WGS-based susceptibility testing for TB: from research to service delivery Update from National Mycobacterial Reference Service

Transcription

1 WGS-based susceptibility testing for TB: from research to service delivery Update from National Mycobacterial Reference Service Grace Smith. National Mycobacterial Reference Service. Public Health England

2 2 Global TB in m active cases, 1.7 m deaths MDRTB estimated 600,000. Only 22% diagnosed and treated - driving spread.

3 Drug resistant TB in England 6% TB cases with initial resistance to isoniazid (without MDR-TB ) fairly stable in past decade 68 cases in 2016 confirmed or treated as MDR/RR-TB) 59 (1.7%) of TB cases confirmed with initial MDR/RR-TB -increased slightly since 2015 (53, 1.5%) 10 cases of XDR-TB in 2016 and in 2015, higher than in previous years 49% of MDR/RR-TB cases notified in 2014 completed treatment by 24 months, the lowest proportion since % of drug resistant TB cases notified in 2014 were lost to follow-up, higher than in previous years (17% in 2014) 3 PHE 2017 report

4 4 Why WGS? A2 Provide universal access to high-quality diagnostics Improve TAT for susceptibility prediction Better resolution for relatedness Single test - cost saving

5 5 WGS: research data

6 6 PHE WGS for mycobacteria: the how NMRS: National Mycobacterial Reference Service. Reference work for NHS funded centrally. Distributed hub model: London and Birmingham All first positive mycobacterial cultures All positives with previous TB and previous isolate >= 2 months previously Resilience Distributed and diffusing expertise Different models for WGS delivery: Birmingham locally run MiSeq, London PHE CSU

7 Conventional Methods 1day ZN stain + HAIN ID WGS versus Current method for MTB diagnosis and typing MGIT positive 1.7ml DNA extraction WGS 1.0 day 2-3 weeks TB Complex Sensitivity NTM library preparation and Sequencing on MiSeq 1 day 4-5 weeks MIRU-VNTR Typing Upload data to BaseSpace or direct to PHE minutes Data analysis via pipeline in PHE or Oxford CS <1 days Species Identification 6-8 weeks Resistance profile SNP Typing (Transmission) 5-7 days

8 TB WORKFLOW - Identifiers sent to PHE Specimen MGIT culture DNA extraction Load MiSEQ - Plate submission generated: LABKEY - Set up BaseSpace and sharing permissions - Basespace genomic data OXFORD ANALYSIS CENTRE: COMPASS (Cloud system based in GEL) Basespace checked Data fetched to PHE ISILON data stored AUTOMATIC Pipeline IDENTIFICATION Kraken (reference choice) MyKrobe TRIMMING/QC MAP (BAM file) Conversion to VCF Elephant walk (distance Matrix) FASTA Walker Resistance Catalog/ HAIN Labkey reports Export to PHE and identifiers added

9 WGS reports: identification Mapping based- works extremely well for MTB Very well for welldescribed species of NTM Less well for minority species, heavily dependent on quality of the reference genome (often single) 9

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

11 Knowledgebase 1. LPA / Xpert mutations 2. Mutations from a systematic review of the literature by Paolo Miotto for ReSeqTB 3. pnca mutations from Alex Pym s latest paper in Nat Comms 4. Any frame-shift mutation in a non-essential gene (pnca, katg) 5. Mutation characterised in Walker et al in Lancet ID 2015 (includes susceptible mutations) 6. fabg1 L203L

12 Genes relevant to a drug

13 Genes relevant to a drug

14 Genes relevant to a drug S S S S Susceptible phenotype predicted

15 Genes relevant to a drug S R S S Resistant phenotype predicted

16 Genes relevant to a drug S S S U No phenotype predicted (uncharacterised mutation present)

17 Genes relevant to a drug S F S S No phenotype predicted (sequence at resistance locus can t be determined)

18 WGS reports: resistotype Sample with an R mutation reported as R With no, or only S mutations reported as S With U (uncharacterised) mutation, reported U With no nucleotide call made at key resistance site, reported F (for fail) 18

19 19 Resistotype

20 20 Resistotype (3)

21 21 BMS verifies Telepath report with original LabKey report

22 22

23 23

24 24 WGS: faster treatment, informing contact tracing Mr B, 49M from Black country, works in large distribution warehouse Smear positive, cavitatory TB MTBC, rif R on Cepheid GeneXpert Gives no TB contacts for likely source, No close work contacts Screen whole warehouse (>150 staff)? WGS: 0 SNP from pt A Full phenotypic sens on pt A used to inform management of Mr B while awaiting phenotypics

25 And again 23M student, living in halls in Northern university Smear positive, PTB Contact tracing underway, 200 students being skin tested Chemoprophylaxis for latent infection? Specimen date 30/1/17 Received BPHL 8/2/17 WGS result available to clinicians 13/2/17 25 Presentation title - edit in Header and Footer

26 International clusters XDR- likely origin E. Europe Within UK transmission MDR Likely origin E Africa No within UK transmission 26

27 Predicting susceptibility to first-line drugs: Can we phase out phenotypic DST and transition to WGS-led diagnostics? Timothy Walker

28 Can we predict enough Mtbc phenotypes from routinely produced WGS data, with sufficient accuracy, to justify a substantial reduction in phenotyping activity. (i) Is the data robust? (ii) Should we transition to WGS-only DST for some isolates now? If not, when?

29 Analysis of 10,000 isolates WGS and phenotypic DST 16 countries in 6 continents Isoniazid, rifampicin, ethambutol and pyrazinamide resistance correctly predicted, meeting the WHO target profiles for new molecular assays of over 90% specificity and 95% sensitivity overall. Targets met for individual drugs except ethambutol specificity-93.6% Targets met for collections not enriched for drug resistance (consecutively sampled isolates from UK, Italy, the Netherlands and Germany ) Targets met for predicted pan-susceptibility in all collections Targets met in simulated drug profiles with drug resistance rates up to 47% 29

30 Identify and drop likely lab errors based on 3 rules: 1. katg S315T and susceptible INH phenotype 2. rpob S450L and susceptible RIF phenotype 3. >=3 pheno/geno discrepancies 81 errors in total, constituting 0.8% of all samples

31 For all isolates Genotypic prediction R Resistant S phenotype, U F n (%) 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

32 For consecutively sampled isolates from UK, Italy, the Netherlands and Germany Genotypic prediction R Resistant S phenotype, U F n (%) 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

33 SSSS SSSU SSUS SSUU SUSS SUSU SUUS SUUU Predicting antibiograms Phenotype Genotypic prediction Some resistance Fully susceptible Sensitivity Specificity PPV NPV % predictions % % % % made Some resistance Fully susceptible No precition

34 Predicting antibiograms (consecutively sampled collections only Italy, Germany, NL, UK) Phenotype Genotypic prediction Some resistance Fully susceptible Sensitivity Specificity PPV NPV % predictions % % % % made Some resistance Fully susceptible No precition

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

36 Discrepancy analysis Genotypic prediction R Resistant S phenotype, U F n (%) Total Susceptible phenotype, n (%) R S U F Total Isoniazid Rifampicin Ethambutol Pyrazinamide /322 (90.1%) had zero mutations Of the 15 mutations found in the other 32 isolates, these predicted susceptibility across the whole data set as follows: INH 286/293 (97.6%) EMB 95/119 (79.8%) PZA 0/2 (0%). This was one mutation that appears wrongly characterised (pnca_d63a) 145 mutations were responsible for these 822 discrepancies, yet they predicted resistance correctly in other isolates when occurring alone: INH 308/371 (83.0%) RIF 548/627 (87.4%)* EMB 1280/1743 (73.4%) PZA 459/663 (69.2%) *14/17 mutations relevant to RIF were RRDR mutations

37 Of all 3,435 Birmingham isolates, 2,961 have a full phenotype: Prediction Number Errors Cumulative % SSSS 2,386 5 (0.21%) 80.6 SSSU SSUS SSUU SUSS SUUS Total 2705 (of 2961) x SSSS vs RSSS No INH mutations

38 Imagined work-flow Predicted S to HREZ Report as S to HREZ, without DST Predicted R, F or U to any of HREZ Clinical failure Clinician request Perform DST for all drugs Background sampling

39 Improve resistance prediction Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) Create a catalogue of all determinants conferring antituberculosis drug resistance. Will investigate a very large number of isolates over-sampled for resisantce: Gates Foundation funded 21,000 isolates (5,000 with extensive DST) Wellcome Funding 80,000 isolates (37,000 with extensive DST) Potential total 100,000 (42,000 with extensive DST)

40 40

41 Acknowledgements NMRS-North and Central (TB lab!) BPHL Newcastle lab staff NMRS-South PHE TB: TBSU, FES PHE E Mids and W Mids HPTs NHS TB: Cathy Browne, Martin Dedicoat MMM/ NDM University of Oxford 41 Presentation title - edit in Header and Footer

42 Oxford, UK: Derrick Crook Tim Peto Sarah Walker David Clifton Danny Wilson Philip Fowler Clara Grazian Yang Yang Jessica Hedge Zam Iqbal Phelim Bradley Ana Gibertoni Cruz Sarah Hoosdally Carlos Del Ojo Elias Tanya Golubchik PHE Birmingham, UK: Grace Smith + team Brighton, UK: John Paul Kevin Cole 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 Loui Grandjean Thailand / Singapore: Ong Twee Hee OUCRU, Vietnam Guy Thwaites Thuong Nguyen Thuy Thuong