27 Ο ΠΑΝ Ε ΛΛΉΝΙΟ ΣΥ ΝΈ ΔΡΙΟ AI DS

ΑΘ ΉΝ Α Ν Ο Έ ΜΒ ΡΙΟ ς 2015

Ελένη Ναστούλη

Διευθύντρια Ιολογικού Τμήματος

University College London Hospitals

Τι νεώτερο στο AIDS

Στο Εργαστήριο...

Clinical VirologyClinical

Virology

Patient care

Patient care

Training & EducationTraining & Education

Research & InnovationResearch & Innovation

DiagnosticsDiagnostics

UCLH Department of Clinical Virology

Trust-wide responsibilitiesInterdisciplinary work

PHE responsibilities Business Development

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EH

UB

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ΣΑφουγκραζόμενοι τους ασθενείς μας...

Ε1; Παίρνω τη σωστή θεραπεία τη στιγμή που πρέπει?

Ε2; Θα πρέπει να παίρνω φάρμακα για όλη τη ζωή μου? Υπάρχουν φάρμακα

καλύτερα από αυτά που παίρνω?

Ε3; Υπάρχει άλλος τρόπος να καταπολεμήσω τον ιό? Να βοηθήσω το ανοσοποιητικό

μου σύστημα?

Ε4; Θα μπορούσα να διαγνωσθώ νωρίτερα?

Ε5; Πώς θα μπορούσα να μην είχα μολυνθεί?

Ηλεκτρονική

Υγεία

Κλινικές

Μελέτες

Βασική

ιολογία/ανοσολογία

μ

Νέες

Διαγνωστικές

μ Μοριακή

Επιδημιολογία

Ε1; Παίρνω τη σωστή θεραπεία τη στιγμή που πρέπει?

ICPICP

AuditAudit

KPIsKPIs

PPIPPI

Guide

l ines

Guide

l ines

Protease Inhibitor Monotherapy: : Effectiveness and Resistance in Clinical Practice

Integrated Care Pathways Key Performance Indicators

Patient Public Involvement

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El Bouzidi et al EACS 2015

• 40% were viraemic at PIMT initiation and at least 30% had pre-existing PI RAVs

• Virological failure occurred in 2/3 - about twice that seen in PIMT trials

• Over half of patients ended the observation period with virological suppression

• Minor mutations continued on PIMT; those with major switched to cART.

• Loss of future PI options was seen in 6% (6/95) compared to 1% (3/296)

of participants in the PIMT arm of the PIVOT trial.

Clinic Audits

UK HIV Drug Resistance Database

The UK Collaborative HIV Cohort

Secure web-based data

collection using REDCap,

using certified research

computing service at

UCL

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ICPICP

AuditAudit

KPIsKPIs

PPIPPI

Guide

l ines

Guide

l ines

Integrated Care Pathways

Key Performance Indicators

Patient Public Involvement

• Clinical care and treatment data

• Started 2001

• >45,000 records, >16yrs, >1996

• Monitor uptake and

response to therapy

• Central repository for resistance tests performed

as part of routine clinical care throughout the UK.

• Started 2001

• By the end of 2013 over 114,000 test results

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12

14

16

2005

(n=3858)

2006

(n=4948)

2007

(n=4962)

2008

(n=5561)

2009

(n=4795)

2010

(n=4467)

2011

(n=4387)

2012

(n=4044)

2013

(n=3527)

% T

DR Total

MSM

Heterosexual Male

Heterosexual Female

Prevalence of transmitted drug resistance by transmission group, 2005-2013

Tostevin et al submitted 2015

Trends in HIV-1 transmitted drug resistance in UK

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No evidence of an intrinsic subtype effect on the rate of virological failure

on first-line tenofovir-containing regimens

• HIV-1 subtype C viruses have a propensity to develop the K65R mutation in RT

• in cell culture and clinical populations

• The likely mechanism for this effect is the poly-adenine stretch at codons 63-65

• ? patients experience higher rates of virological failure on tenofovir

8,746 patients within UK CHIC and HIV Resdb White et al submitted

University College London Hospitals Biomedical Research Centre

UCLH NIHR Health Informatics Collaborative

• The Government’s Plan for Growth details a number of actions designed to promote the UK as a competitive global hub for life sciences

• The CMO Grand Challenge to BRC supported Trusts collaborate in the use of NHS data & realise benefits to

translational health research,

frontline care,

health services planning

patients and the public.

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Concept of

Standardised

research database

CDR

UCLH

research

systems

winPATHWord

Proforma

(MDT)

Excel

Spread

sheet

UCLH

clinical

systems

Shared IT

environmentCarecast

Current UCLH IT architecture

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CA

PT

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EA

T

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LH:

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GO

AL

Standardised

research

database

Theme specific

research

database

Carecast

Data extract for

specific research

theme

CDR

UCLH

research

systems

winPATHUCLH

clinical

systems

Shared IT

environmentPatient

portalViralCIS

The goal

UCL Safe Data Haven – Architecture

UCL Safe Haven –

sandbox approach

UCLH Source

Data Systems

UCLH

De-identification

Service

CSV Extracts

Re-identified

XML File

Influenza clinical data

workspace

Norovirus clinical data

workspace

HIV clinical data

workspace

Viral Hepatology

Workspace

12

• ISO 27001

• IG Level 2

• Dual Factor Authentication

• Encrypted Storage

Anonymised /

Pseudonymised

data

• NHS Fi rewall

• Dual Factor Authentication

Ε2; Θα πρέπει να παίρνω φάρμακα για όλη τη ζωή μου? Υπάρχουν φάρμακα καλύτερα από αυτά που παίρνω?

BREATHER: BREaks in Adolescent and Child THerapy using

Efavirenz and two nRtis

Phase II RCT- Short Cycle Therapy (SCT) (5 days on/2 off)

Primary endpoint: HIV RNA >50c/mL Primary endpoint : week 48

Week 48 assessment

Number

of

events

Person

years at

risk

Estimated

probability of

failing*

(90% CI)

SCT 6 99.53 6.1% (2.1,

10.2%)

CT 7 98.75 7.3% (2.9,

11.7%)

Difference (SCT-CT) -1.2% (-7.3, 4.9%)

Non-infe

riority

m

arg

in

-.16 -.12 -.08 -.04 0 .04 .08 .12 .16Estimated difference in proportion of YP with VL failure

SCT betterCT better

Results are consistent with non-inferiority of SCT compared to CT

Ireland •

• USASpain •

• Argentina

• Denmark

• Ukraine• Germany

Belgium

• Uganda

Thailand •

•• 199 Young people (YP)

99 in SCT vs 100 in CT

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Total HIV-1 DNA

Integrated

HIV-1 DNA

2-LTR circles

CA HIV-1 RNA

In the context of highly efficacious treatment do we need “new” virological markers?

Antibody characterisation

Low level viraemia

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015

20

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0 1000 2000 3000 4000 5000HIVcopies/mi llion cells

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Distribution of Total HIV-1 DNA values at baseline, SCT arm then CT arm

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0 1000 2000 3000 4000 5000HIVcopies/million cells

05

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0 1000 2000 3000 4000 5000HIVcopies/million cells

Distribution of Total HIV-1 DNA values at week 48, SCT arm then CT arm

Ferns et al 2015 unpublished data

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Total HIV-1 DNA ; the application of ddPCR

Archin et al. Nature Reviews Microbiology 2014

UCLHqPCR vs ddPCR; unpublished data

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ΙΟΤο λανθάνον ιικό φορτίο; «Μες στον καθρέφτη και τι βρήκε η Αλίκη εκεί».

Υποθεση της Κόκκινης Βασίλισσας

“πρέπει να τρέχεις συνεχώς για να παραμείνεις στην

ίδια θέση” Lewis Carroll 1865

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ΙΟPersistent HIV Infection

Palmer S 2014

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CNS/CSF

Where to Measure

Persistent Virus?

�Peripheral Blood

Plasma

Cells: RNA versus DNA

�Tissue Compartments

T cells

Other cell types

RNA versus DNA

�Role of Replication

Defective HIV

Measuring Persistent HIV

Palmer S 2014

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Lewin & Rouzioux, AIDS 2011

Rouzioux & Richman, 2012

Measuring Persistent HIV

Quantitative Viral Outgrowth Assay

(QVOA)

PROCEDURE: Highly purified latently infected resting CD4+ T cells are plated ina serial dilution and are maximally activated to reverse latency. Activated

peripheral blood mononuclear cells (PBMCs) that are isolated from uninfecteddonors are added to propagate the virus.

It currently remains the most reproducible and reliable method to measure HIV-1latency and assess eradication strategies.

QVOA measures latent replication-competent HIV-1

Archin et al. NatureReviews Microbiology

2014

Technical comparative

DURATION 2-3 weeks 1 day 1 day 1 day

SPECIAL

EQUIPMENTnone Real-Time PCR Real-Time PCR Droplet former and reader

SAMPLE CD4 T cel l s PBMC/CD4 T cel l s

whole blood

leukocytes/PBMC/CD4 T

cel ls

PBMC/CD4 T cel l s

PRECISIONDependent on many variables

Des ired precision can be

achieved by increasing total

number of PCR replicates

SENSITIVITY

Detection is capable down to a 2-

fold change

Linear response to the number

of copies present to a llow small

fold change differences to be

detected

QVOAConventional Real-Time PCR

(Relative quantitation)Diatheva® qPCR Kit

(Relative quantitation)Droplet Digital PCR

(Absolute quantitation)

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Measurement Advantages Disadvantages

HIV RNA in Plasma Relatively inexpensive Difficult to separate reservoir

expression vs HIV replication,

some positive samples

undetectable,

may not be representative of

intracellular HIV RNA and DNA

levels

Infectious Virus: estimates the

number of infectious units of HIV

per million mononuclear cells

(IUPM)

Only direct measurement of

replication competent virus or

number of proviruses capable of

productive infection

Requires large quantities of cells,

£££, large error, often impossible

to detect changes in reservoir size

Total HIV DNA(Peripheral Blood or Tissue

Compartments)

Inexpensive, easy Unintegrated HIV DNA

contributes to signal unless

patients are on HAART for 1-3

years. A lot of virus is defective.

Integrated HIV DNA(Peripheral Blood or Tissue

Compartments)

excludes unintegrated HIV DNA,

less error than IUPM

Requires at least a million cells,

complex assay, defective provirus

Palmer S 2014

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Η ιαση ειναι συνώνυμη της ελλειψης

ανίχνευσης DNA ή RNA?

Ποιοί ιστοί/κύτταρα είναι αντιπροσωπευτικά

για να φτάσουμε σε αυτό το συμπέρασμα?

Ποιοί οι δείκτες «ίασης» που θα επιτρέψουν τη διακοπή της θεραπείας?

Απαιτούνται

νέες ευαίσθητες μέθοδοι ανίχνευσης ιού που μπορεί

δυνητικά να αντιγραφεί

Ανάγκη για μεθόδους πιστοποιημένες, χαμηλού κόστους,

διαθέσιμες στους πληθυσμούς

Τα ερωτήματα.....

Mr W met two other HIV and HCV co-infected

men online. They met, drew up syringes of each

other‘s blood and injected themselves with each

others’ blood

Ε3; Υπάρχει άλλος τρόπος να καταπολεμήσω τον ιό? Να βοηθήσω το ανοσοποιητικό

μου σύστημα?Β

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ΙΟΛ

ΟΓΙΑ

ΚΑ

Ι ΑΝ

ΟΣ

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ΟΓΙΑ

Acute HCV

Sample

number

Days in

follow up

since HCV

RNA

detection

HCV RNA

(IU/ml)Anti-HCV

ALT

IU/L

5’UTR region

Sanger

sequencing

genotype

5’UTR region NGS

% of genotypes present using k-

mer analysis

4d 1c 1a

1 1 13,000,000 Not det 30 4d 100% 0.0% 0.0%

2 8 12,000,000 Not det 31 4d 100% 0.0% 0.0%

3 57 36,000,000 Not det 196 1c 0.9% 99.1% 0.0%

4 71 50,000,000 Not det 391 1c 0.0% 100% 0.0%

5 75 29,000,000 Not det 1487 1c 0.0% 100% 0.0%

6 111 12,500,000 Pos 1091 1a 0.0% 0.0% 100%

7 162 300 Pos 375 1a 0.0% 0.0% 100%

8 183 10,911,300 Pos 221 1a 0.0% 0.0% 100%

9 205 9,000,600 Pos 198 1a 0.0% 0.0% 100%

10 254 9,729,600 Pos 126 1a 0.0% 0.0% 100%

Acute HCV : Next generation sequencing resultsΒ

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Tsang et al J Clin Virol 2015

GT4d GT1c GT1a

HCV Ab +HCV Ab-

Acute HCV case follow up New infection

Tsang et al 2015 submitted

R155K and V36M mutations

Acquired not transmittedNo mutations

Week 0 of tx Week 4 of tx Week 1 EOT

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ΓΙΑHCV phylogenetic analysis on treatment

0

500

1000

1500

2000

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1,00E+04

1,00E+06

1,00E+08

0 100 200 300 400 500 600 700

ALT

(I

U/L

)

HC

V

VIR

AL

LOA

D (

IU/M

L)

DAYS SINCE ESTIMATED TIME OF HCV INFECTION

B

0

500

1000

1500

2000

1,00E+02

1,00E+04

1,00E+06

1,00E+08

0 100 200 300

ALT

(IU

/L)

HC

V

VIR

AL

LO

AD

(IU

/ML)

DAYS SINCE ESTIMATED TIME OF HCV INFECTION

C

0

500

1000

1500

2000

1,00E+02

1,00E+04

1,00E+06

1,00E+08

0 100 200 300 400 500 600 700 800 900 1000

ALT

(IU

/L)

HC

V

VIR

AL

LOA

D (

IU/M

L)

DAYS SINCE ESTIMATED TIME OF HCV INFECTION

D

0

500

1000

1500

2000

1,00E+02

1,00E+04

1,00E+06

1,00E+08

0 100 200 300

ALT

(IU

/L)

HC

V

VIR

AL

LO

AD

(IU

/ML)

DAYS SINCE ESTIMATED TIME OF HCV INFECTION

M

Tp1

Tp2

Tp3

Tp4 Tp3

Tp1

Tp2

Tp4

Tp1Tp2

Tp3

Tp4 Tp1Tp3

Tp2

HCV genotypic and phenotypic diversity in HIV-1 co-infected patients

during progression to chronic infection: single genome analysis over HCV E1-NS3 regions

Ferns et al 2015 submitted

Acute HCV: evolution using Single Genome Amplification and Sequencing

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Transmitted/Founder (T/F) virus by SGA reveals almost star-like phylogenies

Ferns et al 2015 submitted

Ε4; Θα μπορούσα να διαγνωσθώ νωρίτερα?

Early Warning Sensing Systems for Infectious DiseasesΝ

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Vision: Mobile HIV Diagnostics in

A&Es, Sexual health clinics, Primary Care and Community Settings

Human & Economic Benefits to NHS

HIV/AIDS

Data Sources: WHO, PHE, NICE (2012/13)

Early diagnosis = health and economic

benefits to patients and populations

Diagnosis Gateway to Treatment and Prevention

35.3 millionpeople living with HIV

98,400 HIV infected people in UK

21,900unaware of their infection in UK

>1M HIV tests in (STI) clinics in 2013 (up 5%)

Testing coverage in STI clinics increased from 69% (2009) to 71% in 2013.

New opportunities in self-testing

UK Market

1

Διαφάνεια 35

1 not sure this is huge increase in 4yrs? + question wil be why? as there are POCT used in this period?maybe refer to 71% only ? and say we need to further increase this.Eleni Nastouli; 10/6/2015

Competitive Advantage

4th Generation Antibody-Antigen Assays

High sensitivity

High specificity

Cost-effective

Wireless data transfer

Primary CareLateral Flow

• Easy to use

• Low cost• Low sensitivity• Low specificity

• No data linkage

Tertiary HospitalsAutomated Immunoassay

• High sensitivity

• High specificity• Complex• High cost• Data linkage

2

3

Διαφάνεια 36

2 just changed the order here to group pros vs consEleni Nastouli; 10/6/2015

3 as previousEleni Nastouli; 10/6/2015

I4i Early Stage Award

Objectives Delivered Outcomes

Prototype Device Handheld device and disposable

USB-like chip cartridge

Multiplexed Ab detection Multichannel chips to detect p24

and Ab to gp41 & p24

Rapid result within 30mins Results in 5 mins

Sensitive Detection Anti-gp41 >94% clinical

sensitivity

p24 1ng/mL LOD

Specific Detection Clinical Specificity for anti-gp41

detection was 100%.

Simple User interface User friendly electronic read

out. iOS and Android phone

app.

Ability to transmit results to

server

Blue tooth connectivity to

immediately transmit results to

a mobile phone and healthcare

system.

～～～～Electric circuit

(Signal generator)

Electric circuit

(Phase & Amplitude

detector)

Input

transducer

Output

transducer

Sensing area

“Listening” to Viruses

0 1 2 3 4 5 6

-15

-10

-5

0

5

Phase s

hift (d

egre

es)

Time (min)

Negative control

sample

Sensitive (µg-pg/ml)

Specific (4 channels)

Rapid (1-5 minutes)

Simple to use

ELE

AN

O

RGR

AY,

LCN

/

UC

L

Mobile Phone Connected Diagnostics for HIVΝ

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Transmission Clustering Among Kiev HIV Sequences

Gourlay A et al EACS 2015

� 36% A1 and 54% of subtype B sequences clustered � the largest clusters contained mixed risk groups

� 16 sequences (4%) evidence of TDR � TDR sequences did not cluster

� 398 new diagnoses (2013 – 2014)� HIV protease and RT sequences

� linked to demographic data

Ε5; Πώς θα μπορούσα να μην είχα μολυνθεί?

ΜΟ

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ΚΗ

ΕΠ

ΙΔΗ

ΜΙΟ

ΛΟ

ΓΙΑΕ5; Πώς θα μπορούσα να μην είχα μολυνθεί?

Full-Genome Deep Sequencing and Phylogenetic Analysis of Novel

Human Betacoronavirus –MERS CoV

Cotten M et al Emerg Infect Dis 2013

Primers designed for reverse

transcription and

overlapping PCR amplification

MO

LEC

ULA

RE

PI

tMRCA analysis across a

range of fixed evolutionary rates

Full-Genome Deep Sequencing and Phylogenetic Analysis of Novel Human

Betacoronavirus –MERS CoV

Phylogenetic analyses on the partial

RNA-dependent RNA polymerase sequence

region (396 bp) of coronaviruses (CoVs).

Wolfe Nature 2007

Infection response through virus

genomics (ICONIC)

Our vision is that a whole pathogen (virus) genome sequence isnow the essential unit of information that will allow evidence basedinfection control health care levels, will enable outbreak/pandemicpreparedness and will allow stratified patient management withcurrent, future and experimental treatments.

HIV Pilot ICONIC dataset

Gonzalo Yebra, 2015 45

• 375 sequences

• Mean length of 6.86kb (0.78-9.2)

• Coverage:

• 292 (78%) include at least 500nt of gag, pol and env

Overview

1. Good genome coverage 2. Reliable variant calling

3. Accurate subtyping 4. Dual infection discovery

Examples of quality metrics: Gene coverage QA

100% 50% 0%

Coverage per gene

Samples

Gene coverage linked to primer efficiency

Small genes can fail because they are within a single primer

75% samples have coverage at all known RAV positions

(~400 for PI, NRTI, NNRTI and INI)

Next step: Transmission networks detection

• Clusters identified in the phylogenetic trees

• Defined by high statistical support and low genetic

distance (GD)– The GD threshold depends on the evolutionary rate of each gene (aprox. twice

as high for env than for gag-pol)

– We will learn more about threshold thanks to these full genomes

Gonzalo Yebra, 2015 48Genetic distance

Statistical support

Cluster

LINKING TO CLINICAL DATA

ANATOMY OF NOSOCOMIAL INFECTION?

ICONIC ID Influenza Genome Assembly IVA 0.7

All Influenza A tree

DETAIL OF LINKED VIRUS GENETICS

AND HOSPITAL INFORMATION

Age Sex SampleDate AdmissionDate Origin DischargeMethod

46 f emale 31 Jan 2013 31 Jan 2013 CA Discharged - Clinical Adv ice

Age Sex SampleDate AdmissionDate Origin DischargeMethod

36 male 31 Jan 2013 1 Feb 2013 CA Discharged - Clinical Advice

Age Sex SampleDate AdmissionDate Origin DischargeMethod Diagnosis

87 f emale 1 Feb 2013 18 Jan 2013 HA Patient Died Septicaemia due to Staphy lococcus aureus

48 male 3 Feb 2013 29 Jan 2013 HA Discharged - Clinical Adv ice

35 f emale 28 Jan 2013 20 Jan 2013 HA Discharged - Clinical Adv ice

TH

AN

KY

OU

!

NIHR-HIC

Ray Wells

Nicola Cooper

Angela Poland

William Rosenberg

Nick Mac Nally

Brian Williams

National Screening Committee

and Institute of Child Health

Sharon Webb

Heather Bailey

Claire Thorne

Catherine Peckham

MRC CTU and PENTA

Di Gibb

Nigel Klein

Ab Babiker

Kholoud Porter

Carlo Giaquinto

ICONIC

Zisis Kozlakidis

Matt Cotton

Dan Frampton

Jade

Anil Gunesh

Andrew Hayward

Paul Kellam

Deenan Pillay

iSENSE

Eleanor Gray

Val Turbe

Rachel MacKendry

UCLH Clinical Teams

Hilary Hewitt

Camille Mallet

Annette Jeanes

Pietro Cohen

Bruce Macrae

David Brealey

Rob Miller

William Rosenberg

UCLH Clinical Virology

Paul Grant Bridget Ferns

Stuart Kirk Shelley Wilson

Mike Kidd Frank Mattes

Jeremy Garson Deenan Pillay

LGC

Alexandra Whale

Eloise Busby

Jim Huggett

OjBio

Vicky Lawson

Dale Athey

Illumina

Roberto Rigatti

Miao HeMortimer Market Centre

Richard Gilson

Laura Waters

Simon Edwards

Virology and Micro/ID SpRs

University College London HospitalsBiomedical Research Centre

RFH Virology

Hepatology & HIV Teams

Examples of quality metrics: Genomic coverage QA

Adapted from “Thomas Splettstoesser (www.scistyle.com) via Wikimedia Commons”

70% of the genomes cover 80% of the subtype B

Examples of quality metrics: Minority variants QA

Filter: variants >=5%; consensus depth >100; variant depth >50

Average reads depth: ~7000

Conclusion: Pan-HIV primers do their job

Genomic coverage independent of subtype (median: 82%)

HIV1-B most prevalent subtype

Output: Detection of Dual infections

Very similar depth profiles can highlight dual infections

Depth from competitive mapping of reads from subtypes B and G

Output: Validation of recombinants

We can use the same technique to validate predicted recombinants

Subtype classification

Gonzalo Yebra, 2015 59

Subtype No. %

A1 18 4.8

B 154 41.1

C 80 21.3

D 7 1.9

F1 7 1.9

G 11 2.9

CRFs 68 18.1

URFs 30 8.0

TOTAL 375

CRFs No. %

CRF01_AE 16 4.3

CRF02_AG 33 8.8

CRF03_AB 1 0.3

CRF06_cpx 8 2.1

CRF07_BC 1 0.3

CRF09_cpx 1 0.3

CRF13_cpx 1 0.3

CRF14_BG 1 0.3

CRF18_cpx 1 0.3

CRF19_cpx 1 0.3

CRF25_cpx 1 0.3

CRF43_02G 1 0.3

CRF49_cpx 1 0.3

CRF60_BC 1 0.3

TOTAL 30

3 A1/D3 B/C3 B/CRF01_AE3 B/CRF02_AG…