scholarly journals Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women

2021 ◽  
Author(s):  
Can Hou ◽  
Daowen Yang ◽  
Yu Hao ◽  
Bin Xu ◽  
Huan Song ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Chinese Women ◽  
Risk Scores ◽  
Polygenic Risk Score ◽  
Nucleotide Polymorphisms ◽  
Cancer Risk Factors ◽  
Polygenic Risk ◽  
Cancer Subtypes

Abstract Background Studies investigating breast cancer polygenic risk score (PRS) in Chinese women are scarce. The objectives of this study were to develop and validate PRSs that could be used to stratify risk for overall and subtype-specific breast cancer in Chinese women, and to evaluate the performance of a newly proposed Artificial Neural Network (ANN) based approach for PRS construction. Methods The PRSs were constructed using the a GWAS dataset and validated in an independent case-control study. Three approaches, including repeated logistic regression (RLR), logistic ridge regression (LRR) and ANN based approach, were used to build the PRSs for overall and subtype-specific breast cancer based on 24 selected single nucleotide polymorphisms (SNPs). Predictive performance and calibration of the PRSs were evaluated unadjusted and adjusted for Gail-2 model 5-year risk or classical breast cancer risk factors. Results The primary PRSANN and PRSLRR both showed good predictive ability for overall breast cancer (IQ-OR 1.76 vs 1.58; AUC 0.601 vs 0.598) and remained to be predictive after adjustment. Although estrogen receptor negative (ER-) breast cancer was poorly predicted by the primary PRSs, the ER- PRSs trained solely on ER- breast cancer cases saw a substantial improvement in predictions of ER- breast cancer. Conclusions The SNP-24 based PRSs can provide additional risk information to help breast cancer risk stratification in the general population of China. The newly proposed ANN approach for PRS construction has potential to replace the traditional approaches, but more studies are needed to validate and investigate its performance.

scholarly journals Combination of a 15-SNP Polygenic Risk Score and Classical Risk Factors for the Prediction of Breast Cancer Risk in Cypriot Women

Cancers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 4568
Author(s):  
Kristia Yiangou ◽  
Kyriacos Kyriacou ◽  
Eleni Kakouri ◽  
Yiola Marcou ◽  
Mihalis I. Panayiotidis ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Model ◽  
Disease Risk ◽  
Polygenic Risk Score ◽  
Nucleotide Polymorphisms ◽  
Cancer Risk Factors ◽  
Breast Cancer Risk Factors

The PRS combines multiplicatively the effects of common low-risk single nucleotide polymorphisms (SNPs) and has the potential to be used for the estimation of an individual’s risk for a trait or disease. PRS has been successfully implemented for the prediction of breast cancer risk. The combination of PRS with classical breast cancer risk factors provides a more comprehensive risk estimation and could, thus, improve risk stratification and personalized preventative strategies. In this study, we assessed the predictive performance of the combined effect of PRS15 with classical breast-cancer risk factors in Cypriot women using 1109 cases and 1177 controls from the MASTOS study. The PRS15 was significantly associated with an increased breast cancer risk in Cypriot women OR (95% CI) 1.66 (1.25–2.19). The integrated risk model obtained an AUC (95% CI) 0.70 (0.67–0.72) and had the ability to stratify women according to their disease status at the extreme deciles. These results provide evidence that the combination of PRS with classical risk factors may be used in the future for the stratification of Cypriot women based on their disease risk, and support its potential clinical utility for targeted preventative actions and population screening.

scholarly journals Discovery of breast cancer risk genes and establishment of a prediction model based on estrogen metabolism regulation

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Feng Zhao ◽  
Zhixiang Hao ◽  
Yanan Zhong ◽  
Yinxue Xu ◽  
Meng Guo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Healthy Subjects ◽  
Metabolic Enzyme ◽  
Estrogen Metabolism ◽  
Polygenic Risk Score ◽  
Risk Genes ◽  
Polygenic Risk ◽  
Enzyme Gene

Abstract Background Multiple common variants identified by genome-wide association studies have shown limited evidence of the risk of breast cancer in Chinese individuals. In this study, we aimed to uncover the relationship between estrogen levels and the genetic polymorphism of estrogen metabolism-related enzymes in breast cancer (BC) and establish a risk prediction model composed of estrogen-metabolizing enzyme genes and GWAS-identified breast cancer-related genes based on a polygenic risk score. Methods Unrelated BC patients and healthy subjects were recruited for analysis of estrogen levels and single nucleotide polymorphisms (SNPs) in genes encoding 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. An independent sample t-test was used to evaluate the differences between PRS scores of BC and healthy subjects. The discriminatory accuracy of the models was compared using the area under the receiver operating characteristic (ROC) curve. 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 accumulating in the serum of BC patients. We then established a PRS model to evaluate the role of SNPs in multiple genes. PRS model 1 (M1) was established from SNPs in 6 GWAS-identified high risk genes. On the basis of M1, we added SNPs from 7 estrogen metabolism enzyme genes to establish PRS model 2 (M2). The independent sample t-test results showed that there was no difference between BC and healthy subjects in M1 (P = 0.17); however, there was a significant difference between BC and healthy subjects in M2 (P = 4.9*10− 5). The ROC curve results showed that the accuracy of M2 (AUC = 62.18%) in breast cancer risk identification was better than that of M1 (AUC = 54.56%). Conclusion Estrogen and related metabolic enzyme gene polymorphisms are closely related to BC. The model constructed by adding estrogen metabolic enzyme gene SNPs has a good predictive ability for breast cancer risk, and the accuracy is greatly improved compared with that of the PRS model that only includes GWAS-identified gene SNPs.

Impact of Personalized Genetic Breast Cancer Risk Estimation With Polygenic Risk Scores on Preventive Endocrine Therapy Intention and Uptake

2020 ◽  
pp. canprevres.0154.2020
Author(s):  
Julian O. Kim ◽  
Daniel J. Schaid ◽  
Celine M. Vachon ◽  
Andrew Cooke ◽  
Fergus J. Couch ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Endocrine Therapy ◽  
Risk Estimation ◽  
Risk Scores ◽  
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.

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