PARKINSON S PROGRESSION MARKERS INITIATIVE. PPMI 2016 ANNUUAL INVESTIGATORS MEETING May 4-5

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1 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI 2016 ANNUUAL INVESTIGATORS MEETING May 4-5

2 PPMI Update Ken Marek PPMI Annual Meeting May 4, 2016 New York, NY

3 PD patient MAY yo right handed WF in excellent general health History 6 month history of poor tennis play Note 1-2 years mild constipation 2 months intermittent R UE tremor while reading the newspaper, or if in stressful situation Exam Mild R UE resting tremor Reduced R arm swing PD DIAGNOSIS 1 MONTH AGO IF THE SYMPTOMS REMAIN AS THEY ARE NOW I COULD DEAL WITH THIS Two years progression History Continue to work, all activities Requires sinemet 100 mg tid Worried about future Exam MAY 2013 Mild R UE> L UE resting tremor R brady UE>LE PD DIAGNOSIS 24 MONTHS AGO THESE SYMPTOMS ARE ANNOYING AND I WORRY THAT THEY ARE GETTING WORSE. Five years progression History Continue to work, more difficulty with keyboards etc Requires sinemet 150 mg tid, SSRI not working as reliably Notes mild dyskinesia Worried about progression, cognition Exam MAY 2016 Mod R UE> L UE resting tremor, R brady UE>LE Gait stiff, subtle balance Minor fluctuations PD DIAGNOSIS 60 MONTHS AGO HOW CAN I STOP THIS FROM GETTING WORSE.

4 PPMI Biomarker Infrastructure Parkinson s Progression Markers Initiative Specific Data Set Appropriate population (early stage PD and controls, prodromal, genetic PD subjects) Clinical (motor/non-motor) and imaging data Corresponding biologic samples (DNA, blood, CSF) Standardization Uniform collection of data and samples Uniform storage of data and samples Strict quality control/quality assurance Access/Sharing Data available to research community data mining, hypothesis generation & testing Samples available for studies

5 PPMI Study Details: Synopsis Study population Assessments/ Clinical data collection Biologic collection/ Data and Biosamples shared on website de novo PD subjects (newly diagnosed and unmedicated) 196 age- and gender-matched healthy controls 64 SWEDD 67 Prodromal - Olfactory/RBD 250 LRRK2 - PD manifest and non-manifesting family members 250 GBA- PD manifest and non-manifesting family members 100 SNCA - PD manifest and non-manifesting family members Subjects will be followed for 3 to 5 years Motor assessments Neurobehavioral/cognitive testing Autonomic, Olfaction, Sleep DaTSCAN, AV133, DTI/RS MRI, Amyloid DNA collected at screening Serum and plasma collected at each visit; urine collected annually CSF collected at baseline, 6mo 12 mo and then annually Samples aliquotted and stored in central biorepository > 586,000 Data downloads > 70 Sample requests via BRC Ancillary study development

6 PPMI ENROLLMENT GROUP Consented Enrolled (n) Withdrawn Active Complete PD Subjects Healthy Controls SWEDD Subjects Prodromal -Hyposmic Prodromal-RBD LRRK2 PD Cohort LRRK2 UA Cohort SNCA PD Cohort SNCA UA Cohort GBA PD Cohort GBA UA Cohort PD Registry UA Registry TOTAL As of 04/APR/2016 5

7 Goals for PPMI to inform clinical trials Improve diagnostic accuracy (enrich a study population) Develop tools to assess disease progression Establish outcomes prior to onset of motor symptoms Identify PD progression subsets - Develop clinical outcomes (cognition, gait, autonomic) Progression at different rates (fast vs slow) Respond to specific therapy Characterization of Genetic cohorts, Prodromal cohorts Phase 2 - provide an efficacy signal to increase confidence for subsequent Phase 3 Phase 3 studies - enrich the study sample and provide objective outcomes of that reflect clinical benefit

8 PPMI MILESTONES Enrolled PD/HV ENROLLMENT PRODROMAL ADD AMYLOID ENROLLMENT GENETIC Enrolled ADD GBA PRODROMAL 2000 DaTscan 580,000 DOWNLOADS ADD DTI ENROLLMENT PD/HV ADD SWEDD 1000 subjects 3000 LPs 900,000 Genotypes 2000 WRI 70 BRC applications 50 Publications

9 PPMI Deliverables 5 Yrs Operational SOPs, Enrollment/Retention, CSF, DAT, DTI Methods to enroll defined PD cohorts IPD, prodromal, genetic Data to develop clinical outcomes MDS-UPDRS, DAT imaging, cognition, phenoconversion. Data to inform study design enrichment, sample size. Biospecimen repositories with full clinical characterization Data to establish PD subsets Develop clinical outcomes (cognition, gait, autonomic) Progression at different rates (fast vs slow) May respond to specific therapy Characterization of Genetic cohorts, Prodromal cohorts

10 PPMI Infrastructure/Operations Operational SOPs, Enrollment/retention, CSF, DAT, DTI PPMI infrastructure - sites, biorepository, data repository Subject retention (as of 10/1/2015) 1130/1213 all subjects (93.2%) retention 554/619 PD/HV (89.4%) retention

11 PPMI - Enrollment of selected cohorts Untreated PD - enrollment - 1 subject/site /month RBD/Olfaction - Central standardized enrollment LRRK2/GBA - Widespread recruitment initiative Synuclein Geographic focus

12 PPMI - Data to develop clinical outcomes Monitor Progression of clinical outcome MDS-UPDRS DAT imaging Biospecimen data Phenoconversion Prodromal cohorts Genetic cohorts Cognition Progression to MCI/Dementia

13 PPMI - Inform study design Enrichment pf study cohorts SWEDD Genetic risk Sample Size Estimate based on UPDRS, DAT, MOCA, synuclein

14 SWEDD subjects in PPMI (16.4%) PPMI

15 PPMI - Biospecimen repositories Coordinated worldwide repository Robust biofluid collection BRC - independent review of applications Distribution of samples

16 PPMI - Data to establish PD subsets Develop clinical outcomes (cognition, gait, autonomic) Progression at different rates (fast vs slow) Respond to specific therapy Characterization of Genetic cohorts, Prodromal cohorts

17 PD subsets Need for DA meds Subjects with high or low change in UPDRS, DAT, Synuclein Subjects with low MoCa, low amyloid, high tau Subjects with relative hypotension, RBD on questionnaire, low amyloid, high tau Subjects with high depression/anxiety/reduced SERT Data Driven Subsets

18 PPMI Data Sharing

19 PPMI funding partners PPMI is sponsored and partially funded by The Michael J. Fox Foundation for Parkinson s Research. Other funding partners include a consortium of industry players, non-profit organizations and private individuals.

20 PPMI SC and Study Cores Steering Committee Clinical Coordination Core Imaging Core Statistics Core Bioinformatics Core BioRepository Biofluid Genetics Core RBD Core Olfactory Core Genetics Coordinating Core PI-K Marek, C Tanner, T Foroud, D Jennings, K Kieburtz, W Poewe, B Mollenhauer, T Simuni, D Galasko (core leaders, MJFF, ISAB), S Lasch University of Rochester s Clinical Trials Coordination Center PI: Karl Kieburtz, Ray Dorsey, Renee Wilson Institute for Neurodegenerative Disorders; PI: John Seibyl, Norbert Schuff, Duygu Tosun University of Iowa PI: Chris Coffey Laboratory of Neuroimaging (LONI) at UCLA PI: Arthur Toga, Karen Crawford Indiana University/Biorep/Tel Aviv PI: Tatiana Foroud PI: Brit Mollenhauer, Doug Galasko, John Trojanowski, Les Shaw National Institute on Aging/NIH PI: Andy Singleton Hephata Hessisches Diakoniezentrum e. V. PI: Geert Mayer Institute for Neurodegenerative Disorders PI: Danna Jennings Indiana University PI: Tatiana Foroud

21 PPMI MJFF team Sohini Chowdhury, Vanessa Arnedo, Mark Frasier, Katie Kopil, Tara Hastings, Alyssa Reimer, Danielle Papow, Jamie Eberling Todd Sherer Debi Brooks 20

22 PPMI Committees Biologics Brit Mollenhauer Doug Galasko Imaging John Seibyl Norbert Schuff Neuropsych /Neurobehavior Dan Weintraub Sleep Wolfgang Oertel Genetics Andrew Singleton LRRK2 Tatiana Foroud Susan Bressman Statistical/Data Chris Coffey ISAB Jesse Cedarbaum Biospecimen review Kalpana Merchant Data and publication David Standaert Ancillary study Carlie Tanner Recruitment/Retention Danna Jennings Patient Advisory Danna Jennings Ken Marek Website Carlie Tanner CSOC Ron Pfeiffer 21

23 PPMI SITES IN THE UNITED STATES: Arizona PD Consortium (Sun City, AZ) Beth Israel Medical Center (NY, NY) Baylor College of Medicine (Houston, TX) Boston University (Boston, MA) Cleveland Clinic (Cleveland, OH) Columbia University (NY, NY) Emory University (Atlanta, GA) Institute of Neurodegenerative Disorders (New Haven, CT) Johns Hopkins University (Baltimore,MD) Northwestern University (Chicago, IL) Oregon Health and Science University (Portland, OR) The Parkinson s Institute (Sunnyvale, CA) PD & Movement Disorders Center at Boca Raton (Boca Raton, FL) University of Alabama at Birmingham (Birmingham, AL) University of California at San Diego (San Diego, CA) University of California at San Francisco (San Francisco, CA) University of Cincinnati (Cincinnati, OH) University of Pennsylvania (Philadelphia, PA) University of Rochester (Rochester, NY) University of South Florida (Tampa, FL) University of Washington (Seattle, WA) PPMI Sites PPMI SITES IN EUROPE: Foundation for Biomedical Research of the Academy of Athens (Athens, Greece) Imperial College (London, UK) Innsbruck University (Innsbruck, Austria) Norwegian University of Science and Technology (Trondheim, Norway) Paracelsus-Elena Clinic Kassel/University of Marburg (Kassel and Marburg, Germany) Pitié-Salpêtrière Hospital (Paris, France) University of Barcelona (Barcelona, Spain) University of Donostia (San Sebastien, Spain) University of Salerno (Salerno, Italy) University of Tübingen (Tübingen, Germany) PPMI SITES IN AUSTRALIA: Macquarie University (Sydney, Australia) PPMI SITES IN Israel: Tel Aviv Sourasky Medical Center (Tel Aviv, Israel)

24 PPMI MILESTONES Enrolled PD/HV ENROLLMENT PRODROMAL ADD AMYLOID ENROLLMENT GENETIC Enrolled ADD GBA PRODROMAL 2000 DaTscan 580,000 DOWNLOADS ADD DTI ENROLLMENT PD/HV ADD SWEDD 1000 subjects 3000 LPs 900,000 Genotypes 2000 WRI 70 BRC applications 50 Publications

25 Meeting Goals - Review study success Continued outstanding retention Comprehensive clinical, imaging, biofluid data Growth of PPMI longitudinal data set Robust biofluid biorepository Expansion to prodromal/genetic cohorts Data and Specimen sharing

26 Meeting Goals-Identify study challenges Subject Retention Enroll Genetic cohort Maintain Data Quality Longitudinal data analysis to define PD subsets/early biomarker signal PPMI fatigue Increase industry sponsorship/funding

27 Meeting Goals Plan PPMI future Focus on PPMI longitudinal data - PD subsets, prediction of progression Novel analytes, imaging tools, clinical assessments, analyses Develop tools for Prodromal assessments phenoconversion, pre-motor progression Focus on genetic enrollment/retention Implement pathology core Longterm PPMI follow-up How can PPMI inform clinical trials

28 PPMI Annual Meeting Agenda 2016 Day 1 10:30-10:50 am Welcome and Introductions Marek, Sherer, All 10:50-11:10 am PPMI Status Update Marek 11:10-11:40 am PPMI enrollment/retention, demographic, Motor Data Wilson, Kieburtz, Coffey, Simuni 11:40-12:00 pm Neuropsych/Neurobehavior data Non-motor Data Weintraub, Simuni 12:00-1:00 pm Lunch - Riverview Dining Area All 1:00-1:45 pm 1:45-2:15 pm 2:15-2:30 pm Imaging Data DaTSCAN/DTI/rsMRI/ b-amyloid imaging, Longitudinal Analysis Biospecimen Data Biorepository CSF analysis Longitudinal LP Safety and Compliance Review of Longitudinal data Seibyl, Tosun, Schuff Foroud, Mollenhauer Dagostine, Jennings 2:30-2:45 pm Highlights from BRC Projects Frasier 2:45-3:00 pm Bioinformatics and PPMI Website update Toga, Crawford

29 PPMI Annual Meeting Agenda 2016-Day 1 3:00-3:15 pm Break All 3:15-4:15 pm BREAKOUT 1 (3 GROUPS - 20 min each- Rotate) 1) PPMI DATA - Accessing the PPMI Data The Forum 1) PPMI Data Test Case using MDS-UPDRS, ST and, LEDD Exchange Hub North 1) Pathology Core advanced directive Exchange Hub South 4:15-4:30 pm PPMI Data Challenge Frasier Toga, Crawford Simuni, Caspell- Garcia, Foster Leverenz, Wilson Cedarbaum, Kiyasova 4:30-5:30 pm Discussion Panel How PPMI data informs clinical studies 5:30-6:00 pm Site Awards & Study Retention 6:00 pm Closing Remarks Preparation for Thursday May 5 th - Day 2 ISAB panelists: Sevigny, Hung; Kristjansen; Van der Brug, Muglia Jennings, Hastings, Leary Marek 6:30 pm Cocktails & Dinner at: The Down Town Association 60 Pine Street, between William & Pearl St. (walking distance from conference center) All

30 PPMI Annual Meeting Agenda Day 2 Thursday May 5, 2016 All sessions except will be in The Forum unless otherwise noted 7:00-8:00 am Breakfast Riverview Dining Area All 8:00-8:45 am Genetic Cohorts LRRK2/GBA/SNCA Recruitment/enrollment goals & timeline/ Scaling Genetic Widespread Recruitment/ new GBA mutations Genetic Analysis SNCA screening in Contursi region of Italy Foroud, Singleton, Marek 8:45-9:15 LRRK2 and GBA supporting clinical trials Arnedo, Marek 9:15-10:00 am Phenoconversion Update PPMI case studies to date Jennings, Kieburtz, Tolosa 10:00-10:20am Break All 10:20 11:10am Translating AD biomarker in prodromal and pre-clinical cohorts Reisa Sperling 11:10-11:40am 11:40-12:30pm Synuclein as a biomarker for PD Research in Barcelona Systemic Synuclein Sampling Study (S4) BREAKOUT 2 Biologics - Exchange Hub North Imaging - Exchange Hub South Neuropsych/Neurobehavior - The Forum Sleep - Front Studio 12:30-1:30 pm Lunch - Riverview Dining Area All Iranzo, Jennings, Kopil, Mollenhauer, Galasko, Frasier, Taylor, Singleton, Shaw Seibyl, Schuff, Tosun Weintraub, Simuni Oertel, Chahine, Iranzo, Lasch

31 PPMI Annual Meeting Agenda Day 2 12:30-1:30 pm Lunch - Riverview Dining Area All 1:30-1:40 Synuclein Imaging Eberling 1:30-2:00 pm Report from Breakout Groups Goals for Biologics Imaging Neuropsych/Neurobehavior Sleep Coordinators Session Feedback 2:00-2:20pm New in PPMI - Amendment 11 2:00-2:30pm ISAB breakout Exchange Hub South Breakout leaders All James, Linder Wilson, Reece/Kemeny, Mirelman Cedarbaum 2:20-2:40pm PPMI Biologic strategy Mollenhauer, Galasko 2:40-3:00pm FOUND in PPMI Tanner 3:00:3:20pm PPMI Publications Update Marek 3:20-3:30 pm ISAB meeting recap and future plans Cedarbaum, Kiyasova 3:30-4:00 pm Goals/Plans/Closing remarks Marek 4:00 pm Car/Coach pickups to airports All

32 CSF Acquisition Group Baseline Month 6 Month 12 Month 24 Month 36 PD 98% (423) 83% (414) 81% (409) 77% (399) 72% (259) Healthy 97% (196) 83% (192) 82% (189 ) 75% (182) 76% (141) SWEDD 92% (62) 76% (62) 77% (62) 64% (61) NA LP well tolerated HA 4-7% CSF Volume collected (mean) Sprotte needle used in 82% Syringe suction 63% Sitting position in 63% Flouroscopy in < 5%

33 DAT Acquisition Group PD HC Baseline 12 month 24 month 48 month 423 (99%) 196 (91%) 409 (92%) 399 (88%) 91 (78%) N/A N/A N/A SWEDD 64 (97%) N/A 61 (85%) N/A DAT standardized acquisition and read (<0.5% excluded) DTI/RS multi-center acquisition and analysis (n=678, <5% excluded)

34 Ascertainment/Baseline of PPMI-RBD Cohort 115 RBD consented RBD Subjects (N = 39) Males 33 (85%) Age (mean) 70 (5.5) 18 PSG Not eligible 97 PSG Eligible 15 No DAT 82 Completed DAT MDS-UPDRS - Total Score - Part I - Part II - Part III (Motor Exam) MOCA Total Score 14.0 (7.6) 7.3 (4.0) 2.2 (2.6) 4.5 (3.8) 25.5 (4.1) Caudate 2.0 (0.52) 41 PPMI ELIGIBLE 41 NOT ELIGIBLE Putamen 1.1 (0.33)

35 RRK2/GBA/SNCA Enrollment (Oct 2015) PD Subjects enrolled cohort LRRK2 88 SNCA 11 Unaffected Subjects enrolled cohort LRRK2 68 SNCA 4 WRI N % % M % F Consented through WRI site, no previous testing % 37% 63% Qualified and confirmed through WRI % 40% 60% Did not qualify through WRI % 28% 72% Testing for LRRK2 G2019S % 45% 55% LRRK % 46% 54% LRRK % 38% 62% Tested with PD % 59% 41% LRRK % 61% 39% LRRK % 46% 54% Tested without PD % 33% 67% LRRK % 34% 66% LRRK % 30% 70%

36 Preliminary Sample Size Estimate Sample Sizes Necessary to Detect Differences in Mean UPDRS Scores in PD subjects who began treatment within one year Total number of subjects assume 2 arms

37 Preliminary Sample Size Estimate Sample Sizes Necessary to Detect Differences in Mean UPDRS Scores in PD subjects who did not begin treatment within one year Total number of subjects assume 2 arms

38 Consented Enrolled Withdrawn No DAT Deficit scan 2 DAT Def scan 2 SWEDD Subjects

39 Consented Enrolled Withdrawn No DAT Deficit scan 2 DAT Def scan 2 SWEDD Subjects

40 PPMI UPDRS CHANGE IN UPDRS at 12 months BY TREATMENT CHANGE IN UPDRS by TREATMENT

41 Preliminary Sample Size Estimate - ALL PD Subjects Total number of subjects assume 2 arms

42 Defining Phenoconversion to motor PD in the PPMI cohort Primary Measure: Based on UK Brain Bank Criteria from PPMI Data mapped from the Diagnostic Features Questionnaire Secondary Measures: Diagnostic Questionnaire Phenoconverters at 18 Months 12/64 (18.6%)

43 % of subjects Clinical Diagnosis at Phenoconversion Idiopathic PD (n=3) Prodromal nonmotor PD (n=5) Prodromal motor PD (n=3) other-neuropathy (n=1)

44 LUMBAR PUNCTURE COMPLETENESS Group Baseline # Expected (% Comp) 6 Months # Expected (% Comp) 1 Year #Expected (% Comp) 2 Year #Expected (% Comp) 3 Year #Expected (% Comp) 4 Year #Expected (% Comp) PD 423 (98%) 391 (88%) 393 (84%) 336 (82%) 155 (81%) 32 (72%) HC 196 (97%) 183 (87%) 185 (84%) 161 (80%) 126 (80%) 24 (79%) SWEDD 64 (92%) 55 (85%) 58 (83%) 49 (76%) N/A N/A Hyposmic 22 (91%) 10 (70%) 5 (60%) N/A N/A N/A RBD 37 (92%) 22 (91%) 4 (75%) N/A N/A N/A Genetic PD 69 (90%) 3 (100%) N/A N/A N/A N/A Genetic UA 54 (93%) 8 (50%) N/A N/A N/A N/A 43

45 PPMI Imaging Studies In-house at IND Core Lab SPECT Scans Received: 1909 PD Year 1: 380 PD Year 2: 329 Prodromal Screening: 322 AV PET Received: Baseline: 30 Year 1: 19 Year 2: 9 (added if needed) Structural MRI Baseline: 464 (includes 78 from the prodromal cohort) DTI Baseline: 317 (includes 31 prodromal) DTI Year 1: 224 DTI Year 2: 139 Resting State: 224 As of 11 May

46 Athens Banner Barcelona Baylor Beth Israel Boca Boston Cincinnati Cleveland Clinic Columbia Emory IND Innsbruck Johns Hopkins Kassel London Macquarie Northwestern Norway OHSU Paris Pennsylvania Rochester Salerno San Sebastian Tel Aviv The PI Tuebingen UAB UCSD UCSF USF Washington Number of Kits Study Visits with Samples at Biorepository First Quarter study visits Site

47 Data Flow Clinical data sent to LONI from CTCC weekly Biorepository Imaging data Genetics - Reconciliation of different data streams

48 Requirements for Biomarker Infrastructure Parkinson s Progression Markers Initiative Specific Data Set Appropriate population (early stage PD and controls) Clinical (motor/non-motor) and imaging data Corresponding biologic samples (DNA, blood, CSF) Standardization Uniform collection of data and samples Uniform storage of data and samples Strict quality control/quality assurance Access/Sharing Data available to research community data mining, hypothesis generation & testing Samples available for studies 47

49 Standardization of data acquisition/analysis Training Matrix Training/Re-training Clinical data entry. Biosample collection and shipping UPDRS Neuropsych Imaging acquisition and data transfer Genetics Quality control of biosamples, imaging data

50 Requirements for Biomarker Infrastructure Parkinson s Progression Markers Initiative Specific Data Set Appropriate population (early stage PD and controls) Clinical (motor/non-motor) and imaging data Corresponding biologic samples (DNA, blood, CSF) Standardization Uniform collection of data and samples Uniform storage of data and samples Strict quality control/quality assurance Access/Sharing Data available to research community data mining, hypothesis generation & testing Samples available for studies 49

51 Standardization of data acquisition/analysis Succeeded New stuff always adding Avaialbel and cllaoboartcollabortivr Subjects there Data subsets Publications Gneeteics Phenoconversion Assays Plans for future - funding, clincial studies, new assesmsneet s OPDN at

52 PPMI SC and Study Cores

53 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI 2016 ANNUUAL INVESTIGATORS MEETING May 4-5

54 PPMI STATUS UPDATE RENEE WILSON, MA, CCRP CLINICAL TRIALS COORDINATION CENTER/ CENTER FOR HUMAN EXPERIMENTAL THERAPEUTICS UNIVERSITY OF ROCHESTER MEDICAL CENTER

55 PPMI SITES IN THE UNITED STATES: Arizona PD Consortium (Sun City, AZ) Beth Israel Medical Center (NY, NY) Baylor College of Medicine (Houston, TX) Boston University (Boston, MA) Cleveland Clinic (Cleveland, OH) Columbia University (NY, NY) Emory University (Atlanta, GA) Institute of Neurodegenerative Disorders (New Haven, CT) Johns Hopkins University (Baltimore,MD) Northwestern University (Chicago, IL) Oregon Health and Science University (Portland, OR) The Parkinson s Institute (Sunnyvale, CA) PD & Movement Disorders Center at Boca Raton (Boca Raton, FL) University of Alabama at Birmingham (Birmingham, AL) University of California at San Diego (San Diego, CA) University of California San Francisco (San Francisco, CA) University of Cincinnati (Cincinnati, OH) University of Pennsylvania (Philadelphia, PA) University of Rochester (Rochester, NY) University of South Florida (Tampa, FL) University of Washington (Seattle, WA) PPMI SITES PPMI SITES IN EUROPE: Foundation for Biomedical Research of the Academy of Athens (Athens, Greece) Imperial College (London, UK) Innsbruck University (Innsbruck, Austria) Norwegian University of Science and Technology (Trondheim, Norway) Paracelsus-Elena Clinic Kassel/University of Marburg (Kassel and Marburg, Germany) Pitié-Salpêtrière Hospital (Paris, France) University of Barcelona (Barcelona, Spain) University of Donostia (San Sebastien, Spain) University of Salerno (Salerno, Italy) University of Tübingen (Tübingen, Germany) PPMI SITES IN AUSTRALIA: Macquarie University (Sydney, Australia) PPMI SITES IN Israel: Tel Aviv Sourasky Medical Center (Tel Aviv, Israel) 3

56 PPMI AMENDMENTS»Amendment 9 Testing for GBA»Amendment 10 Extended follow-up for PD and HC cohorts Month 72, 84 and 96 visits ipsc companion protocol Baylor Miraca Genetics Labs 4

57 PPMI AMENDMENTS»Thank you to all the sites for the excellent effort in gaining IRB and EC approval of these amendments! 5

58 PPMI TRAININGS»Amendment 10 protocol training»ipsc companion protocol training with the Biorepository»eClinical and CRF review Review of new CRFs and CRF changes Review of MDS-UPDRS assessments eclinical reminders»good Clinical Practices and Good Documentation Practices»Protocol training with new PPMI site investigators and coordinators 6

59 ENROLLMENT, RETENTION AND COMPLIANCE 7

60 Source of data for this presentation: Information comes from: Tables produced for CSOC report Tables produced for monthly review by steering committee Additional data requests OVERVIEW All data comes from a data freeze based on data obtained from the LONI website on 04/04/

61 PPMI ENROLLMENT GROUP Consented Enrolled (n) Withdrawn Active Complete PD Subjects Healthy Controls SWEDD Subjects Prodromal -Hyposmic Prodromal-RBD LRRK2 PD Cohort LRRK2 UA Cohort SNCA PD Cohort SNCA UA Cohort GBA PD Cohort GBA UA Cohort PD Registry UA Registry TOTAL

62 COHORT ENROLLMENT

63 VISIT COMPLIANCE Group 6 Months # Exp (% Comp) 1 Year # Exp (% Comp) 2 Year # Exp (% Comp) 3 Year # Exp (% Comp) 4 Year # Exp (% Comp) 5 Year # Exp (% Comp) PD 414 (94%) 409 (96%) 399 (94%) 339 (94%) 153 (94%) 27 (89%) HC 192 (95%) 189 (98%) 182 (96%) 162 (93%) 129 (96%) 21 (86%) SWEDD 62 (89%) 62 (94%) 61 (90%) N/A N/A N/A Hyposmic 25 (96%) 22 (95%) 3 (100%) N/A N/A N/A RBD 39 (95%) 36 (94%) 4 (75%) N/A N/A N/A Genetic PD 96 (90%) 69 (84%) 2 (50%) N/A N/A N/A Genetic UA 66 (70%) 51 (80%) 7 (29%) N/A N/A N/A Number Expected = number of active subjects within the milestone window % Completed = number of active subjects that completed the assessment within the milestone window 11

64 DATSCAN COMPLETENESS Group Baseline # Expected (% Comp) 1 Year #Expected (% Comp) 2 Year #Expected (% Comp) 4 Year #Expected (% Comp) PD 423 (99%) 393 (96%) 377 (93%) 144 (83%) HC 196 (91%) N/A N/A N/A SWEDD 64 (97%) N/A 55 (96%) N/A Hyposmic 26 (100%) 21 (95%) 3 (100%) N/A RBD 39 (100%) 34 (97%) 3 (100%) N/A Genetic PD 118 (85%) N/A 1 (100%) N/A Genetic UA 101 (96%) N/A 2 (50%) N/A Number Expected = Number of active subjects with completed visit % Completed = Number of active subjects that completed the assessment when they completed the visit 12

65 LUMBAR PUNCTURE COMPLETENESS Group Baseline # Exp (% Comp) 6 Months # Exp (% Comp) 1 Year #Exp (% Comp) 2 Year #Exp (% Comp) 3 Year #Exp (% Comp) 4 Year #Exp (% Comp) 5 Year #Exp (% Comp) PD 423 (98%) 391 (88%) 392 (84%) 377 (82%) 318 (78%) 144 (73%) 24 (71%) HC 196 (97%) 183 (87%) 185 (84%) 174 (79%) 151 (78%) 124 (69%) 18 (78%) SWEDD 64 (92%) 55 (85%) 58 (83%) 55 (71%) N/A N/A N/A Hyposmic 23 (87%) 21 (76%) 19 (79%) 3 (67%) N/A N/A N/A RBD 38 (92%) 37 (81%) 34 (79%) 3 (67%) N/A N/A N/A Genetic PD 115 (87%) N/A 58 (72%) 1 (100%) N/A N/A N/A Genetic UA 99 (95%) N/A 41 (78%) 2 (100%) N/A N/A N/A Number Expected = Number of active subjects with completed visit % Completed = Number of active subjects that completed the assessment when they completed the visit 13

66 LUMBAR PUNCTURE COMPLIANCE Overall lumbar puncture compliance rates have been decreasing across visits in most cohorts CSF is invaluable because of its scientific importance Sessions planned to discuss challenges in speaking with subjects about LP and how best to convey to subjects the importance of CSF in research 14

67 HOW PPMI CSF SAMPLES ARE BEING USED MJFF hosted a consortium in Feb 2016 to review and evaluate new biomarkers & assay methods PPMI Data Challenge being held in the Fall 2016 to increased interest in PPMI biospecimens NeuroX genotyping to look for genes and proteins in PD to potentially help guide CSF analysis methods 15

68 2010- Why is CSF Collected in the PPMI Study? 2016 Update of CSF Collected in the PPMI Study PPMI would offer one of the largest PD cohorts with CSF o PPMI has collected longitudinal CSF from de novo PD; HC; SWEDD; Prodromal (RBD & HYP); PD & Unaffected subjects with Genetic Mutations of interest Biochemical changes likely reflected in CSF composition o Initial CSF analysis showed differences between PD and HC Anticipation of additional markers during the course of the study o Activity in the research community for new and existing biomarkers is highly active o PPMI Biologic Working Group, BRC, Biologic Committee/Core and the PPMI team are continuously looking for and evaluating new markers and methodology for evaluating biomarkers in CSF 16

69 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI 2016 ANNUUAL INVESTIGATORS MEETING May 4-5

70 PPMI Data Overview Christopher S. Coffey The University of Iowa PPMI Steering Committee Meeting May 4, 2016 New York, NY

71 Source of data for this presentation: Information comes from: Tables produced for CSOC report Tables produced for monthly review by steering committee Additional data requests OVERVIEW All data comes from a data freeze based on data obtained from the LONI website on 04/04/

72 PPMI ENROLLMENT GROUP Consented Enrolled (n) Withdrawn Active Complete PD Subjects Healthy Controls SWEDD Subjects Prodromal -Hyposmic Prodromal-RBD LRRK2 PD Cohort LRRK2 UA Cohort SNCA PD Cohort SNCA UA Cohort GBA PD Cohort GBA UA Cohort PD Registry UA Registry TOTAL

73 Baseline Characteristics 4

74 BASELINE CHARACTERISTICS Family Hx of PD GROUP (n) Males Age Education Hispanic/ First Caucasian Other None (mean) < 13 yrs Latino Degree PD Subjects % 62 18% 2% 92% 13% 11% 75% Healthy Controls % 61 15% 2% 93% 0% 5% 95% SWEDD Subjects 64 63% 61 28% 3% 95% 23% 9% 67% Hyposmic 26 69% 68 12% 8% 88% 19% 0% 81% RBD 39 85% 70 36% 46% 90% 8% 0% 92% LRRK2 PD Cohort 92 42% 62 27% 34% 84% 48% 14% 33% LRRK2 UA Cohort 85 38% 62 29% 13% 89% 71% 12% 6% GBA PD Cohort 17 53% 64 12% 0% 100% 18% 6% 71% GBA UA Cohort 21 38% 63 10% 0% 100% 38% 14% 43% SNCA PD Cohort 14 43% 51 64% 0% 100% 100% 0% 0% SNCA UA Cohort 4 25% 44 0% 0% 100% 75% 25% 0% PD Registry % 71 25% 17% 93% 54% 11% 25% UA Registry % 48 8% 28% 89% 67% 6% 21% 5

75 BASELINE CHARACTERISTICS GROUP (n) Total Score MDS-UPDRS Score Part I Part II Part III (Motor) PD Subjects Healthy Controls SWEDD Subjects Hyposmic RBD LRRK2 PD Cohort LRRK2 UA Cohort GBA PD Cohort GBA UA Cohort SNCA PD Cohort SNCA UA Cohort PD Registry UA Registry

76 BASELINE CHARACTERISTICS Hoehn & Yahr GROUP (n) Stage 0 Stage 1 Stage 2-5 Modified Schwab Duration of Disease PD Subjects 423 0% 44% 56% 93 7 Healthy Controls % 1% 0% N/A N/A SWEDD Subjects 64 0% 58% 42% 95 7 Hyposmic 26 88% 8% 4% 99 N/A RBD 39 97% 0% 0% 99 N/A LRRK2 PD Cohort 92 0% 21% 77% LRRK2 UA Cohort 85 91% 5% 4% 100 N/A GBA PD Cohort 17 0% 12% 77% GBA UA Cohort 21 90% 5% 0% 99 N/A SNCA PD Cohort 14 0% 21% 78% SNCA UA Cohort 4 100% 0% 0% 100 N/A PD Registry 114 0% 7% 91% UA Registry % 4% 2% 99 N/A 7

77 BASELINE CHARACTERISTICS GROUP (n) MOCA Total Score GDS Total Score SCOPA AUT Score STAS QUIP UPSIT Raw Score Epworth Sleep. Scale (Sleepy = 10 or above) REM Sleep Disorder (Positive = 5 or above) PD Subjects % 38% Healthy Controls % 20% SWEDD Subjects % 41% Hyposmic % 42% RBD % 87% LRRK2 PD Cohort % 32% LRRK2 UA Cohort % 19% GBA PD Cohort % 71% GBA UA Cohort % 10% SNCA PD Cohort % 71% SNCA UA Cohort % 25% PD Registry N/A N/A N/A N/A 22 N/A 34% UA Registry N/A N/A N/A N/A 33 N/A 15% 8

78 Longitudinal Data: Original Cohorts 9

79 TIME TO START PD MEDICATIONS 10

80 MDS-UPDRS TOTAL OVER TIME MDS-UPDRS Total Score MDS-UPDRS Total Score over time Report generated on data submitted as of: 04Apr Month Mean for PD Subjects OFF Mean for Healthy Controls Mean for PD Subjects ON Mean for SWEDD Subjects NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group Baseline Mean (N) (Min, Max) Year 1 Mean (N) (Min, Max) Year 2 Mean (N) (Min, Max) Year 3 Mean (N) (Min, Max) Year 4 Mean (N) (Min, Max) Year 5 Mean (N) (Min, Max) PD ( OFF ) 32 (422) (7, 72) 39 (334) (5, 113) 43 (283) (10, 99) 47 (232) (9, 121) 52 (108) (9, 128) 57 (21) (22, 93) PD ( ON ) 32 (422) (7, 72) 38 (382) (4, 113) 39 (354) (5, 99) 42 (313) (7, 118) 48 (143) (9, 142) 53 (29) (17, 115) HC 5 (195) (0, 20) 5 (185) (0, 25) 5 (173) (0, 26) 6 (154) (0, 27) 6 (123) (0, 29) 6 (22) (1, 19) SWEDD 28 (64) (4, 91) 29 (56) (3, 78) 32 (53) (3, 122) N/A N/A N/A 11

81 MDS-UPDRS TOTAL SAMPLE SIZE Required Sample Size for Hypothetical Trial: TIME Mean (SD) Change From Baseline Power Reduce By 100% Reduce by 50% Reduce by 25% 9 Months* 4.1 (11.1) 80% 90% Year 7.5 (11.6) 80% 90% Year 10.4 (12.9) 80% 90% *OFF/ON exam not completed at 9 months 12

82 MDS-UPDRS TOTAL SAMPLE SIZE Required Sample Size for Hypothetical Trial: For Detecting Change Over 1 Year Grouped according to whether or not subject started PD meds within first year TIME Mean (SD) Change From Baseline Power Reduce By 100% Reduce by 50% Reduce by 25% Treated 5.0 (12.0) 80% 90% Untreated 10.0 (10.6) 80% 90%

83 MDS-UPDRS MOTOR OVER TIME MDS-UPDRS Motor Score MDS-UPDRS Motor Score over time 60 Report generated on data submitted as of: 04Apr Month Mean for PD Subjects OFF Mean for Healthy Controls Mean for PD Subjects ON Mean for SWEDD Subjects NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group Baseline Mean (N) (Min, Max) Year 1 Mean (N) (Min, Max) Year 2 Mean (N) (Min, Max) Year 3 Mean (N) (Min, Max) Year 4 Mean (N) (Min, Max) Year 5 Mean (N) (Min, Max) PD ( OFF ) 21 (423) (4, 51) 25 (334) (2, 67) 27 (283) (3, 68) 30 (232) (4, 80) 33 (108) (6, 80) 34 (21) (14, 59) PD ( ON ) 21 (423) (4, 51) 23 (382) (1, 67) 23 (354) (0, 68) 25 (314) (1, 65) 27 (144) (1, 70) 30 (29) (9, 67) HC 1 (195) (0, 13) 2 (185) (0, 20) 2 (173) (0, 19) 2 (154) (0, 26) 2 (123) (0, 19) 2 (22) (0, 10) SWEDD 14 (64) (2, 42) 14 (56) (0, 47) 15 (53) (1, 69) N/A N/A N/A 14

84 MDS-UPDRS MOTOR SAMPLE SIZE Required Sample Size for Hypothetical Trial: TIME Mean (SD) Change From Baseline Power Reduce By 100% Reduce by 50% Reduce by 25% 9 Months* 2.3 (8.1) 80% 90% Year 4.5 (8.2) 80% 90% Year 6.4 (9.3) 80% 90% *OFF/ON exam not completed at 9 months 15

85 MDS-UPDRS MOTOR SAMPLE SIZE Required Sample Size for Hypothetical Trial: For Detecting Change Over 1 Year Grouped according to whether or not subject started PD meds within first year TIME Mean (SD) Change From Baseline Power Reduce By 100% Reduce by 50% Reduce by 25% Treated 2.8 (8.9) 80% 90% Untreated 6.3 (7.0) 80% 90%

86 MOCA OVER TIME 30 MOCA Score over time 28 MOCA Score Report generated on data submitted as of: 04Apr Month Mean for PD Subjects Mean for Healthy Controls Mean for SWEDD Subjects NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group Baseline Mean (N) (Min, Max) Year 1 Mean (N) (Min, Max) Year 2 Mean (N) (Min, Max) Year 3 Mean (N) (Min, Max) Year 4 Mean (N) (Min, Max) Year 4 Mean (N) (Min, Max) PD 27 (423) (17, 30) 26 (392) (15, 30) 26 (375) (9, 30) 26 (333) (13, 30) 26 (153) (11, 30) 27 (29) (16, 30) HC 28 (196) (26, 30) 27 (185) (20, 30) 27 (173) (21, 30) 27 (155) (19, 30) 28 (123) (14, 30) 28 (22) (24, 30) SWEDD 27 (64) (17, 30) 26 (58) (20, 30) 26 (55) (16, 30) N/A N/A N/A 17

87 MOCA SAMPLE SIZE Required Sample Size for Hypothetical Trial: TIME Mean (SD) Change From Baseline Power Reduce By 100% Reduce by 50% Reduce by 25% 1 Year -0.8 (2.7) 80% 90% Year -0.8 (2.7) 80% 90%

88 MEAN PUTAMEN OVER TIME 3.0 Mean Putamen over time 2.5 Mean Putamen Report generated on data submitted as of: 04Apr Month Mean for PD Subjects Mean for Healthy Controls Mean for SWEDD Subjects NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group Baseline Mean (N) (Min, Max) Year 1 Mean (N) (Min, Max) Year 2 Mean (N) (Min, Max) Year 4 Mean (N) (Min, Max) PD 0.8 (405) (0.2, 2.2) HC 2.1 (177) (0.6, 3.9) SWEDD 2.1 (60) (0.8, 3.0) 0.7 (359) (0.1, 1.7) 0.7 (324) (0.1, 1.9) 0.5 (62) (0.1, 1.4) N/A N/A N/A 1.9 (52) (0.6, 3.3) N/A N/A 19

89 MEAN PUTAMEN SAMPLE SIZE Required Sample Size for Hypothetical Trial: For % Change as Outcome TIME Mean (SD) Change From Baseline Power Reduce By 100% Reduce by 50% Reduce by 25% 1 Year 13% (21.9) 80% 90% Year 19% (21.2) 80% 90%

90 MEAN PUTAMEN SAMPLE SIZE Required Sample Size for Hypothetical Trial: For Detecting Change Over 1 Year Grouped according to whether or not subject started PD meds within first year TIME Mean (SD) Change From Baseline Power Reduce By 100% Reduce by 50% Reduce by 25% Treated 13% (24.8) 80% 90% Untreated 14% (17.3) 80% 90%

91 MEAN STRIATUM OVER TIME 3.0 Mean Striatum over time 2.5 Mean Striatum Report generated on data submitted as of: 04Apr Month Mean for PD Subjects Mean for Healthy Controls Mean for SWEDD Subjects NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group Baseline Mean (N) (Min, Max) Year 1 Mean (N) (Min, Max) Year 2 Mean (N) (Min, Max) Year 4 Mean (N) (Min, Max) PD 1.4 (405) (0.3, 2.6) HC 2.6 (177) (1.0, 4.2) SWEDD 2.5 (60) (1.1, 3.4) 1.2 (359) (0.2, 2.4) 1.2 (324) (0.3, 2.4) 1.0 (62) (0.2, 2.0) N/A N/A N/A 2.3 (52) (1.0, 3.4) N/A N/A 22

92 MEAN STRIATUM SAMPLE SIZE Required Sample Size for Hypothetical Trial: For % Change as Outcome TIME Mean (SD) Change From Baseline Power Reduce By 100% Reduce by 50% Reduce by 25% 1 Year -11% (15.1) 80% 90% Year -17% (16.1) 80% 90%

93 MEAN STRIATUM SAMPLE SIZE Required Sample Size for Hypothetical Trial: For Detecting Change Over 1 Year Grouped according to whether or not subject started PD meds within first year TIME Mean (SD) Change From Baseline Power Reduce By 100% Reduce by 50% Reduce by 25% Treated 12% (17.1) 80% 90% Untreated 10% (11.8) 80% 90%

94 ALPHA-SYNUCLEIN OVER TIME 2300 Alpha-Synuclein over time Alpha-Synuclein Report generated on data submitted as of: 13Apr Month Mean for PD Subjects Mean for Healthy Controls Mean for SWEDD Subjects NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group Baseline Mean (N) (Min, Max) PD 1845 (412) (333, 6695) HC 2204 (189) (593, 8609) SWEDD 2141 (59) (743, 7201) Month 6 Mean (N) (Min, Max) 1882 (175) (473, 4659) 2188 (114) (659, 5209) 2154 (24) (1002, 4342) Year 1 Mean (N) (Min, Max) 1872 (176) (352, 5157) 2155 (114) (729, 5295) 1987 (24) (807, 3324) 25

95 T-TAU OVER TIME 60 Tau over time Tau Report generated on data submitted as of: 13Apr Month Mean for PD Subjects Mean for Healthy Controls Mean for SWEDD Subjects NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group Baseline Mean (N) (Min, Max) PD 45 (408) (14, 121) HC 53 (187) (18, 223) SWEDD 48 (59) (23, 141) Month 6 Mean (N) (Min, Max) 42 (176) (16, 135) 51 (114) (17, 181) 53 (23) (25, 138) Year 1 Mean (N) (Min, Max) 43 (175) (17, 129) 53 (114) (18, 216) 52 (24) (27, 133) 26

96 P-TAU OVER TIME 24 P-Tau over time P-Tau Report generated on data submitted as of: 13Apr Month Mean for PD Subjects Mean for Healthy Controls Mean for SWEDD Subjects NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group Baseline Mean (N) (Min, Max) Month 6 Mean (N) (Min, Max) Year 1 Mean (N) (Min, Max) PD 16 (410) (5, 94) 15 (176) (5, 56) 18 (175) (5, 62) HC 18 (189) (5, 73) 16 (114) (6, 53) 20 (114) (6, 90) SWEDD 17 (59) (6, 71) 20 (24) (8, 61) 22 (24) (8, 46) 27

97 A-BETA OVER TIME 450 A-beta over time A-beta Report generated on data submitted as of: 13Apr Month Mean for PD Subjects Mean for Healthy Controls Mean for SWEDD Subjects NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group Baseline Mean (N) (Min, Max) Month 6 Mean (N) (Min, Max) Year 1 Mean (N) (Min, Max) PD 371 (412) (129, 797) 364 (176) (129, 687) 377 (176) (144, 733) HC 378 (189) (89, 880) 373 (114) (98, 610) 387 (114) (95, 691) SWEDD 404 (59) (156, 628) 377 (24) (238, 661) 397 (24) (261, 577) 28

98 Longitudinal Data: Prodromal Cohorts 29

99 PRODROMAL MDS-UPDRS MDS-UPDRS Total Score MDS-UPDRS Total Score over time in Prodromal Cohort Report generated on data submitted as of: 04Apr Month Mean for Hyposmic Subjects OFF Mean for RBD Subjects OFF NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group Baseline Mean (N) (Min, Max) Hyposmic 10 (26) (0, 26) RBD 14 (38) (3, 31) Year 1 Mean (N) (Min, Max) 13 (21) (0, 45) 18 (34) (3, 63) Year 2 Mean (N) (Min, Max) Year 3 Mean (N) (Min, Max) Year 4 Mean (N) (Min, Max) Year 5 Mean (N) (Min, Max) N/A N/A N/A N/A 30 (5) (11, 52) N/A N/A N/A 30

100 PRODROMAL MDS-UPDRS MOTOR MDS-UPDRS Motor Score MDS-UPDRS Motor Score over time in Prodromal Cohort Report generated on data submitted as of: 04Apr Month Mean for Hyposmic Subjects OFF Mean for RBD Subjects OFF NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group Baseline Mean (N) (Min, Max) Hyposmic 2.9 (26) (0, 13) RBD 4.5 (38) (0, 15) Year 1 Mean (N) (Min, Max) 6.1 (21) (0, 36) 7.1 (34) (0, 28) Year 2 Mean (N) (Min, Max) Year 3 Mean (N) (Min, Max) Year 4 Mean (N) (Min, Max) Year 5 Mean (N) (Min, Max) N/A N/A N/A N/A 11.2 (5) (5, 21) N/A N/A N/A 31

101 PRODROMAL MOCA OVER TIME 30 MOCA Score over time in Prodromal Cohort 28 MOCA Score Report generated on data submitted as of: 04Apr Month Mean for Hyposmic Subjects Mean for RBD Subjects NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group Baseline Mean (N) (Min, Max) Year 1 Mean (N) (Min, Max) Year 2 Mean (N) (Min, Max) Year 3 Mean (N) (Min, Max) Year 4 Mean (N) (Min, Max) Year 5 Mean (N) (Min, Max) Hyposmic 27 (26) (23, 30) 27 (21) (19, 30) 26 (5) (22, 28) N/A N/A N/A RBD 25 (39) (11, 30) 24 (35) (13, 30) 26 (5) (22, 29) N/A N/A N/A 32

102 PRODROMAL MEAN PUTAMEN 2.5 Mean Putamen over time in Prodromal Cohort 2.0 Mean Putamen Report generated on data submitted as of: 04Apr Month Mean for Hyposmic Subjects Mean for RBD Subjects NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group Baseline Mean (N) (Min, Max) Hyposmic 1.4 (26) (0.8, 2.1) RBD 1.1 (39) (0.5, 2.0) Year 1 Mean (N) (Min, Max) 1.2 (6) (0.8, 1.6) 1.1 (13) (0.7, 1.5) Year 2 Mean (N) (Min, Max) Year 3 Mean (N) (Min, Max) Year 4 Mean (N) (Min, Max) Year 5 Mean (N) (Min, Max) N/A N/A N/A N/A N/A N/A N/A N/A 33

103 PRODROMAL MEAN STRIATUM 2.5 Mean Striatum over time in Prodromal Cohort 2.0 Mean Striatum Report generated on data submitted as of: 04Apr Month Mean for Hyposmic Subjects Mean for RBD Subjects NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group Baseline Mean (N) (Min, Max) Hyposmic 2.0 (26) (1.2, 3.0) RBD 1.6 (39) (0.8, 2.8) Year 1 Mean (N) (Min, Max) 1.8 (6) (1.3, 2.1) 1.5 (13) (0.8, 2.0) Year 2 Mean (N) (Min, Max) Year 3 Mean (N) (Min, Max) Year 4 Mean (N) (Min, Max) Year 5 Mean (N) (Min, Max) N/A N/A N/A N/A N/A N/A N/A N/A 34

104 Longitudinal Data: Genetic Cohorts 35

105 GENETIC COHORT MDS-UPDRS TOTAL MDS-UPDRS Total Score MDS-UPDRS Total Score over time in Genetic Cohort Report generated on data submitted as of: 04Apr Month Mean for LRRK2 PD OFF Mean for LRRK2 UA Mean for LRRK2 PD ON Mean for SNCA PD ON NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group LRRK2 PD ( OFF ) LRRK2 PD ( ON ) Baseline Mean (N) (Min, Max) 36 (88) (7, 113) 36 (88) (7, 113) LRRK2 UA 8 (83) (0, 36) SNCA PD ( ON ) 45 (14) (14, 92) Year 1 Mean (N) (Min, Max) 38 (43) (12, 105) 32 (52) (9, 100) 10 (38) (0, 47) 57 (6) (23, 122) Year 2 Mean (N) (Min, Max) Year 3 Mean (N) (Min, Max) Year 4 Mean (N) (Min, Max) Year 5 Mean (N) (Min, Max) N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 36

106 GENETIC COHORT MDS-UPDRS MOTOR MDS-UPDRS Motor Score MDS-UPDRS Motor Score over time in Genetic Cohort Report generated on data submitted as of: 04Apr Month Mean for LRRK2 PD OFF Mean for LRRK2 UA Mean for LRRK2 PD ON Mean for SNCA PD ON NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group LRRK2 PD ( OFF ) LRRK2 PD ( ON ) Baseline Mean (N) (Min, Max) 21 (90) (4, 56) 21 (90) (4, 56) LRRK2 UA 3 (84) (0, 16) SNCA PD ( ON ) 22 (14) (7, 51) Year 1 Mean (N) (Min, Max) 23 (43) (6, 43) 16 (52) (2, 35) 3 (39) (0, 15) 26 (7) (7, 60) Year 2 Mean (N) (Min, Max) Year 3 Mean (N) (Min, Max) Year 4 Mean (N) (Min, Max) Year 5 Mean (N) (Min, Max) N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 37

107 GENETIC COHORT MOCA OVER TIME 30 MOCA Score over time in Genetic Cohort 28 MOCA Score Report generated on data submitted as of: 04Apr Month Mean for LRRK2 PD Mean for SNCA PD Mean for LRRK2 UA NOTE: Points are only plotted if 5 or more subjects have data at that visit. Group Baseline Mean (N) (Min, Max) LRRK2 PD 26 (91) (17, 30) LRRK2 UA 26 (85) (19, 30) SNCA PD 23 (14) (11, 30) Year 1 Mean (N) (Min, Max) 26 (52) (14, 30) 27 (40) (20, 30) 21 (7) (8, 30) Year 2 Mean (N) (Min, Max) Year 3 Mean (N) (Min, Max) Year 4 Mean (N) (Min, Max) Year 5 Mean (N) (Min, Max) N/A N/A N/A N/A 28 (5) (26, 29) N/A N/A N/A N/A N/A N/A N/A 38

108

109 CSF SAMPLE SIZE Required Sample Size for Hypothetical Trial: For Detecting Change Over 1 Year Marker Mean (SD) Change From Baseline Power Reduce By 100% Reduce by 50% Reduce by 25% Alpha Synuclein (605.6) 80% 90% 90K 120K 360K 480K > 1 million > 1 million Tau -0.2 (7.2) 80% 90% 56K 75K 225K 300K 900K > 1 million P-Tau 4.4 (12.9) 80% 90% A-Beta 18.4 (71.9) 80% 90%

110 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI 2016 ANNUUAL INVESTIGATORS MEETING May 4-5

111 PPMI Cognitive-Behavioral Working Group Daniel Weintraub, MD Tanya Simuni, MD PPMI Annual Meeting - May 4-5, 2016

112 Membership Daniel Weintraub WG Chair Tanya Simuni Steering Committee Laura Leary IND Chris Coffey, Chelsea Caspell-Garcia Statistics Core Dag Aarsland Roy Alcalay Estibaliz Arce Paolo Barone Matthew Barrett Melanie Braddabur David Burn Cindy Casacelli Lama Chahine William Cho Thomas Comery Autilia Cozzolino Johnna Devoto Chris Dodds Jamie Eberling Alberto Espay Stewart Factor Hubert Fernandez Regan Fong Douglas Galasko Sandeep Gupta Keith Hawkins David Hewitt Jim Leverenz Irene Litvan Anita McCoy Susanne Ostrowitzki Bernard Ravina Alistair Reith Irene Richard Liana Rosenthal Holly Shill Andrew Siderowf John Sims Gretchen Todd Eduardo Tolosa Michael Ward Michele York

113 Study Assessments

114 Cognitive Assessments Global - Montreal Cognitive Assessment (MoCA) Memory - Hopkins Verbal Learning Test (HVLT) 2. Visuospatial - Judgment of Line Orientation (JOLO) 3. Working memory - Letter-Number Sequencing (LNS) Executive - Semantic fluency (animals, fruits, vegetables) 4. Attention - Symbol-Digit Modalities Test (SDMT)

115 Cognitive Assessments in Neurodegenerative Disease Studies: Focus on PD Cognitive Assessment Global / Screening NINDS CDE MoCA DRS-2 ADAS-cog MMSE Udall Recommended MoCA MDS MCI Task Force MoCA DRS-2 Memory - HVLT-R HVLT-R Executive / Working memory - LNS TMT B Fluency-P DSC Visuospatial - JLO Clock-D LMT LNS TMT B Fluency-P DS-B JLO Clock-D Clock-C PPMI MoCA NACC UDS (*DLB module) MMSE CDR ADNI MoCA MMSE ADAS-cog CDR HVLT-R LMT LMT (I and II) LNS TMT B DS-B DSC (*Stroop) AVLT TMT B JLO - Clock-D Attention - TMT A TMT A SDMT TMT A Language - Fluency-S BNT Fluency-S BNT Fluency-S DSC DS-F Fluency-S BNT TMT A Fluency-S BNT

116 Behavioral Assessments Depression - Geriatric Depression Scale (GDS-15) Anxiety - State-Trait Anxiety (STAI) ICDs - Questionnaire for Impulsive- Compulsive Disorders in Parkinson's Disease (QUIP) Psychosis, apathy, fatigue - MDS-UPDRS Part I

117 Workgroup Manuscripts

118 20% of PD patients screen positive for MCI and 10% meet cognitive test-based criteria Multiple NPS (e.g., depression, anxiety and apathy) more common in untreated PD patients compared with general population Rates of NPS associated with DRT (e.g., psychosis and ICDs) either low or similar to controls NPS=neuropsychiatric symptoms DRT=dopamine replacement therapy Weintraub et al. Movement Disorders 2015;30:

119 Multiple modality biomarker prediction of cognitive impairment in prospectively followed de novo Parkinson disease

120 Methods Cognitive impairment diagnosis assigned annually for out to 3 years: (1) Montreal Cognitive Assessment score <26 (2) Any 2 specific cognitive test scores >1.5 standard deviation below standardized mean (3) Site investigator diagnosis of cognitive impairment (either MCI or dementia) Biomarkers: DAT SPECT scan Structural MRI (volume and thickness) Diffusion tensor imaging (mean diffusivity and fractional anisotropy) CSF (amyloid beta [Aβ], tau and synuclein) 11 single nucleotide polymorphisms (SNPs) previously associated with cognition in PD Baseline biomarkers available for patients; models run twice, first including all participants and then for just those with MRI data Annual (out to year 2) structural MRI and DAT scan data also included Longitudinal logistic regression mixed effect models run after selecting the most appropriate covariates and random effects for each cognitive definition Univariate analyses run initially with FDR=0.2 to select biomarker variables for inclusion in multivariable models. Variables removed from the multivariable model individually in backwards selection process until all remaining variables were p<0.1 level

121 Results Biomarker Definition of cognitive impairment MoCA<26 Cognitive test deficits ( 2 tests >1.5 SD below mean) Site investigator diagnosis (MCI or PDD) DAT imaging - - ipsilateral caudate DAT *** contralateral caudate DAT *** CSF - - CSF Aβ 1-42 *** Structural MRI (volume) - - lateral occipital *** inferior parietal *** fusiform *** pars orbitalis *** Structural MRI (thickness) - - caudal anterior cingulate *** fusiform *** DTI (FA) DTI (MD) Genetics - - MAPT rs (T/T genotype) * COMT val158met (val/val genotype) * (Unpublished data).

122 Conclusions By year 3, cognitive impairment diagnosed in 15-38% participants depending on the criteria in frequency of % over initial 3 years of disease Independently predicted by biomarker changes suggesting: 1. Nigrostriatal dopaminergic deficits 2. Diffuse cortical neurodegenerative disease 3. Co-morbid Alzheimer s disease pathology

123 Predictors of Incident ICD Behaviors

124 Decreased Dopamine Transporter Availability Associated With Incident Behaviors in Early PD All subjects Subjects on DRT OR P OR P Baseline DAT binding Right caudate Left caudate Right putamen Left putamen Mean total striatal Change in DAT binding (baseline-year 1) Right caudate Left caudate Right putamen Left putamen Mean total striatal DAT binding (post-baseline) Right caudate Left caudate Right putamen Left putamen Mean total striatal Smith et al. JNNP /jnnp

125 Clinical-Genetic Model Predicts Incident ICD Behaviors Heritability of symptom = 57% Adding 13 candidate SNPs increased ICD prediction in DA-treated patients (AUC from 71% to 87%) Strongest SNP predictors Serotonin 2A receptor Kappa opioid receptor Dopamine decarboxylase Kraemmer et al. JNNP (in press).

126 Prodromal Cohorts

127 PARS Study Cognitive Data Chahine et al. Movement Disorders 2016;31:86-94.

128 GBA and Cognition Mata et al. Movement Disorders 2016;31:

129 Miscellaneous

130 Total Cognitive-Behavioral Publications Over 1/3 of PPMI manuscripts

131 Similar Studies or Cohorts PD ICICLE-PD DeNoPa CAMPAIGN Fox-funded PD-MCI consortium PD Cognitive Genetics Consortium Udall Centers (PDBP) Uses Combination Comparison Validation Arizona Study of Aging and Neurodegenerative Disorders DLB E-DLB AD ADNI (upcoming ADNI 3)

132 New PPMI Cognition Advisory Group Chaired by Jamie Eberling Members: Glenn Stebbins, Jennifer Goldman, Irene Litvan, David Burn, Ian McKeith, John Paul Taylor, Dag Aarsland, Alex Troster, Bernard Ravina, Andrew Siderowf, Clive Ballard, Connie Marras, Dan Weintraub

133 Baseline Prevalence and Longitudinal Evolution of Non-motor Symptoms in Early Parkinson s Disease: PPMI cohort Tanya Simuni, Chelsea Caspell-Garcia, Michael Walker, Christopher Coffey, Shirley Lasch, Daniel Weintraub, Caroline Tanner, Danna Jennings, Karl Kieburtz, and Ken Marek on behalf of the PPMI Investigators

134 Study Objectives To examine baseline prevalence and longitudinal change in non-motor symptoms (NMS) in cohort of de novo PD (at baseline) subjects vs. matched healthy controls (HC) NMS defined as MDS-UPDRS Part I total score and subscores for cognition (Q ), sleep (Q ), autonomic dysfunction (Q ), and other validated scales available in the PPMI dataset

135 Results 423 PD subjects and 196 age- and gender-matched HC at baseline Longitudinal data at time of analysis available for 398 PD subjects at 1 year and 380 at 2 years High prevalence of NMS in early untreated PD Significant change on NMS over time in PD but not HC Significant change in 3 key domains of NMS in PD but not HC

136 Baseline Predictors of Baseline NMS in PD Subjects Univariate Analysis Number of Multivariable Analysis Variable Parameter Estimate p-value Observations Parameter Estimate p-value (95% CI) Missing (95% CI) Age (-0.018, 0.063) Gender (-1.644, ) (-1.659, ) Education (-1.503, 0.521) Ethnicity (-3.166, 2.221) Race (-2.302, 0.635) Family History of PD (-0.887, 0.927) UPSIT (-0.078, 0.016) MDS-UPDRS Part II Score (0.417, 0.577) < (0.351, 0.534) <.0001 MDS-UDPRS Part III Score (0.048, 0.134) < N.S. N.S. Modified Schwab & England ADL (-0.273, ) < N.S. N.S. Hoehn & Yahr (-1.780, ) N.S. N.S. TD/non-TD Classification (-2.228, ) Not included Not included PIGD Score (4.896, 8.149) < (0.645, 4.072) Tremor Score (-0.371, 2.084) N.S. N.S. Side Most Affected (-0.765, 0.688) Disease Duration (-0.028, 0.093) Age of PD Onset (-0.019, 0.060) Contralateral Caudate (-1.400, 0.003) N.S. N.S. Ipsilateral Caudate (-1.122, 0.202) Not included Not included Contralateral Putamen (-1.289, 1.613) Ipsilateral Putamen (-1.436, 0.612) A-Beta (-0.005, 0.002) t-tau (-0.001, 0.005) p-tau (-0.005, 0.002) t-tau/a-beta (0.000, 0.007) (-0.000, 0.006) p-tau/a-beta (-0.004, 0.003) p-tau/t-tau (-0.007, ) N.S. N.S. Alpha-Synuclein (-0.002, 0.005)

137 Baseline Predictors of Change in NMS From BL to Year 2 in PD Subjects Univariate Analysis Number of Multivariable Analysis Variable Parameter Estimate p-value Observations Parameter Estimate p-value (95% CI) Missing (95% CI) Age (0.023, 0.106) (0.015, 0.099) Gender (-0.134, 1.578) N.S. N.S. Education (-0.461, 1.706) Ethnicity (-5.011, 0.302) N.S. N.S. Race (-1.048, 1.961) Family History of PD (-1.601, 0.274) N.S. N.S. UPSIT (-0.079, 0.019) MDS-UPDRS Part II Score (-0.039, 0.161) MDS-UDPRS Part III Score (-0.046, 0.047) Modified Schwab & England ADL (-0.077, 0.065) Hoehn & Yahr (-0.873, 0.767) TD/non-TD Classification (-0.665, 1.137) PIGD Score (-2.797, 0.824) Tremor Score (-1.383, 1.162) Side Most Affected (-0.194, 1.323) N.S. N.S. Disease Duration (-0.029, 0.095) Age of PD Onset (0.019, 0.101) N.S. N.S. Contralateral Caudate (-1.088, 0.431) Ipsilateral Caudate (-1.224, 0.164) N.S. N.S. Contralateral Putamen (-2.565, 0.791) Ipsilateral Putamen (-2.381, ) N.S. N.S. A-Beta (-0.009, ) (-0.008, ) t-tau (-0.006, 0.001) N.S. N.S. p-tau (-0.004, 0.003) t-tau/a-beta (-0.004, 0.003) p-tau/a-beta (-0.001, 0.006) p-tau/t-tau (-0.002, 0.005) Alpha-Synuclein (-0.005, 0.002) *NMS is defined as MDS-UPDRS Part I Score. Report Generated on Data Submitted as of: 04Jan2016.

138 Conclusions Results support prior reports of high prevalence of NMS in early untreated PD Largest cohort of longitudinal analysis of evolution of NMS cohort of de novo (at recruitment) PD subjects First study to demonstrate impact of higher t-tau/a-beta on baseline prevalence and lower a-beta on longitudinal evolution of NMS in early PD Underlying mechanism of this biological correlation remains to be determined Initiation of any class of PD medications did not have impact on evolution of NMS However, this could be related to still relatively low LED dose in the cohort

139 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI 2016 ANNUUAL INVESTIGATORS MEETING May 4-5

140 Update from the Parkinson Progression Marker Initiative Trial Imaging Core J. Seibyl, D. Tosun, N. Schuff

141 PPMI Imaging Core 1. Updates 2. Longitudinal DAT analyses 3. Longitudinal VMAT2 analyses 4. Amyloid PET 5. DTI study D. Tosun 2

142 PPMI Imaging Core Lab Updates Dr. Duygu Tosun assuming key role in the DTI analyses Dr. Kathleen Poston working with resting state data

143

144 Volume of Interest Strategy Caudate Ant putamen Post putamen SBR= striatal region - 1 occipital

145 Baseline DAT SBR, Age-corrected Mean Striatal SBR Contralateral Putamen SBR PD n= 423 HC n= 196 SWEDD = 64

146 * DAT SPECT LONGITUDINAL STUDY Previously Year 1 and Year 2 data showed changes in specific binding ratios (SBR) greater in Y 1 compared to Y 2 and a regional pattern of the rate of signal loss with relatively greater changes occurring in relatively less affected striatal subregions. This raised the question of a floor effect, where hypothetically signal change becomes minimal as disease progresses. Regional specific binding ratios (SBR) were measured in ipsilateral and contralateral caudate, anterior putamen, and posterior putamen for each timepoint; baseline and Year 4 (N=82) Percent change from baseline reported for Year 4 as composite SBR and for separate striatal subregions and sides

147 Characteristics of PPMI subjects Cohort Gender %M Age (yrs) Dz duration (months) Part III MDS UPDRS baseline 4 Yr PD n= % 61.2 ± ± ± 8.7 All had an Ioflupane SPECT scan demonstrating presynaptic striatal dopaminergic loss consistent with Parkinsonism prior to enrollment

148 % change SBR * Results- Rate of Change in Striatal Subregions Y1 and Y2 (N=241) Percent Rate of Change of Striatal SBR Depends on Side and Subregion Measured ipsi Caud contr Caud # # * ipsi PutAnt contra PutAnt # # ipsi Put * * contra Put Baseline to Year 1 Baseline to Year 2 * p<.05 ipsi v contra # p<.05 put v caud Ipsi = ipsilateral, contra= contralateral, caud=caudate Put= putamen, PutAnt= anterior putamen

149 Questions Determine what striatal subregions provide highest signal size with lowest noise for longitudinal assessment of DAT density Do the regional difference in rates of DAT signal reduction persist over time?

150 SBR Baseline and Y4 SBR for mean ipsilateral and contralateral striatum 3 * * 2 1 * P < b mean ipsi b mean contra 4 mean ipsi 4 mean contra N = 82

151 SBR SBR Ipsilateral and Contralateral Mean Striatal SBR over Four Years Ipsilateral Contralateral B ipsi y1 ipsi y2 ipsi y4 ipsi 0.0 B con y1 con y2 con y4 con

152 % Change SBR No Differences in % Change SBR between Ipsi and Contra Striatum Over 4 yrs % chg ipsi stria % chg contra stria P= 0.68 N=82 Mean % % Std. Deviation

153 % Change SBR fron Baselline % Change SBR from Baseline Ipsilateral and Contralateral Mean % Change Striatal SBR over Four Years 20 Ipsilateral 20 Contralateral % y1 ipsi % y2 ipsi % y4 ipsi % y1 con % y2 con % y4 con Each timepoint is change from original baseline

154 % Change SBR from Baseline % Change SBR from Baseline Ipsilateral and Contralateral Mean % Change Striatal SBR over Four Years- Annualized Ipsilateral Contralateral an % y1 ipsi an % y2 ipsi an % y4 ipsi an % y1 con an % y2 con an % y4 con

155 SBR Striatal Subregion SBRs at Baseline and Year b ipsi Caud-s b contra Caud-s b ipsi PutAnt b contra PutAnt b ipsi Put-s b contra Put-s 4 ipsi Caud-s 4 contra Caud-s 4 ipsi PutAnt 4 contra PutAnt 4 ipsi Put-s 4 contra Put-s N=82

156 SBR Striatal Subregion SBRs at Baseline and Year b ipsi Caud-s 4 ipsi Caud-s b contra Caud-s 4 contra Caud-s b ipsi PutAnt 4 ipsi PutAnt b contra PutAnt 4 contra PutAnt b ipsi Put-s 4 ipsi Put-s b contra Put-s 4 contra Put-s P< For all b v Y4 subregion comparisons

157 % Change SBR Baseline to Y4 Mean % Change SBR are Similar Across all Striatal Subregions % chg ipsi caud % chg contra caud % chg ipsi ant put % chg contra ant put % chg ipsi put % chg contra put

158 Mean % change SBR are similar in striatal subregions over 4 years ipsi caud contra caud ipsi ant put contra ant put ipsi put contra put ipsi stria contra stria Mean Std. Deviation % COV Mean baseline SBR N= 82 %COV variability between regions may be inversely related to baseline SBR

159 * Conclusions Annualized rates of % change in composite SBR are 10-12% in the first year with lower reductions in SBR by year 4, for an average annualized change of 7%. When parsed out into subregions, in Y 1 there is a progressive increase in the rate of change in SBR in the ipsilateral striatum in an anterior to posterior pattern moving from caudate to anterior putamen to posterior putamen. This same pattern is not seen on the contralateral side, and disappears by Y4, when rates SBR change become equal across subregions, without hitting a floor. While subregions have equivalent % CHANGE SBR, the variance is higher for lower uptake areas like contralateral putamen. The use of 123-I ioflupane SPECT as a biomarker of PD progression seems feasible from the perspective of the size of signal change in an early PD cohort with the better regions for tracking early change the composite SBR or caudate.

160 * Additional Questions 1. Are there gender differences in rates of SBR change? 2. Are there associations between medication requirement/treatment and rates of SBR change? - Do those with greatest rate of change of SBR go on to require treatment sooner, or is there a threshold effect on absolute SBR? - Does medication effect percent change SBR? 3. Is there better correlation of 4 year DAT data with clinical ratings or other biomarkers than seen with the 2 year data? 4. Where does DAT imaging fit into a biomarker schema for PD therapeutic; trials eligibility alone or is the signal:noise good enough for cohorts of reasonable subject numbers studied over 1-2 years? 5. Are there lesson to be learned from the groups of fast DAT vs slow DAT reducers?

161 HV PD

162 SBR 18F AV-133 PET: Binding ratios at baseline, Y1, & Y N = 20 Scan 1-2 N = 10 Scan mean ipsi b mean contra b mean ipsi 2 mean contra 2 mean ipsi 3 mean contra 3

163 % Change SBR % Change SBR over 2 Year with AV % chg ipsi stria 3 % chg contra stria 3 2 years ipsi contra Mean SD

164 SBR 18F AV-133 PET: Striatal subregion SBRs, baseline & Y IPSI CAUD b CONTRA CAUD b IPSI PutANT b CONTRA PutANT b IPSI PutPost b CONTRA PutPost b IPSI CAUD 2 CONTRA CAUD 2 IPSI PutANT 2 CONTRA PutANT 2 IPSI PutPost 2 CONTRA PutPost 2

165 % Change SBR AV133 Percent Change in Striatal Subregions over 2 Yrs % chg ipsi caud 3 % chg contra caud 3 % chg ipsi put 3 % chg contra put 3 % chg ipsi ant put 3 % chg contra ant put 3

166 Mean % change SBR are similar in striatal subregions over 2 years ipsi caud contra caud ipsi ant put contra ant put ipsi put contra put ipsi stria contra stria Mean Std. Deviation % COV N= 10

167 Percent Change DAT and VMAT2 SBR in Ipsilateral Striatum DAT B to Yr 2 % SBR Striatum Mean SD SE 1.12 %COV 106% Annualized VMAT2 B to Yr 2 % SBR Striatum Mean SD 9.13 SE 3.04 %COV 43% Annualized N= 241 N= 10

168 FLORBETABEN PET AMYLOID

169

170

171 SUVr FLORBETABEN PET Composite SUVr N =

172 What have we learned thus far? Dopaminergic biomarkers of longitudinal change Enough signal change in early PD No floor effect Composite sampling of striatum lowers variance VMAT2 promising Amyloid PET PD looks like general population

173 DTI STUDIES

174 Acknowledgements PPMI is sponsored by the Michael J. Fox Foundation for Parkinson s Research (MJFF) and is co-funded by:

175 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI 2016 ANNUUAL INVESTIGATORS MEETING May 4-5

176 DIFFUSION TENSOR IMAGING (DTI): IMAGE PROCESSING & ANALYSIS Duygu Tosun, PhD Department Of Radiology And Biomedical Imaging, University Of California San Francisco Contributors: I-Wei Katherine Wu (UCSF) Yu Zhang, MD, PhD (UCSF) Eric Foster, PhD (Iowa) Norbert Schuff, PhD (UCSF)

177 DTI processing status Time Point HC PD SWED D Prodromal GENPD GENUN Reg PD Processe d/receive d Upload BL / m / m / m / Total /808 (94%) Total Excluded Note: Most subjects have 2 DTI scans per session.

178 Site quality monitoring Average SNR(b0) over time of upload, as of April 2016 Cutoff SNR=30

179 DTI processing status Time Point HC PD SWED D Prodromal GENPD GENUN Reg PD Processe d/receive d Upload BL / m / m / m / Total /808 (94%) Total Excluded Note: Most subjects have 2 DTI scans per session.

180 Analyses highlights: Longitudinal brain changes I.Progressive regional brain atrophy in PD II.Progressive microstructural changes using diffusion tensor imaging III.Correlations of brain changes with clinical and CSF biomarker measures 6

181 Longitudinal data sample Baseline Change Measure PD HC P-value PD HC P-value (n=144) (n=61) (n=144) (n=61) Female 48 (33%) 20 (33%) Age [years] 60 ± ± % on PD medication 0 NA NA 67 NA NA H & Y stage [ 2] (a) 85 (59%) 0 < UPDRS (b) Total 31.6 ± ± 3.7 < ± ± 3.1 < UPDRS- Motor (c) 21.1 ± ± 1.4 < ± ± MoCA (d) 27.5 ± ± ± ± ICV [liter] (e) 1.52 ± ± α-synuclein [pg/ml] 1780 ± ± ± ± Aβ I-42 [pg/ml] 368 ± ± ± ± T-tau [pg/ml] 44 ± ± ± ± P-tau 181 [pg/ml] 16 ± ± ± ±

182 Data-driven discovery of regional brain atrophy progression in PD PD HC Brain Region Mean 95% CI (b) Mean 95% CI (b) Amygdala (contra) (-2.87, -1.07) 0.38 (-1.27, 2.03) Putamen (ipsi) (-1.78, -0.61) (-0.79, 0.49) Lingual cortex (contra) (-1.31, -0.42) 0.11 (-0.65, 0.87) Middle-temporal cortex (contra) (-2.03, -1.06) (-1.21, -0.01) Brain regions identified by best subset model selection in differentiating patients from healthy subjects and their corresponding volume changes (in % annual change from baseline) by group 8

183 Longitudinal DTI study: microstructural brain changes in PD Group differences in microstructural rates based on 1 year follow-up DTI; p < 0.05 FDR corrected 9

184 Correlations between DTI changes and CSFbiomarker changes DTI of Substantia Nigra FA radial diffusivity axial diffusivity DTI changes of SN also correlated with CSF aβ and t-tau 10

185 Correlations between DTI changes and DAT changes DTI of Substantia Nigra FA radial diffusivity axial diffusivity 11

186 Correlations between DTI changes and cognitive changes DTI of Thalamus FA radial diffusivity axial diffusivity 12

187 Data-driven discovery of FA changes and CSF α-synuclein changes 13

188 Cross-sectional tractography of brain networks Executive loop (EL): connects Putamen (anterior vertex) and Dorsal Lateral Prefrontal Cortex (DLPFC) DLPFC SMA motor loop executive loop SMA DLPFC Put/Gp SN dopaminergic loop Put/Gp Motor Loop (ML): connects Putamen (medial posterior) and Supplementary Motor Area (SMA) SN Nigrostriatal Tract (NST): connects Substantia Nigra and Putamen (ventral) 14

189 WM integrity of the nigrostriatal tract and motor deficits Replication of previously published results in double the sample size (64 PD and 30 controls): FA, rd, Fiber counts in the nigrostriatal tract (contralateral) correlate selectively with UPDRS-III (not with MoCA) 15

190 WM integrity of the executive loop and executive deficits FA, rd, ad in the executive tract (bilaterally) correlated with executive function FA, rd in the executive tract (contralateral) also correlated with attention function DTI of executive and motor tracts had no correlation with motor function 16

191 Whole brain tractography and network analysis 17

192 Network diffusion model dynamics in PD 18

193 Highlights Stronger longitudinal signal compared to crosssectional Longitudinal changes seen were more strongly associated by the pathology/disease biomarkers Can MR measures be predictive of disease progression? 19

194 Plans MRI/DTI in SWEDD Brain Network Analyses Data-driven (machine learning) analysis of abnormal DTI patterns in PD Predicting PD clinical progression from baseline MRI/DTI 20

195 PPMI task-free functional MRI dataset Timepoint HC PD SWEDD Prodrom al Total bl NA NA NA NA NA m m m Total

196 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI 2016 ANNUUAL INVESTIGATORS MEETING May 4-5

197 BIOREPOSITORY UPDATE Tatiana Foroud

198 PPMI BIOREPOSITORIES... 3

199 PPMI BIOSPECIMENS DNA RNA Plasma Serum CSF Urine PD HC SWEDD Prodromal PD Cohort Unaffected Cohort PD Registry NA 122 Unaffected Registry NA 137 As of May 1;

200 Number of Kits PPMI NONCONFORMANCE 600 Kits received 1 nonconformance >1 nonconformance Q1 Q2 Q3 Q4 5

201 Number of Kits PPMI NONCONFORMANCE 600 Kits received 1 nonconformance >1 nonconformance Most common issues: - No advanced notification - Sample forms filled out incorrectly - Low volume 0 Q1 Q2 Q3 Q4 6

202 INDUCED PLURIPOTENT STEM CELLS (IPSCs) IPSC are adult cells that have been genetically reprogrammed to an embryonic stem cell-like state 7

203 WHAT CAN YOU DO WITH AN IPSC? 8

204 PERIPHERAL BLOOD MONONUCLEAR CELL (PBMC)» PPMI will collect blood and isolate PBMCs» PBMCs can be used in the future to develop IPSC» PPMI is creating an important resource for future research studies Bring to all of PPMI in Amendment 11 9

205 PPMI BIOSPECIMENS DNA Whole Blood RNA Plasma Serum Urine CSF PBMC PD X X X X X X X X HC X X X X X X X X SWEDDS X X X X X X X Prodromal X X X X X X X X PD Cohort X X X X X X X X Unaffected Cohort PD Registry Unaffected Registry X X X X X X X X X X X X X X X X X X X X 10

206 PPMI SAMPLE DISTRIBUTIONS»DNA NeuroX, whole exome sequencing, whole genome sequencing, methylation» Serum and RNA Blood-based biomarker assay evaluation» CSF Several studies to evaluate cross-sectional and longitudinal biomarkers (Hemoglobin, alpha synuclein, a- beta, total tau, ptau) 11

207 PPMI BIOSPECIMENS» Overall, sites are doing very well collecting, processing and shipping samples» Samples being collected are used in ongoing high priority research studies» Continue to expand types of samples being collected in order to maximize the ability to perform key experiments in the future 12

208 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI 2016 ANNUUAL INVESTIGATORS MEETING May 4-5

209 Biospecimen Data CSF analysis PPMI Bioanalytics Core Brit Mollenhauer Douglas Galasko Lesley M. Shaw John Q. Trojanowski

210 Published data Longitudinal CSF analysis Re-analysis plan with completed 3 year follow-up Upcoming other analyses

211

212 Longitudinal data analysis (Mollenhauer et al., submitted to JAMA Neurology) COHORT: PPMI subset 173 PD patients 112 healthy controls SAMPLES: CSF at Baseline, 6 and 12 months follow-up ANALYTES: Total α-synuclein, total and phosphorylated tau protein (181p), β-amyloid 1-42 (Aβ42) RESULTS: Cerebrospinal fluid biomarkers generally remained stable over 6 and 12 months follow up Aβ42 increase slightly in PD and HC from BL to 12 mo FU P-Tau protein increased slightly more in PD than in HC α-syn decreased slightly in PD, stable in HC Predictors of CSF marker changes: Genetic polymorphisms at SNCA Levodopaequivalent dosage for PD subjects on dopamine agonists Correlations with CSF changes Small but significant correlation between changes in CSF α-synuclein and MDs-UPDRS motor scores in PD CONCLUSION: Tau, p-tau and Aβ42 do not change dramatically with progression of PD How α-synuclein gets into the CSF during normal neuronal health and how this continues to do so during neurodegeneration is not clear Longer time intervals are needed Better progression markers for early PD are needed Bateman et al., 2012

213 BIOANALYTICS CORE AVAILABLE DATA UPCOMING ANALYSES» CSF total αsyn measured using the Covance/BioLegend immunoassay All baseline PD, HC, and SWEDD CSF samples Subset of 6 and 12 mo PD, HC and SWEDD» CSF Aβ, tau, and p-tau 181 measured using the Innogenetics INNO-BIA AlzBio3 immunoassay» CSF total αsyn measured using the Covance/BioLegend immunoassay Details below» CSF Aβ, tau, and p-tau 181 measured using the Roche Elecsys automated immunoassay Details below All baseline PD, HC, and SWEDD CSF samples Subset of 6 and 12 mo PD, HC, and SWEDD BL V02 (6 mo) V04 (1 yr) V06 (2 yr) V08 (3 yr) Grand Total PD ,612 HC SWEDD Prodromal Genetic Total ,854 5

214 Longitudinal CSF Re-analysis Plan for 2016 / 2017 SAMPLES: ONE aliquot (0.5 ml) CSF VISITS: All available BL, 6 month, 12, 24 and 36 month visits COHORTS: original cohort (PD, HC), prodromal cohort and genetic cohort ANALYTES: (1) CSF alpha-synuclein and hemoglobin by Peggy Taylor at BioLegend (both ELISA) (2) total and phospho-tau,aβ42 by Lesley Shaw at UPenn using the Roche Elecsys automated immunoassay

215 Elecsys Abeta1-42, ttau, ptau181 assays o o o o o High within-run and within laboratoryprecision (CV%<5) Low instrument-to-instrument variability (CV%<5) Low lot-to-lot variability (r>0.99) External reproduciblility (CV%<5) AD QC program with 5 different platforms for Aβ42 (4 ELISA): average CV% 17.34; for Roche CV% 3.2 Bittner et al.,

216 ALPHA-SYNUCLEIN ASSAY STANDARDIZATION LEAPS Massspec Assays: (3 platforms) Roche Elecsys J. Zhang (H. Zetterberg) (PNNL) (ISB) Samples: CSF, saliva, whole blood J. Zhang B. Mollenhauer Covance /BioLegend ELISA 1. Covance/BioLegend /Taylor 2. B. Mollenhauer 3. H. Zetterberg 4. J. Zhang 5. O. El-Agnaf (Luminex) 1. J. Zhang 2. B. Mollenhauer 3. H. Zetterberg Standard/ calibrator production MSD Comparison of MSD and Mollenhauer Singleplex 1. MSD/Umek 2. B. Mollenhauer 3. H. Zetterberg 4. J. Zhang MJFF/ TBD To be used in 3 assay platforms and verified by mass-spec satellites 8

217 BRC SAMPLE APPROVALS (9/100) ANALYTE Matrix Visit(s) PROJECT DETAILS αsyn transcripts ApoA1 and EGF RNA from whole blood Plasma BL IGF-1 Serum BL mrna transcripts SNCA genotyping mrna transcripts Oligomeric and ps129 asyn RNA from whole blood DNA from whole blood RNA from whole blood CSF BL, V02 (6 mo), V04 (12 mo) BL BL DNA methylation Whole blood BL PD2 peptoid Serum BL BL, V04 (12 mo), V06 (24 mo), V08 (36 mo) BL thru V10 (48 mo) PI: Clemens Scherzer, MD, Harvard University Title: Evaluation of PD-linked transcripts in PPMI PI: Alice Chen-Plotkin, MD, MSc, University of Pennsylvania Title: Plasma apolipoprotein A1 level as a biomarker in Parkinson's disease PI: Maria Teresa Pellecchia, PhD, University of Salerno Title: Insulin-like growth factor-1 as a biomarker of early cognitive impairment in Parkinson s disease PI: Judith Potashkin, PhD, Rosalind and Franklin University Title: Whole blood RNA biomarkers of PD PI: Matthew Farrer, PhD, University of British Columbia Title: Clinical, imaging, and plasma and CSF correlates of alpha-synuclein genomic variability PI: Martin Rabey, BioShai Ltd. Title: Validation of a blood-based gene expression assay for early diagnosis and monitoring disease PI: Omar El-Agnaf, PhD, Hamad Bin Khalifa University Title: Validation of CSF alpha-synuclein species as surrogate biomarkers for Parkinson's disease PI: Dena Hernandez, PhD, National Institute on Aging Title: DNA methylation in PPMI PI: Dwight German, PhD, UT Southwestern Title: Blood biomarker for PD Data Data Data Data Data 9

218 UPCOMING PPMI CSF SAMPLE ANALYSES OTHER SYNUCLEIN SPECIES 10

219 Alpha-Synuclein Pathology LEAPS: Characterization & validation of α-syn species in CSF B. Mollenhauer; P. Taylor; P. Juhasz; H. Lashuel; M. Ahlijania; H. Zetterberg; A. Jeromin GOAL 1: IDENTIFICATION of disease-specific asyn species in CSF by mass spectrometry (IP-MS/MS) ongoing GOAL 2: ANTIBODY DEVELOPMENT, ASSAY DEVELOPMENT AND ASSAY VALIDATION of identified top 3-5 asyn species GOAL 3: BIOMARKER VALIDATION of the established assays in existing CSF collections 11

220 12

221 DEBRIEF FROM BIOMARKER DISCOVERY WORKSHOP 2/2016» Objective: Identify priority platforms and laboratories for biomarker discovery studies in PPMI» MJFF convened KOLs from academia, government, biotech, and industry to define recommendations for advancing PD biomarkers» Key takeaways: Recommend pursuing RNAseq to analyze the cellular transcriptome of de novo PD and healthy control subjects in PPMI; proposal to be presented to PPMI SC in Q Proteomics and metabolomics not yet mature enough for analysis of PPMI samples; recommend measuring targeted candidate biomarkers in PPMI samples based on strength of connection to PDrelevant pathways (ie. inflammation, synaptic integrity/dysfunction, protein handling) Given the limited number of clear recommendations for PPMI sample analysis coming out of the workshop, need to develop PPMI advisory task forces to pursue further due diligence and develop additional recommendations for PPMI sample analysis 13

222 PPMI ADVISORY TASK FORCES Goal Advisors Deliverable/Timeline 1. Nominate candidate markers based on analyses in ADNI, PDBP, BioFIND, etc. 2. Assay/platform due-diligence for candidate markers Jesse Cedarbaum Thomas Kremer And others Kalpana Merchant Marcel van der Brug Present to Biologics WG on 6/3 Present to PPMI BRC on 5/11 3. Standards for metadata and curation/annotation of high dimensional datasets 4. SOP development for reference pools and standards 5. Define pipeline and go/no-go criteria for advancing candidate markers thru validation Mark Cookson Andy Singleton Tatiana Foroud Brit Mollenhauer Jesse Cedarbaum Alice Chen-Plotkin Brit Mollenhauer Present WES case study to Genetics WG on 5/31 Present to Biologics WG on 6/3 Present to PPMI SC on 4/12 14

223 Goal Advisors Deliverable/Timeline 1. Nominate candidate markers based on analyses in ADNI, PDBP, BioFIND, etc. Jesse Cedarbaum Thomas Kremer Brit Mollenhauer Alice Chen-Plotkin Present to Biologics WG on 6/3 Proteomic profiling of exosomes Neurodegeneration markers (neurogranin, neurofilaments) from recent AD meta-analysis Cytokine and neuroinflammation panel Glucosphingolipids and ceramide pathway Lysosomal and autophagy panel; GBA activity assay Somalogic Screen; Caprion targeted proteome 15

224 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI 2016 ANNUUAL INVESTIGATORS MEETING May 4-5

225 MICHELLE LAVALLEE DAGOSTINE, MD FEASIBILITY AND SAFETY OF LUMBAR PUNCTURES IN THE PARKINSON'S PROGRESSION MARKER INITIATIVE

226 AUTHORS Michelle Lavallee Dagostine Samuel Frank Chelsea Caspell Liz Uribe Chris Coffey Shirley Lasch Danna Jennings Ken Marek And the Parkinson s Progression Markers Initiative* *see PPMI-infor.org for full authors list 3

227 INTRODUCTION The safety of LPs in the research setting has not been systematically studied in subjects with PD. Many variables exist which may be modified such as needle type, positioning of subject, and the site at which the LP is performed. LP s have become a standard clinical test used in research designed to identify biomarkers for PD. The purpose of our analysis, is to determine the feasibility, safety, and tolerability of LPs in early Parkinson s disease (PD), healthy volunteers (HV), and subjects who are symptomatic without evidence of dopaminergic deficit (SWEDD) participants in PPMI. 4

228 METHODS All subjects enrolled in PPMI are scheduled to undergo LP at baseline, 6, 12 months, and annually for a total of 7 LPs. The preferred LP technique for PPMI includes: Seated position 24 gauge Sprotte needle Acquisition of at least 15 mls of CSF Subjects are instructed to remain horizontal for at least 30 minutes following the procedure and to minimize intense physical activity for 24 hours. The small gauge needle requires aspiration of CSF, permitted by the atraumatic nature of the Sprotte needle. Adverse events are monitored by phone contact one week after LP completion. Data reviewed in this analysis is limited to the initial cohort: de novo PD, HC, and SWEDD subjects. 5

229 BASELINE DATA 6

230 BASELINE LP CHARACTERISTICS 7

231 LP-RELATED AES BY GROUP - HIGHLIGHTS Group Adverse Event PD Subjects (N=423) Healthy Controls (N=196) SWEDD Subjects (N=64) RR (95% CI) # of Subjects % of Subjects # of Events Rate # of Subjects % of Subjects # of Events Rate # of Subjects % of Subjects # of Events Rate PD vs. HC PD vs. SWEDD TOTAL % % % (0.75, 1.10) 1.00 (0.74, 1.36) Injection Site Pain % % % (0.54, 1.24) 1.15 (0.55, 2.42) Back Pain % % % (0.40, 1.52) 0.48 (0.21, 1.08) Headache % % % (0.67, 1.49) 1.09 (0.57, 2.08) Post lumbar puncture syndrome % % % (0.62, 1.83) 0.84 (0.39, 1.80) Other % % % N/A N/A 8

232 LP-RELATED HEADACHE Headache was the most commonly reported AE in all cohorts. Subjects who underwent LP with the Quincke needles reported 26.56% LP-related headache rate, as compared to those who underwent the procedure using the Sprotte needles, 9.53%. In subjects who had LP s performed in the seated position,14.90% reported an LPrelated headache, compared to those done in the lateral decubitus position who reported only 7.69%. 9

233 COMPARING THE INCIDENCE OF LP RELATED AES ACROSS LP RELATED CHARACTERISTICS 10

234 NON-SIGNIFICANT FACTORS LP site CSF Volume acquired Age of subject BMI of subject 11

235 SIGNIFICANT FACTORS CONTRIBUTING TO AES Needle type Higher rate of AEs when performed using Quincke needle (p=0.0049) LP Position Higher rate of AEs when performed in the seated position (p= ) Gender In male,(but not female) subjects there was a higher percentage of LPrelated AEs (30.42% vs %, p = ). Of note, there is no statistically significant difference amongst male and female subjects with Parkinson s disease, however both SWEDD and HC groups showed differences with p values of and respectively. 12

236 OTHER FACTORS CONTRIBUTING TO AES Experience Higher risk for the first 10 LPs compared to the subsequent LPs when at least 20 LPs were completed [RR = 1.41 (CI 1.06, 1.86)] 13

237 OTHER RESULTS Subjects with LP-related AEs at baseline were less likely than those without AEs to complete a year-1 V04 LP (72.37% vs 85.01%, p ) 14

238 CONCLUSIONS Obtaining CSF in a cohort of PD, SWEDD, and HV subjects with a mean age of 61 years is safe and feasible. Specific LP techniques (gauge and type of needle, as well as subject position) may reduce the overall incidence of AEs. There was no association of BMI, Age, LP site, or volume of CSF and any LP-related AE, including headache. With experience, there appears to be a reduced risk of AE. Minimizing LP-related AEs is one way to improve subject retention. 15

239 REFERENCES 1. Bezov D, Lipton RB, Ashina S. Post-dural puncture headache:part I diagnosis, epidemiology, etiology and pathology. Headache 2010;50: Birnbach DJ, Kuroda MM, Sternman D, Thys DM. Use of Atraumatic Spinal Needles Among Neurologists in the United States. Headache 2001;41: ). 3. Dakka Y, Warra N, Albadareen R.J., Janowski M, Silver B. Headache rate and cost of care following lumbar puncture at a single tertiary care hospital. Neurology 2011;77: H. M. Greene. Lumbar Puncture and the prevention of postpuncture headache. JAMA 1926; 68(6): Hindley NJ, Jobst, KA, King, E, Barnetson, L, Smith A, Haigh A-M. High acceptability and low morbidity of diagnostic lumbar puncture in elderly subjects of mixed cognitive status. Acta Neurol Scand 1995:91: G. Linker, MD; N. Mirza, MD; G. Manetti, BA; M. Meyer, RN; K.T. Putnam, MS; and T. Sunderland, MD. Neurology 2002;59; Sergio Monteiro de Almeida, MD, PhD, Stephaie D. Shumaker, BS, Shannon K. LeBlance, BA, Patrick Delaney, MD, Jennifer Marquie-Beck, MPH, Susan Ueland, RN, Terry Alexander, RN, and Ronald J. Ellis, MD, PhD. Incidence of post-dural puncture headache in research volunteers, Headache November ; 51(10): Elaine R. Peskind, MD, Robert, Riekse, MD, Joseph F. Quinn, MD, Jeffrey Kaye, MD, Christopher M. Clark, MD, Martin R. Farlow, MD, Charles DeCarli, MD, Charles Chabal, MD, Darcy Vavrek, MS, Murray A. Raskind, MD, and Douglas Galasko, MD. Safety and Acceptability of the Research Lumbar Puncture, Alzheimer s Dis Assoc Disord, 2005; 19: Sprotte G, Schedel R, Pajunk H, Pajunk H. An atraumatic universal needle for single-shot regional anesthesia: clinical results and a 6 year trial in over 30,000 regional anesthesias [in German]. Reg Anesth. 1987;10: Tourtelotte W, Haerer AF, Heller AH, Somers JE. Post-lumber puncture headaches. Illinois: Thomas, ST Vilming, R Kloster & L Sandvik, The importance of sex, age, needle size, height and body mass index in post-lumbar puncture headache. Cephalgia, 2001,21, Hammond ER, Wang, Z, Bhulani, N, et. Al. Needle type and the risk of post-lumbar puncture headache in the outpatient neurology clinic. J Neurologic Sci 2011;306: Hart JR, Whitacre RJ. Pencil-point needle in prevention of post-spinal headache. JAMA. 1951;14:

240 PARKINSON S PROGRESSION MARKERS INITIATIVE Biospecimen Analyses May 2016

241 GENETICS CORE PPMI Genetics Core Chair: Andrew Singleton, PhD AVAILABLE DATA» NeuroX and Immunochip genotyping PD Healthy Control (HC) SWEDD» Whole exome sequencing PD HC SWEDD vcf, fastq, and bam files available ONGOING ANALYSES» NeuroX genoty ping Prodromal cohort Genetic cohort (enrolled as of 1/1/16)» Whole genome sequencing PD HC SWEDD Prodromal cohort Genetic cohort (enrolled as of 1/1/16) Genetic registry (enrolled as of 1/1/16) Genotyping and WES data are already available from over 600 subjects and WGS from over 1,100 PPMI subjects will be available in

242 BIOANALYTICS CORE PPMI Bioanalytics Core Chair: Brit Mollenhauer, MD AVAILABLE DATA UPCOMING ANALYSES» CSF total αsyn measured using the Covance/BioLegend immunoassay All baseline PD, HC, and SWEDD CSF samples Subset of 6 and 12 mo PD, HC and SWEDD» CSF Aβ, tau, and p-tau 181 measured using the Innogenetics INNO-BIA AlzBio3 immunoassay» CSF total αsyn measured using the Covance/BioLegend immunoassay Details below» CSF Aβ, tau, and p-tau 181 measured using the Roche Elecsys automated immunoassay Details below All baseline PD, HC, and SWEDD CSF samples Subset of 6 and 12 mo PD, HC, and SWEDD BL V02 (6 mo) V04 (1 yr) V06 (2 yr) V08 (3 yr) Grand Total PD ,612 HC SWEDD Prodromal Genetic Total ,854 3

243 BRC SAMPLE APPROVALS ANALYTE Matrix Visit(s) PROJECT DETAILS αsyn transcripts ApoA1 and EGF RNA from whole blood Plasma BL IGF-1 Serum BL mrna transcripts SNCA genotyping mrna transcripts Oligomeric and ps129 asyn RNA from whole blood DNA from whole blood RNA from whole blood CSF BL, V02 (6 mo), V04 (12 mo) BL BL DNA methylation Whole blood BL PD2 peptoid Serum BL BL, V04 (12 mo), V06 (24 mo), V08 (36 mo) BL thru V10 (48 mo) PI: Clemens Scherzer, MD, Harvard University Title: Evaluation of PD-linked transcripts in PPMI PI: Alice Chen-Plotkin, MD, MSc, University of Pennsylvania Title: Plasma apolipoprotein A1 level as a biomarker in Parkinson's disease PI: Maria Teresa Pellecchia, PhD, University of Salerno Title: Insulin-like growth factor-1 as a biomarker of early cognitive impairment in Parkinson s disease PI: Judith Potashkin, PhD, Rosalind and Franklin University Title: Whole blood RNA biomarkers of PD PI: Matthew Farrer, PhD, University of British Columbia Title: Clinical, imaging, and plasma and CSF correlates of alpha-synuclein genomic variability PI: Martin Rabey, BioShai Ltd. Title: Validation of a blood-based gene expression assay for early diagnosis and monitoring disease PI: Omar El-Agnaf, PhD, Hamad Bin Khalifa University Title: Validation of CSF alpha-synuclein species as surrogate biomarkers for Parkinson's disease PI: Dena Hernandez, PhD, National Institute on Aging Title: DNA methylation in PPMI PI: Dwight German, PhD, UT Southwestern Title: Blood biomarker for PD Data Data Data Data Data 4

244 PPMI CELL LINES» PPMI cell lines are available through two different biologic ancillary studies: Partner Subjects Number of Sites Types of Cell Lines Status New York Stem Cell Foundation (NYSCF) 20 ipd 5 HC 1 Fibroblasts Induced pluripotent stem cells (ipscs) Closed Cellular Dynamics International (CDI) 10 ipd 5 HC 10 Prodromal 30 Genetic PD 30 Genetic Unaffected 7 ipscs Ongoing» Investigators using PPMI cell lines are granted an exception to the PPMI Intellectual Property (IP) Policy. IP generated from use of PPMI cell lines may remain with the inventor.» Timeline: The availability of the NYSCF cell lines will be announced in early May. The first batch of CDI cell lines will be available mid-summer. Cell lines from this resource are appropriate for biomarker discovery projects in PD or other neurodegenerative disease areas. 5

245 PPMI UPDATE: COMPLETED ANALYSES (BASELINE VISIT) T o t a l C S F ( α S yn, H b ) * C S F (Aβ, T a u, p - T a u ) * D N A ( S N C A G e n o t yp i n g ) D N A ( N e u r o X ; W h o l e Exome S e q u e n c i n g ) S e r u m ( I G F - 1) R N A ( α S yn ) R N A ( m R N A T r a n s c r i p t s ) P l a s m a ( Ap o A1 a n d EGF) Control PD SWEDD 8 analyses conducted across patients from the Original Cohort. 6

246 PPMI UPDATE: UPCOMING ANALYSES T o t a l C S F ( α S yn, H b ) C S F (Aβ, T a u, p - T a u ) D N A ( N e u r o X ; W E S ; W G S ) D N A ( S N C A G e n o t yp i n g ) C S F (Oligomeric / p S α S yn ) S e r u m ( I G F - 1) R N A ( α S yn ) R N A ( m R N A T r a n s c r i p t s ) P l a s m a ( Ap o A1 a n d EGF) Control PD SWEDD PRDRML GC UN GC PD Longitudinal CSF analysis of oligomeric and ps129 αsyn, mrna transcripts, DNA methylation and serum PD2 peptoid analyses planned for Fall

247 PPMI UPDATE: SUBJECTS WITH MULTIPLE BIOCHEMICAL ANALYSES (BASELINE VISIT) αsyn (CSF) αsyn (RNA) Oligomeric / ps129 αsyn (CSF) Aβ, Tau, p-tau (CSF) SNCA Genotyping NeuroX WES WGS mrna Transcripts (RNA) ApoA1, EGF (Plasma) IGF-1 (Serum) Total 64 HC 47 PD 146 HC 253 PD 186 HC 393 PD 47 PD and 64 Control patients will be characterized across all analyses. 8

248 STRATEGIC OPPORTUNITY TO LEVERAGE PPMI INFRASTRUCTURE FOR BIOMARKER DISCOVERY CHALLENGES» Over 100 unique applications for PPMI samples since 2012» Only 10 requests have been approved to date (< 10%) OPPORTUNITIES» Advances in discovery platforms» Genomics and Transcriptomics» Proteomics and Metabolomics» Exponential growth in computational power and data analytics PRIORITY 1 PRIORITY 2 PRIORITY 3» Few markers are ready for validation» Candidate pipeline is not robust» Robust marker of disease progression» Prognostic marker» Predictive marker» Patient selection/ enrichment» Patient stratification Timing is optimal for integrating strategic biomarker discovery projects in PPMI with a focus on markers that will advance clinical development. 9

249 DEBRIEF FROM BIOMARKER DISCOVERY WORKSHOP» Objective: Identify priority platforms and laboratories for biomarker discovery studies in PPMI» MJFF convened KOLs from academia, government, biotech, and industry to define recommendations for advancing PD biomarkers» Key takeaways: Recommend pursuing RNAseq to analyze the cellular transcriptome of de novo PD and healthy control subjects in PPMI; proposal to be presented to PPMI SC in Q Proteomics and metabolomics not yet mature enough for analysis of PPMI samples; recommend measuring targeted candidate biomarkers in PPMI samples based on strength of connection to PDrelevant pathways (ie. inflammation, synaptic integrity/dysfunction, protein handling) Given the limited number of clear recommendations for PPMI sample analysis coming out of the workshop, need to develop PPMI advisory task forces to pursue further due diligence and develop additional recommendations for PPMI sample analysis 10

250 PPMI ADVISORY TASK FORCES Goal Advisors Deliverable/Timeline 1. Nominate candidate markers based on analyses in ADNI, PDBP, BioFIND, etc. 2. Assay/platform due-diligence for candidate markers Jesse Cedarbaum Thomas Kremer Kalpana Merchant Marcel van der Brug Present to Biologics WG on 6/3 Present to PPMI BRC on 5/11 3. Standards for metadata and curation/annotation of high dimensional datasets 4. SOP development for reference pools and standards 5. Define pipeline and go/no-go criteria for advancing candidate markers thru validation Mark Cookson Andy Singleton Tatiana Foroud Brit Mollenhauer Jesse Cedarbaum Alice Chen-Plotkin Brit Mollenhauer Present WES case study to Genetics WG on 5/31 Present to Biologics WG on 6/3 Present to PPMI SC on 4/12 11

251 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI 2016 ANNUUAL INVESTIGATORS MEETING May 4-5

252 Informatics Core Update May 2016

253 Data Repository News New data available for download: Surgery for Parkinson Disease Skin Biopsy Olfactory UPSIT Symptomatic Therapy Visit Catalog Derived Variable Definitions and Score Calculations

254 Neuroimaging Data Archived Structural MR SPECT Diffusion MR Functional MR PET CT Processed Raw

255 Downloads Image and Clinical Data Download Activity Images Projected Clinical Projected

256 Data Use Applications Applicants by Sector Applicants by Year Biotech, 130 Government, 44 Other, 248 Pharmaceutical, 149 Scanner Mfg, University/Res earch Institute,

257 Manuscript/Abstract Submissions 194 submitted via online system

258 Whole Exome Data Created Exome on a Disk lending drives for sharing large exome data files Activity BAM file requests: 6 FASTQ file requests: 6

259 Exome Data Distribution Process Determine country specific rules and documents required. Those can be: Air Waybill Commercial Invoice Certificate of Origin Pro Forma Invoice Packing List Electronic Export Information (EEI) Estimate duties and taxes Create a shipping label Schedule a pick up Keep track of the package, verify customs clearance process

260 New: Cell Line Request System

261 PPMI Website

262 Request Cell Lines

263 Request Cell Lines (cont)

264 PPMI Visit Statistics

265 PPMI Website Page Views

266 PPMI Website Visits by Country

267 PPMI Website Visits by Country (Top 10)

268 Coming Soon IDA improvements New technology Responsive design Image viewer Download UI Upload UI Have suggestions? Contact us

269 Coming Soon Analysis Ready Datasets Organized to allow statistical analysis to be done with minimal manipulations. Flexible search, selection and exporting options Browse attributes in tree structure Search attributes using keywords View and manipulate values incharts Have suggestions? Contact us:

270 Next Steps Gather user needs Survey for a link to the survey Focus group ppmi@loni.usc.edu and ask to join Breakout session follows this presentation. Stop by and share your thoughts.

271 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI 2016 ANNUUAL INVESTIGATORS MEETING May 4-5

272 PPMI Annual Meeting - BREAKOUT 1 Wednesday May 4th, 2016 Groups will rotate between the 3 breakout sessions for a 20-minute discussion LAST NAME starts with: A - G H O P Z Group 1 (Forum/South/North) Group 2 (North/Forum/South) Group 3 (South/North/ Forum) Topic Room Chairs Group rotation PPMI DATA - Accessing the PPMI Data The Forum Toga, Crawford PPMI Data Test Case: MDS- UPDRS, ST & LEDD Exchange Hub North Simuni, Caspell-Garcia, Foster PPMI Pathology Core Roll-out Exchange Hub South Leverenz, Wilson

273 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI 2016 ANNUUAL INVESTIGATORS MEETING May 4-5

274 PPMI Annual Meeting - BREAKOUT 1 Wednesday May 4th, 2016 Groups will rotate between the 3 breakout sessions for a 20-minute discussion LAST NAME starts with: A - G H O P Z Group 1 (Forum/South/North) Group 2 (North/Forum/South) Group 3 (South/North/ Forum) Topic Room Chairs Group rotation PPMI DATA - Accessing the PPMI Data The Forum Toga, Crawford PPMI Data Test Case: MDS- UPDRS, ST & LEDD Exchange Hub North Simuni, Caspell-Garcia, Foster PPMI Pathology Core Roll-out Exchange Hub South Leverenz, Wilson

275 PPMI DATA EXAMPLES USING MDS-UPDRS, SYMPTOMATIC THERAPY, AND LEDD CHELSEA CASPELL-GARCIA TANYA SIMUNI ERIC FOSTER

276 OUTLINE 1. MDS-UPDRS a. ON and OFF definitions for Part III (Motor) Exam b. Data tables c. Updates to MDS-UPDRS Data 2. ST Visits 3. Example 1: Finding the mean 1 year change in MDS-UPDRS OFF/Untreated scores for enrolled PD subjects while accounting for ST visits 4. Levodopa Equivalent Daily Dose a. LEDD formula b. LEDD data 5. Example 2: Finding total LEDD for treated PD subjects at year 1 3

277 ON AND OFF DEFINITIONS 1. At the annual visits only, subjects taking Levodopa/DA or a combination of DA & non-da complete a defined ON and defined OFF medication score a. Subjects are asked to withhold their medication prior to the visit for 12 hours for a defined OFF medication score b. During the annual visit, Part III (Motor) of the MDS-UPDRS is assessed with the subject in the defined OFF medication state c. The subject takes standard dose of medication. Approximately 1 hour after the in clinic dose, Part III (Motor) of the MDS-UPDRS is assessed again for a defined ON medication score 2. The time between last dose and either assessment is calculated for all subjects who are taking Levodopa/DA OR are taking a combination of Levodopa/DA & Other PD drug from the PD MED Use CRF 3. Note that OFF definition requires that subjects are off Levodopa/DA for > 6 hours a. That time point was chosen as a compromise for feasibility of collecting the data b. Investigators can choose only the subset of subjects with > 12 hours OFF if they elect to run such analysis 4

278 MDS-UPDRS DATA TABLES 1. The MDS-UPDRS is made up of 4 parts that are collected using 6 CRFs 2. These are stored in 5 separate data files, often referred to by the following PAG_NAMES (PAG_NAMES in parentheses): a. Part 1 i. MDS_UPDRS_Part_I.csv (NUPDRS1) ii. MDS_UPDRS_Part_I_Patient_Questionnaire.csv (NUPDRS1P) b. Part 2: MDS_UPDRS_Part_II_Patient_Questionnaire.csv (NUPDRS2P) c. Part 3: MDS_UPDRS_Part_III_Post_Dose_.csv (NUPDRS3 / NUPDRS3A) NOTE: Both CRFs are in a single data file d. Part 4: MDS_UPDRS_Part_IV.csv (NUPDRS4) 5

279 UPDATES TO MDS-UPDRS DATA» The following variables were recently added to the MDS-UPDRS Part III (NUPDRS3) data to help distinguish between ON and OFF assessments: ON_OFF_DOSE whether assessment is defined as ON or OFF (1 = OFF; 2 = ON) ANNUAL_TIME_BTW_DOSE_EXAM number of hours between most recent dosing and Part III exam PD_MED_USE subject s class of PD medication (corresponds to PD Med Use CRF) 0 = Unmedicated for PD 1 = Levodopa 2 = Dopamine Agonist 3 = Other PD Med 4 = Levodopa + Other 5 = Levodopa + Dopamine Agonist 6 = Dopamine Agonist + Other 7 = Levodopa + Dopamine Agonist + Other Note 1: ON/OFF definitions do NOT apply to subjects with PD_MED_USE coded as 0 or 3 Note 2: For untreated subjects (PD_MED_USE = 0 ) or 3, ON_OFF_DOSE and ANNUAL_TIME_BTW_DOSE_EXAM are missing values 6

280 UPDATES TO MDS-UPDRS DATA The first two records above correspond to assessments that were made while receiving PD medication #5 (Levodopa + DA). The first record above corresponds to an OFF measurement (ON_OFF_DOSE = 1) that was taken 6.33 hours after the last dose. The second record corresponds to an ON measurement (ON_OFF_DOSE = 2) that was taken 1.25 hours after the last dose. The last two records correspond to assessments that were made while either receiving another PD medication (PD_MED_USE = 3) or being unmedicated (PD_MED_USE = 0). 7

281 ST VISITS 1. These are visits where symptomatic therapy is begun 2. These do not necessarily align with planned study visits, but sometimes do take the place of a regular study visit 3. The new data file ST_CATALOG contains a list of all subjects who have had an ST visit a. If the ST visit replaced a regular visit, the visit will be noted in the variable STRPLCVS b. If the ST visit did NOT replace a regular study visit, the variable STRPLCVS will be blank 4. Note: There are special circumstances surrounding ST visits that replaced either Visit 01 (3 Months) or Visit 03 (9 Months): a. If an ST visit replaced Visit 01 or Visit 03, then per protocol the subject was supposed to skip Visit 02 (6 Months) or Visit 04 (12 Months), respectively. b. If Visit 02 or Visit 04 are missing because of an ST visit that replaced Visit 01 or Visit 03, that ST visit is grouped with Visit 02 or Visit 04. c. The schedule of activities for Visit 01 and Visit 03 are limited compared to Visits 02 and 04. d. The schedule of activities for an ST visits more closely resemble those of Visits 02 and 04 than they do Visits 01 and 03. 8

282 EXAMPLE 1» Goal: Finding the mean 1 year change in MDS-UPDRS OFF/Untreated scores for enrolled PD subjects while accounting for ST visits.» What we will need: Take the NUPDRS3 table and merge it with the ST_CATALOG table based on the subject ID (PATNO) variable. Find all enrolled PD subjects (2015 PPMI Annual Meeting Breakout Presentation). Link the PD subjects to their records in the NUPDRS3 table. Analyze the change from baseline in MDS-UPDRS OFF/Untreated scores. 9

283 EXAMPLE 1 CONTINUED 1. Get the ST_CATALOG table 2. Merge the ST_CATALOG and the NUPDRS3 Tables and calculate the NEW_EVENT_ID coding. 10

284 EXAMPLE 1 CONTINUED 3. Be sure to use the PPMI Derived Variables Definitions and Score Calculations Document! 4. Selecting APPRDX = 1 (PD Subjects) and Paying attention to the new PD_MED_USE and ON_OFF_DOSE variables, we now have the Baseline and Year 1 (V04) UPDRS Part III scores for PD subjects OFF/unmedicated. 11

285 LEVODOPA EQUIVALENT DAILY DOSE Levodopa Equivalent Daily Dose (LEDD) was calculated using methods described in the 2010 Tomlinson, et al, publication in Movement Disorders Drug Class Dopamine Replacement COMT Inhibitors Dopamine Agonists MAOB Inhibitors Other Drug Conversion factor Levodopa x 1 Controlled-release Levodopa x 0.7 Duodopa x 1.11 Entacapone LD x 0.33* Tolcapone LD x 0.5* Pramipexole, Lisuride, Pergolide x 100 Ropinirole x 20 Rotigotine x 30.3 Piribedil x 1 Bromocriptine x 10 Cabergolin x DHEC x 5 Selegiline (oral) x 10 Selegiline (sublingual) x 80 Rasagiline x 100 Amantadine x 1 Apomorphine x 10 *Regardless of the Entacapone/Tolcapone dose, the Levodopa dose is multiplied by the conversion factor and added to the other subtotal LEDDs to find the total LEDD. 12

286 LEDD DATA 1. LEDD has been added to the Concomitant Medications (CONMED) data table a. The variable LEDD shows the Levodopa equivalent daily dose for each individual PD medication b. Note: The variable LEDD is only updated quarterly, so this variable will not necessarily be up-to-date with the other data in the CONMED data file i. Existing LEDD data is current for all data that was submitted as of 07Mar Special considerations: a. Anticholinergics and a few other PD medications that are not included in the calculation of the total LEDD will have a missing value for the variable LEDD b. For COMT inhibitors, the variable LEDD will read LD x 0.5 or LD x To find the LEDD subtotal for COMT inhibitors, first find the total dose of Levodopa only, and then multiply that value by either 0.5 or 0.33 as instructed. 13

287 EXAMPLE 2» Goal: Finding the total LEDD for treated PD subjects at year 1 1. Find enrolled PD subjects as previously described 2. Find the STARTDT and STOPDT for each PD medication in the CONMED data table a. All relevant PD medications will have a non-missing LEDD variable b. If STOPDT is missing, the medication is currently being taken 14

288 EXAMPLE 2 CONTINUED 3. Find the year 1 visit dates by looking in the Signature form (SIG) data table at the variable INFODT for observations with EVENT_ID = V04 a. Note that the year 1 visit may have been an ST visit b. Use ST_CATALOG to find subjects with STRPLCVS = V03 or V04, as previously described 4. Merge the year 1 visit dates with the PD medications based on subject ID (PATNO) 15

289 EXAMPLE 2 CONTINUED 5. Keep the PD medications whose STARTDT and STOPDT surround the year 1 visit date 6. To find total LEDD at the year 1 visit, add all values of the variable LEDD for each PD medication being taken at the visit 16

290 PPMI PATHOLOGY CORE JAMES LEVERENZ, M.D. LOU RUVO CENTER FOR BRAIN HEALTH NEUROLOGIC INSTITUTE CLEVELAND CLINIC

291 OVERVIEW 1. Purpose of PPMI Pathology Core (PPMI Brain & Tissue Bank ) 2. Introduction to key staff 3. Logistics for brain donation a. Advanced Directive b. Autopsy Service c. Neuropathologic assessment, tissue storage, report 4. Common Questions 3

292 PURPOSE OF PPMI PATH CORE 1. To establish a brain donation program for participants in PPMI 2. To collect and store fixed and frozen brain tissue 3. To systematically evaluate and characterize the neuropathologic changes a. Share results with PPMI database 4. To distribute tissues for additional investigations 4

293 KEY STAFF 1. James Leverenz, M.D. a. Core director b. Neurologist/Neuropathologist 2. Lynn Bekris, Ph.D. a. Co-investigator b. Supervisor of wet lab 3. Danielle Kemeny a. Primary coordinator for PPMI PC program 4. Christine Reece a. Program manager, Lou Ruvo Center for Brain Health 5

294 LOGISTICS FOR BRAIN DONATION 1. Enrollment a. Initial contact through PPMI site coordinator/pi b. Contact form sent to PPMI Pathology Core c. PPMI Pathology Core outreach to potential PPMI donor i. Advanced Directive Participant, next of kin (NOK) ii. Collection of contact information Participant and NOK One year updates or updates through PPMI coordinators 6

295 LOGISTICS FOR BRAIN DONATION 1. At time of death a. PPMI PC contact from NOK or site coordinator i. Confirm donation with NOK b. Contact with National Autopsy Experts (NAE) i. Travel to site for brain removal ii. Ship to Cleveland Clinic for processing 7

296 LOGISTICS FOR BRAIN DONATION 1. Brain processing and evaluation a. Fresh tissue to Cleveland Clinic i. Dissection fresh for frozen and fixed tissue storage at Cleveland Clinic Lerner Research Institute ii. Fixed tissue for neuropathologic assessment b. Neuropathologic results sent to LONI/ PPMI database c. Neuropathologic report to NOK i. Cover letter ii. Detail of neuropathologic findings iii. Option for conference call with Dr. Leverenz 8

297 COMMON QUESTIONS 1. Why do you need a brain donation program? a. Important to confirm diagnosis and other aging changes to compare with the biomarkers and clinical course b. Tissue can be stored used for future research 2. Who will pay for transportation and costs of autopsy? a. Costs of removal of the brain, shipping, and pathology all covered by MJFF and study funding b. We do not assume cost of funeral, burial, transport to funeral home 9

298 COMMON QUESTIONS 1. Can there be an open casket? a. Yes 2. Can I be cremated and still be a donor? a. Yes 3. Can I donate if I don t live in the Cleveland area? a. Yes, the donated tissue will be stored in the PPMI Brain and Tissue Bank at the Cleveland Clinic, but the body remains at the funeral home 10

299 TIMELINE/ NEXT STEPS PPMI Brain and Tissue Bank at the Cleveland Clinic is included in PPMI Protocol Amendment 11 a. Central IRB approval for AM11 completed (4/2016) b. PPMI site need to submit for review and approval from local IRB and Ethical Committees c. Protocol training for PPMI sites with PPMI Pathology Core will be scheduled by CTCC Roll-out will start in US and expand to include the EU, AUS, and other locations 11

300 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI 2016 ANNUUAL INVESTIGATORS MEETING May 4-5

301 PPMI PATHOLOGY CORE JAMES LEVERENZ, M.D. LOU RUVO CENTER FOR BRAIN HEALTH NEUROLOGIC INSTITUTE CLEVELAND CLINIC

302 OVERVIEW 1. Purpose of PPMI Pathology Core (PPMI Brain & Tissue Bank ) 2. Introduction to key staff 3. Logistics for brain donation a. Advanced Directive b. Autopsy Service c. Neuropathologic assessment, tissue storage, report 4. Common Questions 3

303 PURPOSE OF PPMI PATH CORE 1. To establish a brain donation program for participants in PPMI 2. To collect and store fixed and frozen brain tissue 3. To systematically evaluate and characterize the neuropathologic changes a. Share results with PPMI database 4. To distribute tissues for additional investigations 4

304 KEY STAFF 1. James Leverenz, M.D. a. Core director b. Neurologist/Neuropathologist 2. Lynn Bekris, Ph.D. a. Co-investigator b. Supervisor of wet lab 3. Danielle Kemeny a. Primary coordinator for PPMI PC program 4. Christine Reece a. Program manager, Lou Ruvo Center for Brain Health 5

305 LOGISTICS FOR BRAIN DONATION 1. Enrollment a. Initial contact through PPMI site coordinator/pi b. Contact form sent to PPMI Pathology Core c. PPMI Pathology Core outreach to potential PPMI donor i. Advanced Directive Participant, next of kin (NOK) ii. Collection of contact information Participant and NOK One year updates or updates through PPMI coordinators 6

306 LOGISTICS FOR BRAIN DONATION 1. At time of death a. PPMI PC contact from NOK or site coordinator i. Confirm donation with NOK b. Contact with National Autopsy Experts (NAE) i. Travel to site for brain removal ii. Ship to Cleveland Clinic for processing 7

307 LOGISTICS FOR BRAIN DONATION 1. Brain processing and evaluation a. Fresh tissue to Cleveland Clinic i. Dissection fresh for frozen and fixed tissue storage at Cleveland Clinic Lerner Research Institute ii. Fixed tissue for neuropathologic assessment b. Neuropathologic results sent to LONI/ PPMI database c. Neuropathologic report to NOK i. Cover letter ii. Detail of neuropathologic findings iii. Option for conference call with Dr. Leverenz 8

308 COMMON QUESTIONS 1. Why do you need a brain donation program? a. Important to confirm diagnosis and other aging changes to compare with the biomarkers and clinical course b. Tissue can be stored used for future research 2. Who will pay for transportation and costs of autopsy? a. Costs of removal of the brain, shipping, and pathology all covered by MJFF and study funding b. We do not assume cost of funeral, burial, transport to funeral home 9

309 COMMON QUESTIONS 1. Can there be an open casket? a. Yes 2. Can I be cremated and still be a donor? a. Yes 3. Can I donate if I don t live in the Cleveland area? a. Yes, the donated tissue will be stored in the PPMI Brain and Tissue Bank at the Cleveland Clinic, but the body remains at the funeral home 10

310 TIMELINE/ NEXT STEPS PPMI Brain and Tissue Bank at the Cleveland Clinic is included in PPMI Protocol Amendment 11 a. Central IRB approval for AM11 completed (4/2016) b. PPMI site need to submit for review and approval from local IRB and Ethical Committees c. Protocol training for PPMI sites with PPMI Pathology Core will be scheduled by CTCC Roll-out will start in US and expand to include the EU, AUS, and other locations 11

311 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI 2016 ANNUUAL INVESTIGATORS MEETING May 4-5

312 PPMI DATA CHALLENGE Challenge Format o Award $50,000 in total prizes to support novel and predictive statistical analysis of the PPMI dataset, by posing two questions considered crucial to Parkinson s disease progression: o At baseline, determine what are factors that predict clinical progression? o What are the sub-types of Parkinson s disease based on clinical, molecular and imaging data? Objective o Open call to expanding network of computational scientists, data scientists and neuroscientists o Stimulate analysis by tapping into unique perspectives of data scientists o Cross-collaboration between data scientists and PD experts May 4-5, 2016 June 1, 2016 June 22, 2016 July 14, 2016 September 16, 2016 October 5, 2016 Tentative Challenge Timeline Pre-launch at PPMI Annual Steering Committee Meeting Public Launch Informational webinar Submissions due MJFF-hosted Review Meeting Announce Prize Potential Co-sponsor: GE Healthcare 1

313 PARKINSON S PROGRESSION MARKERS INITIATIVE PPMI 2016 ANNUUAL INVESTIGATORS MEETING May 4-5

314 ISAB PANEL DISCUSSION J. CEDARBAUM & V. KIYASOVA

315 QUESTIONS FOR THE GROUP 1. Please describe your company s interest in PD 2. How has your company used PPMI data to date? 3. If you have not used PPMI data yet, how might you think of using it in the future? 4. From a corporate point of view, what role do initiatives such as PPMI play in the drug development process? 5. What are the key strengths of PPMI? 6. What could PPMI do better to facilitate the drug development enterprise? 3

316 PPMI RETENTION UPDATE Annual Investigators Meeting May 4, 2016

317 PPMI STATUS UPDATE Cohort Total Enrolled Retention Rate Status De Novo PD % Complete Controls % Complete SWEDDs 64 86% Complete Prodromal 65 97% Complete Genetic Cohort % Recruiting Genetic Registry % Recruiting Total % Active=1067 Overall study retention is 93%! (Original cohort: 89%) As of 04APR2016 2

318 RETENTION STRATEGIES OVERVIEW Goal: Maintaining the stamina and trust of enrolled participants over time» Site relationship with participants Primary mechanism for maintaining relationships with participants, accommodating their needs» Annual Retention Events Participant appreciation lunch/dinner to update participants on study status and results» Scientific Update Packets (Vol. 3 coming soon!) Lay abstracts of results using PPMI data, ideally to be distributed at retention events» Participant newsletters Bi-annual study updates and profiles» Study update calls Quarterly calls featuring presentation by researcher using PPMI data or samples Next call: June 12pm ET Blood-based biomarker research» Visit giveaways Token of appreciation at each visit 3

319 PPMI PATIENT COMMITTEE»Peter Burne»Jean Burns»Carey Christensen»Linda Comerci»Sheryl Jedlinski»Kevin Kwok»Phillip Myers»Bill Shepard 4

320 PPMI SITE AWARDS Recognizing site teams for outstanding effort

321 SITES WITH HIGHEST LONGITUDINAL LP COMPLIANCE AT V06 FOR THE DE NOVO PD, HC AND SWEDD COHORTS Banner Sun-Health The University of Pennsylvania Boston University 6

322 HONORABLE MENTION SITES WITH HIGHEST LONGITUDINAL LP COMPLIANCE AT V06 FOR THE DE NOVO, PD, HC AND SWEDD COHORTS The Institute for Neurodegenerative Disorders Emory University Northwestern University 7

323 SITES WITH HIGHEST LONGITUDINAL LP COMPLIANCE FOR BOTH THE PRODROMAL AND GENETIC COHORTS AT V04 University of California at San Diego Boston University University of Pennsylvania 8

324 HONORABLE MENTION SITES WITH HIGHEST LONGITUDINAL LP COMPLIANCE FOR PRODROMAL COHORT AT V04 Oregon Health and Science University University of California at San Francisco 9

325 HONORABLE MENTION HIGHEST LONGITUDINAL LP COMPLIANCE FOR GENETIC COHORT AT V04 The Cleveland Clinic Banner Sun Health Imperial College The Parkinson s Institute 10

326 SITES WITH 90% OF DATA ENTERED AT V08 (ALL COHORTS) Boston University The Institute for Neurodegenerative Disorders 11

327 SITES WITH HIGHEST COMPLIANCE FOR BOTH BIOSPECIMEN COLLECTION AND SHIPMENT (ALL COHORTS) Paracelsus-Elena Clinic Kassell University of Barcelona University of California at San Diego 12

328 HONORABLE MENTION SITES WITH HIGHEST COMPLIANCE FOR BIOSPECIMEN COLLECTION (ALL COHORTS) University of South Florida Oregon Health and Science University Boston University University of Cincinnati PD & Movement Disorders Center at Boca Raton University of Rochester Columbia University The Parkinson s Institute Banner Sun Health 13

329 HONORABLE MENTION SITES WITH HIGHEST COMPLIANCE FOR BIOSPECIMEN SHIPMENT (ALL COHORTS) Northwestern University Baylor College of Medicine Oregon Health and Science University University of Alabama at Birmingham University of Pennsylvania University of Cincinnati Banner Sun Health 14

330 BEST BINDERS FOR SOURCE DOCUMENTATION The Parkinson s Institute University of Pennsylvania 15

331 HONORABLE MENTION QUICKEST SAFETY ASSESSMENT SIGN OFF Banner Sun Health Northwestern University Oregon Health & Science University University of Pennsylvania 16

332 WELCOME TO PPMI! NEW COORDINATORS THAT HAVE COME ONTO THE PPMI STUDY / MAY 2015 APRIL 2016 Alicia Portillo, Oregon Health & Science University Debra Smejdir, The Institute for Neurodegenerative Disorders Krista Specketer, VA Pudget Sound Health Care System Zoran Obradov, Arizona PD Consortium, Banner-Sun Health Laura- Marie Payne, Imperial College London - England Etienne Debecq, Pitie Salpetreiere Hospital, France 17

333 WELCOME TO PPMI! NEW CO-COORDINATORS THAT HAVE COME ONTO THE PPMI STUDY / MAY 2015 APRIL 2016 Madeline Cresswell, Oregon Health & Science University Leila Schwanemann, Oregon Health & Science University Katie Forsberg, Emory University Samantha Konz, University of California, San Francisco Anna Vaigast, Beth Israel Medical Center Natalia Okon, Northwestern University 18

334 WELCOME TO PPMI! Cont.. NEW CO-COORDINATORS THAT HAVE COME ONTO THE PPMI STUDY / MAY 2015 APRIL 2016 Victoria Brown, Banner-Sun Health Kelly Clark, Banner-Sun Health Lauren Mullins, PD & Movement Disorders Center at Boca Raton Christine Haaga, University of Tubingen - Germany 19

335 LONGITUDINAL MILESTONE AWARD The Institute for Neurodegerative Disorders Activated June 2010 Arizona PD Consortium, Banner-Sun Health Activated July 2010 Northwestern University Activated August 2010 Baylor College of Medicine Activated August 2010 The Parkinson s Institute Activated August 2010 Oregon Health and Science University Activated September 2010 Recognizing sites who have reached the planned longitudinal study milestone 20

336 LONGITUDINAL MILESTONE AWARD University of Pennsylvania Activated September 2010 Boston University Activated September 2010 University of Alabama at Birmingham Activated September 2010 John Hopkins University Activated October 2010 Paracelsus-Elena Clinic Kassel/University Of Marburg Activated March 2011 Cleveland Clinic Activated May 2011 Recognizing sites who have reached the planned longitudinal study milestone 21