caBIG: Building a

Biomedical e-Ecosystem

Ken Buetow, Ph.D. Associate Director for Biomedical

Informatics and Information Technology National Cancer Institute

Sage Congress

The cancer Biomedical Informatics Grid® (caBIG®) is a virtual network of interconnected data, individuals, and organizations that redefines how research is conducted, care is provided, and patients/participants interact with the biomedical research enterprise.

caBIG®: Biomedical Information Highway

21st Century Biomedicine requires connection of diverse information

Clinical Research

Pathology Molecular Biology

Imaging

Molecular Medicine

Boundaries and Interfaces

•  focus on boundaries and interfaces, how things fit together, not on the internal details •  acknowledge the inherent heterogeneity of biomedical data and applications •  assume that the diverse landscapes is ever changing

Research Unit

IT-enabled ecosystem!

Imaging

Microarray Data

Biospecimens

Analytical Tools

Research Center

Security Advertisement / Discovery

Federated Query Workflow

Metadata Management

Dorian GTS Index Service

Federated Query

Service

Workflow Management

Service

Vocabularies & Ontologies

GME Schema Management

Common Data Elements

Medical Center

Research Center

Research Center

Medical Center

Medical Center

Medical Unit

Research Center

Research Center

Classifying Lymphoma

•  Scientific value •  Use gene-expression patterns

associated with two lymphoma types to predict the type of an unknown sample.

•  Connect caGrid data service (caArray) with analytical services (PreProcess, SVM and KNN from GenePattern).

•  Major steps •  Querying training data from

experiments stored in caArray. •  Preprocessing, i.e., normalizing the

microarray data. •  Predicting lymphoma type using SVM

& KNN services. •  Extension

•  Generalized the workflow into a cancer type prediction routine that can be used on other caArray data sets.

*Fig. from MA Shipp. Nature Medicine, 2002

Ravi Madduri Univ. Chicago

Carole Goble U. Manchester, UK

Wei Tan Univ. Chicago

Dinanath Sulakhe Univ. Chicago

Stian Soiland-Reyes U Manchester, UK

MicroArray from tumor tissue

Microarray preProcessing

Lymphoma classification

Lymphoma Prediction Workflow

Ack. Juli Klemm, Xiaopeng Bian, Rashmi Srinivasa (NCI) Jared Nedzel (MIT)

The I-SPY trial (Investigation of Serial studies to Predict Your Therapeutic Response with

Imaging And moLecular analysis):

a national study to identify biomarkers predictive of response to therapy throughout the treatment cycle for women with Stage 3 breast cancer.

(Laura Esserman, UCSF )

Multiple Morphologic Patterns of Breast Cancer

•  Clinical diagnosis

•  Treatment history

•  Histologic diagnosis

•  Pathologic status

•  Tissue anatomic site

•  Surgical history

•  Gene expression

•  Chromosomal copy

number

•  Loss of heterozygosity

•  Methylation patterns

•  miRNA expression

•  DNA sequence

Specialized Programs of Excellence (SPOREs)

Multiple Sites/ Organizations

Multiple Data Types

Cancer and Leukemia Group B (CALGB)

American College of Radiology Imaging Network (ACRIN)

University of California at San Francisco (UCSF)

I-SPY Trial: Identify biomarkers predictive of therapeutic response in Stage 3 breast cancer

10

I-SPY Adaptive Trial Outline

Accrual: Anticipate 800 patients over 3–4 years

Enroll ~20 patients per month

Participating Sites: 15–20 across US and Canada

On Study

MRI MRI Biopsy Blood

MRI Blood

Surgery

Biopsy Blood

MRI Blood

Tissue

Taxol +/–New Drug (12 weekly cycles)

AC (4 cycles)

Taxol + Trastuzumab* + New Agent A

Taxol + New Agent C

Taxol + Trastuzumab*

Taxol + Trastuzumab* + New Agent B

Taxol

AC

AC HER 2 (+)

HER 2 (–)

Randomize

Randomize

Surgery Taxol + New Agent D *Or Equivalent

On Study Surgery

Taxol + Trastuzumab* + New Agent C

Surgery

Learn, Adapt from each patient

Taxol + Trastuzumab* + New Agent A

Taxol + New Agent C

Taxol + Trastuzumab*

Taxol + Trastuzumab* + New Agent B

Taxol

AC

AC HER 2 (+)

HER 2 (–)

Randomize

Randomize

Surgery

Taxol + New Agent F

Taxol + New Agent D

Taxol + New Agent G *Or Equivalent

Learn, Adapt from each patient

On Study Surgery

Taxol + Trastuzumab* + New Agent C

Taxol + Trastuzumab* + New Agent F

Surgery

I-SPY Trial IT Infrastructure

Expression Array Data!SNPArray Data! Radiological Data!Clinical Data!Patient Samples!

Data Mart

API

Local System

caExchange - Hub

Tolven

API caBIG® Applications

caTissue caArray

API

Perspectives for building a Community where disease data and models are shared

Lessons Learned: •  Culture eats Strategy for Lunch: Most of what prevents data-sharing is not technical, but reflects the segmentation of stakeholders into silos (by discipline, by geography, by sector), or concerns about intellectual capital, getting credit

•  Begin with a “coalition of the willing” •  There are no free lunches

•  Failure to collect appropriate structured data can result in loss of information or inability to connect

•  context is critical for clinical data •  Exchange AND use of information requires more than the ability to electronically move it from point to point. •  Pay me now or pay me later

Challenges Ahead: •  The culture of science is driven towards doing “new” not leveraging the work of others and incentives are generally aligned that way – “not invented here”. We don’t move forward because we are always starting over.