WGS for diagnosis of DR in TB: are we there yet? Union NAR, Vancouver 2/22/2017

Transcription

1 WGS for diagnosis of DR in TB: are we there yet? Union NAR, Vancouver 2/22/2017 Maha Farhat MD MSc Department of Biomedical Informatics, HMS Pulmonary and Critical Care, MGH

2 Two routes to drug resistance Inadequate treatment Acquired resistance Drug-sensitive Drug-resistant Transmission Primary resistance Uninfected Drug-resistant New York outbreak

3 Growth of resistant TB strains Single amino acid substitutions in the 81 bp coreregion of the rpob gene responsible for conferring rifampicin (RIF) resistance. rpob codes for RNA polymerase. Ordway et al, Inf Immun, 1995 Billington O, Antimicrob Agents and Chemother, 1999 As mean number of generations increases, relative fitness increases

4 Percentage infected Prevalence of infectious TB Projections from dynamic model of MDR TB Infection Active Disease Years after drug introduction Years after drug introduction =time of switch to DOTS-like strategy Drug sensitive MDR (RF=120%) MDR (RF=80%) Future trajectory of MDR epidemic depends on fitness of DR TB Cohen, Murray, Modeling epidemic of multidrug resistant tuberculosis of heterogeneous fitness, Nat Med, 2004

5 123 Isolates Farhat et al. Nat Gen 2013

6 Isolate Phylogeny Farhat et al. Nat Gen 2013

7 Loci under positive selection Mycobacterial Cell Wall Farhat et al. Nat Gen 2013

8 RpoC Farhat et al. Nat Gen 2013 Comas et al. Nat Gen 2011

9 Translating genomic data to individual patient care Prediction Sample collection Whole Genome Sequencing INH R RIF S EMB S PZA S MXF S More fit phenotype Treatment and Public health interventions personalized + Others

10 Whole genome sequencing in clinical practice Can we use it directly on sputum? biotinylated RNA baits Single molecule nanopore sequencing Others What is the cost? Whole Genome Sequencing Can it be taken to resource limited laboratories without consistent internet or electricity? Can it be scaled? Brown A et al. JCM 2015 Bates M et al. Int J of Mycobacteriology 2016

11 Coming to an office/lab near you: The USB Sequencer Advantages: Size & portability Cheapest technology on the market Can be scaled Disadvantages: Large sized library required Internet connection Low accuracy Lu et al. Genom Proteom Bioinf 2016

12 WGS from early MIGIT Koser et al. NEJM 2013

13 The bioinformatic/analytical bottleneck Prediction INH R RIF S EMB S PZA S MXF S Whole Genome Sequencing More fit phenotype + Others

14 Geographic source and diversity of 1800 TB isolates Farhat et al. American Journal of Respiratory and Critical Care 2016

15 Evidence for gene-gene interaction Farhat et al. JCM 2016 Nebenzahl-Guimaraes et al. JAC 2014 mg/ L

16 Random Forest Classifier

17 Identifying the minimum set of mutations predictive of drug resistance Specificity Farhat et al. AJRCCM 2016 Sensitivity

18 Validated Predictive performance for drug resistance diagnosis Drug Selected Mutations Isonaizid 19 Rifampicin 14 Pyrazinamide 124 Ethambutol 18 Streptomycin 39 Ethionamide 20 Kanamycin 3 Capreomycin 5 Amikacin 2 Ciprofloxacin 7 Levofloxacin 8 Ofloxacin 6 p-aminosalicylic acid 4 Total 250 Farhat et al. AJRCCM 2016

19 How to relay actionable data from WGS Prediction Whole Genome Sequencing INH R RIF S EMB S PZA S MXF S More fit phenotype + Others Treatment and Public health interventions personalized

20 gentb update gentb.hms.harvard.edu

21 Heat map of prediction

22 Personalizing fluoroquinolone treatment in MDR-TB Farhat et al. JCM 2016

23 Different gyr mutations result in differences in Fluoroquinolone response Covariate Hazard Ratio 95% CI P-value treatment with 3rd generation FLQ 0.85 ( ) 0.66 intermediate gyr mutation (A90V, D94A) 1.31 ( ) 0.54 high level gyr mutation 2.60 ( ) 0.01* Farhat et al. under review

24 In Summary The new generation of sequencing technologies are getting closer to the field and are improving in accuracy. Novel associations between TB genome and drug resistance data are needed to close the sensitivity and specificity gap. Both fitness and resistance causing mutations are discoverable and both may be relevant for DR diagnosis or surveillance Prediction of the drug resistance phenotype from genotype is likely best done through machine learning and open informatics solutions are needed to allow for this. Data on patient treatment outcome is the gold standard measure of drug response/resistance

25 With thanks Megan Murray, Department of Global Health and Social Medicine Benjamin Medoff, Chief MGH Pulmonary and Critical Care PIH/DGHSM (Molly Franke, Carole Mitnick) MSLI (Alex Sloutsky and Devinder Kaur) Stellenbosch University (Rob Warren, Tommie Victor and Lizma Streicher) RIVM (Dick van Soolingen, Hanna Guimaraes) US CDC (Bonnie Plikyatis, Jamie Posey) PHRI (Barry Kreiswirth, Natalia Kurepina) WHO - TDR (Leen Rigouts) Radboud University Nijmegen Medical Center (Reinout van Crevel, Arjan van Laarhoven) Boston Medical Center (Karen Jacobson) IQSS (Gary King, Merce Crosas, Christine Choirat, Raman Prasad, James Honaker) Genomics England (Razvan Sultana) Novartis (Oleg Iartchouk) Vancover CDC (Jennifer Gardy, James Johnston) Imperial College London (Caroline Colijn) University of Montreal (Jesse Shapiro) Broad Institute (Pardis Sabeti) Funders