Genes in hormone metabolism pathway and breast cancer
Creators: name: University Of California Office Of The President
Publisher: name: University of California Office of the President
publisherIdentifier: https://ror.org/00dmfq477
publisherIdentifierScheme: ROR
Year: 2024

description: Numerous epidemiological and experimental studies have established that hormonal and reproductive factors are associated with breast cancer. Consistent with this, high hormone levels are also associated with increased risk of breast cancer in postmenopausal women. It therefore seems reasonable that genes that code for enzymes that regulate how these hormones are broken down in the body would also be important. A number of studies have investigated the role of such genetic variants, and results so far have been mixed. However, this may be because most of these studies have focused on one gene at a time. We hypothesize that these genetic variants act in concert, and that one needs to study many of them at the same time in order to find an effect. It is also possible that these genetic variants, or combinations of them, modify the effect of reproductive and hormonal factors on breast cancer risk.
 
 
 
 
 
 
 
 In this proposal, we will address questions on whether variations in hormone metabolism pathways interact with each other or an environmental factor to increase the breast cancer risk more than expected than when the factors action singly. The investigation of these questions is not simple because of the enormous amount of data to be analyzed: many genes are involved and there are many variants in one gene. We will study over 400 such variants. We will use data from a study of 1500 breast cancer cases and 1500 non-cases within the California Teachers Study (CTS) cohort. We will use newer statistical methods to examine the effects of many different genetic variants as well as the environmental factor (estrogen/progesterone combined therapy, EPT use) in combination. We will investigate genetic effects using functional SNPs (single nucleotide polymorphisms = single base paring changes in DNA) and haplotypes (a combination of alleles that are transmitted together on the same chromosome).We will take advantage of two innovative statistical approaches to correct for this multiple testing problem. Further, we will use “Hierarchical candidate gene pathway modeling”, a new statistical approach that takes advantage of previous biologic knowledge to select which sets of genes will be included in the final model. For this proposal, we will focus on one hormonal factor, EPT, as an “environmental factor.” If we could identify genetic risk factors of breast cancer and genetic modifier of the effect of EPT use, it will help us predict which subgroup of women will be at increased risk of breast cancer. If our modeling is successful, we could use the same technique to identify other important gene-environment interactions for breast cancer risk both in the hormonal pathway as well as in other pathways.
 
 
 
 
 
 
 
 It is important to understand whether certain combinations of genetic variants put women at higher risk of breast cancer, and to determine whether such combinations interact with non-genetic factors. These approaches may ultimately improve risk management of genetically predisposed women. Furthermore, it will help identifying women who could safely use EPT – at least with regard to breast cancer risk.