ID-Predict (Tuberculosis): Predicting recent Tuberculosis infection and future disease risk in Victorian TB contacts
Associate Professor Sarah DunstanFull bio
Professor Justin DenholmFull bio
Dr Kasha SinghFull bio
Professor Sammy BedouiFull bio
Professor Tim StinearFull bio
Associate Professor Anna CoussensFull bio
MACH OmicsLearn more
University of Melbourne at the Peter Doherty Institute for Infection and Immunity
Royal Melbourne Hospital
Walter and Eliza Hall Institute
The 2015 WHO End TB strategy stated that new tools are needed if we are to ever achieve TB elimination. One diagnostic test that could radically change the course of the TB pandemic towards elimination, would be one that indicated who would benefit most from preventative therapy: those recently infected and those at greatest risk of future disease. Combining clinically well characterised TB patients and their immediate contacts, with state-of-the-art multiple omics analyses, statistical modelling and innovative machine learning approaches to find the predictive factors for progression to active disease, will provide a modern lens to illuminate the answer to this century-old dilemma.
The purpose of ID Predict (TB) is to identify diagnostic biomarkers of recent Mtb infection and predictive biomarkers of TB progression risk that have the potential as diagnostic tests to identify those who will benefit most from preventative treatment. Our aim is to be able to differentiate between recent infection and early-stage asymptomatic subclinical TB disease to inform the most appropriate treatment choice (short-course prevention, vs long-course treatment).
ID Predict (TB) is a prospective study which can be divided into 3 distinct phases.
PHASE 1 is the establishment of a cohort of individuals experiencing the spectrum of TB infection and clinical disease. This will be achieved by the recruitment and longitudinal sampling of patients with active TB disease and their contacts.
PHASE 2 is multiple-omic data generation of bio-banked samples from the clinically well-characterised TB patients and close contacts.
PHASE 3 is individual omic analysis, clinical data and omic data integration, and statistical and machine learning approaches will be used to identify diagnostic and prognostic biomarkers.
Seed funding provided by the Doherty Institute will fund a pilot study of PHASE 1.
Actual key impacts
- The establishment of a process to recruit study participants embedded within the structure of the Victorian TB program (VTP) utilising VTP data and programmatic contract tracing activities and databases curation of epidemiological and clinical data
- The creation of a bioresource of samples from clinically well-defined TB contacts and cases suitable and accessible for phenotyping and multi-omic applications to investigate critical question in the development of TB infection and disease
- The establishment of strong relationships between clinical and research partners with the public health TB program in Victoria to ensure relevance of the research and facilitate interventions for rapid translation into future downstream health impacts
- The development of cross institution and disciplinary partnerships to facilitate large-scale multiple-omic projects with potential commercial applications
Intended future impacts
- The identification of predictive factors that are important in determining the risk of progression to active disease amongst those exposed to active TB, will support treatment of latent disease as a more targeted, efficient and feasible tool in TB elimination