Prof Derrick Crook's presentation to the Monday session of the Chief Scientific Officer's #HCS15 2015 Annual Conference

1. Shaping the Public Health and
NHS Interface
Derrick Crook
Public Health England
University of Oxford
Oxford University Hospitals

2. What is our diagnostic ambition?
• We are starting a journey to near completely
digitalise the entire microbial information for
every ‘isolate’ yielding the entire diagnostic
content precisely
– Analogous to The Yellow Pages finding a place in the
modern digital world
– Or closer to home: radiology
• This will replace routine microbiology as we
know it

3. The NHS and Public Health converge
on a single solution

4. Vision for the future
Culture/sample
1 Species Identification,
2 Resistance prediction
3 Typing:
strain-typing
patho-typing
Now Future

5. Current processes for bacteria
Nature Reviews Genetics 13, 601-612 (September 2012)

6. Future practice
In one step generate the complete diagnostic,
typing and surveillance information
Nature Reviews Genetics 13, 601-612 (September 2012)

7. Challenges with creating a robust
WGS solution
• A triad:
1. Robust evaluative sampling frames
2. Processing, storage of sequence data and linkage to
clinical data
a. Assembly of genomes
b. Analysis of sequence data
c. Storage
d. Combined genomic and epidemiological data analysis
3. Validation of WGS solutions vs current methods

8. Clostridium difficile

9. Role of symptomatic patients in C. difficile
transmission
• We sequenced 1223 of
all 1251 hospital and
community CDI cases
(98%) in Oxfordshire,
September 2007 –
March 2011
• Hospital admission and
ward movement data,
and home postcode
district and GP location
available for each case
• 3 Hospitals
– Typical CDI incidence
– Infection control in line
with published guidelines
Oxford
Banbury
23 miles
Eyre: N Engl J Med 2013; 369:1195-1205

10. Source of new C. difficile cases
All cases
No genetic matches
624 (65%) > 10 SNVs
Genetic matches
333 (35%)
Genetic Matches (0-2 SNV)
No known contact
Community
Other same Hospital
Medical
specialty
Direct Ward contact
Indirect ward (‘spore’)
957
333
120 (12%)
32 ( 3%)
12 ( 1%)
26 ( 3%)
126 (13%)
17 ( 2%)
N Engl J Med 2013; 369:1195-1205

11. Outcome of study
• Only 20% of C. difficile infections are caught in
the hospital from another case
• Most cases caught C. difficile from a very large
reservoir
• Have developed software to monitor and track
infection in the hospital and nationally

12. TB
• We have developed an end-to-end whole
genome sequence based diagnostic solution
for mycobacteria

13. Pilot study for mycobacterial processing
October 2013 – April 2014
• Participants: Oxford, Birmingham, Brighton,
Leeds, Lille, Borstal, Dublin & Vancouver.
• Clinical samples requiring mycobacterial
culture were processed in MIGT tubes
• When they flagged positive, 1 ml was
removed for DNA extraction and preparation
• Sequenced by MiSeq locally
ECCMID 2014

14. Bioinformatics processing
– 331 samples processed
– Data Transferred via the cloud to
Oxford
– Analysed and generated reports
recording:
1. Species
2. Resistance prediction
3. Nearest genomics match
(against 4000 WGS TB in
database)
ECCMID 2014

15. Summary – Species ID
For MTBC: Sensitivity 95.4%
Specificity 98.1%
Based on single sequencing event (no repeating of tests)
Identification Concordant WGS only Routine only
M. tuberculosis complex 157 1
M. tuberculosis complex (BCG) 8
M. tuberculosis complex (africanum) 2
M. tuberculosis complex & M. avium complex 1 5 3
M. avium complex 71 1 1
M. avium complex & M. malmoense 1
M. abscessus complex 39 1
M. gordonae 18
M. xenopi 11
M. kansasii 6 1
M. malmoense 3
M. fortuitum 2 1
M. szulgae 2
M. celatum 1
M. lentiflavum 1
M. scrofulaceum 1
Failed to identify species 9 10 2
Total 331 (96%) 18 (5%) 10 (3%)

16. Summary – Resistance prediction
Resistant DST Sensitive DST DST not attempted DST failed
WGS WGS WGS WGS
R S M F R S M F R S M F R S M F
Isoniazid 13 2 1 0 0 143 0 7 0 0 0 0 0 1 0 1
Rifampicin 5 1 0 0 0 148 4 9 0 0 0 0 0 0 0 1
Ethambutol 5 1 0 0 1 153 0 7 0 0 0 0 0 0 0 1
Pyrazinamide 8 1 0 0 0 149 1 8 0 0 0 0 0 0 0 1
Streptomycin 5 1 0 0 0 14 0 0 2 138 0 8 0 0 0 0
Fluoroquinolones 3 1 0 0 0 6 0 0 2 148 0 8 0 0 0 0
Aminoglycosides 1 0 0 0 0 5 0 0 2 141 7 12 0 0 0 0
TOTAL 40 7 1 0 1 618 5 31 6 427 7 28 0 1 0 4
WGS resistance predictions for MTB isolates compared to phenotypic DST. R = Resistant, S =
Sensitive, M = Mixed (resistant and sensitive), F = failed WGS prediction

18. Speed – Analysis & reporting
WGS faster
WGS slower

19. Cost
• WGS 50% of routine reference lab costs

20. Mycobacterial WGS diagnostic
solution gone live this week
• Currently Birmingham processing samples
• Next month Leeds, Oxford and Brighton will
start processing
• Linked to national epidemiological data
sources and tracking outbreaks presented as
they emerge
• It is faster, cheaper and better than current
methods

21. The message is
• Expect a shift in skills and knowledge towards
– Computer science
• High performance computing
• Software engineering
• Data linkage
– Computational biology (bioinformatics)
– More sophisticated molecular processing of samples