[This page is intended to provide a study summary, the sections of which are below. Please complete these sections, as applicable. The headings below are suggested headings. You can remove inapplicable sections, or add new ones relevant to your study]
Investigator Names and Contact Information
Jielin Sun [firstname.lastname@example.org]
Hormone Therapy (HT) is commonly used to relieve
the symptoms associated with menopause. However, studies show HT is associated
with increased risk of breast cancer and cardiovascular disease. Some women may
have different susceptibility to HT use with respect to breast cancer risk,
based on genetic background. This proposal aims to identify common genetic
variants (SNPs) that classify women into low or high risk groups for developing
breast cancer with HT use (gene-hormone interaction). We will test this among women
who participated in the Hormone Therapy Trial (HT) of the Women’s Health
Initiative (WHI) study using a genome-wide association (GWA) approach.
Considering the different risk effect of estrogen plus progestin (E+P) and
estrogen alone (E), we will test our hypothesis in women taking these two types
of hormones separately in Aims 1 and 2. We expect to identify a subset of SNPs
that interact with hormone use and breast cancer. We also plan to further evaluate/confirm the top
200-500 significant SNPs (P~10-4) among women in the OS arm using a case-control
study design in Aim 3.These variants will be used to create a risk-benefit
profile for HT treatment and improve clinical decision making for HT use.
Individual HT-breast cancer risk assessment is important for identifying women for
whose benefit may outweigh the risk from those whose risk outweighs the
Aim1: Identify SNP-hormone (Estrogen + Progestin) interactions that are associated with breast cancer risk.
We will use a case-only study design, including all 583 cases women who participated in the Hormone Therapy Trial (HT) arm (E+P) of the Women's Health Initiative (WHI) study. The cases include all incident cases of invasive breast cancer in participants of the Clinical Trial (CT) arm who were randomized to receive HT or placebo.
We will first perform a genome-wide (GWA) association study in women of the HT arm. 583 cases from the E+P use group and placebo group will be screened using an Affymetrix 6.0 Chip. We will then impute ~2 million SNPs with MAF ≥ 1% in the genome based on our genotyped SNPs and haplotype information from HapMap, using the IMPUTE computer program. Association analysis will be performed for ~2 million SNPs. Interactions between HT use and each of the ~2 millon SNPs with breast cancer risk will be tested using assuming an additive mode of inheritance and adjusting for demographic covariates and known risk factors for breast cancer. We will identify a subset of SNPs (200-500) that interact with hormone replacement therapy use on breast cancer risk, using a P-value of 10-4 from directed genotyped SNPs and imputed SNPs.
Aim 2: Identify SNP-hormone (Estrogen only) interactions that are associated with breast cancer risk.
Perform a genome-wide (GWA) association study in women with Estrogen use only and placebo use in Women of the HT arm. 307 cases from the Estrogen use only group and placebo group will be screened. We will use the same approaches as detailed in Aim 1. Similarly, we will expect to identify a subset of SNPs (200-500) that interact with Estrogen only therapy on breast cancer risk.
Aim 3: Confirm the top 500 significant SNPs identified in Aim 1 and Aim 2 among women in the OS arm using a case-control study design in Aim3.
We expect that some of the significant SNPs shown in Aim1 and Aim 2 may overlap, and this is why we estimate ~500 SNPs will be evaluated in Aim 3. We will use a case-control study design, including 1,800 cases and 1,800 controls in the OS arm of the Women's Health Initiative (WHI) study using a Sequenom platform. Approximately 40% of women can be classified as past or current users of E+P, and ~30% of women can be classified as past or current users of E alone. Interaction between SNPs and hormone therapy with breast cancer risk will be tested using a logistic regression approach. SNP-hormone interactions will be considered as confirmed using a P-value of 10-3 .