Neuroimaging techniques, such as magnetic resonance imaging (MRI) and positron emission tomography (PET) have provided important advances in our understanding of Huntington's disease and may be a suitable biomarker for monitoring disease progression in HD and for assessing the efficacy of future disease modifying therapies.

1. Neuroimaging for HD: Successes and
Future Applications
Thursday, November 3
10:30-11:30am
Chair:
Victor Sung, MD
University of Alabama, Birmingham

2. Presenters
HSG 2016: DISCOVERING OUR FUTURE
Sarah Tabrizi, FMedSci
University College London
Jeffrey Long, PhD
University of Iowa

3. Neuroimaging endpoints for HD studies
and Track-HD data
Sarah J Tabrizi MD PhD FMedSci
Dept of Neurodegenerative Disease
UCL Institute of Neurology and
National Hospital for Neurology and Neurosurgery
Queen Square, London
HSG 2017 Nashville
3rd November 2016
Neuroimaging for HD: Successes and Future Applications

4. HD clinical trials: challenges
• Slowly progressive disease
• Long presymptomatic phase – how we do measure
progression?
• Endpoints that are
– biologically relevant
– clinically relevant to the patient’s function
– responsive to treatment in a clinically meaningful way
• Optimal duration of clinical trials

5. HD Biomarkers: A Proximal to Distal Categorisation
improved quality
of life
improved
lifespan
behavioural & structural changes
specific cognitive changes
electrophysiological changes
cellular changes
HTT protein reduction (esp mHTT)
mHTT mRNA reduction
mHTT mRNA cleavage (e.g. 5’RACE assay)
Early read-out vs. longer time to see effect
Predictive of
an inevitable
benefit to
patient
Little
relationship to
eventual
patient benefit
Measuring vs
predicting a
benefit
Slide courtesy of Doug Macdonald, CHDI
The most valuable biomarkers will be those of “intermediate proximity”
Not sufficient to predict
benefit (in trials)
“Manipulation checks”
e.g PET - D2R
e.g MRI
e.g cortical-striatal
connectivity
e.g Executive function

6. 123
Controls
120
Premanifest
• 3T MRI (DTI, PET, MRS)
• Novel quantitative motor
tasks
• Cognitive battery
• Oculomotor tasks
• Videotaped psychiatric
assessment
• Blood biosamples
• Quality of life, and
functional assessments
4 study sites:
London (UCL)
Leiden (LUMC)
Paris (UPMC)
Vancouver (UBC)
Baseline
2008
12-month
2009
36-month
2011
24-month
2010
58 PreB
62 PreA
123
Early HD
77 HD1
Clinical trial design: rigorous training, data monitoring, blinded QC/QA, centralized analysis
Centralized repositories for biosamples, data and images
46 HD2

8. 12 and 24-month change in whole brain atrophy
Control Premanifest Early HD
Tissue loss
Tissue gain
*p<0.05
**p<0.01
***p<0.001

9. 12 and 24-month change in caudate volume
baseline 12 months 24 months
*p<0.05
**p<0.01
***p<0.001

10. *p<0.05
**p<0.01
***p<0.001
12 and 24-month change in white matter

11. Orange nodes - caudate
Blue nodes – cortical rich club regions,
Grey nodes – non-rich club regions,
Yellow edges – cortico-caudate connections.
Rich Club structural connectivity loss: PreHD vs. controls shows reduced
connectivity in cortico-caudate connections
McColgan, Seunarine, et al Brain 2015

12. • We now have potential outcome measures for clinical
trials in early HD over 12 and 24 months – longer
time (3 years or more) is needed for premanifest HD
trials
- The TRACK-HD battery
• Practical, well-powered potential outcome measures
for these disease-modifying trials – now being used
in clinical trials design

13. • Insights into Huntington’s disease natural history
pre- and post-symptom-onset
• Track-HD battery now used in all current global clinical trials

14. What about short-interval POC
6 month trials in early HD?

16. 6 month effect sizes in early HD
*Difference in mean change between HD subjects and controls, divided by the residual SD in HD
Unpublished data
Hobbs et al JNNP 2015

17. Cortical thickness: Early HD compared with controls
All analyses adjusted for age, gender and site. Significance maps are additionally adjusted for multiple comparisons; FDR correction (p<0.05).
Cross-sectional
between-group
differences
Hobbs et al JNNP 2015
No between-group
differences at 6 months
No between-group
differences at 15-months

18. 36-month TRACK-HD data analyses:
identified predictors of
disease and progression
in premanifest and early HD

19. Atrophy:
the first reliably detectable sign
in HD expansion carriers
Merely a morphological observation
or a FUNCTIONAL change?

20. Progressor
or
Non-progressor
?
Premanifest HD subjects who progressed had higher rates of change in...
Grey
matter
atrophy
White
Matter
atrophy
Whole-brain atrophy
Caudate atrophy
Speeded tappingNegative emotion recognition

21. Problem behaviours assessment (PBA) apathy
Grey
matter
atrophy
Indirect circle tracing
Caudate atrophy
Declining functional
capacity
?
Early-HD subjects with a declining TFC had higher rates of change in...

22. HD Biomarkers: A Proximal to Distal Categorisation
improved quality
of life
improved
lifespan
behavioural & structural changes
specific cognitive changes
electrophysiological changes
cellular changes
HTT protein reduction (esp mHTT)
HTT mRNA reduction
HTT mRNA cleavage (e.g. 5’RACE assay)
Early read-out vs. longer time to see effect
Predictive of
an inevitable
benefit to
patient
Little
relationship to
eventual
patient benefit
Measuring vs
predicting a
benefit
Slide courtesy of Doug Macdonald, CHDI
The most valuable biomarkers will be those of “intermediate proximity”
Not sufficient to predict
benefit (in trials)
“Manipulation checks”
e.g PET - D2R or MRS
e.g MRI
e.g cortical-striatal
connectivity
e.g Executive function

23. PET Imaging markers in HD trials
Which imaging or functional marker in
clinical trials targeting Htt?
[18F]FDG
Synaptic
activity
Global network
CB1R ligand
CB1 receptors
Cortical projections?
Cortex
5-HT2A/1A/1B ligand
Other cortical markers?
Cortex
Courtesy of Dr. Andrea Varrone, Karolinska Institutet, Stockholm, Sweden
[11C]raclopride
D2 receptors
Striatal neurones
PDE10A
Striatum
Basal ganglia
D2 receptor

24. Overall conclusions
• Potential measures for future clinical trials in early
and premanifest HD over 6 months to 3 years
• We have identified baseline predictors of disease
onset and progression in pre- and early HD
• We have identified characteristics of progressors
versus stable subjects in pre- and early stage HD
• PET studies are yielding useful functional receptor
markers

25. Premanifest
Motor diagnosis
Manifest
Years
Cortical
grey matter
Globus
pallidus etc.
White matter
Striatal volume
Adapted from Ross, C. A.......Tabrizi S. J. (2014) Huntington disease: natural history, biomarkers and
prospects for therapeutics Nat. Rev. Neurol. 2014
PET striatal/cortical
cellular receptors
CSF/blood
mHTT

26. Neuroimaging Data from PREDICT-HD
Jeffrey D. Long, PhD
Department of Psychiatry, Carver College of Medicine
Department of Biostatistics, College of Public Health
University of Iowa
HSG November 2016

27. Conflict of Interest
Consulting Agreement
Neurophage Inc
Paid Consulting
Azevan Inc (clinical trial for Huntington’s disease)
RochePharma (clinical trial for Huntington’s disease)
Funding
NINDS, CHDI Inc, Michael J. Fox
Important Point
No financial gain from this talk

28. Goals of Talk
Overview
(1) Change of imaging variables versus clinical variables
Linear and non-linear
Rates of change
(2) Predicting motor diagnosis
Results
PREDICT-HD recent published papers
Collaborator Dr. Jane S. Paulsen, PI of PREDICT-HD

29. Neurobiological Predictors of Huntington’s Disease
PREDICT-HD
Longitudinal observational study enrolling people
without any HD signs (no motor diagnosis)
Purpose: identify earliest changes
Dr. Jane S. Paulsen, Principal Investigator
Funding: NIH (NINDS) and the CHDI Foundation, Inc
Data collection 2002-2014 (up to 12 years of data)
Variables
32-sites in 6 countries
N > 1400 to date; N = 1013 gene-expanded
Over 80 variables collected annually

30. Indexing Disease Progression in PREDICT-HD
Zhang, Long, et al. (2011) Am J Med Genet
CAG-Age Product (CAP)
CAP = Age · (CAG − 34)
Interpretation
Age adjusted for CAG expansion (time-varying)
Average CAP at motor diagnosis = 445
CAP Groups (Time-Static)
Low: CAP <290
Medium: 290 ≤ CAP ≤ 368
High: CAP >368

31. UHDRS Clinical Variables Paulsen, Long, et al. (2014)
Total Motor Score (TMS) and Total Functional Capacity (TFC)
0
70
60
50
40
30
20
10
100 150 200 250 300 400 450 500 550 600350
CAP
TMS
Entry CAP
Low
Medium
High
13
12
11
10
9
8
7
6
5
4
3
2
100 150 200 250 300 400 450 500 550 600350
CAP
TFC

32. Imaging Variables Paulsen, Long, et al. (2014), Front Aging Neurosci
Imaging variables corrected for ICV
0.008
0.007
0.006
0.005
0.004
0.003
0.002
100 150 200 250 300
Putamen
0.30
0.28
0.26
0.24
0.22
0.20
0.18
0.16
0.14
0.12
0.10
0.08
0.06
0.04
100 150 200 250 300 400 450 500 550 600350
CAP
CSFluid

33. Rate of Change of Imaging and Clinical Variables
High CAP Group: Rate of Change
Ranking of Rate (1 = fastest)
(1) Putamen
(2) Caudate
(3) Accumbens
(4) Total Motor Score (TMS)
(5) Symbol Digit Modalities Test
Paulsen, Long, et al. (2014), Front Aging Neurosci

34. Predicting Motor Diagnosis Long & Paulsen (2015) Mov Disord
Motor Diagnosis
UHDRS Diagnostic Confidence Level (DCL) = 4
≥ 99% confident participant meets definition of HD
Analysis
Measured at baseline predicting time to first DCL = 4
Survival analysis (using machine learning methods)
Analysis
Model 1: CAG, AGE
Model 2: CAG, AGE, TMS, SDMT
Model 3: CAG, AGE, TMS, SDMT, PUTAMEN, CAUDATE

35. Performance of Three Models Long & Paulsen (2015) Mov Disord
10/10