Is the TB field ready for routineIs the TB field ready for routine use of NGS?use of NGS?

ABSOLUTELY!ABSOLUTELY!Dr Vlad Nikolayevskyyy yy

Senior Clinical Scientist/ERLTB‐Net Project LeadN ti l M b t i R f S i P bli H lth E l dNational Mycobacterium Reference Service, Public Health England, 

London, United Kingdom

Talk outline• Definitions• Definitions• NGS/WGS vs Sanger sequencing. Role and place of TB WGSNGS/WGS vs Sanger sequencing. Role and place of TB WGS in diagnostic laboratories • Implementation of TB WGS in routine reference lab settings: UK experienceUK experience• Future work

Talk outline

• Definitions• Definitions• NGS/WGS vs Sanger sequencing. Role and place of TB WGSNGS/WGS vs Sanger sequencing. Role and place of TB WGS in diagnostic laboratories • Implementation of TB WGS in routine reference lab settings: UK experienceUK experience• Future work

DefinitionsDefinitions  • WGS vs NGS

h l f h l• Whole Genome Sequencing: sequencing of a whole genome using NGS technologies

• Targeted NGS (eg Deeplex®‐MycTB Genoscreen, France)

DefinitionsDefinitionsTB Di i R f i• TB Diagnosis vs Reference service• WGS allows to obtain information on speciation, drug resistance p , gprediction and relatedness of unprecedented accuracy and scale –at a cost of speed;at a cost of speed;

• “Absolutely” vs “Yes”• Settings matter• Settings matter• Will be sharing experience Implementing WGS in low incidence high income settings

• Well aware of challenges on both technical and programmaticWell aware of challenges on both technical and programmatic sides

Talk outline

• Definitions• Definitions• NGS/WGS vs Sanger sequencing. Role and place of TB WGSNGS/WGS vs Sanger sequencing. Role and place of TB WGS in diagnostic laboratories • Implementation of TB WGS in routine reference lab settings: UK experienceUK experience• Future work

From Sanger to WGS

Loman, Pallen, Nat Rev Mic 2015

Principal steps

Courtesy of M.Merker, Borstel, Germany

Principal platforms (WGS)

Targeted NGSg

Courtesy of P.Supply

Utility of WGS in TB diagnostic laboratories

Speciation Relatedness Drug resistance prediction• NTM vs MTBC

• Differentiation• Ruling out transmission

prediction• 1st line – M/XDRDifferentiation 

within MTBC • Ruling in transmission?

• 1 line  M/XDR• 2nd/3rd line

transmission?

Transition to WGS based diagnosis evidenceTransition to WGS‐based diagnosis: evidenceA (P kh t t l 2016)• Accuracy (Pankhurst et al., 2016):

• Species prediction: 93% (95% CI 90–96; 322 of 345 i )specimens)

• Drug susceptibility prediction: 93% (91–95; 628 of 672 specimens);672 specimens);

• Implementation  • TAT:  median of 9 days (IQR 6–10), • Cost of £481 per culture‐positive specimen, whereas routine diagnosis costs £518.

• Relatedness (Walker et al 2014 2015)Relatedness (Walker et al, 2014, 2015)• 6‐12 SNP cutoff• Not absolute!Not absolute!

12

Accuracy for drug resistance predictionDrug Genes and other relevant M.tuberculosis 

genome regions

Sensitivity, % (range) Specificity, % (range)

Low High Low HighRifampicin (RIF) rpoB, rpoA, rpoC 89.2 100.0 66.7 100.0

•

Rifampicin (RIF) rpoB, rpoA, rpoC 89.2 100.0 66.7 100.0Isoniazid (INH) katG, inhA, oxyR‐ahpC, fpbC, Rv1592C, Rv1772, Rv2242, 

fabD, fabG1, kasA, accD, oxyR, ndh, fadE24, nat, kasA, mabA, p_inhA, accD6, efpA,  

90.0 100.0 83.3 100.0

Ethambutol (EMB) embA embB embC embR iniA iniB iniC Rv3124 manB 71 4 100 0 15 4 95 8Ethambutol (EMB) embA, embB, embC, embR, iniA, iniB, iniC, Rv3124, manB, PPE49, rmlD, manB

71.4 100.0 15.4 95.8

Pyrazinamide (PZA) pncA, p_pncA, rpsA, panD 43.2 100.0 66.7 100.0Streptomycin (STR) rpsL, rrs, gidB 57.1 100.0 40.0 100.0Amikacin (AMK)2 rrs, eis, gidB, tlyA 80.0 100.0 50.0 100.0Capreomycin (CAP)2 rrs, eis, gidB, tlyA 60.7 100.0 13.7 100.0Kanamycin (KAN)2 rrs, eis, tlyA 75.0 100.0 0 100.0Injectable drugs4 rrs, eis, gidB, tlyA 37.0 100.0 50.0 100.0Ciprofloxacin (CIP)6 gyrA, gyrB 100.0 100.0 98.9 98.9Ofloxacin (OFL)3 gyrA, gyrB 80.0 100.0 80.0 100.0Moxifloxacin (MXF)3 gyrA, gyrB 60.0 90.9 68.7 100.0Levofloxacin (LVX)3 gyrA, gyrB ‐ ‐ ‐ ‐( ) gy , gyGatifloxacin (GFX)3 gyrA, gyrB ‐ ‐ ‐ ‐Fluoroquinolones (FQ)5 gyrA, gyrB 89.2 100.0 100.0 100.0Ethionamide (ETH) ethA, ethR, p_inhA, inhA, fabG1 16.7 100.0 50.0 100.0Prothionamide (PTH) p ethA ethA 40 0 100 0 29 4 80 0Prothionamide (PTH) p_ethA, ethA 40.0 100.0 29.4 80.0Rifabutin (RFB) 6 rpoC 100.0 100.0 ‐ ‐PAS6 thyA, folC, ribB 75.0 75.0 100.0 100.0Trimethoprim/sulfamethoxazole   dfrA ‐ ‐ ‐ ‐

13Minocycline (MINOC)6 whiB7 100.0 100.0 100.0 100.0Linezolid (LNZ) Rrl, rplC ‐ ‐ ‐ ‐Bedaquiline (BDQ) Rv0678 ‐ ‐ ‐ ‐Clofazimine (CFZ) Rv0678 ‐ ‐ ‐ ‐

Papaventsis et al., 2017

Accuracy for RIFAccuracy for RIF

•

Papaventsis et al., 2017

Accuracy for INHAccuracy for INH

•

15

Papaventsis et al., 2017

TB WGS vs ”conventional” diagnosticsg

Performance of targeted NGS

Courtesy of P.Supply

Performance of targeted NGS

Courtesy of P.Supply

Talk outline

• Definitions• Definitions• NGS/WGS vs Sanger sequencing. Role and place of TB WGSNGS/WGS vs Sanger sequencing. Role and place of TB WGS in diagnostic laboratories • Implementation of TB WGS in routine reference lab settings: UK experienceUK experience• Future work

Implementation timeline:‐ Birmingham December 2016‐ London January 2018

Routine WGS‐based lab diagnostics in the UKRoutine WGS based lab diagnostics in the UK• Mycobacteria speciationy p

• k‐mer based method that interrogates a catalogue of mycobacterial type strains

• Kraken for genus identification (Wood, Salzberg 2014)• Mykrobe for differentiaon within Mycobacterium genus (Bradley et al., 2015)y y g ( y , )

• Drug resistance prediction (INH, RIF, STR, EMB, PZA, FQ, AG)• International databases including PhyResSE ReSeqTB and proprietary• International databases including PhyResSE, ReSeqTB and proprietary –continuous monitoring for new evidence and updates (CRyPTIC, ReSeqTB) etc

R l t d• Relatedness• Mtb sequence is aligned to existing sequences in the database so that a 

i di t b l l t d (i SNP ) t h t igenomic distance can be calculated (in SNPs) to each strain. • Strains within a given SNP distance are reported to public health as ‘nearest genomic neighbours’ and phylogenetic relationships illustrated throughgenomic neighbours  and phylogenetic relationships illustrated through automatically produced trees

WGS versus Current method for MTB di i d t i

MGIT positive

MTB diagnosis and typingWGS

Current Method1.7ml  DNA extraction ZN stain + HAIN ID

1.5 day1.5 day

Current Method

1day

TB Complex

ZN stain + HAIN ID

library preparation and Sequencing on 1 day1 dayNTM

1day

SensitivitySensitivity

q gMiSeq

upload data to

2‐3 weeks2‐3 weeks

NTM

upload data to BaseSpace

4 ‐5 weeks4 ‐5 weeksminutesminutes

MIRU‐VNTR       Typing

MIRU‐VNTR       Typing

Data analysis via pipeline in Genome England

1‐2 days1‐2 days

Species IdentificationResistance profile

6 8 k6 8 kSNP Typing (Transmission)

6 ‐8 weeks6 ‐8 weeks 6‐8 days6‐8 days

PHE (UK) routine pipeline

TB pipeline output: internal report

TB pipeline output: internal report

Implementation: key stepsImplementation: key steps

• Pre‐implementation• WorkloadWorkload• Compatibility of IT platforms• Personnel training• Personnel training• Method verification• Communication with customers

• Implementation phase: QAImplementation phase: QA• IQA – 4 strains a month I t l t l P iti (H37R d 2 NTM) d ti• Internal controls: Positive (H37Rv and 2 NTM) and negative

• EQA: schemes are being piloted (ERLTB‐Net)

Implementation: current routine performanceImplementation: current routine performance• Workload: ~1200 isolates monthlyWorkload:  1200 isolates monthly

• London: ~200 isolates/week• Birmingham ~100 isolates/week• Birmingham ~100 isolates/week

• WGS platformp• London HiSeq• BirminghamMiSeqBirmingham MiSeq

• TAT• 7 working days from specimen receipt to report to the end user

• Failure rates• Failure rates • ~7% speciation

d• <5% FLD resistance prediction in MTBC

ConclusionsConclusions• WGS in routine settings in the UK• WGS in routine settings in the UK

• Simplified workflowsf• Massive time savings vs routine workflows 

including species ID, DST, and VNTR• Significant reduction in false clustering –elimination of unnecessary public health actions –money saving

• EU levelEU level• Use of TB WGS for tracing cross‐border M/XDR TB outbreaksoutbreaks

Future workFuture work• Failures predominantly due to low DNA conc/human DNA• Failures predominantly due to low DNA conc/human DNA contamination – improvements

• Turning off DST for first line drugs – very soon• Shortening TAT• Shortening TAT

• Technically possible; resources needed• Standardization and QA:

• Further development and implementation of EQA schemes• Further development and implementation of EQA schemes • Internal controlsR i l• Reporting language

• Accreditation • Linking mutations to MICs

• Adjustment of drug regimens• Adjustment of drug regimens

AcknowledgementsAcknowledgements•United Kingdom

•NMRS Central and North•NMRS South•University of Oxford•PHE TB surveillance unit•PHE TB surveillance unit•PHE Field Epidemiology Service•PHE Bioinformatics unit

•ECDCd h l f l b•Swedish National TB Reference laboratory

•Ospedale/University San Raffaele Milan ItalyOspedale/University San Raffaele, Milan, Italy•Centre for Mycobacteria, Borstel, Germany• Institute Pasteur Lille, France

Thank you for your attentiony y

vlad.nikolayevskyy@phe.gov.uky yy p g