# Presentation: 071052

## CaBIG Adopter Project :Integrative Cancer Biology A Structure for an Integrated Database and Data Management System for Translational Research

- Judith D. Goldberg, Sc.D.
- Professor of Environmental Medicine
- Director, Division of Biostatistics
- Director, NYU Cancer Institute
- Biostatistics Shared Resource

## Resources:Biostatistics Division, NYU School of Medicine& NYU Cancer Institute Biostatistics Shared Resource

- Faculty - 6:
- Project Co-Investigators:
- Judith D. Goldberg, Sc.D.,  Professor
- YongZhao Shao, Ph.D., Associate Professor
- Arthur Nadas, Ph.D., Associate Professor
- Ilana Belitskaya, Ph.D., Assistant Professor
- Jomol Mathew, Ph.D., Assistant Professor
- Staff:
- Assistant Research Scientists (statistician/programmers) -3
- Ph.D. Students - 6

## Other Resources:

- NYU School of Medicine Information Technology Services
- NYU School of Medicine Research Computing Facility Bioinformatics (Stuart Brown, Ph.D., Co-investigator)
- NYU Cancer Institute Bioinformatics
- NYU Cancer Institute Genomics Facility
- NYU Courant Institute Bioinformatics Group

## Division of Biostatistics

- Department of Environmental Medicine

- Center for
- Aids
- Research

- General
-  Clinical
- Research
- Center

- BSR
- NYU
- Cancer
- Institute

- NIEHS
- Center

- NYU Oral
-  Cancer
- RAAHP
- Center

- NYU Gene
- Therapy
-  Center

## Expertises

- Statistics
- 		Clinical trials, translational research
- 		Statistical genetics
- 		Genomics
- 		Multivariate analysis
- 		Longitudinal data analysis
- Database design
- Statistical programming
- Bioinformatics
- Evaluation and implementation of database systems, statistical analysis systems

## A Structure for an Integrated Database and Data Management System for Translational Research

- Objective:
- To develop a central database and data management system that enables
- Conduct of translational research across multiple cancer centers and laboratories using common clinical protocols
- Capture, storage, and linkage of clinical and laboratory data (e.g. functional genomics, molecular markers, immunonomics (Serex analysis), hormonal assays, mutational analyses, etc.)
- Integration of data from multiple cancer centers, sites and laboratories
- Integrated statistical analysis and reporting of research results

## Locally Advanced Breast Cancer (LABC): Treatment and PrognosisS. Formenti, R. Schneider, J. Goldberg, et al.

- Goal: identification of characteristics predictive of pathological response, progression and survival of LABC based on clinical and laboratory data generated by
- Functional genomics,  genotyping, immunomics, mutational analyses, molecular markers, hormonal assays, etc
- Standard chemo-radiation protocol and patient follow-up
- Multi-ethnic cohorts
- Five cancer centers world wide
- Expected accrual of 750 patients

## The Proposed System	

- For storage and management of clinical data from multiple centers:
- Oracle Clinical (implemented) with Remote Data Capture Module
- For storage and management of microarray data:
- Gene Traffic
- For linking the databases and data visualization:
- SpotFire, GeneSpring
- For statistical data analyses:
- S-plus, MatLab, BRB Array tools, SAS, R, Bioconductor, ...

**Notes:**

4. Followed security protocols 

## Remote Capture Schema

## Benefits of Proposed System

- Central database accessible from all centers and laboratories without duplication of effort
- Capability to merge data from different types of translational studies
- Standardized data structure with each clinical trial or observational study as the primary unit of the clinical database; patients within trials are the subunits
- Seamless integration of genetic and clinical data which can lead to development of novel treatment methods based on new findings
- Component systems are publicly or commercial available, validated, and maintained

**Notes:**
Depending on HIPPA, we can select patients for future studies

## Scientific Advantages of the Proposed System

- Ability to select patients for inclusion in translational research studies from a common cohort of patients
- Integration of data of different types from clinical and translational research programs
- Scientific ability to conduct integrated statistical analyses within and across cohorts using data from all sources
- Generation of hypotheses for future studies

## Example: LABC data structure

|  | Types of Variables |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
| N of Patients | Clinical | Genomics | Molecular markers | Immunology | Mutational analyses | Hormonal assays |
| 150  | + | +
 | +
 | +
 | +
 | +
 |
| 600 | +
 |  |  |  |  | 
 |

-  Sampling strategies
-  Data mining methods
-  Treatment of incomplete data

## Strategies for Statistical Study Design and Analysis

- Using state-of-the-art statistical procedures for
- Variable selection
- Performance assessment
- Patient selection for translational substudies
- Treatment of data with partially observed covariates to provide inferences about entire cohorts with incomplete data

## Statistical Issues in DNA Microarrays

**High-dimensionality of data:**
- many genes (thousands)
- few samples (tens)
**Multiple sources of variation that can confound effects:**
- background level
- dye, slide, spot
- experimental condition
- ...

## Statistical Methods Specific to DNA Microarrays

**To control for multiple sources of variation: **
- Replication, dye color reversal
- Centering, scaling, transformation
- Normalizing to common reference sample
**To detect differential gene expression: **
- Significance Analysis of Microarrays (SAM) [Tusher et al, 2001]
**To control for multiplicity: **
- False Discovery Rate (FDR) [Benyamini and Hochberg, 1995; Storey et al, 2002]

## Statistical Methods Specific to DNA Microarrays - continued

**To identify clusters of genes and samples:**
- Hierarchical, partitioning clustering methods
- Clustering on subsets of variables
- [Alon et al, 1999; Getz et al, 2000; Belitskaya, 2002]
**To predict clinical outcomes based on gene expression profiles:**
- Supervised learning methods
- Predictive Analysis of Microarrays (PAM)“nearest centroid classification”
-    [Tibshirani et al, 2002]

## End User Perspective for Adoption of Software Systems or Components

- Meets functional requirements
- Validated system, component
- Integrates easily into NYUCI environment, platforms
- 		Oracle Clinical repository for patient data

## End User Testing of Systems, Components

- Develop scenarios for stress testing
- Test data sets
- Comparison with alternative or existing systems, components (if available)
- Interaction with developers to modify functionality, usability, documentation

## Required Biostatistics Resources for Implementation

- Personnel:
- Database administrator
- Systems analyst
- Programmers
- Infrastructure:
- Server
- Networking support
- Client machines
- Software licenses

## Summary

- Central database accessible from all centers and laboratories without duplication of effort
- Capability to merge data from different types of translational studies
- Standardized data structure
- Components of system available (public domain, commercially), validated, maintained
- Ability to select patients for inclusion in translational research studies from a common cohort of patients
- Seamless integration of genetic and clinical data which can lead to development of novel treatment methods based on new findings
- Scientific ability to conduct integrated statistical analyses within and across cohorts using data from all sources
- Generation of hypotheses for future studies

**Notes:**
Depending on HIPPA, we can select patients for future studies