||5R21CA208938-02 Interpret this number
||University Of California-Irvine
||Mammographic Density and Metabolic Genotyping for Predicting Cancer Prognosis
Title: Mammographic Density and Metabolic Genotyping for Predicting Cancer Prognosis
This project will investigate the role of quantitative mammographic density (MD) and cytochrome P450
CYP2D6 metabolic genotyping in the prognosis of breast cancer (BC) patients, with the ultimate goal of
using them as prognostic predictors for improving the treatment that can be provided to each individual
patient. Mammographic density is an established risk factor for developing breast cancer, and there is also
evidence suggesting that cancers arising from dense tissue area are more aggressive; therefore collectively
they suggest MD may serve as a prognostic predictor. In this project we will utilize a single-institution, all-
Chinese, patient cohort that is treated in one hospital using similar strategies (Taichung Veteran's General
Hospital in Taiwan). Their Breast Care Center has established a detailed registry, and each patient's
personal factors, TNM staging, molecular biomarkers, imaging findings, and treatment protocols (surgery,
radiation, chemotherapy and hormonal therapy) are all well documented in the database. Patients with
newly diagnosed Stage I, II, and III invasive breast cancer will be identified from the registry as the inclusion
criteria. The majority of patients are continuingly being followed in the same hospital, so it is very easy to
find their prognostic information, including development of recurrence, secondary BC, distant metastasis
and BC-specific death. This registry provides a great resource for investigating the association of MD with
patients' prognosis (Aim-1). For patients diagnosed with hormonal receptor positive BC, it is the standard of
care to give them hormonal therapy, e.g. tamoxifen for pre- and peri-menopausal women. Although the
treatment has been shown very effective in improving disease-free survival and overall survival on a
statistical basis, many patients still develop progressive disease, raising the question of individual
responsiveness. The hormonal therapy drugs are associated with various side effects; thus there is a strong
interest to find biomarkers that can predict the responsiveness of each individual patient to ensure a
favorable benefit-to-risk ratio. MD reduction has been shown as a valid surrogate marker for predicating
tamoxifen response, and it would be very interesting to understand why some women would respond and
show MD reduction but others not. Aim-2 was designed to predict tamoxifen treatment efficacy based on
MD reduction and the CYP2D6 genotyping that are known to affect the metabolism of tamoxifen to active
compounds that have a high affinity for estrogen receptors. Patients returning to hospital for in-person
follow-up will be invited to provide blood samples for the CYP2D6 genotyping, by using a new method
based on the high-resolution melting curve analysis (HRM), which has been validated in Chinese women
and proven to be efficient and low-cost. Based on the gene alleles patients will be determined as extensive
metabolizers or intermediate metabolizers. The CYP2D6 metabolic status will be correlated with MD
reduction, and then both correlated with prognosis. Further, they will be combined to investigate whether
these two factors can be added to improve the prediction of prognosis.
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