www.1mind4research.org

An Open-Science Framework• Generate and Share Data:

– Use a portal system to access global coherent datasets – Build an inter-operable infrastructure for collation, curation and hosting of

datasets for the R&D community– Common standards and methods for the collection of the world’s largest

biomarker repositories for brain disorders

• Generate and Share Tools:– Collectively develop next-generation animal models, imaging tools, stem cell

lines, biosensors, and more…

• Generate and Share Models: – Apply advanced clinical informatics, computational biology and visualization

tools to create dynamic interactive Disease Maps and inform Clinical Decisions Support and Practice Guidelines

• Create Insight: – Tap Global Expertise (multi-disciplinary & global) – crowd-sourced solutions

THE WAY FORWARD……….

THE CHALLENGE

DATA SHARING IS POORLY INCENTIVIZED

DATA AND TOOL SHARING IS NOT SIMPLE ENOUGH

???

DATA SETS ARE BALKANIZED AND HARD TO FIND

THE APOLLO PROGRAM

PORTAL ARCHITECTURE

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Engagement Tools• Facebook-like Activity Stream• LinkedIn-like social networking• Patient Engagement platforms

Analytics Marketplace• Access to the world’s most advance Big Data Analytics Tools• API allows both Open-Source and Proprietary Software to Plug-In

Links Data – Experts - Analysis

Data Hub• OS Infrastructure for Comprehensive Data Management• Tiered Resource Ingestion Pipeline, DataSpace, Object Models,

Search, Web Services, Encyclopedias, Ontologies

CASE STUDY

tranSMART CASE STUDY: TRACK-TBIPrepared for ONE MIND

Sirimon O’Charoen, PhDManager, Translational Medicinesirimon.ocharoen@thomsonreuters.com

• ~1.7 Million Reported TBI’s in US / Year– ~52,000 Deaths ----------------------------------

– ~275,000 Severe TBIs (sTBIs) -------------

– ~1.37 M Mild TBIs (mTBIs)------------

– ?? M Unreported TBIs ---------------

• ~ 360,000 servicemen/veterans with war-related TBI

• BUT: TBIs can happen to anyone:– ~3.8 million sports related TBIs/yr in US (3.6 M unreported)

– ~300,000 vehicular TBI’s / yr in US (17% of TBIs)

– 35.2% of reported TBIs from falls

• TBI is a contributing factor to a third (30.5%) of all injury-related deaths in the United States.

Traumatic Brain Injury Numbers

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3.4M

The Goal: Establishing the Natural History of the TBI Spectrum

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Accelerate the understanding, both

clinically and mechanistically, of the natural course of TBI

Develop a new classification of

TBI

Identify predictive

measures of outcomes

Develop new diagnostic tools

Identify new targets for therapeutic intervention

Develop new treatments, preventions and cures and move the discoveries into clinical practice for

improved patient outcomes

In order to:

TRACK-TBI MULTICENTER INITIATIVE

• Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot– Multicenter Implementation of the Common Data

Elements for Traumatic Brain Injury

• Aims– Test and validate Traumatic Brain Injury Common

Data Elements (TBI-CDEs)

– Collect neuroimaging, proteomic, and genetic biomarkers, clinical and outcome data

TRACK-TBI DATA

• Approximately 650 patients

• Mostly mild TBI

• Principle Investigators– UCSF, San Francisco

General Hospital• Geoffrey Manley, MD, PhD

– University of Pittsburgh Medical Center• David Okonkwo, MD, PhD

– University Medical Center Brackenridge• Alex Valadka, MD

– Mount Sinai Rehabilitation Center• Wayne Gordon, PhD

Yue JK, et al., J Neurotrauma, 2013 Jul 2.

TRACK-TBI DATA IN tranSMART

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TRACK-TBI Data Curation Process Delivery in tranSMART

Ready for analysis- Exploratory analysis (tranSMART features)- Advanced workflow (tranSMART features)- Custom analysis (database access or R-

plugin)

QuesGen DatabaseSPSS

Collaborators

Goal: Curate/upload TRACK-TBI Data for data sharing and analysis at researcher’s fingertip

CDEs MappingData QC

ETL

tranSMART USER INTERFACE

• Data navigation: folder-structure• Analysis: drag & drop

• Trainings were provided for all collaborators• Same curated dataset can be accessed

without compressing and emailing files

ANALYSIS EXAMPLES

• 2 Publications focus on– Follow-up prediction

– GFAP-BDP as a diagnostic biomarker

• How do analyses in tranSMART confirm the published results?

• How quickly we can generate new hypotheses and perform exploratory analyses in tranSMART?– Data can be across domains (i.e. genotype/biomarker vs.

outcome, medical history vs. follow-up)

FOLLOW-UP PREDICTION

• Outcome follow-up is challenging*– 3-month GOS-E: 77%

– 6-month GOS-E: 69%

• Rate of successful follow-up significantly depends*– Years of Education

– History of Developmental Disorders

– Toxicology Lab Results at ED

– Homelessness

* Result from

FOLLOW-UP PREDICTION Lost-to-follow up

(6-month)Successful follow up

(69% at 6-month)

Years of Education

History of Developmental Disorders

LIVE DEMO

GFAP-BDP DIAGNOSTIC MARKER

• Glial fibrillary acidic protein (GFAP)– Acute plasma level

– Optimal cutoff 0.68 ng/ml*

• Discriminate*– CT intracranial lesion result

– Outcomes

• Other protein biomarkers may improve prediction

* Result from

GFAP-BDP DIAGNOSTIC MARKER:CT RESULT

Publication Figure 1Subset1: CT negativeSubset2: CT positive

GFAP-BDP DIAGNOSTIC MARKER:OUTCOME GOS-E

Publication Figure 5

GFAP-BDP DIAGNOSTIC MARKER: OPTIMAL CUTOFF

Subset1: GFAP<0.68 ng/mlSubset2: GFAP≥0.68 ng/ml

ACCOMPLISHMENTS

Pilot

• tranSMART implementation

• TRACK-TBI Pilot curation and upload

Evaluation

• User trainings (>25 users)

• Feedbacks• Validation of

publication results in seconds

On-going

• Data access for collaborators

• New data analysis

• Part of One Mind Portal