# Presentation: 199030

## Management and Integration Across Sites,

- Initial Results, & Plans for the Future

**David J Hunter**

***BPC3***

## Outline

- Success in integration across cohorts
- Met interim goals
- Plans for future studies and synergies

## Comparing Minimum 18-month Performance Criteria to Actual Accomplishments (1)

- Performance Criteria
- Organizational Needs:
- Hold Monthly Conference Calls and Semi-Annual Meetings
- Establish Subcommittees
- Determine Work Scope and Data Sharing Methods
- Identify Data Availability With Power Calculations
- Prepare a Quality Control Plan
- Genetic Plans:
- Resequencing
- Develop Haplotype and Haplotype Tags
- Share Data Across Genotyping Centers
- Identify Specific Case-Control Groups
- Genotype

## Comparing Minimum Performance Criteria to Actual Accomplishments (2)

- Performance Criteria
- Data Congruency:
- Identify Common Questionnaire Elements
- Determine Methods for Parallel Analyses
- Develop Plans to Share Data Post-
- Extra Efforts:
- Have Results from Parallel Analyses for First Genes Typed
- Complete Pooled Analyses Only Available in Pooled Setting
- Draft Manuscript Describing Baseline Characteristics
- Preliminary Manuscript for Multivariate Analyses of Gene-Environment Interactions for Prostate Cancer

## Organizational Structure BPC3

**Organizational Structure BPC3**

- STEERING
- COMMITTEE

**GENOTYPING**
**Working group**

- POPULATION GENETICS
-  Working group

- Data Pooling Center

- PUBLICATIONS Working group

- Harvard
- cohorts

- ACS
- cohort

- EPIC
- cohort

- Multiethnic
- cohort

- PLCO
- cohort

- ATBC
- cohort

- Laboratories for:
- Gene sequencing, SNP Identification & Haplotypes
- Genotyping

- STATISTICS
- Working Group

**Harvard**

**IARC**

**Cambridge**

**U. Hawaii**

**USC**

## Genotyping Summary: Number of Cases, Controls, Total Genotypes in the Consortium as of May 1, 2005

| Cohorts | Breast
(Ca+Co) | Prostate
(Ca+Co) | +5-10% 
QC | Number of Genotypes;
6 Genes Completed*
(60 htSNPs) |
| --- | --- | --- | --- | --- |
| MEC | 3,579 | 4,620 | 9,019 | 541,140 |
| EPIC | 4,751 | 1,850 | 7,261 | 435,660 |
| HARVARD | 4,839 | 4,906 | 10,720 | 643,200 |
| ACS | 1,200 | 2,355 | 3,911 | 243,660 |
| PLCO | - | 2,812 | 3,093 | 185,580 |
| ATBC | - | 2,116 | 2,328 | 139,680 |
| TOTAL | 14,369 | 18,659 | 36,331 | 2.2 million genotypes |

- *6 Genes: AR, CYP19, HSD17B1, CYP17, PGR, ESR2 (~0.4% error)

## Public Access to Results & Data

- Available websites for BPC3 background and data
- http://epi.grants.cancer.gov/BPC3
- http://uscnorris.com/MECgenetics
- http://snp500cancer.nci.nih.gov
- http://cgf.nci.nih.gov

## http://epi.grants.cancer.gov/BPC3

## http://uscnorris.com/MECgenetics

## http://cgf.nci.nih.gov

## RR and 95% CI of Breast Cancer Associated With S312G Variant of HSD17B1 

**                 ****RR**

**Cohort**

***BPC3***

**NHS**

**WHS**

**MEC**

**EPIC**

**CPS-II**

- Results from the pooled data are certainly more precise than results from individual studies

## Relative Risk (and 95% CI) of Breast Cancer Associated With AAGC Haplotype of HSD17B1 

**RR**

**Cohort**

***BPC3***

**NHS**

**WHS**

**MEC**

**EPIC**

**CPS-II**

## Ethnicity-specific estimates require further follow-upe.g. HSD17B1 CAGC haplotype for CaP

- Ethnicity-specific estimates require further follow-upe.g. HSD17B1 CAGC haplotype for CaP

## Important scientific/technical issues to be addressed

- How to deal with large and complex genes?
- A subset of genes require many htSNPs
- Consider alternative methods:
- Different algorithm for htSNP selection
- Sliding r2 pairwise analysis

## ESR1

**3’UTR**

**GENE**

**5’UTR**

***ESR1***
- 484 kb
- 294 SNPs genotyped
- >70 htSNPs

## Important scientific/technical issues to be addressed

**Transition to high-throughput genotyping platform**
- 	Distributed genotyping model has been highly successful to date, permitting low-cost genotyping without the need to exchange and track samples while taking advantage of existing infrastructure that supports other NCI/NIH studies.
- 	However, new and future genotyping platforms may not be so adaptable and will require centralized genotyping

## Scientific Plans

**Expanding the size of case-control studies  **
**Selecting next hypotheses, genes, pathways**
**	****Genome scan may generate new candidate genes/pathways**
**	****Replication of novel findings**
**New Hypotheses Committee composed of younger investigators will recommend worthwhile studies with limited numbers of SNPs that can be performed with available resources**
**Public access to the genotyping data needs to be made more user-friendly**