Using WGS data to predict DRTB phenotypes: A CRyPTIC experience. Timothy M Walker

Transcription

1 Using WGS data to predict DRTB phenotypes: A CRyPTIC experience Timothy M Walker

2 CRyPTIC Comprehensive Resistance Prediction for Tuberculosis: an international consortium

3 Whole genome sequencing a global collection of 100,000 M. tuberculosis genomes in an attempt to define the M. tuberculosis resistome phenotype vs. genotype Machine learning Genome wide association studies Bayesian methods Nonparametric methods Structural approaches Robust to artefact and noise Principled & probabilistic Incorporate prior clinical knowledge Scale to massive datasets Learn model structure from the data Impose minimal assumptions

4 Genotype vs phenotype or Genotype vs outcome Phenotype is what we are aiming to replace in the first instance Outcome is down to more than just pathogen factors Compliance Co-morbidities Co-administered drugs PK/PD Extent of disease and timing of presentation

5 Can we already predict enough Mtbc phenotypes from routinely produced WGS data, with sufficient accuracy, to justify a substantial reduction in phenotyping activity.

6 Candidate gene approach Isoniazid Rifampicin Ethambutol Pyrazinamide katg inha fabg1 ahpc rpob emba embb embc pnca

7 Sampling frame Table 1. Numbers of Isolates According to Country of Sample Origin and Drug-Resistance Profile. Country Period Isolated Enriched for Resistance Susceptible to All Four Drugs Susceptible to Three Drugs* Isoniazid-Resistant, Rifampin-Susceptible Isoniazid-Susceptible, Rifampin-Resistant Isoniazid-Resistant, Rifampin-Resistant Australia Yes Belgium Yes Canada Yes 11 1, ,343 China Yes Germany No Italy Yes and no Netherlands Yes and no Pakistan Yes Peru Yes Russia Yes Serbia Yes South Africa Yes Spain Yes eswatini Yes Thailand Yes United Kingdom Yes and no 3, ,887 Total 4,911 1, , ,209 * Isolates in this category were missing results for pyrazinamide. More than one collection was derived from Italy, the Netherlands, and the United Kingdom, some of which were enriched and some of which were not enriched for resistance. Details are provided in the Supplementary Appendix. Until recently, eswatini was known as Swaziland. Other Pattern Total

8 For all isolates Genotypic prediction Resistant phenotype, n (%) R S U F Total Susceptible phenotype, n (%) R S U F Total Isoniazid Rifampicin Ethambutol Pyrazinamide Genotypic prediction PPV, % NPV, % Sensitivity (%) Specificity (%) No genotypic prediction made, (%) Resistance prevalence (%) Isoniazid Rifampicin Ethambutol Pyrazinamide

9 negative predictive value (%) Rifampicin Isoniazid Drug Profiles Ethambutol Pyrazinamide prevalence of resistance (%)

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15 Each image is shown to 15 different people Since April 2017, 15,743 people from around the world have done 1,239,548 classifications between them.

16 Dealing with the phenotype 1. MIC reading by laboratory scientist using ThermoFisher Vizion Agree? yes Label as high quality MIC 2. AMyGDA automated reading of plate growth no 3. BashTheBug Citizen Science project 4. BashTheBug PRO (single expert)

17 No more PAS

18 Ethambutol UKMYC5 UKMYC6 predicted R predicted S

19 Differential effect of known DR-mutations on MIC

20 What CRyPTIC has so far Over 10,000 microtitre plates Over 6,600 with WGS data and more to come imminently Preliminary analyses under way Two years still to run

21 Where are we heading? Current question: Is the organism susceptible to the drugs I d like to give? R vs S Future model: How much drug to give to this particular patient for this particular organism? Predicted MIC Pharmacokinetics Dosing Clinical outcome

22 Oxford, UK: Derrick Crook Tim Peto Sarah Walker Sarah Hoosdally Ana Gibertoni Cruz Zam Iqbal Martin Hunt Phelim Bradley David Clifton Danny Wilson Philip Fowler Clara Grazian Yang Yang Jessica Hedge Carlos Del Ojo Elias Tanya Golubchik PHE Birmingham, UK: Grace Smith + team Leeds, UK: Mark Wilcox Deborah Gascoyne-Binzi Acknowledgements National Institute for Communicable Diseases, South Africa: Nazir Ismail Shaheed Valley Omar Forschungs Institute Borstel, Germany: Stefan Niemann Thomas Kohl Matthias Merker Genoscreen, Lille: Philip Supply Serbia: Irena Zivanovic Pakistan TB control programme: Sabira Tahseen Mumbai, India: Nerges Mistry Camilla Rodrigues Anirvan Chatterjee Kayzad Nilgiriwala China / CDC China: Guangxue He Qian Gao Yanlin Zhao Joy Flemming Baoli Zhu Sydney, Australia: Vitali Sinchenko Vancouver, Canada: Jennifer Gardy London / Russia: Francis Drobniewski Valencia, Spain: Iñaki Comas San Rafaele, Milano Daniela Cirillo Paolo Miotto Andrea Cabbibe Maria Rosaria De Filippo Lele Borroni RIVM, Netherlands Dick van Soolingen Han de Neeling Harvard Medical School Maha Farhat LSHTM/Peru David Moore Louis Grandjean Thailand / Singapore: Ong Twee Hee OUCRU, Vietnam Guy Thwaites Thuong Nguyen Thuy Thuong

23 Thanks to Phil Fowler for slides on MICs, Bash the Bug and AMyGDA

24 University of Oxford, Oxford, UK University of Oxford Clinical Research Unit, Ho Chi Minh City, Vietnam European Bioinformatics Institute, Cambridge, UK Public Health England, Birmingham, UK San Raffaele Scientific Institute, Milan, Italy National Tuberculosis Control Program, Islamabad, Pakistan The Foundation for Medical Research, Mumbai, India PD Hinduja National Hospital, Mumbai India Research Centre Borstel, Germany Institute of Microbiology & Laboratory Sciences, Gauting, Germany London School of Tropical Medicine, London, UK Universidad Peruana Cayetano Heredá, Lima, Peru China CDC, Beijing, China Institute for Microbiology, Chinese Academy of Science, Beijing, China Mycobacteria Reference Laboratory, Edinburgh, UK University of Cape town, Cape town, South Africa Imperial College London, London, UK National University of Singapore, Singapore CDC Atlanta, Atlanta, USA University of British Columbia, Vancouver, Canada Instituto Adolfo Lutz, São Paulo, Brazil CDC Taiwan, Taipei, Taiwan Public Health Agency of Sweden, Sweden Institut Pasteur Madagascar, Antananarivo, Madagascar African Health Research Institute, Durban, South Africa TORCH Consortium World Health Organisation