CRyPTIC: Comprehensive Resistance Prediction For Tuberculosis: An International Consortium

Our ultimate aim is to achieve sufficiently accurate genetic prediction of resistance to all anti-tuberculosis drugs for whole genome sequencing (WGS) to replace slow, cumbersome culture-based drug susceptibility testing (DST) for Mycobacterium tuberculosis complex (MTBC).  This would enable rapid-turnaround near-to-patient assays to revolutionize drug-resistant TB identification and management.

This multidisciplinary collaboration including different professional profiles of TB experts from five continents, statisticians/mathematicians and software engineers will integrate bioinformatics, machine-learning and statistical genetic methods to uncover all, or nearly all, genomic variation causing at least 1% resistance to first-line anti-TB drugs (not second and third line drugs), and demonstrate proof-of-principle for future extenstion to second- and third-line drugs.  This will be achieved as follows:
  1. We will develop and validate a high-throughput ≥15-drug micotitre plate (clinical plate) assay including all relevant anti-tuberculosis drugs in order to provide standardized quantitative median inhibitory concentrations for drug-susceptibility phenotypes.  Its performance will also be evaluated on >4,000 isolates.  This work will provide data to support association studies as outlined in 2 below and is a feasibility study for initiation of regulatory approval as a diagnostic test by Thermo Fisher.
  2. We will assemble a moderate-scale global and clade-representative WGS collection (~21,000; 4,500 sequenced within this project, over-sampling drug-resistant isolates).  We will develop and use (i) better WGS assembly methods to identify more genomic variants with more precision and (ii) improved statistical methods, both statistical genetic and machine learning, to detect associations between variants and both quantitative (median inhibitory concentration) and binary (susceptible vs resistant) DST.