Visiting Scholars Seminar Series - 2006-2007

Monday, June 4, 2007
Noon – 1:00 p.m.
Executive Plaza North
6130 Executive Blvd.
Conference Room H
Rockville, Md.

Beyond heritability:  Using twins to study immune phenotypes for chronic disease
Wendy Cozen, D.O., M.P.H.
Associate Professor of Research
Keck School of Medicine
University of Southern California

Monday, May 21, 2007
Noon – 1:00 p.m.
6116 Executive Blvd.
Conference Room 210
Rockville, Md.

Chronic Inflammation and the Microenvironment of the Cancer Cell: Etiology, Pathogenesis and the Basis for Preventive Interventions
David Schottenfeld, M.D., M.S.
John G. Searle Professor Emeritus of Epidemiology
Professor of Internal Medicine
University of Michigan School of Public Health

A causal link between chronic inflammation and carcinogenesis will be explored by reviewing illustrative examples of cancers in the gastrointestinal tract, and causal agents and mechanisms. Recurrent or persistent inflammation may induce, promote, or influence susceptibility to carcinogenesis by causing DNA damage, inciting tissue reparative proliferation, and/or by creating a stromal "soil" that is enriched with cytokines and growth factors. Future research on the complex cascade of cellular and humoral factors participating in the chronic inflammatory process may further understanding of the pathogenesis of cancer in a more universal context, and provide a rationale for targeted preventive interventions.

Monday, April 23, 2007
Noon– 1:00 p.m.

Executive Plaza North
6130 Executive Blvd.
Conference Room G
Rockville, Md.

The NIH Peer Review Process
Denise Wiesch, M.P.H., Ph.D.
Scientific Review Administrator
Center for Scientific Review
National Institutes of Health

Dr. Denise Wiesch is the Scientific Review Administrator for the Center for Scientific Review’s (CSR) Epidemiology of Cancer Study Section (EPIC) and a referral officer in CSR’s Division of Receipt and Referral.  Dr. Wiesch earned a M.P.H. and a Ph.D. in genetic epidemiology from Johns Hopkins University and she subsequently joined the Center for the Genetics of Complex Diseases at the University of Maryland at Baltimore.  Her research focused on the genetic epidemiology of allergy and asthma in occupational settings. Dr. Wiesch has spent much of her career at NIH where she has worked in the intramural research programs at NHLBI, NICHD, and NIMH and also for the National Library of Medicine.  Prior to joining CSR, Dr. Wiesch was a program officer at NIAID’s Division of Allergy, Immunology, and Transplantation.  She directed the clinical research program in primary immune deficiency diseases as well as part of the autoimmune disease research program.

Monday, January 8, 2007
Noon -1:00 p.m.

Executive Plaza North
6130 Executive Boulevard
Conference Room G
Rockville, Md.

Inflammation and Ovarian Cancer From an Epidemiologist's Perspective
Roberta B. Ness, M.D., M.P.H.
Chair, Department of Epidemiology
Professor of Epidemiology, Medicine and Ob/Gyn
Graduate School of Public Health
University of Pittsburgh

Tuesday, December 5, 2006
2:00 -3:00 p.m.

Executive Plaza North
6130 Executive Boulevard
Conference Room J
Rockville, Md.

Vitamin D and Colorectal Cancer
Edward Giovannucci, M.D., Sc.D.
Professor, Nutrition and Epidemilogy
Harvard School of Public Health

Monday, November 20, 2006
Noon - 1 p.m.

Executive Plaza North
6130 Executive Boulevard
Conference Room G
Rockville, Md.

Semiparametric Methods for Gene-Environment Case-Control Studies
Raymond J. Carroll, Ph.D.
Distinguished Professor of Statistics and Nutrition
Department of Statistics
Texas A&M University

We consider population-based case-control studies of gene and environment interactions using prospective logistic regression models. In a typical case-control study, neither the intercept of the logistic regression nor the population probability of disease can be identified. However, in many cases it is reasonable to assume that genotype and environment are independent in the population, possibly conditional on covariates to account for population stratification. In such a case, we show that the intercept and population probability of disease are identified. We develop a modern semiparametric likelihood approach for this problem, showing that it leads to much more efficient estimates of gene-environment interaction parameters and then gene main effect than the standard approach: decreases of standard errors for the former are often by factors of 50% and more.

In addition, if the probability of disease is known in the population, we show efficiency gains for estimating gene-environment interactions, again in contrast to the standard approach. Multiple extensions are discussed, with applications to an important data set involving BRCA 1/2. The most important extensions are to the problems of missing genotype data (our example) and unphased haplotype data. This is joint work with Nilanjan Chatterjee, Ph.D., senior investigator in NCI’s Division of Cancer Epidemiology and Genetics.

Dr. Carroll is an NCI Method to Extend Research in Time (MERIT) Award winner. The award provided him with an additional 5 years of funding for his research. MERIT Awards are given to investigators who have Research Project Grants (R01), whose competence and productivity are distinctly superior, and who are likely to continue to perform in an outstanding manner. Recipients are nominated by NCI staff and approved by the National Cancer Advisory Board.

Monday, October 30, 2006
Noon - 1 p.m.

Executive Plaza North
6130 Executive Boulevard
Conference Room G
Rockville, Md.

Genetic Epidemiologic Approaches to Elucidate Ovarian Cancer Susceptibility
Thomas A. Sellers, Ph.D.
Associate Center Director, Cancer Prevention and Control
H. Lee Moffitt Cancer Center & Research Institute
University of South Florida

Several lines of evidence suggest that ovarian cancer has a genetic component. In addition to several rare single gene mutations that confer high risk, it is hypothesized that common genetic polymorphisms that each confer smaller risks exist. This talk will cover epidemiologic approaches to identify these genetic risk factors, including candidate genes, pathways, and genome screens.