The CardioVascular Research Grid (CVRG):The CardioVascular Research Grid (CVRG):A National Infrastructure for A National Infrastructure for Representing, Sharing, Analyzing, Representing, Sharing, Analyzing, and Modeling Cardiovascular Dataand Modeling Cardiovascular Data

Stephen J. Granite, MSDirector of Database/Software Development The Johns Hopkins University Center for Cardiovascular Bioinformatics and Modeling

Why Is There A Need For The Why Is There A Need For The CVRG?CVRG?

The challenge of how best to represent CV dataEmerging data representation standards are seldom used

No standards for representing and no culture of sharing electrophysiological data

The challenge of sharing dataNational initiatives in CV genetics, genomics and proteomics are underway but there is no direct, easy way to discover data

Facilitate data discovery

The challenge of how best to develop and deploy “hardened” data analysis workflows

The challenge of discovering new knowledge from the CV data itself

Grids And E-ScienceGrids And E-Science

Grids1 Interconnected networks of computers and storage systems

Running common software

Enabling resource sharing and problem solving in multi-institutional environments

E-Science Computationally intensive science carried out on grids

Science with immense data sets that require grid computing

Two major “bio-grids” are active todayThe Biomedical Informatics Research Network (BIRN; http://www.nbirn.net/)The Cancer Biomedical Informatics Grid (caBIG; http://cabig.nci.nih.gov/)1I. Foster and C. Kesselman (2004). The Grid: Blueprint for a New Computing Infrastructure.

Elsevier.

The Biomedical Informatics Research Network The Biomedical Informatics Research Network (BIRN)(BIRN)Principal Investigator: Mark Ellisman UCSDPrincipal Investigator: Mark Ellisman UCSD

Grid infrastructure for sharing, analyzing and visualizing brain imaging data sets32 participating research sites, > 400 investigators4 “driving biological projects”BIRN is a “bottom up” effort (scientific applications drive technology)

UCLA ImageAcquisition

BWH ImageSegmentation

JHU Shape Analysis

End User Shape

Visualization

NCI intramural research effort, with selected external collaboratorsDevelop open-source software

Enables cancer researchers to become a caBIG node

Share data with the cancer research community

Develop controlled vocabularies for describing cancer phenotypes and multi-scale dataDevelop grid analytic services for analyzing cancer data sets

The Cancer Biomedical Informatics Grid (caBIG)The Cancer Biomedical Informatics Grid (caBIG)caGrid Lead Development Team: Joel Saltz OSUcaGrid Lead Development Team: Joel Saltz OSU

Center studies the cause and treatment of Sudden Cardiac Death (SCD) in the setting of heart failure (HF)

HF is the primary U.S. hospital discharge diagnosis

Incidence of ~ 400,000 per year, prevalence of ~ 4.5 million

Prevalence increasing as population ages

Leading cause of SCD (30-50% of deaths are sudden)

Medical expenditures ~ $20 billion per year

Manifestation of HF occurs at multiple biological levels

Genetic Predisposition via Single/Multi-Gene Mutations

Modified Gene/Protein Expression

Electrophysiological Remodeling and Altered Cellular Function

Heart Shape and Motion Changes

Reduced cardiac output, mechanical pump failure

The CVRG Driving Biological Project The CVRG Driving Biological Project (DBP)(DBP)The D. W. Reynolds Cardiovascular Clinical Research Center (PI - E. The D. W. Reynolds Cardiovascular Clinical Research Center (PI - E. Marban)Marban)

The CVRG Driving Biological Project The CVRG Driving Biological Project (DBP)(DBP)The D. W. Reynolds Cardiovascular Clinical Research Center (PI - E. The D. W. Reynolds Cardiovascular Clinical Research Center (PI - E. Marban)Marban)

Large patient cohort (~ 1,200) at high risk for SCDAll have received ICD placement to prevent SCDCollecting multi-scale data for all these patientsPatients with ICD firings are defined as high risk for SCD; patients without as low riskWithin the 1st year, only 5% of the ICDs implanted have actually firedChallenge – discover multi-scale biomarkers that are predictive of which patients should receive ICDs

Genetic Variability(SNPs)

Gene ExpressionProfiling

Protein ExpressionProfiling

Multi-ModalImaging

ElectrophysiologicalData

Multi-Scale Data

The CVRG ProjectThe CVRG Project

R24 NHLBI Resource, start date 3/1/07

3 development teamsWinslow, Geman, Miller, Naiman, Ratnanather, Younes (JHU)

Saltz, Kurc (OSU)

Ellisman, Grethe (UCSD)

AimsDevelop tools for representing, managing and sharing multi-scale data

SNP, genomic and proteomic data (Project 1)Electrophysiological data (Projects 1 & 2)Heart Shape and Motion (Cardiac Computational Anatomy) data (Projects 1, 3 & 4)

Use multi-scale data to discover biomarkers that predict need for ICD placement (Project 5)

The CVRG-Core (Projects 1-5)

EP Data

(Project 2)

Gene

Expression

(Project 1)

Project 1: The CVRG Core Project 1: The CVRG Core InfrastructureInfrastructure

Develop and deploy CVRG-Core middlewareReuse components and assure interoperability with BIRN and caBIGOpen-source software stack that instantiates a CVRG node

BioINTEGRATE

(Project 1)

BioMANAGE

(Project 1)

BioPORTAL

(Project 1)

SNP

(Project 1)Imaging

(Project 1)

Patient/

Study

(Project 1)

Protein

Expression

(Project 1)

Multiple Analytical Methods

(Projects 2,3,4&5)

Data Services

CVRG Data Services CVRG Analytic Services

Project 2: Electrophysiological (EP) Project 2: Electrophysiological (EP) Data Management And DisseminationData Management And Dissemination

ECG EP data

ONTOLOGIES

XMLDATABASES

ECG & EPData AnalysisPortal

Goal Adopt/develop data models to represent cardiovascular EP dataCreate databases for managing and sharing these data

Project 3: Mathematical Project 3: Mathematical Characterization Of Cardiac Ventricular Characterization Of Cardiac Ventricular Anatomic Shape And MotionAnatomic Shape And Motion

Goal Develop methods for statistically characterizing variability of heart shape and motion in health and disease Use these methods to discover shape and motion biomarkers for CV disease

MethodsMeasure heart shape and motion over time in the Reynolds population using multi-modal imaging (MR, multi-detector CT and Gd+ contrast-enhanced MR)Model variation of heart shape/motion in both the low/high risk Reynolds patientsDiscover shape and motion parameters that predict who should receive ICD placement

Cardiac Computational Cardiac Computational Anatomy And Shape Analysis Anatomy And Shape Analysis LLarge arge DDeformation eformation DDiffeomorphic iffeomorphic MMetric etric MMappingapping22

2Beg et al (2004). Mag. Res. Med. 52: 1167 Template

(smoothed)

Targets(Diseased Training Set)

Targets(Normal Training Set) ?

Cardiac Computational Cardiac Computational Anatomy And Shape Analysis Anatomy And Shape Analysis LLarge arge DDeformation eformation DDiffeomorphic iffeomorphic MMetric etric MMappingapping22

2Beg et al (2004). Mag. Res. Med. 52: 1167 Template

(smoothed)

Targets(Diseased Training Set)

Targets(Normal Training Set)

DiseasedHeart

Project 4: Grid-Tools for Cardiac Computational Project 4: Grid-Tools for Cardiac Computational AnatomyAnatomy

Segmentatio

n

Landmarking, Affine &LDDMM Shape Analysis

BioMANAGE

Visualization

12

3

4 5

Supercomputing

TeraGrid

De-identificationand upload

6

Statistical

Analysis

Goal – predict risk of SCD and identify patients to receive ICDs

Develop learning methods that work in the “small sample regime”

Patient A Patient A

SCD HIGH RISK

Patient B Patient B

SCD LOW RISK

Deploy these multi-scale biomarker discovery tools on the CVRG Portal

Project 5: Statistical Learning Project 5: Statistical Learning With Multi-Scale Cardiovascular DataWith Multi-Scale Cardiovascular Data

Algorithms3-

6

3Geman et al (2004). Stat. Appl. Genet. Mol. Biol. 3(1): Article 19.4Xu et al (2005). Bioinformatics, 21(20): 3905-39115Anderson et al (2007). Proteomics 7(8): 11976Price et al (2007). PNAS 104(9): 3414

Project 6: Resource Project 6: Resource ManagementManagement

Establish CVRG Working Groups to create a mechanism for community input on design and function of CVRG-Core and the CVRG

CV ontologies/data models Testbed Projects

(HLB-STAT)

New Technologies Data Sharing/IRB

Undertake outreach efforts to inform, train, and support researchers in use of CVRG tools and resources

AcknowledgementsAcknowledgementsThe CVRG Development Team

Johns HopkinsUniversity

Siamak Ardekani

Donald Geman

Stephen Granite

Joe Henessy

David Hopkins

Anthony Kolasny

Aaron Lucas

Michael Miller

Daniel Naiman

Tilak Ratnanather

Kyle Reynolds

Aik Tan

Rai Winslow

Gem Yang

Ohio StateUniversity

Shannon Hastings

Tahsin Kurc

Stephen Langella

Scott Oster

Tony Pan

Justin Permar

Joel Saltz

UCSD

Mark Ellisman

Jeff Grethe

Ramil Manasala

NHLBI (R24 HL085343)

Microsoft Research Faculty Summit 2007