Embed Size (px)
Citation preview
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
Global TB in 2016 10 m active cases , 1.7 m deaths MDRTB estimated 600,000. Only 22% diagnosed and treated - driving spread.
2
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 2000
20% 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
Why WGS? • A2 Provide universal access to high-quality
diagnostics
• Improve TAT for susceptibility prediction • Better resolution for relatedness • Single “test”- cost saving
4
WGS: research data
5
PHE WGS for mycobacteria: the how • NMRS: National Mycobacterial Reference
Service. Reference work for NHS funded centrally.
• Distributed hub model: London and Birmingham
• Resilience • Distributed and diffusing expertise • Different models for WGS delivery:
Birmingham locally run MiSeq, London PHE CSU
• All first positive mycobacterial cultures
• All positives with previous TB and previous isolate >= 2 months previously
6
Sensitivity
1.7ml DNA extraction
MGIT positive
TBComplex
ZNstain+HAINID
library preparation and Sequencing on
MiSeq
Upload data to BaseSpace or direct
to PHE
Data analysis via pipeline in PHE or Oxford CS
WGSversusCurrentmethodforMTBdiagnosisandtyping
2-3weeks
4-5weeks
Species Identification
SNP Typing (Transmission)
Resistance profile
1.0day
1day
minutes
<1days
MIRU-VNTRTyping
WGS Conventional Methods
6-8weeks 5-7days
NTM
1day
TB WORKFLOW
Specimen MGIT culture DNA extraction Load MiSEQ
- Identifiers sent to PHE
- Plate submission generated: LABKEY
- Set up BaseSpace and sharing permissions - Basespace genomic data
OXFORD ANALYSIS CENTRE: COMPASS (Cloud system based in GEL)
Basespace checked AUTOMATIC Pipeline Data fetched to PHE
TRIMMING/QC Kraken (reference choice)
MyKrobe
MAP (BAM file) Conversion to VCF
Elephant walk (distance Matrix) FASTA
Walker Resistance Catalog/ HAIN
Labkey reports
Export to PHE and identifiers added
IDENTIFICATION
ISILON data stored
WGS reports: identification
9
• Mapping based- works extremely well for MTB
• Very well for well-described species of NTM
• Less well for minority species, heavily dependent on quality of the reference genome (often single)
Candidate gene approach
katG inhA
fabG1 ahpC
rpoB embA embB embC
pncA
Isoniazid Rifampicin Ethambutol Pyrazinamide
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
Genes relevant to a drug
1 1000
1 1000
1 1000
Genes relevant to a drug
1 1000
1 1000
1 1000
Genes relevant to a drug
S
S S
S
1 1000
1 1000
1 1000
Susceptible phenotype predicted
Genes relevant to a drug
S
R S
S
1 1000
1 1000
1 1000
Resistant phenotype predicted
Genes relevant to a drug
S
S S
U
1 1000
1 1000
1 1000
No phenotype predicted (uncharacterised mutation present)
Genes relevant to a drug
S
F S
S
1 1000
1 1000
1 1000
No phenotype predicted (sequence at resistance locus can’t be determined)
WGS reports: resistotype
18
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)
Resistotype
19
Resistotype (3)
20
BMS verifies Telepath report with original LabKey report
21
22
23
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
24
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
International clusters
26
XDR- likely origin E. Europe Within UK transmission
MDR Likely origin E Africa No within UK transmission
Predicting susceptibility to first-line drugs: Can we phase out phenotypic DST and
transition to WGS-led diagnostics?
Timothy Walker
“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?
• 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
Analysis of 10,000 isolates WGS and phenotypic DST 16 countries in 6 continents
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
For all isolates
Resistant phenotype, n (%)Genotypic prediction
R S U F Total
Isoniazid 3067 90 93 44 3294Rifampicin 2743 69 7 84 2903Ethambutol 1410 81 94 55 1640Pyrazinamide 863 82 117 77 1139
Susceptible phenotype, n (%)R S U F Total
65 6313 215 117 671085 6763 232 147 7227468 6835 781 70 8154204 6146 197 108 6655
Genotypic prediction
PPV, % NPV, % Sensitivity (%)
Specificity (%)
No genotypic prediction made, (%)
Resistance prevalence
(%)
Isoniazid 97.9 98.6 97.1 99.0 4.7 32.9Rifampicin 97.0 99.0 97.5 98.8 4.6 28.7Ethambutol 75.1 98.8 94.6 93.6 10.2 16.7Pyrazinamide 80.9 98.7 91.3 96.8 6.4 14.6
For consecutively sampled isolates from UK, Italy, the Netherlands and Germany
Resistant phenotype, n (%)Genotypic prediction
R S U F Total
Isoniazid 314 8 9 4 335Rifampicin 126 0 0 9 135Ethambutol 72 1 0 0 73Pyrazinamide 109 6 4 6 125
Susceptible phenotype, n (%)R S U F Total
15 3770 104 90 397931 3958 103 116 420847 3711 458 36 425230 4003 14 58 4105
Genotypic prediction
PPV, % NPV, % Sensitivity (%)
Specificity (%)
No genotypic prediction made, (%)
Resistance prevalence
(%)
Isoniazid 95.4 99.8 97.5 99.6 4.8 7.8Rifampicin 80.3 100.0 100.0 99.2 5.2 3.1Ethambutol 60.5 100.0 98.6 98.7 11.4 1.7Pyrazinamide 78.4 99.9 94.8 99.3 1.9 3.0
Predicting antibiograms SSSS SSSU SSUS SSUU SUSS SUSU SUUS SUUU
PhenotypeGenotypic prediction
Some resistance
Fully susceptible Sensitivity Specificity PPV NPV % predictions
made% % % %Some resistance 1828 55
94.6 98.8 97.0 97.8 85.1Fully susceptible 101 4481
No precition 731 375
Predicting antibiograms (consecutively sampled collections only – Italy, Germany, NL, UK)
PhenotypeGenotypic prediction
Some resistance
Fully susceptible Sensitivity Specificity PPV NPV % predictions
made% % % %Some resistance 269 38
96.1 98.9 87.6 99.7 91.0Fully susceptible 11 3439
No precition 81 291
0
20
40
60
80
100
20 40 60 80 100
95
Pyrazinamide
Ethambutol
IsoniazidRifampicin
39 55 57
prevalence of resistance (%)
nega
tive
pred
ictiv
e va
lue
(%)
Drug Profiles
4634
Discrepancy analysis
290/322 (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
Resistant phenotype, n (%)Genotypic prediction
R S U F Total
Isoniazid 3067 90 93 44 3294Rifampicin 2743 69 7 84 2903Ethambutol 1410 81 94 55 1640Pyrazinamide 863 82 117 77 1139
Susceptible phenotype, n (%)R S U F Total
65 6313 215 117 671085 6763 232 147 7227468 6835 781 70 8154204 6146 197 108 6655
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 6 0 80.8 SSUS 242 0 88.9 SSUU 2 0 89.0 SUSS 52 0 90.1 SUUS 17 0 91.3 Total 2705 (of 2961) 91.3
5x SSSS vs RSSS No INH mutations
Imagined work-flow
Predicted ‘S’ to HREZ
Predicted ‘R’,’F’ or ‘U’ to any of HREZ
Report as ‘S’ to HREZ, without DST
Clinical failure Perform DST for all drugs
Clinician request
Background sampling
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)
http://bashthebug.net/about/
40
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
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
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
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
OUCRU, Vietnam Guy Thwaites Thuong Nguyen Thuy Thuong
Serbia: Irena Zivanovic
Pakistan TB control programme: Sabira Tahseen Mumbai, India: Nerges Mistry Camilla Rodrigues Anirvan Chatterjee Kayzad Nilgiriwala
Sydney, Australia: Vitali Sinchenko
Vancouver, Canada: Jennifer Gardy
Valencia, Spain: Iñaki Comas
Thailand / Singapore: Ong Twee Hee
Leeds, UK: Mark Wilcox Deborah Gascoyne-Binzi
Brighton, UK: John Paul Kevin Cole
London / Russia: Francis Drobniewski
China / CDC China: Guangxue He Qian Gao Yanlin Zhao Joy Flemming Baoli Zhu
