Neuroimaging for HD: Successes and Future

ApplicationsThursday, November 3

10:30-11:30amChair: Victor Sung, MDUniversity of Alabama, Birmingham

Presenters

HSG 2016: DISCOVERING OUR FUTURE

Sarah Tabrizi, FMedSciUniversity College London

Jeffrey Long, PhDUniversity of Iowa

Neuroimaging endpoints for HD studies and Track-HD data

Sarah J Tabrizi MD PhD FMedSciDept of Neurodegenerative Disease

UCL Institute of Neurology andNational Hospital for Neurology and Neurosurgery

Queen Square, London

HSG 2017 Nashville 3rd November 2016

Neuroimaging for HD: Successes and Future Applications

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

value

as

predict

or

HD Biomarkers: A Proximal to Distal Categorisation

Proxim

al

marker

improved quality of life

improved lifespan

behavioural & structural changesspecific 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

Distal

marker 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

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)

Baseline2008

12-month2009

36-month2011

24-month2010

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

12 and 24-month change in whole brain atrophy

Control Premanifest Early HDTissue lossTissue gain

*p<0.05**p<0.01

***p<0.001

12 and 24-month change in caudate volume baseline 12 months 24 months

*p<0.05**p<0.01

***p<0.001

*p<0.05**p<0.01

***p<0.001

12 and 24-month change in white matter

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

• 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

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

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

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

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

36-month TRACK-HD data analyses:identified predictors of

disease and progressionin premanifest and early HD

Atrophy:the first reliably detectable sign

in HD expansion carriers

Merely a morphological observation or a FUNCTIONAL change?

Progressoror

Non-progressor?

Premanifest HD subjects who progressed had higher rates of change in...

Greymatteratrophy

WhiteMatteratrophy

Whole-brain atrophy

Caudate atrophy

Speeded tappingNegative emotion recognition

Problem behaviours assessment (PBA) apathy

Greymatteratrophy

Indirect circle tracing

Caudate atrophyDeclining functional

capacity?

Early-HD subjects with a declining TFC had higher rates of change in...

value

as

predict

or

HD Biomarkers: A Proximal to Distal Categorisation

Proxim

al

marker

improved quality of life

improved lifespan

behavioural & structural changesspecific 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

Distal

marker 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

PET Imaging markers in HD trialsWhich 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 receptorsStriatal neurones

PDE10A

StriatumBasal ganglia

D2 receptor

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 progressorsversus stable subjects in pre- and early stage HD

• PET studies are yielding useful functional receptor markers

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/corticalcellular receptors

CSF/bloodmHTT

Neuroimaging Data from PREDICT-HD

Jeffrey D. Long, PhDDepartment of Psychiatry, Carver College of Medicine Department of Biostatistics, College of Public Health

University of Iowa

HSG November 2016

Conflict of Interest

Consulting Agreement

Neurophage Inc

Paid Consulting

Azevan Inc (clinical trial for Huntington’s disease)

Roche Pharma (clinical trial for Huntington’s disease)

Funding

NINDS, CHDI Inc, Michael J. Fox

Important Point

No financial gain from this talk

Goals of Talk

Overview

(1) Change of imaging variables versus clinical variablesLinear 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

Neurobiological Predictors of Huntington’s Disease

PREDICT-HD

Longitudinal observational study enrolling people without any HD signs (no motor diagnosis)Purpose: identify earliest changesDr. Jane S. Paulsen, Principal InvestigatorFunding: NIH (NINDS) and the CHDI Foundation, Inc Data collection 2002-2014 (up to 12 years of data)

Variables

32-sites in 6 countriesN > 1400 to date; N = 1013 gene-expanded Over 80 variables collected annually

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 < 290Medium: 290 ≤ CAP ≤ 368High: CAP > 368

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 600350CAP

TMS

Entry CAP

Low

Medium

High

13121110

98765432100 150 200 250 300 400 450 500 550 600350

CAP

TFC

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

0.002100 150 200 250 300 350 400 450 500 550 600

CAP

Put

amen

0.300.280.260.240.220.200.180.160.140.120.100.080.060.04

100 150 200 250 300 400 450 500 550 600350CAP

CS

Flu

id

Rate of Change of Imaging and Clinical Variables

High CAP Group: Rate of ChangeRanking 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

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, AGEModel 2: CAG, AGE, TMS, SDMTModel 3: CAG, AGE, TMS, SDMT, PUTAMEN, CAUDATE

Performance of Three Models Long & Paulsen (2015) Mov Disord

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