MODELING INHERITED SUSCEPTIBILITY TO BREAST CANCER 

	Giovanni Parmigiani
	Institute of Statistics and Decision Sciences,
	Duke University

	gp@isds.duke.edu
	http://www.isds.duke.edu/~gp	


Recent advances in the understanding of genetic susceptibility to
breast cancer, notably identification of the BRCA1 and BRCA2
susceptibility genes, raise important questions for clinicians,
patients and policy makers.  Answers to many of these questions hinge
on accurate assessment of the probability of carrying a genetic
susceptibility mutation.  In particular, it is important to predict
genetic susceptibility based on easy-to-collect data about family
history of breast and related cancers.

In this talk I will describe a simple prediction model of genetic
susceptibility. Our model is based on combining published data about the
genes' prevalence, penetrance and inheritance mechanism, together with a
straightforward application of Bayes' rule.  I will briefly outline the
basic elements of our model and then describe four areas of current
research: (i) directly using the model in genetic counseling; assessing
and communicating uncertainty about the model's estimated probability;
(ii) validating the model using pedigree data on tested individuals,
accounting for errors in genetic testing; (iii) using the model for
exploring differences in prognosis between carriers and noncarriers of
the genes, by incorporating detailed pedigree information in a survival
analysis; (iv) developing a comprehensive model of breast cancer risk,
including pedigree information as well as other risk factors. The latter
requires some thought about how to jointly model case-control and
prospective studies.