In the broader realm of the advancement of science and the betterment of the human condition, there are several purported benefits for sharing clinical trials and research data. The scientific community has just begun to embrace open-access datasets to build their knowledge base, gain insight into new discoveries, and generate novel data-driven hypotheses that were not initially formulated in the studies. With the increasing amount of clinical trial data available, comes the need to leverage a multitude of shared datasets. Your knowledge base needs to facilitate discovery across research domains. This talk highlights the data sharing, dissemination, and repurposing of clinical and molecular studies generated by government-funded research consortia. Further, we are building a new knowledge base resource, IMMGRAKN to facilitate translational discovery from crowd-sourced clinical trials data in ImmPort (www.immport.org), an NIH-NIAID funded open-access immunology database and analysis portal. The case studies demonstrating the use of IMMGRAKN will be discussed

1. Toward leveraging publicly accessible clinical
trials data- sharing, dissemination, and
repurposing
Sanchita Bhattacharya
Bioinformatics Project Leader
Bakar Institute of Computational Health Sciences
University of California, San Francisco

2. 2
v Clinical trials generate vast amounts of data, a large portion is
never published or made available to other researchers.
v Data sharing could advance scientific discovery and improve
clinical care by maximizing the knowledge gained from data
collected in trials, stimulating new ideas for research, and
avoiding unnecessarily duplicative trials.
Motivation

3. Clinical Trial Life Cycle: When to Share Data
3
2
.
C L I N I C A L
T R I A L M I L E S T
O N E
K E Y
:
M E TA D ATA INDIVIDU AL
PARTICIPANT D A T A
SUMMAR Y D A T A
At t r i a l
re g i st rat i o n
1 2 m o n t h s
a f te r st u d y
co m p l e t i o n
1 8 m o n t h s a f t e r
p ro d u c t a b a n d o n m e n t
O R 3 0 d a y s a f t e r
re g u l a t o r y a p p r o v al**
6 M o n t h s
a f te r
p u b l i c at i o n *
DA T A
S H A R I N G
P L A N
R E G I ST R AT I O N
E L E M E N TS
F U L L DA TA
P A CKA GE
P OST- R E G U L ATO RY
DA TA P AC KAG E
1 8 m o n t h s
a f te r st u d y
co m p l e t i o n
S U M M A RY-
L E V E L
R E S U LTS
LAY
S U M M A R I E S
P A R T I C I PA N T
E N R O L L M E N T
N O
5
B
T R I A L D E S I G N
& R E G I S T R AT I O N1
ST U DY CO M P L E T I O N
O R T E R M I N AT I O N 3
Y E S
5
A
R E G U L ATO R Y
A P P L I C AT I O N ?
2
P OST- P U B L I C A T I O N
DA TA P AC KAG E
P U B L I C AT I O N
4
Sharing Clinical Trial Data: Maximizing Benefits, Minimizing Risk.
http://www.iom.edu/Reports/2015/

4. Institute of Medicine Report on
Sharing Clinical Trial Data: Maximizing Benefits, Minimizing Risk
http://www.iom.edu/Reports/2015/Sharing-Clinical-Trial-Data.aspx
• Benefits of Data sharing
ü confirm published results,
ü generate better evidence,
ü facilitate additional findings,
ü prevent duplicate trials, and
ü ultimately improve public health.
4

5. • Risks of Data Sharing
ü privacy of clinical trial participants
ü legitimate economic interests of sponsors
ü invalid secondary analyses
ü appropriate recognition of the researchers for their
intellectual contributions
ü fear of research institutions that data sharing will be
unfunded mandates.
Institute of Medicine Report on
Sharing Clinical Trial Data: Maximizing Benefits, Minimizing Risk
http://www.iom.edu/Reports/2015/Sharing-Clinical-Trial-Data.aspx
5

6. Non-Profit
Organizations
Government Health
Agencies
Pharmaceutical
Companies
6
Market and Regulatory forces are driving initiatives to publicly
share patient-level data from clinical trials

7. Non-Profit
Organizations
Government Health
Agencies
Pharmaceutical
Companies
7
Electronic portals for requesting, sharing and analyzing clinical
trial data

8. 8
Yu et al., Current Opinion in Systems Biology, 2019
Molecular Portraits of Immune System

9. ImmPort.org
ImmPort data portal was developed to collect and share research
and clinical trials data from NIAID/DAIT* funded researchers
*National Institute of Allergy and Infectious Diseases, Division of
Allergy, Immunology and Transplantation (NIAID-DAIT)

10. 10
ImmPort redistributes data from
major NIAID-funded programs and more
Data from 300+ trials and studies already released, involving:
• Human Immunology Project Consortium (HIPC)
• Accelerating Medicines Partnership (AMP) in Rheumatoid
Arthritis and Lupus (AMP)
§ National Cancer Institute, Oncology Models Forum
§ March of Dimes: Preterm Birth Research
• Immune Tolerance Network (ITN)
• Atopic Dermatitis Research Network (ADRN)
• Clinical Trials in Organ Transplantation (CTOT) and in Children
(CTOT-C)
• Population Genetics Analysis Program
• Protective Immunity for Special Populations
• HLA Region Genomics in Immune-mediated Diseases
• Modeling Immunity for Biodefense
• Reagent Development for Innate Immune Receptors
• Adjuvant Development Program
• Immunity in Neonates and InfantsAsthma and Allergic Diseases
Cooperative Research CentersHLA and KIR Region Genomics in
Immune-Mediated Diseases
§ Cooperative Study Group for Autoimmune Disease Prevention
§ Immunobiology of Xenotransplantation
§ Centers for Medical Countermeasures against Radiation
Consortium
§ Inner City Asthma Consortium
§ Systems Approach to Immunity and Inflammation
§ Innate Immune Receptors and Adjuvant Discovery Program
§ Maintenance of Macaque Specific Pathogen-Free Breeding
Colonies
§ Non-human Primate Transplantation Tolerance Cooperative
Study Group
§ Consortium for Food Allergy Research
§ Development of Sample Sparing Assays for Monitoring Immune
Responses (U24)

11. Data Flow
Standards
De-Identification
Standardization
Semantic Harmonization
Private Data
Data Analysis
Flow Cytometry
Data Analysis
ImmPort Galaxy
immuneXpresso
Gene Lists
Cell Ontology Browser
Shared Data
ImmPort APIs
Data Catalogs
Database
Format
Flat Files
Resources
Templates
Validation Service
Ontologies
Tutorials
ImmPort Ecosystem
Data providers
Research Community
Shared Data Repositories
dbGAP, SRA, ...
NIAID ImmPort Data
ReUse Initiative
Indexing
DOIs
ImmuneSpace
Data Re-Analysis
RImmPort
10K Immunomes
MetaCyto
Trajectory-based Visualization
Repurposing
Research Community, Citizens
and Data Enthusiasts

12. Data Model Schematic

13. Focus Areas and Data Types
Total Human Subjects (46937)
Total Studies (413) Total Experiments (1638)
Immunology Research Studies- 291
Clinical Trials- 122

14. Influenza Vaccination Studies in ImmPort

15. Reproducibility
Re-Analyze
Repurpose

16. Reanalysis of the Rituximab in ANCA-Associated Vasculitis trial
identifies granulocyte subsets as a novel early marker of
successful treatment.
Nasrallah, M., Pouliot, Y., Hartmann, B. et al. Arthritis Res Ther 17, 262
(2015)

17. RAVE Trial: Rituximab in ANCA-Associated Vasculitis
COMPLETE REMISSION
● Randomized, double-blind, active-
controlled, non-inferiority, phase II/III
trial comparing Rituximab to
Cyclophosphamide for induction of
remission
● 63 of the 99 patients in the rituximab
group (64%) reached the primary end
point, as compared with 52 of 98 in the
cyclophosphomide group (53%).
197 participants Multi-center
Stone et al., NEJM (2010)
Primary Endpoint
● 35% of patients treated with rituximab and 47% of patients
treated with cyclophosphamide failed to achieve remission.
● In retrospect, do any measured factors
predict response to therapies?

18. Proposed personalized treatment option based on
cellular profiling
Nasrallah et al., Arthritis Res.Ther (2015)
C
R

19. 20
Targeted_therapy_graph
ANCA-
associated
Vasculitis
Profiled
Therapy
~ 54% of patients
Non-profiled
Therapy
~46% of patients
Treat with
Rituximab
~ 30% of patients
Remission Rate ~ 83%
Treat with
Cyclophosphamide
~24% of patients
Remission Rate ~ 66%
Do not treat with
Cyclophosphamide
Failure rate ~ 67%
Do not treat with
Rituximab
Failure rate ~ 70%
GI ≤ -9.25%
OR
GI ≥ 47.6%
GI ≤ -9.25% GI ≥ 47.6%
Treat with either Rituximab or
Cyclophosphamide
according to best clinical judgement
Average Remission Rate ~ 60%
Non-profiled
Therapy
100% of patients
NO
Proposed
Method
Current
Method
Measure the Granularity Index
(GI)
YES
Proposed Personalized Treatment on the Basis of Granularity Index

20. The 10,000 Immunomes project
• Large, diverse, cleaned reference dataset
for human immunology
• Interactive data visualization
• Custom control cohorts and standardized
data download

21. 85 Studies
10,344 Subjects
42,000+ Samples
Manual curation of studies, arms, and planned
visits to filter for normal human subjects
Data available in the
10,000 Immunomes Project
Total Samples 42117
Total Distinct Subjects 10344
MEASUREMENT
Secreted Proteins
SUBJECTS
4835
ELISA 4035
Multiplex ELISA 1286
Virus Titer 3609
Virus Neutralization Titer 2265
HAI Titer 1344
Clinical Lab Tests 2639
Complete Blood Count 1684
Comprehensive Metabolic Panel 664
Fasting Lipid Profile 664
Questionnaire 1422
Cytometry 1415
Flow Cytometry (PBMC) 907
CyTOF (PBMC) 583
Flow Cytometry (Whole Blood) 164
HLA Type 1093
Gene Expression Array 476
Whole Blood 311
PBMC 165
10kimmunomes.org

22. Journal of the American Medical Informatics Association, Volume 23, Issue 3, 1 May 2016
Extracting PubMed record-associated MeSH
descriptors

23. The Knowledge model
ImmPort
Cytokines
Therapeutic
target
Diagnostic
marker
Clinical data
Expression data
Text mining
ArrayExpress GEO
Open Source
Clinical trials
Nophar Geifman

24. The Knowledge model
DiseaseCell types Cytokines
Clinical data
(ELISA, Elispot)
Clinical data
(Blood tests, Flow cytometry)
Curated knowledge Expression data
Text miningGender
Ethnicity
Age
Diagnostics and
Therapeutics

25. 26

26. Different Clusters are Characterized By Different Cytokine Patterns
IL-6
TNF-a
4

27. Different Clusters are Characterized By Different Cytokine Patterns
IL-4
IL-5
IL-8
IFNg
IL-
10
IL-
13
CCL11
15
TNF-a

28. Different Clusters are Characterized By Different Cytokine Patterns
TNF-aIFNg
23

29. 30
TherapeuticsDiagnostics

30. immGrakn: Vaccine Knowledgebase
31

31. Influenza Vaccination Studies in ImmPort

32. 33
Challenges and Opportunities
• Crowd-sourced
• Data silos
• Interoperability- federated repositories
• Lack of data standards
§ Effect of age, gender, race on vaccination
§ Immune response in vaccinated individuals with disease conditions
(e.g., acute, chronic)
§ Effect of medications on vaccination response
§ Improving standard of care
v Better understanding of Vaccination Response

33. 34
Ø limit complexity and organize information into data and knowledge.
Ø compilation of facts and figures that can be used to provide contextual
meaning to searches.
Knowledge Graphs: Connecting the dots

34. Sub Types:
*Categories (e.g., cardiac,
autoimmune etc.)
*Condition (e.g., Asymptomatic,
Acute, Chronic)
Sub Type:
* Condition
(e.g.,
Pregnant
Subject
age
gender
race
zygosity
has
has
has
has
Clinical
Phenotype
disease
health
type
Vaccine
year
strain
outcome
Interventionreceives
patient
has
has
has
ImmGRAKN
knowledgebase
Drug
Molecular (DNA, mRNA,
protein, cell,)
Microbiome
has
preventiontherapeutic
Biosample
assessment

35. 36
ImmGRAKN
knowledgebase model

36. Take Home Messages
• Holistic approach to analyzing clinical trials data
• Open-access clinical trials are a valuable resource to evaluate new
hypotheses, gain novel insights, and inform a better trial design
• 10Kimmunomes- reference dataset generated by integrating individual
level data from publicly available immunology studies.
• Knowledge Graphs can be used as a semantic search engine sparking
new ideas and finding unexpected connections in research and knowledge
discovery applications.
Embrace open-access clinical trials!

37. Acknowledgements
Atul Butte
Northrop Grumman
Health Solutions
Elizabeth Thomson
Patrick Dunn
Henry Schaefer
John Campbell
Morgan Crafts
Zicheng Hu
Kelly Zalocusky
Matthew Elliott
Daniel Wu
Data Providers
@ImmPortDB bit.ly/10kimmu
Funding Support
National Institute of Allergy and Infectious
Diseases (NIAID)
National Institutes of Health (NIH)
Health and Human Services (HHS)
Contract #: HHSN316201200036W
Priscilla Chan and Mark Zuckerberg
Distinguished Professor.
Director, Bakar Computational
Health Sciences Institute, UCSF
Tomas Sabat
Soroush Saffari
Daniel Crowe
Nophar Geifman
University of Manchester

38. THANKS
Sanchita.Bhattacharya@ucsf.edu
@sanchitab
39
Bakar Institute of Computational Health Sciences
University of California, San Francisco