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Neuroimaging for HD: Successes and Future Applications

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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.

Published in: Health & Medicine

Neuroimaging for HD: Successes and Future Applications

  1. 1. Neuroimaging for HD: Successes and Future Applications Thursday, November 3 10:30-11:30am Chair: Victor Sung, MD University of Alabama, Birmingham
  2. 2. Presenters HSG 2016: DISCOVERING OUR FUTURE Sarah Tabrizi, FMedSci University College London Jeffrey Long, PhD University of Iowa
  3. 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. 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. 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. 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
  7. 7. 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
  8. 8. 12 and 24-month change in caudate volume baseline 12 months 24 months *p<0.05 **p<0.01 ***p<0.001
  9. 9. *p<0.05 **p<0.01 ***p<0.001 12 and 24-month change in white matter
  10. 10. 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
  11. 11. • 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
  12. 12. • Insights into Huntington’s disease natural history pre- and post-symptom-onset • Track-HD battery now used in all current global clinical trials
  13. 13. What about short-interval POC 6 month trials in early HD?
  14. 14. 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
  15. 15. 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
  16. 16. 36-month TRACK-HD data analyses: identified predictors of disease and progression in premanifest and early HD
  17. 17. Atrophy: the first reliably detectable sign in HD expansion carriers Merely a morphological observation or a FUNCTIONAL change?
  18. 18. 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
  19. 19. 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...
  20. 20. 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
  21. 21. 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
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 25. 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
  26. 26. 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
  27. 27. 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
  28. 28. 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
  29. 29. 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
  30. 30. 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
  31. 31. 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
  32. 32. 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
  33. 33. Performance of Three Models Long & Paulsen (2015) Mov Disord 10/10

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