiological management of tuberculosis, particularly in Eastern European regions, CARE focuses on:
Identification of biomarkers of drug-sensitive and MDR-TB and association with treatment failure/cure by analysing blood/plasma and urine in a longitudinal treatment cohort
Development of non-invasive early TB diagnostics and definition of prognostic markers based on urine analysis
Definition of novel biomarkers of TB disease progression or cure, particularly in the context of multidrug resistant TB (MDR-TB)
Investigation of the role of SIGLEC 1 in TB by identifying and analysing polymorphisms in an extra-pulmonary TB cohort
We identified and further evaluated at least 2 candidate urine biomarkers of response to treatment.
We analysed plasma and urine specimens taken at regular intervals from an earlier longitudinal cohort of susceptible (DSTB) and multidrug resistant (MDRTB) TB patients from Russia and Lithuania and realized the TB H2020 database. Using Linear Mixed Models for longitudinal analysis, we identified metabolites whose abundance changes in response to TB treatment.
After applying this procedure to all metabolic signals in an assay, we selected a number of statistically significant features (p-values threshold set at 5% to account for false discovery rate), shown in Table1. Some of the signatures are quite large (i.e. the RPOS untargeted XCMS dataset has >100 features passing false discovery rate correction).
Table 1 – Number of features in each assay for which the trend with time was found to be significant
All these are possible biomarkers of the effect of anti TB drugs.
Some of the possible markers we identified, had been found in a diagnostic study by Isa et al (Isa, F. et al. Mass Spectrometric Identification of Urinary Biomarkers of Pulmonary Tuberculosis. EBioMedicine 31, 157–165 (2018)) and we evaluated them for their changes across time during TB treatment in the original reference. Table 2 summarizes the results of such longitudinal analysis in our TB H2020 dataset, indicating the potential value in defining cure.
Table 2 - Results from the TB H2020 dataset of the longitudinal replication analysis of the markers reported by Isa et al.
If successful, this research will contribute to the development of reliable non-invasive TB diagnostics.