scholarly journals Breast cancer risk prediction using a clinical risk model and polygenic risk score

2016 ◽  
Vol 159 (3) ◽  
pp. 513-525 ◽  
Author(s):  
Yiwey Shieh ◽  
Donglei Hu ◽  
Lin Ma ◽  
Scott Huntsman ◽  
Charlotte C. Gard ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Risk Score ◽  
Risk Model ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Clinical Risk

scholarly journals The Discovery of Breast Cancer Risk Gene and Establishment of Prediction Model Based on Estrogen Metabolism Regulation

2020 ◽  
Author(s):  
Feng Zhao ◽  
Zhixiang Hao ◽  
Yanan Zhong ◽  
Yinxue Xu ◽  
Meng Guo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Score ◽  
Healthy Subjects ◽  
Estrogen Metabolism ◽  
Polygenic Risk Score ◽  
Polygenic Risk

Abstract Background In this study, we aim to uncover the relationship between estrogen levels and the genetic polymorphism of estrogen metabolism-related enzymes with breast cancer (BC) and establish a risk prediction model based on polygenic risk score. Methods Unrelated BC patients and healthy subjects were recruited for analysis of the estrogen levels and the single nucleotide polymorphisms (SNPs) of estrogen metabolism-related enzymes. The polygenic risk score (PRS) was used to explore the combined effect of multiple genes which was calculated using a Bayesian approach. The independent sample t test was used to evaluate the difference between PRS scores of BC and healthy subjects. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (ROC). Results The estrogen homeostasis profile was disturbed in BC patients, with parent estrogens (E1, E2) and carcinogenic catechol estrogens (2/4-OHE1, 2-OHE2, 4-OHE2) significantly accumulated in the serum of BC patients. Then,we established PRS model to evaluate the role of multiple genes SNPs. The PRS model 1 (M1) was established from 6 GWAS-identified high risk genes SNPs. On the basis of M1, we added 7 estrogen metabolism enzyme genes SNPs to establish PRS model 2 (M2). The independent sample t test results show that there is no difference between BC and healthy subjects in M1 (P = 0.17), however, there is significant difference between BC and healthy subjects in M2 (P = 4.9*10− 5). The ROC curve results also show that the accuracy of M2 (AUC = 62.18%) in breast cancer risk identification was better than M1 (AUC = 54.56%). Conclusion Estrogens and the related metabolic enzymes gene polymorphisms are closely related to BC. The model constructed by adding estrogen metabolic enzyme genes SNPs has a good ability in breast cancer risk prediction, and the accuracy is greatly improved comparing PRS model only includes GWAS-identified genes SNPs.

scholarly journals A streamlined model for use in clinical breast cancer risk assessment maintains predictive power and is further improved with inclusion of a polygenic risk score

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245375
Author(s):  
Richard Allman ◽  
Erika Spaeth ◽  
John Lai ◽  
Susan J. Gross ◽  
John L. Hopper
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
African American ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Score ◽  
Clinical Risk Factors ◽  
Polygenic Risk Score ◽  
Gail Model ◽  
Clinical Risk

Five-year absolute breast cancer risk prediction models are required to comply with national guidelines regarding risk reduction regimens. Models including the Gail model are under-utilized in the general population for various reasons, including difficulty in accurately completing some clinical fields. The purpose of this study was to determine if a streamlined risk model could be designed without substantial loss in performance. Only the clinical risk factors that were easily answered by women will be retained and combined with an objective validated polygenic risk score (PRS) to ultimately improve overall compliance with professional recommendations. We first undertook a review of a series of 2,339 Caucasian, African American and Hispanic women from the USA who underwent clinical testing. We first used deidentified test request forms to identify the clinical risk factors that were best answered by women in a clinical setting and then compared the 5-year risks for the full model and the streamlined model in this clinical series. We used OPERA analysis on previously published case-control data from 11,924 Gail model samples to determine clinical risk factors to include in a streamlined model: first degree family history and age that could then be combined with the PRS. Next, to ensure that the addition of PRS to the streamlined model was indeed beneficial, we compared risk stratification using the Streamlined model with and without PRS for the existing case-control datasets comprising 1,313 cases and 10,611 controls of African-American (n = 7421), Caucasian (n = 1155) and Hispanic (n = 3348) women, using the area under the curve to determine model performance. The improvement in risk discrimination from adding the PRS risk score to the Streamlined model was 52%, 46% and 62% for African-American, Caucasian and Hispanic women, respectively, based on changes in log OPERA. There was no statistically significant difference in mean risk scores between the Gail model plus risk PRS compared to the Streamlined model plus PRS. This study demonstrates that validated PRS can be used to streamline a clinical test for primary care practice without diminishing test performance. Importantly, by eliminating risk factors that women find hard to recall or that require obtaining medical records, this model may facilitate increased clinical adoption of 5-year risk breast cancer risk prediction test in keeping with national standards and guidelines for breast cancer risk reduction.

Abstract P1-09-04: Association between a breast cancer polygenic risk score and contralateral breast cancer risk

2019 ◽  
Author(s):  
I Kramer ◽  
MJ Hooning ◽  
N Mavaddat ◽  
S Canisius ◽  
R Keeman ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Score ◽  
Contralateral Breast Cancer ◽  
Contralateral Breast ◽  
Polygenic Risk Score ◽  
Polygenic Risk

scholarly journals Polygenic risk score for the prediction of breast cancer is related to lesser terminal duct lobular unit involution of the breast

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Clara Bodelon ◽  
Hannah Oh ◽  
Andriy Derkach ◽  
Joshua N. Sampson ◽  
Brian L. Sprague ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Intermediate Step ◽  
Breast Cancers ◽  
Tissue Samples ◽  
Polygenic Risk ◽  
Age Related

Abstract Terminal duct lobular units (TDLUs) are the predominant anatomical structures where breast cancers originate. Having lesser degrees of age-related TDLU involution, measured as higher TDLUs counts or more epithelial TDLU substructures (acini), is related to increased breast cancer risk among women with benign breast disease (BBD). We evaluated whether a recently developed polygenic risk score (PRS) based on 313-common variants for breast cancer prediction is related to TDLU involution in the background, normal breast tissue, as this could provide mechanistic clues on the genetic predisposition to breast cancer. Among 1398 women without breast cancer, higher values of the PRS were significantly associated with higher TDLU counts (P = 0.004), but not with acini counts (P = 0.808), in histologically normal tissue samples from donors and diagnostic BBD biopsies. Mediation analysis indicated that TDLU counts may explain a modest proportion (≤10%) of the association of the 313-variant PRS with breast cancer risk. These findings suggest that TDLU involution might be an intermediate step in the association between common genetic variation and breast cancer risk.

Abstract PS7-31: Breast cancer risk assessment combined with a polygenic risk score in the general population for personalized screening

2021 ◽  
Author(s):  
Mahasti Saghatchian ◽  
Marc Abehsera ◽  
Robert Sigal ◽  
Emilien Gauthier ◽  
Valerie Helin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Breast Cancer Risk ◽  
General Population ◽  
Cancer Risk ◽  
Risk Score ◽  
Cancer Risk Assessment ◽  
Polygenic Risk Score ◽  
Polygenic Risk ◽  
Breast Cancer Risk Assessment

scholarly journals Joint association of mammographic density adjusted for age and body mass index and polygenic risk score with breast cancer risk

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Celine M. Vachon ◽  
Christopher G. Scott ◽  
Rulla M. Tamimi ◽  
Deborah J. Thompson ◽  
Peter A. Fasching ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Body Mass Index ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Body Mass ◽  
Mammographic Density ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Polygenic Risk

A polygenic risk score for breast cancer risk in a Taiwanese population

2017 ◽  
Vol 163 (1) ◽  
pp. 131-138 ◽  
Author(s):  
Yi-Chen Hsieh ◽  
Shih-Hsin Tu ◽  
Chien-Tien Su ◽  
Er-Chieh Cho ◽  
Chih-Hsiung Wu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Taiwanese Population ◽  
Polygenic Risk

Abstract 2419: A polygenic risk score predicts breast cancer risk in Latinas

2019 ◽  
Author(s):  
Yiwey Shieh ◽  
Laura Fejerman ◽  
Sarah D. Sawyer ◽  
Donglei Hu ◽  
Scott Huntsman ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Score ◽  
Polygenic Risk Score ◽  
Polygenic Risk
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