Quantitative Medicine Feb 2009

Quantitative medicine A “killer app” for grid Ian Foster Computation Institute Argonne National Lab & University of Chicago

Thanks in particular to … Carl Stephan Steve Ravi Jonathan Kesselman Erberich Tuecke Madduri Silverstein

Quantitative medicine is the key to reducing healthcare costs and improving healthcare outcomes Patients with same diagnosis

Quantitative medicine is the key to reducing healthcare costs and improving healthcare outcomes Patients with same diagnosis Misdiagnosed Non-responders, toxic responders Non-toxic responders

Major drugs ineffective for many… Asthma Drugs 40-70% Beta-2-agonists Hypertension Drugs 10-30% ACE Inhibitors Heart Failure Drugs 15-25% Beta Blockers Anti Depressants 20-50% SSRIs Cholesterol Drugs 30-70% Statins Source: Amy Miller, Personalized Medicine Coalition

Same clinical disease, but different response to same chemotherapy, depending on gene expression profile Patient ID Number Danenberg Tumor Profile Scale Colorectal cancer: clinical trial data Salonga et al. Clin Cancer Res 2000; 6: 1322-1327.

Personalized medicine is quantitative The right treatment for the right person at the right time Trial and Error Personalized Medicine Current Practice One size fits all Trial and error Source: Amy Miller, Personalized Medicine Coalition

To realize the promise of quantitative medicine, we must break down barriers to information sharing … Discovering effective personalized treatments Determining the right treatment for the individual … and deliver new analytical tools to make sense of large quantities of data

Why it is hard?

A large, dispersed community

Huge quantities of data

Great diversity of data

Inadequate computing capabilities

Lack of a culture of sharing

Privacy concerns

Basic Research Clinical Practice Clinical Trials trial subjects, outcomes library Outcomes, tissue bank screening tests ongoing investigative studies pathways

Healthcare and infrastructure

Increased recognition that information systems and data understanding are limiting factor

… much of the promise associated with health IT requires high levels of adoption … and high levels of use of interoperable systems (in which information can be exchanged across unrelated systems) ….

RAND COMPARE

Health system is complex, adaptive system

There is no single point(s) of control. System behaviors are often unpredictable and uncontrollable, and no one is “in charge.”

W Rouse, NAE Bridge

Need to blur boundary from research to clinical

… I advocate … a model of virtual integration rather than true vertical integration….

George Halvorson, CEO Kaiser

Virtual organizations Grids and SOA

Children’s Oncology Group Grid Globus

Childrens’ Oncology Grid clinical imaging trials (Erberich)

Wide-area medical interface service

Maps local medical workflow actions to wide area ops

Image workflow, EHR, …

Transparently manages federation of

Security

Data replication and recovery

Data discovery

Enterprise/Grid Interface Service DICOM protocols Grid protocols (Web services) DICOM XDS HL7 Vendor-specific Wide Area Service Actor Plug-in adapters

US National Institutes of Health infrastructure activities

Biomedical Research Informatics Network (BIRN)

National Center for Research Resources (NCRR)

General infrastructure, with initial focus on neuroscience applications

Cancer Biology Informatics Grid (caBIG)

National Cancer Institute (NCI)

Initial focus on the cancer research community; BIGhealth initiative seeks to broaden it

Globus

Service oriented medicine: caGrid, Introduce, and gRAVI

Introduce

Define service

Create skeleton

Discover types

Add operations

Configure security

G rid R emote A pplication V irtualization I nfrastructure

Wrap executables

Index service Repository Service Introduce Container Ohio State University and Argonne/U.Chicago Appln Service Create Store Advertize Discover Invoke; get results Transfer GAR Deploy Globus

As of Oct 19 , 2008: 122 participants 105 services 70 data 35 analytical

Microarray clustering using Taverna

Query and retrieve microarray data from a caArray data service: cagridnode.c2b2.columbia.edu:8080/wsrf/services/cagrid/CaArrayScrub

Normalize microarray data using GenePattern analytical service node255.broad.mit.edu:6060/wsrf/services/cagrid/PreprocessDatasetMAGEService

Hierarchical clustering using geWorkbench analytical service: cagridnode.c2b2.columbia.edu:8080/wsrf/services/cagrid/HierarchicalClusteringMage

Workflow in/output caGrid services “ Shim” services others Wei Tan

Outsourcing analysis: caBIG’s geWorkbench/TeraGrid interface R. Madduri, U.Chicago, Taverna team

Schizophrenia as a neuropsychiatric model (Potkin, UCI)

A brain illness with subtle structural and functional changes

Active area of imaging research with many competing theories and approaches

Progress hampered by

Inconsistent data & lack of replications

Noncomparable imaging techniques

Small, diverse patient populations

Functional BIRN (fBIRN) information integration vision Multi-Site User Query Data Provenance Information Derived data processing FIPS Results FMRI/MRI Images Processing Pipelines HIDB(s) (Distributed) Data Grid Clinical Data Input DICOM, NIFTI fMRI Scanner

FBIRN multi-site study, 2006 UNM UMN UI UCI BWH MGH UCLA UCSD Stanford Duke/ UNC Yale = 3 or 4T site = 1.5T site = Development site

Lessons learned from BIRN (G. Farber)

There is little point in sharing data unless there is community agreement on how to standardize data collection

There continues to be a communications/ease of use gap between computer scientists and biomedical researchers

Sharing heterogeneous data from biomedical experiments is a challenge to existing data sharing infrastructures

Complex queries are a really hard problem

Health informatics services model Analysis Management Integration Publication Policy and Security Decision Support Radiology Medical Records Labs Pathology Genomics Applications Source: Carl Kesselman

Decision support for HIV drug ranking (Peter Sloot et. al)

Clinical Parameters: -weight - opportunistic infections and tumors -survival Molecular Dynamics Binding Affinity Protein Structure & Binding Affinity VIROLAB DRUG RANKING DECISION SUPPORT Text Mining  Drugranking  1 st order logic Complex Networks Epidemics Agent-Based Entry Simulation Phenotype CA Based Immune Response Protease and RT mutations

Virolab: DSS Virtual Laboratory Experiment developer Scientist Clinical Virologist Experiment Planning Environment Experiment scenario ViroLab Portal Virtual Laboratory runtime components (Required to select resources and execute experiment scenarios) Computational services (WS, WSRF, components, jobs) Data services (DAS data sources, standalone databases) Grids (EGEE), Clusters, Computers, Network Users Interfaces Runtime Services Infrastructure Drug Ranking Scenario

Many many tasks: Identifying potential drug targets 2M+ ligands Protein x target(s) (Mike Kubal, Benoit Roux, and others)

start report DOCK6 Receptor (1 per protein: defines pocket to bind to) ZINC 3-D structures ligands complexes NAB script parameters (defines flexible residues, #MDsteps) Amber Score: 1. AmberizeLigand 3. AmberizeComplex 5. RunNABScript end BuildNABScript NAB Script NAB Script Template Amber prep: 2. AmberizeReceptor 4. perl: gen nabscript FRED Receptor (1 per protein: defines pocket to bind to) Manually prep DOCK6 rec file Manually prep FRED rec file 1 protein (1MB) PDB protein descriptions For 1 target: 4 million tasks 500,000 cpu-hrs (50 cpu-years) 6 GB 2M structures (6 GB) DOCK6 FRED ~4M x 60s x 1 cpu ~60K cpu-hrs Amber ~10K x 20m x 1 cpu ~3K cpu-hrs Select best ~500 ~500 x 10hr x 100 cpu ~500K cpu-hrs GCMC Select best ~5K Select best ~5K

DOCK on BG/P: ~1M tasks on 118,000 CPUs