Multimodal Biomarkers for Alzheimer’s disease

Candidates versus genomics and proteomics

Research collaborations relevant to biomarkers for AD:

• Proteome Sciences, Millipore Merck and GSK

• J&J and GE

• Precompetitive collaborative projects with multiple European Federation of

Pharmaceutical Industry Associations (EFPIA) partners

Other, non-biomarker, collaborations

• Astra Zeneca

• J&J

Consultancy / speaker fees

• Astra Zeneca

• Lundbeck

• Lilly

Conflict of interests

Karolinska

Johan Bengtsson

Tony Segerdahl

Christian Spenger

Eric Westman

King’s College

Simon Lovestone

Andy Simmons

Catherine Tunnard

University of Kuopio

Mervi Kononen

Hilkka Soininen

Ritva Vanninen

University of Perugia

Emanuela Costanzi

Patrizia Mecocci

Roberto Tarducci

Aristotle University

of Thessaloniki

Eleni Kantoglou

Penelope Mauredaki

Magda Tsolaki University of Lodz

Tadeusz Biegański

Iwona Kłoszewska

Radoslaw Magierski

University of Toulouse

Celine Caillaud

Pierre Payoux

Bruno Vellas

McGill University

Louis Collins

Alan Evans

Sebastian Muehlboeck

AddNeuroMed academic clinical and imaging team

NA-ADNI

J-ADNI

WW-ADNI

AIBL

C-ADNI AddNeuroMed

E-ADNI

Biomarkers for dementia – core pathology markers

Biomarkers for dementia – alternative approaches

-

Albumin

Pre-clinical studies Human studies

Biomarker discovery

Samples

Pre-clinical models

Clinical studies Validation

Samples

Biomarker discovery Validation

Human studies

Long term follow-on sample

Case control sample

Short term follow-on sample

Pro

teom

ics

Progression

markers

Diagnostic

markers

Surrogate markers

for use in trials

Bioinformatics

Intelligence

networks

Genom

ics

transcripto

mic

s

Candidate markers – hypothesis and informatics

Page 9

1.5 T sMRI and automated analysis

Regional cortical thickness – 34 areas

Regional cortical volume – 24 areas

36 cytokines measured by Luminex

commercial 30 plex and customised 6 plex

Analysis by Support Vector Machines to find the best

combination of predictors

Candidate markers – inflammatory proteins

Biomarkers of Mild Cognitive Impairment -

progression to dementia

Deborah Kronenberg, Simon Furney, Andreas Guentert, Andy Simmons

Total n= 48; 22 convertors Total n= 253 (99 convertors; 48 with imaging)

Intelligence networks to determine candidate

biomarkers

BioWisdom Sofia platform database access

Databases

Gene Ontology

NCBI Gene Expression Omnibus

Gensat Database

Diseases Database

HuGE Index – Tissue specific expression of mRNA database

KEGG – protein pathway information

Uni-Prot – database of proteins

On-line Mendelian Inheritance in Man – disease-associated genetic mutations

Biomarkers in AD knowledgebase (http://www.telemakus.net/AD/)

Textual Data

Medline

Text books (Adams and Victor’s Principles of Neurology)

Full text papers

Alzheimer Research Forum (http://www.alzforum.org/)

Essential Science Indicator’s (http://www.esi-topics.com/alzheimer/)

Workflow results

Proteins with a functional involvement

in defined pathological processes and

hallmarks of AD

26

Proteins/mRNAs expressed in AD

(defined brain regions)

5257

Proteins/mRNAs upregulated in AD

67

Candidates discovered through Intelligence Network

Candidates known to be involved in core pathology e.g:

• Apolipoprotein E

• BACE1

Candidates known to be involved in core pathology e.g:

• Clusterin

• Transthyretin

Candidates not previously thought of as markers e.g:

• Choline acetyltransferase

• Urokinase Plasminogen Activator Receptor (uPAR)

Validation process in uPAR and ChAT

Ines Greco, Julie Barnes, Niki Day

Discovery of markers based on disease hypothesis has proved successful

Discovery of markers beyond the core hypothesis has merit

Immune based markers show a clear but not consistent signal

Informatics based candidate discovery reveals novel markers

Summary – candidate markers

Candidates are limited by ‘what is known’ …….. genomics and proteomics are not

Blood based transcriptomic Markers

• Illumina Human HT-12 v3 Expression BeadChips (Illumina)

• N=332 (106 AD, 118 MCI [77 MCIStable, 41 MCIConverter] and 108 control

• Feature selection by random forests; t-tests with feature sets of 5, 10, 20, 50,

100 probes

• Random forests and SVM used as class prediction; combined with APOE and

MRI data and independently

Angela Hodges, Katie Lunnon, Richard Dobson

Blood based transcriptomic Markers – AD vs controls

Blood based transcriptomic Markers – AD vs controls

Blood based transcriptomic Markers

• Oxidative phosphorylation (OXPHOS) ; mitochondrial complexes I to V

• Transcription (GO:0006350; P = 9.1x10-147) / Translation (GO:0006412; P = 2.1x10-116)

• RNA Splicing (GO:0008380; P = 8.2 x10-102) / Processing (GO:0006396; P = 6.5 x10-98)

• ATP Binding (GO:0005524; P = 3.2 x10-94) / Protein Catabolism (GO:0044257 P = 1.7 x10-89)

• Ribosome Biogenesis (GO:0042254 P = 7.6 x10-37)

• Microtubule Cytoskeleton (GO:0015630; P = 1.8x10-33)

• Vesicle-mediated Transport (GO:0016192; P = 2.8 x10-28)

• Apoptosis (GO:0006915; P = 1.7 x10-7) and regulation (GO:0042981; P = 5.2 x10-13)

• Immune response and adhesion molecules

Gel based biomarker discovery in plasma

M.Wt

14 kDa

220 kDa

-

pH 3 Non-linear IPG pH 10

Albumin

IgG g chain

IgG l and k chain

Abdul Hye, Madhav Thambisetty, Latha Velayudhan

Limitations of gel based proteomics:

- Dynamic range is challenging

- To deplete or not to deplete ?

Case - control study

- two dimensional gel electrophoresis (2-DGE)

126.1 127.1 128.1 129.1 130.1

Study Reference Pool n=80

131.1

Plasma S1

Excise the complete

gel lane into ~ 10

individual eppendorfs

Reduce/Alkylate

+ trypsin

Discovery Proteomics Experiment using Isobaric Tandem

Mass Tags (TMT)

Quantitation

Plasma S2 Plasma S3 Plasma S4 Plasma S5

23

Proteomics Workflow Dec. 9th 2011

-

1.0

0.6

0.2

1.4

1.8

Rela

tive

op

tica

l d

en

sity

P<0.05

0.0 0.2 0.4 0.6 0.8 1.0

1 - Specificity

0.0

0.2

0.4

0.6

0.8

1.0

Sen

sit

ivit

y

ROC Curve

Complement Factor H (CFH)

n>500

Validation of CFH as a marker

CFH biomarker replication

Cutler et al (2008) Proteomics Clin Appl

Gel based separation, depletion, MS identification

47 x 47 (cases x controls)

CFH identified as predominant marker on 2DGE

Akuffo et al (2008) Biomarkers 13 618-636

Rosiglitazone AD trial

Gel based proteomics at baseline and 24 weeks of therapy

CFH shows correlation with response to rosiglitazone

Mueller et al. (2010) J Alzheimers Dis ePub

CFH identified as altered in AD using proteomics

Correlation with cortical atrophy

Correlation with cognition (MMSE)

Correlation with speed of decline

Using endophenotypes to search for biomarkers

Protein ID - O

1Complement C3

2g -Fibrinogen

3Serum albumin

4Complement Factor-I

Clusterin

Clusterin

7Serum amyloid P-

component

8α1-microglobulin

Protein ID - O

1Complement C4-A

2γ -Fibrinogen

3Complement

component C8 g

chain

Clusterin

5Complement C4-A

6Complement C4-A

7Apolipoprotein A-I

8Apolipoprotein A-I

9Transthyretin

[s1]

Hippocampal atrophy

n=44 Progression

n=51

Madhav Thambisetty, Latha Velayudhan

Validation – correlation with imaging, cognition and progression

AD only : n=113,R= -0.31 and p=0.001

AD and MCI: n=220, R=-0.14 and p=0.04

Imaging: entorhinal atrophy Cogntion : MMSE

AD only : n=576, r=-0.22; p<0.001

Andreas Guentert, Abdul Hye,

Anna Kinsey

Non-rapid

decline

Non-rapid

decline

Rapid

decline

Rapid

decline

Retrospective decline

n=344

Prospective decline

n=237

[Clu

ste

rin

] pla

sm

a

Progression: before and after sample point

Plasma Clusterin association with brain amyloid

In man….

Susan Resnick, Madhav Thambisetty and the

BLSA study team

W/T APP/PS1

In brain….

In mouse….

Plasma clusterin in life correlates with brain clusterin

in superior temporal gyrus

R=0.47 ; p = 0.027 ; N=22

David Howlett, Paul Francis, Andreas

Guentert

Muzamil Saleem, Andreas Guentert

September 6th 2009

N=14,000

N=16,000

Next steps – assay design for qualification

Intellectual property on ~30-protein panel protected by

KCL/Proteome sciences. Licensed for research use to Millipore

Joint development with Proteome Sciences and Millipore

Funding from MRC

Luminex xMAP panel

MS panel using Reaction Monitoring and isobaric tags

Validation / qualification study design

Validation / qualification workflow

AddNeuroMed

DCR

genADA

TOTAL

595 126 275 996

Normal 213 68 114 395

MCI 172 - - 172

AD 210 58 161 429

Normal MCI AD Sig.

n=395 n=172 n=429

Age, yrs 76.22

(±6.0, 54-93)

76.34

(±5.8, 65-90)

77.21

(±6.3, 58-96)

Non-sig

Sex (% female) 51.6% 49.4% 55.5% Non-sig

APOE genotype

(% e4)

27.1% 35.0% 59.9% P<0.001*

MMSE

28.92

(±1.26, 22-30)

26.91

(±2.9, 0-30)

21.15

(±5.2, 0-30)

P<0.001*

N=1000; validation study

Marker correlation with ADAS-cog

CFH CRP

Classifier scheme

AD vs. Control models

Train Test

Used variables Sens.

[%]

Spec.

[%]

Acc.

[%]

Sens.

[%]

Spec.

[%]

Acc.

[%]

1. Proteins 79.2 66.7 73.8 75.7 71.6 73.7

2. APOE, age,

gender

67.5 66.7 67.5 69.9 56.8 63.6

MCI vs. Control models

Train Test

Used variables MCI [%] CTL [%] Acc.

[%]

MCI [%] CTL [%] Acc.

[%]

1. Proteins 74.2 67.4 68.0 85.7 61.1 68.6

2. APOE, Age,

gender

59.7 61.8 60.2 19.1 72.6 56.2

AD vs. MCI models

Train Test

Used variables AD [%] MCI [%] Acc.

[%]

AD [%] MCI [%] Acc.

[%]

1. Proteins 75.3 72.6 75.9 71.9 66.7 70.4

2. APOE, age,

gender

71.4 54.8 65.7 92.2 14.3 69.7

Both candidate and data-driven approaches confirm a signature of potential

markers in blood

Candidate studies repeatedly find an inflammatory signal but the exact nature of

the signal differs between studies

Transcript based data-driven studies find a signature but further replication and

refinement is needed

Proteomics yields a consistent signature from discovery by endophenotypes,

through replication, biological confirmation and qualification studies

Summary

BRC Bioinformatics team

Martina Sattlecker

Anbarasu Lourdasamy

Simon Furney

Gerome Breen

Richard Dobson

KHP Biomarkers team

Abdul Hye

Joanna Riddoch-Contreras

Rufina Leung

Andreas Guentert

Mohammed Aiyaz

Madhav Thambisetty (NIA)

Katie Lunnon

Latha Velayudhan

Angela Hodges

Simon Lovestone

AddNeuroMed Imaging team

Eric Westman

Sebastian Muehlboeck

Sergi Costafreda

Lars-Olof Wahlund

Christian Spenger

Alan Evans

Andrew Simmons

Collaborators in Proteome Sciences, Millipore, GSK, GenADA, UCLA

AddNeuroMed collaborators

Supported by:

FP6 EU funding to AddNeuroMed

NIHR Biomedical Research Centre for Mental Health

at the South London and Maudsley NHS Foundation

Trust and King’s College London

MRC Centre for Neurodegeneration research

Additional funding from Alzheimer’s Research Trust