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.

1. Meaningful (meta)data at scale:
removing barriers to precision medicine research
Nolan Nichols
Neuroinformatics 2018
Montréal, Canada
August 9, 2018

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. 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. 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. 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. 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. 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. 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. Beyond the clinical trial data lifecycle
9
Data from the Completed
Clinical Trials
Targets Biomarkers
Biology
Actionable
Scientific Insights

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. 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
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. 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. Clinical Data Interchange Standards Consortium:
the CDISC standards model
14

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. Mapping study data to SDTM: Lab Test Results
16

17. The need for data governance and a metadata repository
17

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. 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. - 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. 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. The GREX Pillars and FAIR
22

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. 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. Discovery Interface
25

26. Result Page
26

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. Clinical Study Data Request
28
https://www.clinicalstudydatarequest.com/

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. 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