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Grant Details

Grant Number: 5R21CA208938-02 Interpret this number
Primary Investigator: Su, Min-Ying
Organization: University Of California-Irvine
Project Title: Mammographic Density and Metabolic Genotyping for Predicting Cancer Prognosis
Fiscal Year: 2018
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ABSTRACT 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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Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue.
Authors: Zhou J. , Zhang Y. , Chang K.T. , Lee K.E. , Wang O. , Li J. , Lin Y. , Pan Z. , Chang P. , Chow D. , et al. .
Source: Journal of magnetic resonance imaging : JMRI, 2020 Mar; 51(3), p. 798-809.
EPub date: 2019-11-01.
PMID: 31675151
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Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI.
Authors: Lang N. , Zhang Y. , Zhang E. , Zhang J. , Chow D. , Chang P. , Yu H.J. , Yuan H. , Su M.Y. .
Source: Magnetic resonance imaging, 2019 12; 64, p. 4-12.
EPub date: 2019-02-28.
PMID: 30826448
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Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net.
Authors: Zhang Y. , Chen J.H. , Chang K.T. , Park V.Y. , Kim M.J. , Chan S. , Chang P. , Chow D. , Luk A. , Kwong T. , et al. .
Source: Academic radiology, 2019 11; 26(11), p. 1526-1535.
EPub date: 2019-01-31.
PMID: 30713130
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Feasibility and Diagnostic Performance of Voxelwise Computed Diffusion-Weighted Imaging in Breast Cancer.
Authors: Zhou J. , Chen E. , Xu H. , Ye Q. , Li J. , Ye S. , Cheng Q. , Zhao L. , Su M.Y. , Wang M. .
Source: Journal of magnetic resonance imaging : JMRI, 2019 06; 49(6), p. 1610-1616.
EPub date: 2018-10-16.
PMID: 30328211
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Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs.
Authors: Pandey D. , Yin X. , Wang H. , Su M.Y. , Chen J.H. , Wu J. , Zhang Y. .
Source: Heliyon, 2018 Dec; 4(12), p. e01042.
EPub date: 2018-12-17.
PMID: 30582055
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Role of dynamic contrast-enhanced MRI in evaluating the association between contralateral parenchymal enhancement and survival outcome in ER-positive, HER2-negative, node-negative invasive breast cancer.
Authors: Shin G.W. , Zhang Y. , Kim M.J. , Su M.Y. , Kim E.K. , Moon H.J. , Yoon J.H. , Park V.Y. .
Source: Journal of magnetic resonance imaging : JMRI, 2018 12; 48(6), p. 1678-1689.
EPub date: 2018-05-07.
PMID: 29734483
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Quantitative analysis of peri-tumor fat in different molecular subtypes of breast cancer.
Authors: Chen J.H. , Zhang Y. , Chan S. , Chang R.F. , Su M.Y. .
Source: Magnetic resonance imaging, 2018 11; 53, p. 34-39.
EPub date: 2018-06-30.
PMID: 29969646
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Breast density quantification using structured-light-based diffuse optical tomography simulations.
Authors: Ruiz J. , Nouizi F. , Cho J. , Zheng J. , Li Y. , Chen J.H. , Su M.Y. , Gulsen G. .
Source: Applied optics, 2017-09-01; 56(25), p. 7146-7157.
PMID: 29047975
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3D MRI for Quantitative Analysis of Quadrant Percent Breast Density: Correlation with Quadrant Location of Breast Cancer.
Authors: Chen J.H. , Liao F. , Zhang Y. , Li Y. , Chang C.J. , Chou C.P. , Yang T.L. , Su M.Y. .
Source: Academic radiology, 2017 07; 24(7), p. 811-817.
EPub date: 2017-01-26.
PMID: 28131498
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Microstructural white matter abnormalities independent of white matter lesion burden in amnestic mild cognitive impairment and early Alzheimer disease among Han Chinese elderly.
Authors: Liao J. , Zhu Y. , Zhang M. , Yuan H. , Su M.Y. , Yu X. , Wang H. .
Source: Alzheimer disease and associated disorders, 2010 Oct-Dec; 24(4), p. 317-24.
PMID: 20592582
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