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Meaningful (meta)data at scale: removing barriers to precision medicine research

Randomized controlled trials (RCTs) are the gold standard for evaluating therapeutics in patient populations. The data collected during RCTs include a wealth of clinical measures, biomarkers, and tissue samples – the analysis of which can lead to the approval of new medicines that improve the lives of patients. The secondary use of these data can also fuel the discovery of novel targets and biomarkers that support precision medicine, but a lack of metadata standards creates substantial barriers to reuse.

For this talk, I will discuss the challenges that arise when aggregating diverse types of data from a large number of RCTs and present a case study in how to apply (meta)data standards for the scalable curation and integration of these data into an analysis ready form.

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Meaningful (meta)data at scale: removing barriers to precision medicine research

  1. 1. Meaningful (meta)data at scale: removing barriers to precision medicine research Nolan Nichols Neuroinformatics 2018 Montréal, Canada August 9, 2018
  2. 2. Alzheimer’s disease: a profound and growing unmet need 2 ▪ No effective treatment ▪ AD accounts for 60-80% of all dementia ▪ Global dementia prevalence to triple from 46M in 2016 to 130M in 2050 ▪ AD will become leading cause of death in many developed countries in next ten years ▪ Global costs will rise from ~$800 billion in 2015 to $2T by 2030 ▪ Caregivers also carry a huge direct burden. The average caregiver in the US provides 22hrs/wk of active support with $5k/yr additional out of pocket expenses Source: Alzheimer’s Association; 2016 AD Facts & Figures; World Alzheimer’s Report 2015 The Global Impact of Dementia
  3. 3. Biomarkers and AD pathological Cascade 3 Aβ accumulation and amyloid pathology usually occurs first, and may plateau when clinical symptoms manifest. Tau pathology generally appears later and bears a closer temporal relationship to AD symptoms than amyloid. Adapted from Jack et al., Lancet Neurol, 2013 Symptomatic Therapies Earlier treatment considered key to prevent or delay onset of neuronal degeneration Preclinical AD Prodromal AD AD dementia >10 years ~5-7yrs ~7-10 yrs Time (yrs) Mild-moderate-severe
  4. 4. Targeting key pathways involved in AD pathophysiology 4 Aβ42 monomers Toxic Aβ42 oligomers Amyloid plaque Amyloid precursor protein Tau pathology Crenezumab Humanized anti-amyloid-beta IgG4 mAb Targets multiple β-amyloid forms - preference for oligomers Phase 3 ongoing (CREAD program) Gantenerumab Fully human anti-amyloid beta IgG1 mAb Targets aggregated β-amyloid forms - binds oligomers & plaques Phase 3 starting (GRADUATE program)
  5. 5. Crenezumab Mild to Moderate AD Phase II Program 5 IV SC 2011 2012 2013 2014 ABBY IV SC BLAZE OLE • MMSE score at screening 18-26 (mild-to-moderate AD), age 50 – 80 years • SC dose is 300mg/q2w and IV dose is 15mg/kg/q4w (~2.5 fold higher exposure) • Primary analysis after 72 weeks, IV and SC separately; pre-specified subpopulation: MMSE ≥ 20; further post-hoc analyses ABBY “Cognition Study” 446 enrolled • Primary endpoint: reduction in cognitive decline as measured by ADAS-Cog and CDR-SB • Additional endpoints included ADCS-ADL, MMSE, DSST, optional CSF sub-study BLAZE “Biomarker Study” 91 enrolled • Primary endpoint: changes in brain amyloid load by florbetapir-PET • Additional endpoints included: CSF, FDG-PET, vMRI • Enrollment required “amyloid positive” PET
  6. 6. ABBY primary endpoint: change in ADAS-Cog12 Mild-to-moderate population 6 Pl Cr Diff (SE) %Red P–value 7.85 7.81 0.04 (1.40) 0.5% 0.977 Pl Cr Diff (SE) %Red P–value 10.56 8.79 1.78 (1.35) 16.8% 0.190 300 mg q2w SC (Low Dose) 15 mg/kg q4w IV (High Dose) Pl n=58 n=55 n=45 Week 73 ADAS–Cog12ChangefromBaseline Week 1 25 49 73 2 0 -2 -4 4 6 10 12 14 16 8 Week 73 2 0 -2 -4 4 6 10 12 14 16 8 Week 1 25 49 73 Cr n=113 n=105 n=58 Pl n=76 n=71 n=64 Cr n=148 n=130 n=122 Placebo Crenezumab Primary endpoint (ADAS-Cog) not met; however, higher dose showed treatment effect suggesting ‘higher is better’
  7. 7. ABBY high dose cohort showed increasing effect on cognition in progressively milder subsets of AD 7 Pl Cr Diff (SE) %Red P–value 10.56 8.79 1.78 (1.35) 16.8% 0.190 ADAS–Cog12 ChangefromBaseline ImprovementDecline 2 0 -2 -4 4 6 10 12 14 16 8 MMSE 18–26 Wee k 1 25 49 73 Week 73 Pl n=76 n=71 n=64 C r n=148 n=130 n=122 Pl Cr Diff (SE) %Red P–value 9.43 7.18 2.24 (1.47) 23.8% 0.128 2 0 -2 -4 4 6 10 12 14 16 8 MMSE 20–26 Wee k 1 25 49 73 Week 73 Pl n=54 n=51 n=47 C r n=111 n=101 n=93 Pl Cr Diff (SE) %Red P–value 9.70 6.26 3.44 (1.61) 35.4% 0.036 2 0 -2 -4 4 6 10 12 14 16 8 MMSE 22–26 Wee k 1 25 49 73 Week 73 Pl n=39 n=36 n=33 C r n=82 n=75 n=70 Placebo Crenezumab MMSE N (plc) N (active) Δ (SE) % ES (SD) p 18-26 64 122 1.78 (1.35) 16.8% 0.2 (9.08) 0.190 20-26 47 93 2.24 (1.47) 23.8% 0.27 (8.44) 0.128 22-26 33 70 3.44 (1.61) 35.4% 0.44 (7.80) 0.036 18-21 31 52 -0.15 (2.25) -1.3% -0.01 (10.38) 0.947 16.8% 23.8% 35.4%
  8. 8. Conclusion & decisions from Phase II program 8 CREAD Phase III program higher dose prodromal to mild population Clinical data with high dose (IV) crenezumab suggests consistency with emerging data from other anti-amyloid studies that earlier treatment and higher doses are associated with improved clinical outcomes Phase Ib study explore safety at higher doses
  9. 9. Beyond the clinical trial data lifecycle 9 Data from the Completed Clinical Trials Targets Biomarkers Biology Actionable Scientific Insights
  10. 10. The cycle of translational science 10 Using insights from clinical trials and clinical practice to further research and development. Using translational research to inform clinical trials and clinical practice. Forward Translation Reverse Translation Research & Development Clinical Trials & Clinical Practice Components of Precision Medicine Targets Biomarkers Biology
  11. 11. How do we pay the price only once? https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-m ost-time-consuming-least-enjoyable-data-science-task-survey-says
  12. 12. 12 FAIR data for precision medicine research Wilkinson, M. D., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data.
  13. 13. The path to making data analysis ready Site 1 Site 2 MRI Data Standard Derived Data Trial Operations Trial Analysis Neuro Lab... Site N Sample Biobank Vendor-based Services Clinical Data Warehouse Mappings Clinical Scientist
  14. 14. Clinical Data Interchange Standards Consortium: the CDISC standards model 14
  15. 15. Data tabulations have domain specific standards 15 https://www.cdisc.org/standards Class Example Special Purpose Demographics (DM) General Observation - Interventions Exposure (EX) General Observation - Events Adverse Events (AE) General Observation - Findings Laboratory Test Results (LB) Findings About Findings About (FA) Trial Design Domains Trial Arms (TA) Relationship Datasets Related Records (RELREC)
  16. 16. Mapping study data to SDTM: Lab Test Results 16
  17. 17. The need for data governance and a metadata repository 17
  18. 18. The path to making data analysis ready Discovery Research Site 1 Site 2 MRI Data Standard Derived Data Trial Operations Trial Analysis Neuro Lab... Site N Sample Biobank Vendor-based Services Clinical Data Warehouse 18 Exploratory Assays Tabulations Mappings Bioinformatics Scientist
  19. 19. Exploratory assays and analysis ready data 19Multi Assay Experiment Object https://doi.org/doi:10.18129/B9.bioc.MultiAssayExperiment ● Straight to analysis, eliminate time wasted on data munging ● A variety of assay types ○ RNASeq ○ Nanostring ○ Fluidigm ○ Etc… ● Bioconductor’s MultiAssayExperiment ○ implements data structures and methods for representing, manipulating, and integrating multi-assay experiments via efficient construction, subsetting, and extraction operations.
  20. 20. - Data preparation for discovery research is highly custom - Manual quality assurances are critical - patient-sample identifier mappings - standardized assay processing steps - many more… - How can analysts - discover available data and results? - trust the data they receive? - conduct data forensics? - share their results? Making fully integrated data FAIR is hard! 20
  21. 21. What is it? ● A system for recording, tracking, storing, finding and retrieving computational results (including data) in a FAIR manner ● Key Goal: apply and adapt the FAIR principles to human generated exploratory and one-off analyses GREX 21
  22. 22. The GREX Pillars and FAIR 22
  23. 23. The R in FAIR is not for Reproducibility GREX extensions/additions to FAIR ● Intrinsically link code to results ● Link result to recreatable description of environment ● Client automatically generates metadata ● (forthcoming) automatic result reproduction/code verification 23
  24. 24. Submitting data or results for publication 24 R Client Controller Client Bundle 1417e07cacada19fa73 ├── metadata │ └── metadata.json ├── results │ └── SpkyV2_a9.rda ├── outputs │ ├── MAE.html │ ├── SpkyV2_a9.png └── transformation └── MAE_poplar.Rmd Archive Discoverability Provenance Store Publish Download Query
  25. 25. Discovery Interface 25
  26. 26. Result Page 26
  27. 27. ● Store results and associated metadata with persistent IDs ● Results are Findable via discoverability portal ● Registered Results are Accessible ● Results and metadata are stored in standard (~ Interoperable) ways ○ JSON-LD for metadata and R serialized objects for results ● Results are Reusable - Provenance records model links between data, code, and results ○ histry and trackr R packages for capture FAIR principles that GREX implements 27
  28. 28. Clinical Study Data Request 28 https://www.clinicalstudydatarequest.com/
  29. 29. Alzheimer’s Prevention Initiative Columbia Trial 29 Colombian Study* (300 patients) N=100 N=100 N=100 Baseline Data Requests Investigators can apply for use of baseline demographic, clinical, and imaging data from the API ADAD Colombia Trial as soon as these data are uploaded to API’s data-sharing portal. Until then, investigators may send data queries to APIdata@bannerhealth.com Primary endpoints: • API Cognitive Test Battery Secondary endpoints: • AV-45 PET • FDG-PET • vMRI • CSF analysis 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 FPI LPI LPLD
  30. 30. Acknowledgements GREX Team & Bioinformatics - Gabe Becker - Dana Caulder - Altaf Kassam - Kiran Mukhyala 30 AD and Crenezumab - Jasi Atwal - Heather Guthrie - Brad Friedman - Helen Lin Clinical Data Standards - Jonathan Chainey - Nelia Lassiera

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