The Impact Of Biomarkers On The Diagnosis Of … · The Impact Of Biomarkers On The Diagnosis Of...
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The Impact Of Biomarkers On The Diagnosis Of Alzheimer’s
Disease
John Q. Trojanowski, M.D., Ph.D.
NIA Alzheimer’s Disease Core Center, NINDS Udall Center of Excellence For Parkinson’s Disease Research, Center for
Neurodegenerative Disease Research, Marin S. Ware Alzheimer Program, Institute on Aging, Department of Pathology and Laboratory
Medicine, University of Pennsylvania, Philadelphia, PA
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Global Population Forecast: The Silver Tsunami & The Rising Tide (Epidemic) Of AD
AD is the most common dementia with ~5 M patients now & ~13 M by 2025, but a treatment now that will slow disease by ~5 years will decrease AD prevalence and cost ~50% by 2050.
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Aging Related Neurodegenerative Diseases Characterized by Filamentous
Aggregates of Misfolded Proteins Disease Lesions Components
Alzheimer’s Disease (A multi-proteinopathy)
SPs (100%) NFTs (100%) LBs (50%)
TDP-43 (50%)
Aβ Tau
α-Synuclein TDP-43
Frontotemporal Lobar Degeneration Inclusions Tau, TDP-43, FUS
Amyotrophic Lateral Sclerosis Inclusions TDP-43, FUS, Tau
Dementia with Lewy Bodies LBs a-Synuclein
Multiple System Atrophy GCIs a-Synuclein
Prion diseases SPs Prions
Trinucleotide repeat diseases Inclusions Expanded polyglutamine repeats
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Mechanisms and Consequences of Protein Misfolding
and Brain Amyloidosis in Neurodegenerative Diseases
(Forman M, Trojanowski, JQ, Lee VM-Y. Nat Med, 2004)
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Hypothetical Timeline for the Onset and Progression of AD Neurodegeneration and Dementia: The Need For AD
Biomarkers Is Urgent
Shaw LM, Korecka M, Clark CM, Lee VM.-Y, Trojanowski JQ. Nat Rev Drug Discovery, 6:295-303, 2007.
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Targets for Aβ and Tau Focused AD Drug Discovery
Shaw LM, Korecka M, Clark CM, Lee VM.-Y, Trojanowski JQ. Nat Rev Drug Discovery, 6:295-303, 2007.
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M. Weiner, P. Aisen, R Peterson, C. Jack, W. Jagust, J Trojanowski, L. Shaw, A. Toga, L. Beckett, D. Harvey, C Mathis,
A. Gamst. R. Green. A. Saykin, S. Potkin, J Morris, L Thal (D) Neil Buckholz, David Lee, Holly Soares
Industry Scientific Advisory Board (ISAB) and Site PIs, Study Coordinators, and
821 subjects enrolled in 58 sites in US and Canada
FUNDED BY THE NATIONAL INSTITUTE ON AGING
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ADNI-1 Governance Advisory NIA Staff Steering
Committee
PI: Weiner, UCSF Administrative Core
Executive Committee
Biomarkers Core
Trojanowski and Shaw,
Penn
Biostatistics Core
Beckett, UCD
Coordinating Center (CC) Aisen, UCSD
Peterson, Mayo
Clinical Sites: Clinical, MRI, PET,
Biomarkers
Informatics Core
Toga, UCLA
Neuroimaging Center (NC)
MRI Jack, Mayo
PET Jagust, Berkeley
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ADNI Industry Scientific Advisory Board
PIB/PET Supplement : Alzheimer’s Association and GE Healthcare Cerebrospinal Fluid Extension: Alzheimer’s Association, AstraZeneca, Cure
Alzheimer’s Fund, Merck, Pfizer and an anonymous foundation Genome-Wide Genotyping :Gene Network Sciences, Merck, Pfizer and an
anonymous foundation Genome-Wide Genotyping Genetic Analysis: NIBIB, Merck, Pfizer and an
anonymous foundation
New members Abbott, Genentech, Roche, Bayer
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GOALS OF ADNI-1 ($40 M From NIH, $25 M From ISAB, Foundations & FNIH; Funded
From 10/1/2004 To 9/30/2009 With 1 Year No Cost Extension To 9/30/2010)
• Optimize and standardize biomarkers for clinical trials
• Validate biomarkers as measures of change • Validate biomarkers as diagnostics or
predictors • Establish world-wide network for clinical AD
studies and treatment trials
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ADNI-1: Naturalistic study of AD progression • 200 NORMAL 3 yrs • 400 MCI 3 yrs • 200 AD 2 yrs • Visits every 6 mo
• 57 sites • Clinical, blood, LP • Cognitive Tests • 1.5T MRI
Some also have • 3.0T MRI (25%) • FDG-PET (50%) • PiB-PET (approx 100)
All data in public database: UCLA/LONI/ADNI No embargo of data
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NIA GRAND OPPORTUNITES (GO) ARRA GRANT
($24 M From NIH; Funded From 9/30/2009 to 9/30/2011) • Adds cohort of 200 very mild “early”
MCI, (EMCI) • LPs on 100% of new subjects • Follow ADNI-1 controls/MCI additional yr • F18 amyloid imaging on ALL existing
and new ADNI/GO subjects (AV-45) • Complete analysis of all ADNI data
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SCOPE OF ADNI-2 ($40 M From NIH & $29 M From ISAB, Foundations & FNIH; Funded
From 10/1/2010 To 9/30/2015) • Goal to continue to follow >400 controls and
MCI from ADNI-1 for 5 more years and enroll: – 100 additional EMCI (supplements 200 from GO) – 150 new controls, LMCI, and AD
• MRI at 3,6, months and annually • F18 amyloid (AV-45)/FDG baseline and
Yr 2 • LP on 100% of subjects at enrollment • Genetics
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NA-ADNI
J-ADNI E-ADNI
WW-ADNI
A-ADNI
C-ADNI K-ADNI
T-ADNI
Arg-ADNI
Future ADNI sites
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Criteria for Ideal Biochemical Biomarker for Early Detection of AD
• Detect fundamental feature of neuropathology & validation in neuropathology-confirmed cases
• Diagnostic sensitivity for detection of AD >80% & specificity >80% for distinguishing from other dementias
• Reliable, reproducible test • Non-invasive/minimally invasive • Simple to perform • Reasonable cost
Frank, RA et al, 2003; NIA Reagan Institute, et al, 1998
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Diagnostic Performance of Laboratory Tests for AD
Sensitivity Proportion of patients with AD who are correctly identified by the laboratory test
Specificity Proportion of patients without AD who are correctly identified by the laboratory test
Prior probability Frequency of AD in a particular group of patients
Positive predictive value
Proportion of patients with a positive test who are correctly diagnosed as AD
Negative predictive value
Percentage of people with a negative test who are negative for AD at autopsy
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Since there is significant variation in CSF biomarker levels between studies, there is an urgent need to standardize and validate AD biomarkers
Study design: All studies on CSF T-tau with >25 AD cases
Innogenetics T-tau ELISA
34 studies, 2600 AD cases
Comparison of: mean level of CSF T-tau
→ Need for standardization: CSF sampling / handling procedures
Laboratory procedures
External control program
CSF
T-ta
u p
g/m
L
0
100
200
300
400
500
600
700
800
900
1000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
336 pg/mL 919 pg/mL
Study no.
Blennow K, NYAS 2006
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CSF through year 4 Plasma through year 4
N 948 2621
Average time (mins) 35.11 67.94
95% CI (mins) 32.46-37.78 66.32-69.57
• After overnight fast • Collect into polypropylene
tube • Transfer to polypropylene
transfer tube • No centrifugation • Freeze at site, thaw & aliquot
at UPenn, storage at -80 0C
Number of biofluids collected as of 6/30/2010: 13,122 Number of aliquots in biofluid bank: 126,681
Pre-Analytical Issues are Critical: CSF & Plasma Collections For ADNI
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Microspheres coupled with antibody
Each type of microsphere coded with fluorochromes
Up to 100 proteins can be analyzed at once Sample volume = 75 µL.
Microspheres pass 2 lasers:
a) Identification
b) Quantification
Olsson A, et al, Clin Chem 2005
Luminex xMAP Technology for Multiparameter Immunoassays
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Analytical Issues Are Critical: ADNI CSF Biochemical Biomarker Inter-laboratory Study
• University of Pennsylvania: Leslie M Shaw, John Trojanowski, Virginia M-Y Lee, Margaret Knapik-Czajka
• Innogenetics: Hugo Vanderstichele • Sahlgrenska University Hospital: Kaj Blennow • Friedrich-Alexander-Universitat Erlangen-Nurnberg: Jens
Wiltfang, Piotr Lewczuk • Pfizer Global Research & Development: Holly Soares,
Nancy Raha • Eli Lilly & Company: Robert A Dean, Eric Siemers, Richard
Lachno, Brent Salfen, (Linco) • Merck Research Laboratories: Adam Simon
Participating Centers & Investigators
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CSF Biomarker Validation (All Data Are on ADNI Website) • Calibration curve stability • Aliquot reproducibility • Short- & long-term within- and between-
day reproducibility • Stability of biomarkers in CSF
– Freeze-thaw – Room temp – +40C
•
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Within-Center Reproducibility and Between Center
Repeatability for CSF Ab1-42
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CSF pool 00012 CSF pool 00040
Day to Day Variability of CSF Pools
Test Retest Sample Performance
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Tau Aβ1-42 p-Tau181p
Tau/Aβ1-42
p-tau181p/Aβ1-42
LR TAA
ROC AUC 0.831 0.913 0.753 0.917 0.856 0.938
Threshold values
93 ng/mL
192 ng/mL
23 ng/mL 0.39 0.10 0.22
Sensitivity (%) 69.6 96.4 67.9 85.7 91.1 100
Specificity (%) 92.3 76.9 73.1 84.6 71.2 76.9
Test accuracy (%)
80.6 87.0 73.1 85.2 81.5 88.9
Positive predictive value (%)
90.7 81.8 67.9 85.7 77.3 82.4
Negative predictive value (%)
73.8 95.2 70.4 84.6 88.1 100
CSF Biomarker Cutpoints Established Using CSFs Collected from ADNI-Independent Autopsy-Based AD
and Age-Matched Cognitively Normal Subjects
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CSF Aβ1-42 Is Most Informative Single AD Biomarker & LRTAA Best Delineates Mild AD from MCI and NC in ADNI Cohort
Non-ADNI AD cases vs Non-ADNI NC subjects
ADNI Probable AD vs NC subjects
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Survival Analyses for ADNI MCI Subjects: Progression to AD for BASELINE CSF Biomarkers > or < Cut Points
Aβ42<192 pg/mL
t-tau/Aβ4 >0.39
riskTAA2 > 0.34
As of June 28, 2010
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Analysis of the ADNI CSF Data Set By Unsupervised Mixture Modeling
De Meyer G, Shapiro F, Vanderstichele H, Vanmechelen E, Engleborghs B, De Deyn P-P, Coart E, Hanson O, Minthon L, Zetterberg H, Blennow K, Shaw LM, Trojanowski Q.,
The ADNI. Diagnosis-independent Alzheimer’s disease biomarker signature in cognitively normal elderly people. Arch. Neurol., 67:949-956, 2010.
• Unsupervised mixture modeling method: – Assumes that the biomarker data are
obtained from 2 populations – US-ADNI dataset was modeled, without
using the group labels – Start with single biomarker models and
proceed to multiple biomarker models
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Analysis of the ADNI CSF Data Set By Unsupervised Mixture Modeling De Meyer G, Shapiro F, Vanderstichele H, Vanmechelen E, Engleborghs B, De Deyn P-P, Coart E, Hanson O, Minthon L,
Zetterberg H, Blennow K, Shaw LM, Trojanowski Q., The ADNI. Diagnosis-independent Alzheimer’s disease biomarker signature in cognitively normal elderly people. Arch. Neurol., 67:949-956, 2010.
• Mixture model classification based on Aβ1-42 and P-Tau181P gave best fit
• Break down of the signatures obtained by mixture modelling over the clinical diagnosis groups: – AD signature (in red):
• 36% of the normal subjects, 72% of the MCIs and 90% of the AD patients
Normal MCI AD
Aβ1-42 level (pg/ml)
P-T
au18
1 lev
el (p
g/m
l)
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Results: Mixture Model for Multiple Biomarkers (2)
• Validation of the multi-biomarker mixture model on 2 datasets:
P-T
au18
1 le
vel (
pg/m
l)
Aβ1-42 level (pg/ml)
P-T
au18
1 le
vel (
pg/m
l)
1. Data Hansson et al. Lancet Neurol, 2006: 57 of 57 subjects with MCI progressing to AD had an AD-signature (sensitivity=100%)
2. Data Engelborghs et al. Neurobiol Aging, 2008: Sensitivity on autopsy confirmed cases: 63 of 68 (94%)
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ADNI Data Integration Is Off And Running
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OBJECTIVES: Investigate effect of CSF abnormalities on rate of functional decline in NC, MCI, and mild AD.
DESIGN: T-tau, p-tau181, and Aβ42 assayed in CSF from ADNI participants. Random effects regressions to examine the relationship between CSF abnormalities, cognitive impairment (ADAS-Cog), and functional decline (Pfeffer’s FAQ);.
SETTING: ADNI. PARTICIPANTS: 114 NC, 195 MCI, 100 mild AD. OUTCOME MEASURE: Decline in Pfeffer’s FAQ.
RESULTS: Across all groups, persons with a combination of tau and Aβ42 abnormalities exhibited the steepest rate of functional decline.
CONCLUSIONS: CSF abnormalities are associated with functional decline, and the development of AD in NC and MCI subjects, and those persons with tau and Aβ42 abnormalities are at greatest risk of functional impairment.
Arch Neurol, 67:688-696, 2010
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Temporal Ordering of AD Biomarkers Reflects Disease Progression
Shaw et al., 2007; Jack et al., 2010; Trojanowski JQ, et al, 2010
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OTHER INITITATIVES – Michael J. Fox Foundation Parkinson’s Progression Marker Initiative (PPMI)
PPMI: Identify tools to inform PD clinical trial design and decisions
PPMI
Dataset/ sample
collection
Standardized protocols
Biomarker verification
studies
Identify progression
markers
PPMI comprises 4 core objectives
1. Develop/collect comprehensive clinical/imaging dataset and biological samples, which is made available
2. Establish standardized protocols for acquisition, transfer and analysis of clinical, imaging and biologic data
3. Conduct preliminary verification and validation studies on imaging and biologic markers
4. Identify and correlate clinical, imaging and biologic markers for use in future trials.
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PPMI SC and Study Cores Steering
Committee
PI-K Marek, A Siderowf, C Scherzer, D Jennings, K Kieburtz, W Poewe, B Mollenhauer, C Tanner, B Ravina (core leaders, MJFF, ISAB)
Clinical Coordination Core
University of Rochester’s Clinical Trials Coordination Center PI: Bernard Ravina
Imaging Core Institute for Neurodegenerative Disorders PI: John Seibyl
Statistics Core University of Iowa PI: Chris Coffey
Bioinformatics Core
Laboratory of Neuroimaging (LONI) at UCLA PI: Arthur Toga
BioRepository Coriell/BioRep PI: Alison Ansbach, Pasquale De Blasio, Michele Piovella
Bioanalytics Core University of Pennsylvania PI: John Trojanowski, Les Shaw
Genetics Core National Institute on Aging/NIH PI: Andy Singleton
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PPMI CLINICAL SITES
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THE SEARCH FOR NEW AD BIOMARKERS: THE PFIZER-PENN ALLIANCE RULES BASED MEDICINE INITIATIVE
WT Hu, A Chen-Plotkin, SE Arnold , M Grossman, CM Clark, LM Shaw, E Pickering, M Kuhn, Y Chen, L McCluskey, L Elman, J Karlawish, HI Hurtig , A Siderowf, VM-Y Lee, H Soares, JQ Trojanowski.
Novel CSF Biomarkers for Alzheimer’s Disease and Mild Cognitive Impairment Acta Neuropath, 119:669-678, 1010.
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WT Hu, A Chen-Plotkin, SE Arnold , M Grossman, CM Clark, LM Shaw, E Pickering, M Kuhn, Y Chen, L McCluskey, L Elman, J Karlawish, HI Hurtig , A Siderowf, VM-Y Lee, H Soares, JQ Trojanowski. Novel CSF Biomarkers for Alzheimer’s Disease and Mild Cognitive Impairment Acta Neuropath, 119:669-678, 1010.
Random Forests PAM
Traditional AD biomarkers
Traditional + MAP
biomarkers Traditional AD
biomarkers
Traditional + MAP
biomarkers
Sensitivity (%) 88.6 92.4 97.0 97.0
Specificity (%) 86.2 97.0 66.7 87.9
Accuracy (%) 87.9 93.9 86.9 93.9
Improved AD Diagnostic Accuracy
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IMPACT OF ADNI (>150 Published And In Press Papers)
• Provide new information on AD pathophysiology
• Develop early detection methods – Identify those at risk for future AD
• Develop improved treatment trials – ADNI data indicate that biomarkers
increases statistical power over traditional cognitive measures, aid in subject selection, and could reduce clinical trial sample size
• Lead to AD treatment/prevention
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“A Scary Idea: Pre-emptive Brain Scans For Alzheimer's” FORBES, 7/15/2010 - Robert Langreth is a senior editor at Forbes, in
charge of health care coverage
“You feel fine and have no symptoms, but your brain is slowly rotting away. And there is nothing we can do about it. Have a nice day.”
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Integrating AD Diagnosis and Therapy/Prevention Societal Cost: Prevalence: 5 M and rising rapidly; $150B- one of the largest single health care burdens
Genomic • ApoE allele assessment: homozygous 4/4 increase risk
• APP/PS1: familial AD risk
Symptomology • Patient/family complaints about “forgetfulness”
Biomarkers • Aβ42/40 ratio in CSF
• CSF phosphotau
• Deviation from normal suggest pathology of an amyloid type or neurodegeneration
Pet/MR Imaging • Aggregated Aβ42 specific PET measure on PET-CT
• Hippocampal localization; extent and severity of disease confirmed
• Confirm therapy effectiveness
Therapy • Beta secretase inhibitor (APP processing to Aβ)
Therapy Evaluation
Risk, Biomarker, Disease and Therapeutic Evaluation
• Passive/active
immunization
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Biomarkers Will Accelerate Efforts To Treat And Delay The Onset of AD
Delay (years)
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WE AS A NATION HAVE THE PEOPLE, IDEAS AND CAPACITY TO ACCLERATE THE PACE OF FINDING CURES FOR AD, PD, ALS, FTLD AND OTHER AGING RELATED
NEURODEGENERATIVE DISORDERS NOW BUT IT IS A MATTER OF RESOURCES. THUS, WE NEED A PUBLIC-PRIVATE COMPACT TO CURE THESE DISEASES ALONG THE LINES OF
SIMILAR PAST INITIATIVES LIKE BUILDING THE PANAMA CANAL, LAUNCHING THE SPACE PROGRAM AND MANY OTHERS
So where do we go from here?
• The US spends $53 Billion/year on anti-aging balms, salves, lotions, etc. with no proven efficacy.
• The US spends $2.6 Billion/year on Viagra and Cialis.
• The US spends $2 Billion/year on popcorn.
• French President Sarkozy recently unveiled a plan to spend $480 Million per year – or $558 per person – for 5 years to fight AD which afflicts 860,000 people in France.
• And in the US, the NIH spends only $600 Million/year – or $120 per person - for research on AD which afflicts 5,000,000 US citizens. Thus, France spends 4 times more than the US for its citizens with AD while the amount spent on healthy aging is far less and difficult to estimate.
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ADNI Biomarker Team:
Les Shaw, Virginia M-Y Lee, Chris Clark, Steve Arnold, Hugo Vanderstichele, Magdalena Korecka, Margaret Knapik-Czajka, Uwe Christians, Kaj Blennow Holly Soares, Eric Siemers, Piotr Lewczuk, William Potter & many more collaborators • NIA: Drs. R Hodes & N. Buckholz • Industry Scientific Advisory Board • Foundation for NIH • ADNI Core leaders and Core
members • ADNI Site PIs • Our volunteer subjects
Supported by the NIH/NIA/NINDS, Michael J. Fox Foundation, The Marian S. Ware Alzheimer Program, The William Maul Measey-Truman G. Schnabel, Jr. Chair of Geriatric Medicine & Gerontology and the Families of our Patients
Penn Biomarker Team: William T Hu , Alice Chen-Plotkin , Murray Grossman, Steve Arnold, Chris Clark , Les Shaw, Leo McCluskey, Laura Elman , Virginia M-Y, Lee,
It Takes a Great Team!