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Liu L, He Y, Kao C, Fan Y, Yang F, Wang F, Yu L, Zhou F, Xiang Y, Huang S, Zheng C, Cai H, Bao H, Fang L, Wang L, Chen Z, Yu Z. An advanced machine learning method for simultaneous breast cancer risk prediction and risk ranking in Chinese population: A prospective cohort and modeling study. Chin Med J (Engl) 2024; 137:2084-2091. [PMID: 38403898 PMCID: PMC11374254 DOI: 10.1097/cm9.0000000000002891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Indexed: 02/27/2024] Open
Abstract
BACKGROUND Breast cancer (BC) risk-stratification tools for Asian women that are highly accurate and can provide improved interpretation ability are lacking. We aimed to develop risk-stratification models to predict long- and short-term BC risk among Chinese women and to simultaneously rank potential non-experimental risk factors. METHODS The Breast Cancer Cohort Study in Chinese Women, a large ongoing prospective dynamic cohort study, includes 122,058 women aged 25-70 years old from the eastern part of China. We developed multiple machine-learning risk prediction models using parametric models (penalized logistic regression, bootstrap, and ensemble learning), which were the short-term ensemble penalized logistic regression (EPLR) risk prediction model and the ensemble penalized long-term (EPLT) risk prediction model to estimate BC risk. The models were assessed based on calibration and discrimination, and following this assessment, they were externally validated in new study participants from 2017 to 2020. RESULTS The AUC values of the short-term EPLR risk prediction model were 0.800 for the internal validation and 0.751 for the external validation set. For the long-term EPLT risk prediction model, the area under the receiver operating characteristic curve was 0.692 and 0.760 in internal and external validations, respectively. The net reclassification improvement index of the EPLT relative to the Gail and the Han Chinese Breast Cancer Prediction Model (HCBCP) models for external validation was 0.193 and 0.233, respectively, indicating that the EPLT model has higher classification accuracy. CONCLUSIONS We developed the EPLR and EPLT models to screen populations with a high risk of developing BC. These can serve as useful tools to aid in risk-stratified screening and BC prevention.
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Affiliation(s)
- Liyuan Liu
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- School of Mathematics, Shandong University, Jinan, Shandong 250100, China
| | - Yong He
- School of Mathematics, Shandong University, Jinan, Shandong 250100, China
- Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan, Shandong 250100, China
| | - Chunyu Kao
- Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan, Shandong 250100, China
| | - Yeye Fan
- School of Mathematics, Shandong University, Jinan, Shandong 250100, China
| | - Fu Yang
- Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan, Shandong 250100, China
| | - Fei Wang
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong 250033, China
| | - Lixiang Yu
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong 250033, China
| | - Fei Zhou
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong 250033, China
| | - Yujuan Xiang
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong 250033, China
| | - Shuya Huang
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong 250033, China
| | - Chao Zheng
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong 250033, China
| | - Han Cai
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong 250033, China
| | - Heling Bao
- Department of Maternal and Child Health, School of Public Health, Peking University, Haidian District, Beijing 100191, China
| | - Liwen Fang
- National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Linhong Wang
- National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Zengjing Chen
- School of Mathematics, Shandong University, Jinan, Shandong 250100, China
| | - Zhigang Yu
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
- Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, Shandong 250033, China
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Liu H, Li K, Xia J, Zhu J, Cheng Y, Zhang X, Ye H, Wang P. Prediction of esophageal cancer risk based on genetic variants and environmental risk factors in Chinese population. BMC Cancer 2024; 24:598. [PMID: 38755535 PMCID: PMC11100074 DOI: 10.1186/s12885-024-12370-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 05/10/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Results regarding whether it is essential to incorporate genetic variants into risk prediction models for esophageal cancer (EC) are inconsistent due to the different genetic backgrounds of the populations studied. We aimed to identify single-nucleotide polymorphisms (SNPs) associated with EC among the Chinese population and to evaluate the performance of genetic and non-genetic factors in a risk model for developing EC. METHODS A meta-analysis was performed to systematically identify potential SNPs, which were further verified by a case-control study. Three risk models were developed: a genetic model with weighted genetic risk score (wGRS) based on promising SNPs, a non-genetic model with environmental risk factors, and a combined model including both genetic and non-genetic factors. The discrimination ability of the models was compared using the area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI). The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to assess the goodness-of-fit of the models. RESULTS Five promising SNPs were ultimately utilized to calculate the wGRS. Individuals in the highest quartile of the wGRS had a 4.93-fold (95% confidence interval [CI]: 2.59 to 9.38) increased risk of EC compared with those in the lowest quartile. The genetic or non-genetic model identified EC patients with AUCs ranging from 0.618 to 0.650. The combined model had an AUC of 0.707 (95% CI: 0.669 to 0.743) and was the best-fitting model (AIC = 750.55, BIC = 759.34). The NRI improved when the wGRS was added to the risk model with non-genetic factors only (NRI = 0.082, P = 0.037). CONCLUSIONS Among the three risk models for EC, the combined model showed optimal predictive performance and can help to identify individuals at risk of EC for tailored preventive measures.
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Affiliation(s)
- Haiyan Liu
- Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou City, 450001, Henan Province, China
- Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou City, 450052, Henan Province, China
| | - Keming Li
- Zhengzhou Center for Disease Control and Prevention, Zhengzhou City, 450042, Henan Province, China
| | - Junfen Xia
- Office of Health Care, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou City, 450052, Henan Province, China
| | - Jicun Zhu
- Department of Pharmacy, the First Affiliated Hospital of Zhengzhou University, Zhengzhou City, 450052, Henan Province, China
| | - Yifan Cheng
- Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou City, 450001, Henan Province, China
- Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou City, 450052, Henan Province, China
| | - Xiaoyue Zhang
- Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou City, 450001, Henan Province, China
- Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou City, 450052, Henan Province, China
| | - Hua Ye
- Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou City, 450001, Henan Province, China
- Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou City, 450052, Henan Province, China
| | - Peng Wang
- Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou City, 450001, Henan Province, China.
- Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou City, 450052, Henan Province, China.
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Jia G, Ping J, Guo X, Yang Y, Tao R, Li B, Ambs S, Barnard ME, Chen Y, Garcia-Closas M, Gu J, Hu JJ, Huo D, John EM, Li CI, Li JL, Nathanson KL, Nemesure B, Olopade OI, Pal T, Press MF, Sanderson M, Sandler DP, Shu XO, Troester MA, Yao S, Adejumo PO, Ahearn T, Brewster AM, Hennis AJM, Makumbi T, Ndom P, O'Brien KM, Olshan AF, Oluwasanu MM, Reid S, Butler EN, Huang M, Ntekim A, Qian H, Zhang H, Ambrosone CB, Cai Q, Long J, Palmer JR, Haiman CA, Zheng W. Genome-wide association analyses of breast cancer in women of African ancestry identify new susceptibility loci and improve risk prediction. Nat Genet 2024; 56:819-826. [PMID: 38741014 PMCID: PMC11284829 DOI: 10.1038/s41588-024-01736-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 03/25/2024] [Indexed: 05/16/2024]
Abstract
We performed genome-wide association studies of breast cancer including 18,034 cases and 22,104 controls of African ancestry. Genetic variants at 12 loci were associated with breast cancer risk (P < 5 × 10-8), including associations of a low-frequency missense variant rs61751053 in ARHGEF38 with overall breast cancer (odds ratio (OR) = 1.48) and a common variant rs76664032 at chromosome 2q14.2 with triple-negative breast cancer (TNBC) (OR = 1.30). Approximately 15.4% of cases with TNBC carried six risk alleles in three genome-wide association study-identified TNBC risk variants, with an OR of 4.21 (95% confidence interval = 2.66-7.03) compared with those carrying fewer than two risk alleles. A polygenic risk score (PRS) showed an area under the receiver operating characteristic curve of 0.60 for the prediction of breast cancer risk, which outperformed PRS derived using data from females of European ancestry. Our study markedly increases the population diversity in genetic studies for breast cancer and demonstrates the utility of PRS for risk prediction in females of African ancestry.
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Affiliation(s)
- Guochong Jia
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jie Ping
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yaohua Yang
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Public Health Genomics, Department of Public Health Sciences, UVA Comprehensive Cancer Center, School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bingshan Li
- Department of Molecular Physiology & Biophysics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
| | - Stefan Ambs
- Laboratory of Human Carcinogenesis, Center of Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Yu Chen
- Division of Epidemiology, Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Jian Gu
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jennifer J Hu
- Department of Public Health Sciences, University of Miami School of Medicine, Miami, FL, USA
| | - Dezheng Huo
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Esther M John
- Departments of Epidemiology & Population Health and of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Christopher I Li
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - James L Li
- Department of Public Health Sciences, University of Chicago, Chicago, IL, USA
| | - Katherine L Nathanson
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Basser Center for BRCA, Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Barbara Nemesure
- Department of Family, Population and Preventive Medicine, Renaissance School of Medicine, Stony Brook University, New York, NY, USA
| | - Olufunmilayo I Olopade
- Center for Clinical Cancer Genetics and Global Health, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Tuya Pal
- Division of Genetic Medicine, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michael F Press
- Department of Pathology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Maureen Sanderson
- Department of Family and Community Medicine, Meharry Medical College, Nashville, TN, USA
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Melissa A Troester
- Department of Epidemiology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Song Yao
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Elm & Carlton Streets, Buffalo, NY, USA
| | - Prisca O Adejumo
- Department of Nursing, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Thomas Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Abenaa M Brewster
- Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anselm J M Hennis
- George Alleyne Chronic Disease Research Centre, University of the West Indies, Bridgetown, Barbados
- Department of Family, Population and Preventive Medicine, Stony Brook University, New York, NY, USA
| | | | - Paul Ndom
- Yaounde General Hospital, Yaounde, Cameroon
| | - Katie M O'Brien
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Andrew F Olshan
- Department of Epidemiology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Mojisola M Oluwasanu
- Department of Health Promotion and Education, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Sonya Reid
- Division of Hematology and Oncology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ebonee N Butler
- Department of Epidemiology and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Maosheng Huang
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Atara Ntekim
- Department of Radiation Oncology, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Huijun Qian
- Department of Statistics and Operation Research, University of North Carolina, Chapel Hill, NC, USA
| | - Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Christine B Ambrosone
- Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Elm & Carlton Streets, Buffalo, NY, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Julie R Palmer
- Slone Epidemiology Center, Boston University, Boston, MA, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, University of Southern California Keck School of Medicine, Los Angeles, CA, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.
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Zhu J, Wang L, Gong W, Li X, Wang Y, Zhu C, Li H, Shi L, Yang C, Du L. Development and evaluation of a risk assessment tool for the personalized screening of breast cancer in Chinese populations: A prospective cohort study. Cancer 2024; 130:1403-1414. [PMID: 37916832 DOI: 10.1002/cncr.35095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/06/2023] [Accepted: 09/28/2023] [Indexed: 11/03/2023]
Abstract
INTRODUCTION Breast cancer is a significant contributor to female mortality, exerting a public health burden worldwide, especially in China, where risk-prediction models with good discriminating accuracy for breast cancer are still scarce. METHODS A multicenter screening cohort study was conducted as part of the Cancer Screening Program in Urban China. Dwellers aged 40-74 years were recruited between 2014 and 2019 and prospectively followed up until June 30, 2021. The entire data set was divided by year of enrollment to develop a prediction model and validate it internally. Multivariate Cox regression was used to ascertain predictors and develop a risk-prediction model. Model performance at 1, 3, and 5 years was evaluated using the area under the curve, nomogram, and calibration curves and subsequently validated internally. The prediction model incorporates selected factors that are assigned appropriate weights to establish a risk-scoring algorithm. Guided by the risk score, participants were categorized into low-, medium-, and high-risk groups for breast cancer. The cutoff values were chosen using X-tile plots. Sensitivity analysis was conducted by categorizing breast cancer risk into the low- and high-risk groups. A decision curve analysis was used to assess the clinical utility of the model. RESULTS Of the 70,520 women enrolled, 447 were diagnosed with breast cancer (median follow-up, 6.43 [interquartile range, 3.99-7.12] years). The final prediction model included age and education level (high, hazard ratio [HR], 2.01 [95% CI, 1.31-3.09]), menopausal age (≥50 years, 1.34 [1.03-1.75]), previous benign breast disease (1.42 [1.09-1.83]), and reproductive surgery (1.28 [0.97-1.69]). The 1-year area under the curve was 0.607 in the development set and 0.643 in the validation set. Moderate predictive discrimination and satisfactory calibration were observed for the validation set. The risk predictions demonstrated statistically significant differences between the low-, medium-, and high-risk groups (p < .001). Compared with the low-risk group, women in the high- and medium-risk groups posed a 2.17-fold and 1.62-fold elevated risk of breast cancer, respectively. Similar results were obtained in the sensitivity analyses. A web-based calculator was developed to estimate risk stratification for women. CONCLUSIONS This study developed and internally validated a risk-adapted and user-friendly risk-prediction model by incorporating easily accessible variables and female factors. The personalized model demonstrated reliable calibration and moderate discriminative ability. Risk-stratified screening strategies contribute to precisely distinguishing high-risk individuals from asymptomatic individuals and prioritizing breast cancer screening. PLAIN LANGUAGE SUMMARY Breast cancer remains a burden in China. To enhance breast cancer screening, we need to incorporate population stratification in screening. Accurate risk-prediction models for breast cancer remain scarce in China. We established and validated a risk-adapted and user-friendly risk-prediction model by incorporating routinely available variables along with female factors. Using this risk-stratified model helps accurately identify high-risk individuals, which is of significant importance when considering integrating individual risk assessments into mass screening programs for breast cancer. Current clinical breast cancer screening lacks a constructive clinical pathway and guiding recommendations. Our findings can better guide clinicians and health care providers.
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Affiliation(s)
- Juan Zhu
- Department of Cancer Prevention, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Le Wang
- Department of Cancer Prevention, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Weiwei Gong
- Department of Chronic and Noncommunicable Disease Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China
| | - Xue Li
- Department of Cancer Prevention, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Youqing Wang
- Department of Cancer Prevention, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Chen Zhu
- Department of Cancer Prevention, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Huizhang Li
- Department of Cancer Prevention, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Lei Shi
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Chen Yang
- Department of Ultrasound, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Lingbin Du
- Department of Cancer Prevention, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, China
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Mbuya-Bienge C, Pashayan N, Kazemali CD, Lapointe J, Simard J, Nabi H. A Systematic Review and Critical Assessment of Breast Cancer Risk Prediction Tools Incorporating a Polygenic Risk Score for the General Population. Cancers (Basel) 2023; 15:5380. [PMID: 38001640 PMCID: PMC10670420 DOI: 10.3390/cancers15225380] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/26/2023] [Accepted: 11/03/2023] [Indexed: 11/26/2023] Open
Abstract
Single nucleotide polymorphisms (SNPs) in the form of a polygenic risk score (PRS) have emerged as a promising factor that could improve the predictive performance of breast cancer (BC) risk prediction tools. This study aims to appraise and critically assess the current evidence on these tools. Studies were identified using Medline, EMBASE and the Cochrane Library up to November 2022 and were included if they described the development and/ or validation of a BC risk prediction model using a PRS for women of the general population and if they reported a measure of predictive performance. We identified 37 articles, of which 29 combined genetic and non-genetic risk factors using seven different risk prediction tools. Most models (55.0%) were developed on populations from European ancestry and performed better than those developed on populations from other ancestry groups. Regardless of the number of SNPs in each PRS, models combining a PRS with genetic and non-genetic risk factors generally had better discriminatory accuracy (AUC from 0.52 to 0.77) than those using a PRS alone (AUC from 0.48 to 0.68). The overall risk of bias was considered low in most studies. BC risk prediction tools combining a PRS with genetic and non-genetic risk factors provided better discriminative accuracy than either used alone. Further studies are needed to cross-compare their clinical utility and readiness for implementation in public health practices.
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Affiliation(s)
- Cynthia Mbuya-Bienge
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada; (C.M.-B.); (C.D.K.)
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
| | - Nora Pashayan
- Department of Applied Health Research, University College London, London WC1E 6BT, UK;
| | - Cornelia D. Kazemali
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada; (C.M.-B.); (C.D.K.)
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
| | - Julie Lapointe
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
| | - Jacques Simard
- Endocrinology and Nephology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1V 4G2, Canada;
- Department of Molecular Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada
| | - Hermann Nabi
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, QC G1V 0A6, Canada; (C.M.-B.); (C.D.K.)
- Oncology Division, CHU de Québec-Université Laval Research Center, Quebec City, QC G1S 4L8, Canada;
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Choi J, Ha TW, Choi HM, Lee HB, Shin HC, Chung W, Han W. Development of a Breast Cancer Risk Prediction Model Incorporating Polygenic Risk Scores and Nongenetic Risk Factors for Korean Women. Cancer Epidemiol Biomarkers Prev 2023; 32:1182-1189. [PMID: 37310812 PMCID: PMC10472098 DOI: 10.1158/1055-9965.epi-23-0064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 04/19/2023] [Accepted: 06/09/2023] [Indexed: 06/15/2023] Open
Abstract
BACKGROUND To develop a breast cancer prediction model for Korean women using published polygenic risk scores (PRS) combined with nongenetic risk factors (NGRF). METHODS Thirteen PRS models generated from single or multiple combinations of the Asian and European PRSs were evaluated among 20,434 Korean women. The AUC and increase in OR per SD were compared for each PRS. The PRSs with the highest predictive power were combined with NGRFs; then, an integrated prediction model was established using the Individualized Coherent Absolute Risk Estimation (iCARE) tool. The absolute breast cancer risk was stratified for 18,142 women with available follow-up data. RESULTS PRS38_ASN+PRS190_EB, a combination of Asian and European PRSs, had the highest AUC (0.621) among PRSs, with an OR per SD increase of 1.45 (95% confidence interval: 1.31-1.61). Compared with the average risk group (35%-65%), women in the top 5% had a 2.5-fold higher risk of breast cancer. Incorporating NGRFs yielded a modest increase in the AUC of women ages >50 years. For PRS38_ASN+PRS190_EB+NGRF, the average absolute risk was 5.06%. The lifetime absolute risk at age 80 years for women in the top 5% was 9.93%, whereas that of women in the lowest 5% was 2.22%. Women at higher risks were more sensitive to NGRF incorporation. CONCLUSIONS Combined Asian and European PRSs were predictive of breast cancer in Korean women. Our findings support the use of these models for personalized screening and prevention of breast cancer. IMPACT Our study provides insights into genetic susceptibility and NGRFs for predicting breast cancer in Korean women.
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Affiliation(s)
- Jihye Choi
- Department of General Surgery, National Medical Center, Seoul, Republic of Korea
- Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea
| | | | | | - Han-Byoel Lee
- Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea
- DCGen, Co., Ltd., Seoul, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
- Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
| | - Hee-Chul Shin
- DCGen, Co., Ltd., Seoul, Republic of Korea
- Department of Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | | | - Wonshik Han
- Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea
- DCGen, Co., Ltd., Seoul, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
- Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
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7
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Zou S, Lin Y, Yu X, Eriksson M, Lin M, Fu F, Yang H. Genetic and lifestyle factors for breast cancer risk assessment in Southeast China. Cancer Med 2023; 12:15504-15514. [PMID: 37264741 PMCID: PMC10417168 DOI: 10.1002/cam4.6198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 04/01/2023] [Accepted: 05/23/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Despite the rising incidence and mortality of breast cancer among women in China, there are currently few predictive models for breast cancer in the Chinese population and with low accuracy. This study aimed to identify major genetic and life-style risk factors in a Chinese population for potential application in risk assessment models. METHODS A case-control study in southeast China was conducted including 1321 breast cancer patients and 2045 controls during 2013-2016, in which the data were randomly divided into a training set and a test set on a 7:3 scale. The association between genetic and life-style factors and breast cancer was examined using logistic regression models. Using AUC curves, we also compared the performance of the logistic model to machine learning models, namely LASSO regression model and support vector machine (SVM), and the scores calculated from CKB, Gail and Tyrer-Cuzick models in the test set. RESULTS Among all factors considered, the best model was achieved when polygenetic risk score, lifestyle, and reproductive factors were considered jointly in the logistic regression model (AUC = 0.73; 95% CI: 0.70-0.77). The models created in this study performed better than those using scores calculated from the CKB, Gail, and Tyrer-Cuzick models. However, the logistic model and machine learning models did not significantly differ from one another. CONCLUSION In summary, we have found genetic and lifestyle risk predictors for breast cancer with moderate discrimination, which might provide reference for breast cancer screening in southeast China. Further population-based studies are needed to validate the model for future applications in personalized breast cancer screening programs.
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Affiliation(s)
- Shuqing Zou
- Department of Epidemiology and Health Statistics, School of Public HealthFujian Medical UniversityFuzhouChina
| | - Yuxiang Lin
- Department of Breast SurgeryFujian Medical University Union HospitalFuzhouChina
- Department of General SurgeryFujian Medical University Union HospitalFuzhouChina
- Breast Cancer Institute, Fujian Medical UniversityFuzhouChina
| | - Xingxing Yu
- Department of Epidemiology and Health Statistics, School of Public HealthFujian Medical UniversityFuzhouChina
| | - Mikael Eriksson
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
| | | | - Fangmeng Fu
- Department of Breast SurgeryFujian Medical University Union HospitalFuzhouChina
- Department of General SurgeryFujian Medical University Union HospitalFuzhouChina
- Breast Cancer Institute, Fujian Medical UniversityFuzhouChina
| | - Haomin Yang
- Department of Epidemiology and Health Statistics, School of Public HealthFujian Medical UniversityFuzhouChina
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
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8
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Thanh Thi Ngoc Nguyen, Nguyen THN, Phan HN, Nguyen HT. Seven-Single Nucleotide Polymorphism Polygenic Risk Score for Breast Cancer Risk Prediction in a Vietnamese Population. CYTOL GENET+ 2022. [DOI: 10.3103/s0095452722040065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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9
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2q35-rs13387042 variant and the risk of breast cancer: a case-control study. Mol Biol Rep 2022; 49:3549-3557. [PMID: 35445312 DOI: 10.1007/s11033-022-07195-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 01/25/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Breast Cancer is the most frequent neoplasm diagnosed among women worldwide. Genetic background and lifestyle/environment play a significant role in the disease etiology. According to Genome-wide association studies, some single-nucleotide polymorphisms such as 2q35-rs13387042-(G/A) have been introduced to be associated with breast cancer risk and features. In this study, we aimed to evaluate the association between this variant and the risk of breast cancer in a cohort of Iranian women. METHODS Demographics and clinical information were collected by interview and using patients' medical records, respectively. DNA was extracted from 506 blood samples, including 184 patients and 322 controls, and genotyping was performed using allele specific-PCR. SPSS v16 was used for statistical analysis. RESULT Statistically significant association was observed between AA genotype and disease risk in all patients [padj = 0.048; ORadj = 2.13, 95% CI (1.01-4.50)] and also ER-positive breast cancers [padj = 0.015; ORadj = 2.12, 95% CI (1.16-3.88)]. There was no association between rs13387042 and histopathological characteristics of the disease. Furthermore, overall survival was not statistically associated with genotype and allelic models even after adjustment for stage and receptor status (p > 0.05). CONCLUSION There is a statistically significant association between 2q35-rs13387042 and breast cancer risk. rs13387042-AA genotype might be a risk-conferring factor for breast cancer development in the Iranian population. However, further consideration is suggested to confirm its role in risk assessment and probable association with other genetic markers.
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10
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Hou C, Xu B, Hao Y, Yang D, Song H, Li J. Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women. BMC Cancer 2022; 22:374. [PMID: 35395775 PMCID: PMC8991589 DOI: 10.1186/s12885-022-09425-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 03/15/2022] [Indexed: 02/08/2023] Open
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 dataset from a genome-wide association study (GWAS) 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 modest predictive ability for overall breast cancer (odds ratio per interquartile range increase of the PRS in controls [IQ-OR] 1.76 vs 1.58; area under the receiver operator characteristic curve [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 24 SNPs 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. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09425-3.
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Affiliation(s)
- Can Hou
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610047, Sichuan, China.,Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Ren Min Nan Lu, Chengdu, 610041, Sichuan, China.,Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Bin Xu
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Ren Min Nan Lu, Chengdu, 610041, Sichuan, China
| | - Yu Hao
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Ren Min Nan Lu, Chengdu, 610041, Sichuan, China
| | - Daowen Yang
- Robot Perception and Control Joint Lab, Sichuan University & Aisono, Chengdu, China
| | - Huan Song
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610047, Sichuan, China. .,Med-X Center for Informatics, Sichuan University, Chengdu, China.
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16 Ren Min Nan Lu, Chengdu, 610041, Sichuan, China.
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11
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Yang Y, Tao R, Shu X, Cai Q, Wen W, Gu K, Gao YT, Zheng Y, Kweon SS, Shin MH, Choi JY, Lee ES, Kong SY, Park B, Park MH, Jia G, Li B, Kang D, Shu XO, Long J, Zheng W. Incorporating Polygenic Risk Scores and Nongenetic Risk Factors for Breast Cancer Risk Prediction Among Asian Women. JAMA Netw Open 2022; 5:e2149030. [PMID: 35311964 PMCID: PMC8938714 DOI: 10.1001/jamanetworkopen.2021.49030] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
IMPORTANCE Polygenic risk scores (PRSs) have shown promise in breast cancer risk prediction; however, limited studies have been conducted among Asian women. OBJECTIVE To develop breast cancer risk prediction models for Asian women incorporating PRSs and nongenetic risk factors. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study included women of Asian ancestry from the Asia Breast Cancer Consortium. PRSs were developed using data from genomewide association studies (GWASs) of breast cancer conducted among 123 041 women with Asian ancestry (including 18 650 women with breast cancer) using 3 approaches: (1) reported PRS for women with European ancestry; (2) breast cancer-associated single-nucleotide variations (SNVs) identified by fine-mapping of GWAS-identified risk loci; and (3) genomewide risk prediction algorithms. A nongenetic risk score (NGRS) was built, including 7 well-established nongenetic risk factors, using data of 416 case participants and 1558 control participants from a prospective cohort study. PRSs were initially validated in an independent data set including 1426 case participants and 1323 control participants and further evaluated, along with the NGRS, in the second data set including 368 case participants and 736 control participants nested within a prospective cohort study. MAIN OUTCOMES AND MEASURES Logistic regression was used to examine associations of risk scores with breast cancer risk to estimate odds ratios (ORs) with 95% CIs and area under the receiver operating characteristic curve (AUC). RESULTS A total of 126 894 women of Asian ancestry were included; 20 444 (16.1%) had breast cancer. The mean (SD) age ranged from 49.1 (10.8) to 54.4 (10.4) years for case participants and 50.6 (9.5) to 54.0 (7.4) years for control participants among studies that provided demographic characteristics. In the prospective cohort, a PRS with 111 SNVs developed using the fine-mapping approach (PRS111) showed a prediction performance comparable with a genomewide PRS that included more than 855 000 SNVs. The OR per SD increase of PRS111 score was 1.67 (95% CI, 1.46-1.92), with an AUC of 0.639 (95% CI, 0.604-0.674). The NGRS had a limited predictive ability (AUC, 0.565; 95% CI, 0.529-0.601). Compared with the average risk group (40th-60th percentile), women in the top 5% of PRS111 and NGRS were at a 3.84-fold (95% CI, 2.30-6.46) and 2.10-fold (95% CI, 1.22-3.62) higher risk of breast cancer, respectively. The prediction model including both PRS111 and NGRS achieved the highest prediction accuracy (AUC, 0.648; 95% CI, 0.613-0.682). CONCLUSIONS AND RELEVANCE In this study, PRSs derived using breast cancer risk-associated SNVs had similar predictive performance in Asian and European women. Including nongenetic risk factors in models further improved prediction accuracy. These findings support the utility of these models in developing personalized screening and prevention strategies.
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Affiliation(s)
- Yaohua Yang
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Xiang Shu
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Kai Gu
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai Institutes of Preventive Medicine, Shanghai, China
| | - Yu-Tang Gao
- State Key Laboratory of Oncogene and Related Genes and Department of Epidemiology, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Ying Zheng
- Department of Cancer Prevention, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Sun-Seog Kweon
- Department of Preventive Medicine, Chonnam National University Medical School, Hwasun, South Korea
- Jeonnam Regional Cancer Center, Chonnam National University Hwasun Hospital, Hwasun, South Korea
| | - Min-Ho Shin
- Department of Preventive Medicine, Chonnam National University Medical School, Hwasun, South Korea
| | - Ji-Yeob Choi
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, South Korea
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
| | - Eun-Sook Lee
- National Cancer Center Graduate School of Cancer Science and Policy, Goyang, South Korea
- Hospital, National Cancer Center, Goyang, South Korea
- Research Institute, National Cancer Center, Goyang, South Korea
| | - Sun-Young Kong
- National Cancer Center Graduate School of Cancer Science and Policy, Goyang, South Korea
- Hospital, National Cancer Center, Goyang, South Korea
- Research Institute, National Cancer Center, Goyang, South Korea
| | - Boyoung Park
- Research Institute, National Cancer Center, Goyang, South Korea
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, South Korea
| | - Min Ho Park
- Department of Surgery, Chonnam National University Medical School & Hospital, Hwasun, South Korea
| | - Guochong Jia
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Bingshan Li
- Department of Molecular Physiology & Biophysics, Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee
| | - Daehee Kang
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, South Korea
- Institute of Environmental Medicine, Seoul National University Medical Research Center, Seoul, South Korea
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee
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12
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Huang Y, Wang H, Lyu Z, Dai H, Liu P, Zhu Y, Song F, Chen K. Development and evaluation of the screening performance of a low-cost high-risk screening strategy for breast cancer. Cancer Biol Med 2021; 19:j.issn.2095-3941.2020.0758. [PMID: 34570443 PMCID: PMC9500221 DOI: 10.20892/j.issn.2095-3941.2020.0758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 04/06/2021] [Indexed: 01/31/2023] Open
Abstract
OBJECTIVE To develop and evaluate the screening performance of a low-cost high-risk screening strategy for breast cancer in low resource areas. METHODS Based on the Multi-modality Independent Screening Trial, 6 questionnaire-based risk factors of breast cancer (age at menarche, age at menopause, age at first live birth, oral contraceptive, obesity, family history of breast cancer) were used to determine the women with high risk of breast cancer. The screening performance of clinical breast examination (CBE), breast ultrasonography (BUS), and mammography (MAM) were calculated and compared to determine the optimal screening method for these high risk women. RESULTS A total of 94 breast cancers were detected among 31,720 asymptomatic Chinese women aged 45-65 years. Due to significantly higher detection rates (DRs) and suitable coverage of the population, high risk women were defined as those with any of 6 risk factors. Among high risk women, the DR for BUS [3.09/1,000 (33/10,694)] was similar to that for MAM [3.18/1,000 (34/10,696)], while it was significantly higher than that for the CBE [1.73/1,000 (19/10,959), P = 0.002]. Compared with MAM, BUS showed significantly higher specificity [98.64% (10,501/10,646) vs. 98.06% (10,443/10,650), P = 0.001], but no significant differences in sensitivity [68.75% (33/48) vs. 73.91% (34/46)], positive prediction values [18.54% (33/178) vs. 14.11% (34/241)], and negative prediction values [99.86% (10,501/10,516) vs. 99.89% (10,443/10,455)]. Further analyses showed no significant difference in the percentages of early stage breast cancer [53.57% (15/28) vs. 50.00% (15/30)], lymph node involvement [22.73% (5/22) vs. 28.00% (7/25)], and tumor size ≥ 2 cm [37.04% (10/27) vs. 29.03% (9/31)] between BUS and MAM. Subgroup analyses stratified by breast densities or age at enrollment showed similar results. CONCLUSIONS The low-cost high-risk screening strategy based on 6 questionnaire-based risk factors was an easy-to-use method to identify women with high risk of breast cancer. Moreover, BUS and MAM had comparable screening performances among high risk women.
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Affiliation(s)
- Yubei Huang
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Huan Wang
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Zhangyan Lyu
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Hongji Dai
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Peifang Liu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Ying Zhu
- Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Fengju Song
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
| | - Kexin Chen
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Molecular Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin 300060, China
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13
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Leung K, Wu JT, Wong IOL, Shu XO, Zheng W, Wen W, Khoo US, Ngan R, Kwong A, Leung GM. Using Risk Stratification to Optimize Mammography Screening in Chinese Women. JNCI Cancer Spectr 2021; 5:pkab060. [PMID: 34377936 PMCID: PMC8346705 DOI: 10.1093/jncics/pkab060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/30/2021] [Accepted: 04/30/2021] [Indexed: 12/24/2022] Open
Abstract
Background The cost-effectiveness of mammography screening among Chinese women remains contentious. Here, we characterized breast cancer (BC) epidemiology in Hong Kong and evaluated the cost-effectiveness of personalized risk-based screening. Methods We used the Hong Kong Breast Cancer Study (a case-control study with 3501 cases and 3610 controls) and Hong Kong Cancer Registry to develop a risk stratification model based on well-documented risk factors. We used the Shanghai Breast Cancer Study to validate the model. We considered risk-based programs with different screening age ranges and risk thresholds under which women were eligible to join if their remaining BC risk at the starting age exceeded the threshold. Results The lifetime risk (15-99 years) of BC ranged from 1.8% to 26.6% with a mean of 6.8%. Biennial screening was most cost-effective when the starting age was 44 years, and screening from age 44 to 69 years would reduce breast cancer mortality by 25.4% (95% credible interval [CrI] = 20.5%-29.4%) for all risk strata. If the risk threshold for this screening program was 8.4% (the average remaining BC risk among US women at their recommended starting age of 50 years), the coverage was 25.8%, and the incremental cost-effectiveness ratio (ICER) was US$18 151 (95% CrI = $10 408-$27 663) per quality-of-life-year (QALY) compared with no screening. The ICER of universal screening was $34 953 (95% CrI = $22 820-$50 268) and $48 303 (95% CrI = $32 210-$68 000) per QALY compared with no screening and risk-based screening with 8.4% threshold, respectively. Conclusion Organized BC screening in Chinese women should commence as risk-based programs. Outcome data (e.g., QALY loss because of false-positive mammograms) should be systemically collected for optimizing the risk threshold.
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Affiliation(s)
- Kathy Leung
- Division of Epidemiology and Biostatistics, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, New Territories, Hong Kong SAR, China
| | - Joseph T Wu
- Division of Epidemiology and Biostatistics, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, New Territories, Hong Kong SAR, China
| | - Irene Oi-ling Wong
- Division of Epidemiology and Biostatistics, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, and Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, and Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, and Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ui-Soon Khoo
- Department of Pathology, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Roger Ngan
- Department of Clinical Oncology, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Ava Kwong
- Department of Surgery, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Gabriel M Leung
- Division of Epidemiology and Biostatistics, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, New Territories, Hong Kong SAR, China
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14
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Wang F, Duan XN, Ling R, Yu ZG. Clinical practice guidelines for risk assessment to identify women at high risk of breast cancer: Chinese Society of Breast Surgery (CSBrS) practice guidelines 2021. Chin Med J (Engl) 2021; 134:1655-1657. [PMID: 34116529 PMCID: PMC8318626 DOI: 10.1097/cm9.0000000000001502] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Indexed: 12/03/2022] Open
Affiliation(s)
- Fei Wang
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
| | - Xue-Ning Duan
- Breast Disease Center, Peking University First Hospital, Beijing 100034, China
| | - Rui Ling
- Department of Thyroid, Breast and Vascular Surgery, Xijing Hospital, The Fourth Military Medical University, Xi’an, Shaanxi 710032, China
| | - Zhi-Gang Yu
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
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15
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Han Y, Lv J, Yu C, Guo Y, Bian Z, Hu Y, Yang L, Chen Y, Du H, Zhao F, Wen W, Shu XO, Xiang Y, Gao YT, Zheng W, Guo H, Liang P, Chen J, Chen Z, Huo D, Li L. Development and external validation of a breast cancer absolute risk prediction model in Chinese population. Breast Cancer Res 2021; 23:62. [PMID: 34051827 PMCID: PMC8164768 DOI: 10.1186/s13058-021-01439-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 05/17/2021] [Indexed: 01/06/2023] Open
Abstract
BACKGROUNDS In contrast to developed countries, breast cancer in China is characterized by a rapidly escalating incidence rate in the past two decades, lower survival rate, and vast geographic variation. However, there is no validated risk prediction model in China to aid early detection yet. METHODS A large nationwide prospective cohort, China Kadoorie Biobank (CKB), was used to evaluate relative and attributable risks of invasive breast cancer. A total of 300,824 women free of any prior cancer were recruited during 2004-2008 and followed up to Dec 31, 2016. Cox models were used to identify breast cancer risk factors and build a relative risk model. Absolute risks were calculated by incorporating national age- and residence-specific breast cancer incidence and non-breast cancer mortality rates. We used an independent large prospective cohort, Shanghai Women's Health Study (SWHS), with 73,203 women to externally validate the calibration and discriminating accuracy. RESULTS During a median of 10.2 years of follow-up in the CKB, 2287 cases were observed. The final model included age, residence area, education, BMI, height, family history of overall cancer, parity, and age at menarche. The model was well-calibrated in both the CKB and the SWHS, yielding expected/observed (E/O) ratios of 1.01 (95% confidence interval (CI), 0.94-1.09) and 0.94 (95% CI, 0.89-0.99), respectively. After eliminating the effect of age and residence, the model maintained moderate but comparable discriminating accuracy compared with those of some previous externally validated models. The adjusted areas under the receiver operating curve (AUC) were 0.634 (95% CI, 0.608-0.661) and 0.585 (95% CI, 0.564-0.605) in the CKB and the SWHS, respectively. CONCLUSIONS Based only on non-laboratory predictors, our model has a good calibration and moderate discriminating capacity. The model may serve as a useful tool to raise individuals' awareness and aid risk-stratified screening and prevention strategies.
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Affiliation(s)
- Yuting Han
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191 China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191 China
- Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, Beijing, China
- Peking University Institute of Environmental Medicine, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191 China
| | - Yu Guo
- Chinese Academy of Medical Sciences, Beijing, China
| | - Zheng Bian
- Chinese Academy of Medical Sciences, Beijing, China
| | - Yizhen Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191 China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fangyuan Zhao
- Department of Public Health Sciences, The University of Chicago, 5841 S. Maryland Ave., MC2000, Chicago, IL 60637 USA
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN USA
| | - Yongbing Xiang
- State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology, Shanghai Cancer Institute, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yu-Tang Gao
- State Key Laboratory of Oncogene and Related Genes & Department of Epidemiology, Shanghai Cancer Institute, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN USA
| | - Hong Guo
- Medical department, Liuyang Hospital of Traditional Chinese Medicine, Liuyang, China
| | - Peng Liang
- People’s Hospital of Liuyang, Liuyang, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Dezheng Huo
- Department of Public Health Sciences, The University of Chicago, 5841 S. Maryland Ave., MC2000, Chicago, IL 60637 USA
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191 China
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16
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A 23-gene prognostic classifier for prediction of recurrence and survival for Asian breast cancer patients. Biosci Rep 2020; 40:227018. [PMID: 33226082 PMCID: PMC7711061 DOI: 10.1042/bsr20202794] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 11/18/2020] [Accepted: 11/20/2020] [Indexed: 11/17/2022] Open
Abstract
We report a 23- gene-classifier profiled from Asian women, with the primary purpose of assessing its clinical utility towards improved risk stratification for relapse for breast cancer patients from Asian cohorts within 10 years’ following mastectomy. Four hundred and twenty-two breast cancer patients underwent mastectomy and were used to train the classifier on a logistic regression model. A subset of 197 patients were chosen to be entered into the follow-up studies post mastectomy who were examined to determine the patterns of recurrence and survival analysis based on gene expression of the gene classifier, age at diagnosis, tumor stage and lymph node status, over a 5 and 10 years follow-up period. Metastasis to lymph node (N2-N3) with N0 as the reference (N2 vs. N0 hazard ratio: 2.02 (1.05–8.70), N3 vs. N0 hazard ratio: 4.32 (1.41–13.22) for 5 years) and gene expression of the 23-gene panel (P=0.06, 5 years and 0.02, 10 years, log-rank test) were found to have significant discriminatory effects on the risk of relapse (HR (95%CI):2.50 (0.95–6.50)). Furthermore, survival curves for subgroup analysis with N0-N1 and T1-T2 predicted patients with higher risk scores. The study provides robust evidence of the effectiveness of the 23-gene-classifier and could be used to determine the risk of relapse event (locoregional and distant recurrence) in Asian patients, leading to a meaningful reduction in chemotherapy recommendations.
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17
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Choi J, Jia G, Wen W, Long J, Zheng W. Evaluating polygenic risk scores in assessing risk of nine solid and hematologic cancers in European descendants. Int J Cancer 2020; 147:3416-3423. [PMID: 32588423 DOI: 10.1002/ijc.33176] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 06/04/2020] [Accepted: 06/15/2020] [Indexed: 01/04/2023]
Abstract
Genome-wide association studies (GWAS) have identified many genetic risk variants for cancers. The utility of these variants in assessing risk of esophageal, gastric and endometrial cancers, as well as melanoma, glioma, diffuse large B-cell lymphoma, follicular lymphoma, chronic lymphoid leukemia and multiple myeloma, has not been adequately investigated. We constructed a site-specific polygenic risk score (PRS) for each of these nine cancers using their GWAS-identified risk variants. Using data from 400 807 participants of European descent in the UK Biobank, a population-based cohort study, we estimated the hazard ratios of each cancer associated with its PRS using Cox proportional hazard models. During a median follow-up of 5.8 years, 3905 incident cases of these nine cancers were identified in the cohort. The area under the receiver operating characteristic curve ranged from 0.53 to 0.69 for these cancers. Except for esophageal cancer, significant dose-response associations were observed between PRS and cancer risk. Compared to individuals in the middle quintile (40%-60%) at an average risk, those among the highest 5% of the PRS had a twofold elevated risk of melanoma, glioma, follicular lymphoma or multiple myeloma, and a fourfold elevated risk of chronic lymphoid leukemia. Using PRS, 63.0% of the participants could be classified as having an over twofold elevated risk for at least one cancer. The PRS derived using risk variants identified to date by GWAS showed the potential in identifying individuals at a significantly elevated risk of cancer for prevention.
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Affiliation(s)
- Jungyoon Choi
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Guochong Jia
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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18
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Ho WK, Tan MM, Mavaddat N, Tai MC, Mariapun S, Li J, Ho PJ, Dennis J, Tyrer JP, Bolla MK, Michailidou K, Wang Q, Kang D, Choi JY, Jamaris S, Shu XO, Yoon SY, Park SK, Kim SW, Shen CY, Yu JC, Tan EY, Chan PMY, Muir K, Lophatananon A, Wu AH, Stram DO, Matsuo K, Ito H, Chan CW, Ngeow J, Yong WS, Lim SH, Lim GH, Kwong A, Chan TL, Tan SM, Seah J, John EM, Kurian AW, Koh WP, Khor CC, Iwasaki M, Yamaji T, Tan KMV, Tan KTB, Spinelli JJ, Aronson KJ, Hasan SN, Rahmat K, Vijayananthan A, Sim X, Pharoah PDP, Zheng W, Dunning AM, Simard J, van Dam RM, Yip CH, Taib NAM, Hartman M, Easton DF, Teo SH, Antoniou AC. European polygenic risk score for prediction of breast cancer shows similar performance in Asian women. Nat Commun 2020; 11:3833. [PMID: 32737321 PMCID: PMC7395776 DOI: 10.1038/s41467-020-17680-w] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 07/15/2020] [Indexed: 12/02/2022] Open
Abstract
Polygenic risk scores (PRS) have been shown to predict breast cancer risk in European women, but their utility in Asian women is unclear. Here we evaluate the best performing PRSs for European-ancestry women using data from 17,262 breast cancer cases and 17,695 controls of Asian ancestry from 13 case-control studies, and 10,255 Chinese women from a prospective cohort (413 incident breast cancers). Compared to women in the middle quintile of the risk distribution, women in the highest 1% of PRS distribution have a ~2.7-fold risk and women in the lowest 1% of PRS distribution has ~0.4-fold risk of developing breast cancer. There is no evidence of heterogeneity in PRS performance in Chinese, Malay and Indian women. A PRS developed for European-ancestry women is also predictive of breast cancer risk in Asian women and can help in developing risk-stratified screening programmes in Asia.
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Affiliation(s)
- Weang-Kee Ho
- School of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih, 43500, Selangor, Malaysia.
- Cancer Research Malaysia, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia.
| | - Min-Min Tan
- School of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih, 43500, Selangor, Malaysia
- Cancer Research Malaysia, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia
| | - Nasim Mavaddat
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, CB1 8RN, Cambridge, UK
| | - Mei-Chee Tai
- Cancer Research Malaysia, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia
| | - Shivaani Mariapun
- School of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih, 43500, Selangor, Malaysia
- Cancer Research Malaysia, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia
| | - Jingmei Li
- Human Genetics, Genome Institute of Singapore, 60 Biopolis St, 138672, Singapore, Singapore
- Department of Surgery, National University Hospital and NUHS, 1E Kent Ridge Road, 119228, Singapore, Singapore
| | - Peh-Joo Ho
- Human Genetics, Genome Institute of Singapore, 60 Biopolis St, 138672, Singapore, Singapore
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, CB1 8RN, Cambridge, UK
| | - Jonathan P Tyrer
- Strangeways Research Laboratory, University of Cambridge, 2 Worts' Causeway, Cambridge, UK
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, CB1 8RN, Cambridge, UK
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, CB1 8RN, Cambridge, UK
- Biostatistics Unit, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371, Ayios, Dometios, Cyprus
- Cyprus School of Molecular Medicine, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, 2371, Ayios, Dometios, Cyprus
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, CB1 8RN, Cambridge, UK
| | - Daehee Kang
- Department of Preventive Medicine, Seoul National University College of Medicine, 103 Daehak-Ro, Jongno-Gu, 03080, Seoul, Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, 103 Daehak-Ro, Jongno-Gu, 03080, Seoul, Korea
- Cancer Research Institute, Seoul National University, 103 Daehak-Ro, Jongno-Gu, Seoul, 03080, Korea
| | - Ji-Yeob Choi
- Department of Biomedical Sciences, Seoul National University Graduate School, 103 Daehak-Ro, Jongno-Gu, 03080, Seoul, Korea
- Cancer Research Institute, Seoul National University, 103 Daehak-Ro, Jongno-Gu, Seoul, 03080, Korea
| | - Suniza Jamaris
- Department of Surgery, Faculty of Medicine, University of Malaya, Jalan Universiti, 50630, Kuala Lumpur, Malaysia
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, 1161 21st Ave S # D3300, Nashville, TN, 37232, USA
| | - Sook-Yee Yoon
- Cancer Research Malaysia, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia
| | - Sue K Park
- Department of Preventive Medicine, Seoul National University College of Medicine, 103 Daehak-Ro, Jongno-Gu, 03080, Seoul, Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, 103 Daehak-Ro, Jongno-Gu, 03080, Seoul, Korea
- Cancer Research Institute, Seoul National University, 103 Daehak-Ro, Jongno-Gu, Seoul, 03080, Korea
| | - Sung-Won Kim
- Department of Surgery, Daerim Saint Mary's Hospital, 657 Siheung-Daero, Daerim-Dong, Yeongdeungpo-Gu, 07442, Seoul, Korea
| | - Chen-Yang Shen
- Institute of Biomedical Sciences, Academia Sinica, 115128, Section 2, Academia Road, Taipei, Taiwan
- School of Public Health, China Medical University, Taichung, Taiwan
| | - Jyh-Cherng Yu
- Department of Surgery, Tri-Service General Hospital, Taipei, 114, Taiwan
| | - Ern Yu Tan
- Department of General Surgery, Tan Tock Seng Hospital, Singapore, 308433, Singapore
| | - Patrick Mun Yew Chan
- Department of General Surgery, Tan Tock Seng Hospital, Singapore, 308433, Singapore
| | - Kenneth Muir
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, The University of Manchester, Oxford Road, M13 9PL, Manchester, UK
| | - Artitaya Lophatananon
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, The University of Manchester, Oxford Road, M13 9PL, Manchester, UK
| | - Anna H Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, 1975 Zonal Ave, Los Angeles, 90033, CA, USA
| | - Daniel O Stram
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, 1975 Zonal Ave, Los Angeles, 90033, CA, USA
| | - Keitaro Matsuo
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, 1-1 Kanokoden, Chikusa-Ku, 464-8681, Nagoya, Japan
- Division of Cancer Epidemiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, 466-8550, Nagoya, Japan
| | - Hidemi Ito
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, 1-1 Kanokoden, Chikusa-Ku, 464-8681, Nagoya, Japan
- Division of Cancer Epidemiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, 466-8550, Nagoya, Japan
| | - Ching Wan Chan
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore, Singapore
- National University Hospital, National University Health System, Singapore, Singapore
| | - Joanne Ngeow
- Division of Medical Oncology, National Cancer Centre Singapore, 11 Hospital Drive, 169610, Singapore, Singapore
- Oncology Academic Clinical Program, Duke-NUS Graduate Medical School, 8 College Road, 169857, Singapore, Singapore
| | - Wei Sean Yong
- Division of Surgical Oncology, National Cancer Centre, Singapore, Singapore
| | - Swee Ho Lim
- Breast Department, KK Women's and Children's Hospital, Singapore, 100 Bukit Timah Road, 229899, Singapore
| | - Geok Hoon Lim
- Breast Department, KK Women's and Children's Hospital, Singapore, 100 Bukit Timah Road, 229899, Singapore
| | - Ava Kwong
- Hong Kong Hereditary Breast Cancer Family Registry, Cancer Genetics Centre, 18A Kung Ngam Village Road, Happy Valley, Hong Kong
- Department of Surgery, The University of Hong Kong, 102 Pokfulam Road, Pok Fu Lam, Hong Kong
- Department of Surgery, Hong Kong Sanatorium and Hospital, 2 Village Rd, Happy Valley, Hong Kong
| | - Tsun L Chan
- Hong Kong Hereditary Breast Cancer Family Registry, Cancer Genetics Centre, 18A Kung Ngam Village Road, Happy Valley, Hong Kong
- Department of Pathology, Hong Kong Sanatorium and Hospital, 2 Village Rd, Happy Valley, Hong Kong
| | - Su Ming Tan
- General Surgery, Changi General Hospital, Singapore, Singapore
| | - Jaime Seah
- General Surgery, Changi General Hospital, Singapore, Singapore
| | - Esther M John
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, 780 Welch Road, Suite CJ250C, Stanford, 94304 CA, USA
| | - Allison W Kurian
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University School of Medicine, 780 Welch Road, Suite CJ250C, Stanford, 94304 CA, USA
- Department of Health Research and Policy-Epidemiology, Stanford University School of Medicine, 259 Campus Drive, Stanford, 94305, CA, USA
| | - Woon-Puay Koh
- Health Services and Systems Research, Duke-NUS Medical School, Stanford University School of Medicine, 8 College Road, 169857, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, #10-01, 117549, Singapore, Singapore
| | - Chiea Chuen Khor
- Human Genetics, Genome Institute of Singapore, 60 Biopolis St, 138672, Singapore, Singapore
| | - Motoki Iwasaki
- Division of Epidemiology, Center for Public Health Sciences, National Cancer Center, 5-1-1 Tsukiji, Chuo-Ku, 104-0045, Tokyo, Japan
| | - Taiki Yamaji
- Division of Epidemiology, Center for Public Health Sciences, National Cancer Center, 5-1-1 Tsukiji, Chuo-Ku, 104-0045, Tokyo, Japan
| | - Kiak Mien Veronique Tan
- Division of Surgical Oncology, National Cancer Centre, Singapore, Singapore
- Department of General Surgery, Singapore General Hospital, Singapore, Singapore
| | - Kiat Tee Benita Tan
- Division of Surgical Oncology, National Cancer Centre, Singapore, Singapore
- Department of General Surgery, Singapore General Hospital, Singapore, Singapore
| | - John J Spinelli
- Population Oncology, BC Cancer, 675 West 10th Avenue, Vancouver, V5Z 1G1 BC, Canada
- School of Population and Public Health, University of British Columbia, 2329 West Mall, Vancouver, V6T 1Z4 BC, Canada
| | - Kristan J Aronson
- Department of Public Health Sciences, and Cancer Research Institute, Queen's University, 10 Stuart Street, Kingston, K7L 3N6 ON, Canada
| | - Siti Norhidayu Hasan
- Cancer Research Malaysia, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia
| | - Kartini Rahmat
- Biomedical Imaging Department, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Anushya Vijayananthan
- Biomedical Imaging Department, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, #10-01, 117549, Singapore, Singapore
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, CB1 8RN, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, 2 Worts' Causeway, CB1 8RN, Cambridge, UK
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, 1161 21st Ave S # D3300, Nashville, TN, 37232, USA
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, 2 Worts' Causeway, CB1 8RN, Cambridge, UK
| | - Jacques Simard
- Genomics Center, CHU de Québec-Université Laval Research 2705 Blvd Laurier Québec (Québec) G1V 4G2, Quebec, Canada
| | - Rob Martinus van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, #10-01, 117549, Singapore, Singapore
- Departments of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Cheng-Har Yip
- Sime Darby Medical Centre, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia
| | - Nur Aishah Mohd Taib
- Department of Surgery, Faculty of Medicine, University of Malaya, Jalan Universiti, 50630, Kuala Lumpur, Malaysia
| | - Mikael Hartman
- Department of Surgery, National University Hospital and NUHS, 1E Kent Ridge Road, 119228, Singapore, Singapore
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, CB1 8RN, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, 2 Worts' Causeway, CB1 8RN, Cambridge, UK
| | - Soo-Hwang Teo
- Cancer Research Malaysia, 1 Jalan SS12/1A, Subang Jaya, 47500, Selangor, Malaysia.
- Department of Surgery, Faculty of Medicine, University of Malaya, Jalan Universiti, 50630, Kuala Lumpur, Malaysia.
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, CB1 8RN, Cambridge, UK
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19
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Hou C, Zhong X, He P, Xu B, Diao S, Yi F, Zheng H, Li J. Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development. JMIR Med Inform 2020; 8:e17364. [PMID: 32510459 PMCID: PMC7308891 DOI: 10.2196/17364] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 03/28/2020] [Accepted: 04/19/2020] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women. OBJECTIVE This study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors. METHODS A dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance. RESULTS The three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms. CONCLUSIONS The novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.
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Affiliation(s)
- Can Hou
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xiaorong Zhong
- Department of Head, Neck and Mammary Gland Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,Laboratory of Molecular Diagnosis of Cancer, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China
| | - Ping He
- Department of Head, Neck and Mammary Gland Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,Laboratory of Molecular Diagnosis of Cancer, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China
| | - Bin Xu
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Sha Diao
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Fang Yi
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Hong Zheng
- Department of Head, Neck and Mammary Gland Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,Laboratory of Molecular Diagnosis of Cancer, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China
| | - Jiayuan Li
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
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20
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Evaluation of pathogenetic mutations in breast cancer predisposition genes in population-based studies conducted among Chinese women. Breast Cancer Res Treat 2020; 181:465-473. [PMID: 32318955 PMCID: PMC7188717 DOI: 10.1007/s10549-020-05643-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 04/10/2020] [Indexed: 02/08/2023]
Abstract
Purpose Limited studies have been conducted to evaluate pathogenetic mutations in breast cancer predisposition genes among Chinese women. To fully characterize germline mutations of these genes in this population, we used the whole-exome sequencing data in a population-based case–control study conducted in Shanghai, China. Methods We evaluated exonic, splicing, and copy number variants in 11 established and 14 candidate breast cancer predisposition genes in 831 invasive breast cancer cases and 839 controls. We identified 55 pathogenic variants, including 15 newly identified in this study. Results Approximately 8% of the cases and 0.6% of the cancer-free controls carried these pathogenetic variants (P = 3.05 × 10−15). Among cases, 3.7% had a BRCA2 pathogenic variant and 1.6% had a BRCA1 pathogenic variant, while 2.5% had a pathogenic variant in other genes including ATM, CHEK2, NBN, NF1, CDH1, PALB2, PTEN, TP53 as well as BARD1, BRIP, and RAD51D. Patients with BRCA1/2 pathogenic variants were more likely to have a family history of breast cancer and hormone receptor negative tumors compared with patients without pathogenic variants. Conclusions This study highlighted the importance of hereditary breast cancer genes in the breast cancer etiology in this understudied population. Together with previous studies in East Asian women, this study suggested a relatively more prominent role of BRCA2 compared to BRCA1. This study also provides additional evidence to design cost-efficient genetic testing among Chinese women for risk assessment and early detection of breast cancer. Electronic supplementary material The online version of this article (10.1007/s10549-020-05643-0) contains supplementary material, which is available to authorized users.
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21
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Jia G, Lu Y, Wen W, Long J, Liu Y, Tao R, Li B, Denny JC, Shu XO, Zheng W. Evaluating the Utility of Polygenic Risk Scores in Identifying High-Risk Individuals for Eight Common Cancers. JNCI Cancer Spectr 2020; 4:pkaa021. [PMID: 32596635 PMCID: PMC7306192 DOI: 10.1093/jncics/pkaa021] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 01/14/2020] [Accepted: 03/06/2020] [Indexed: 12/13/2022] Open
Abstract
Background Genome-wide association studies have identified common genetic risk variants in many loci associated with multiple cancers. We sought to systematically evaluate the utility of these risk variants in identifying high-risk individuals for eight common cancers. Methods We constructed polygenic risk scores (PRS) using genome-wide association studies–identified risk variants for each cancer. Using data from 400 812 participants of European descent in a population-based cohort study, UK Biobank, we estimated hazard ratios associated with PRS using Cox proportional hazard models and evaluated the performance of the PRS in cancer risk prediction and their ability to identify individuals at more than a twofold elevated risk, a risk level comparable to a moderate-penetrance mutation in known cancer predisposition genes. Results During a median follow-up of 5.8 years, 14 584 incident case patients of cancers were identified (ranging from 358 epithelial ovarian cancer case patients to 4430 prostate cancer case patients). Compared with those at an average risk, individuals among the highest 5% of the PRS had a two- to threefold elevated risk for cancer of the prostate, breast, pancreas, colorectal, or ovary, and an approximately 1.5-fold elevated risk of cancer of the lung, bladder, or kidney. The areas under the curve ranged from 0.567 to 0.662. Using PRS, 40.4% of the study participants can be classified as having more than a twofold elevated risk for at least one site-specific cancer. Conclusions A large proportion of the general population can be identified at an elevated cancer risk by PRS, supporting the potential clinical utility of PRS for personalized cancer risk prediction.
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Affiliation(s)
- Guochong Jia
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yingchang Lu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ying Liu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bingshan Li
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
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Özgöz A, Mutlu İçduygu F, Yükseltürk A, ŞamlI H, Hekİmler Öztürk K, Başkan Z. Low-penetrance susceptibility variants and postmenopausal oestrogen receptor positive breast cancer. J Genet 2020. [DOI: 10.1007/s12041-019-1174-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Genetic Testing to Guide Risk-Stratified Screens for Breast Cancer. J Pers Med 2019; 9:jpm9010015. [PMID: 30832243 PMCID: PMC6462925 DOI: 10.3390/jpm9010015] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 02/18/2019] [Accepted: 02/22/2019] [Indexed: 12/14/2022] Open
Abstract
Breast cancer screening modalities and guidelines continue to evolve and are increasingly based on risk factors, including genetic risk and a personal or family history of cancer. Here, we review genetic testing of high-penetrance hereditary breast and ovarian cancer genes, including BRCA1 and BRCA2, for the purpose of identifying high-risk individuals who would benefit from earlier screening and more sensitive methods such as magnetic resonance imaging. We also consider risk-based screening in the general population, including whether every woman should be genetically tested for high-risk genes and the potential use of polygenic risk scores. In addition to enabling early detection, the results of genetic screens of breast cancer susceptibility genes can be utilized to guide decision-making about when to elect prophylactic surgeries that reduce cancer risk and the choice of therapeutic options. Variants of uncertain significance, especially missense variants, are being identified during panel testing for hereditary breast and ovarian cancer. A finding of a variant of uncertain significance does not provide a basis for increased cancer surveillance or prophylactic procedures. Given that variant classification is often challenging, we also consider the role of multifactorial statistical analyses by large consortia and functional tests for this purpose.
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Fung SM, Wong XY, Lee SX, Miao H, Hartman M, Wee HL. Performance of Single-Nucleotide Polymorphisms in Breast Cancer Risk Prediction Models: A Systematic Review and Meta-analysis. Cancer Epidemiol Biomarkers Prev 2018; 28:506-521. [DOI: 10.1158/1055-9965.epi-18-0810] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 10/30/2018] [Accepted: 12/03/2018] [Indexed: 11/16/2022] Open
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25
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Hao W, Xu X, Shi H, Zhang C, Chen X. No association of TP53 codon 72 and intron 3 16-bp duplication polymorphisms with breast cancer risk in Chinese Han women: new evidence from a population-based case-control investigation. Eur J Med Res 2018; 23:47. [PMID: 30309383 PMCID: PMC6180397 DOI: 10.1186/s40001-018-0345-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Accepted: 09/25/2018] [Indexed: 11/21/2022] Open
Abstract
Background Many studies have demonstrated that the genetic variants of tumor suppressor gene TP53 contribute to the prediction of breast cancer risk. However, most of them focused on Europeans and Americans; the investigations about Asians, especially Chinese women, are scarce. Thus, the aim of this study was to explore the influence of TP53 codon 72 and intron 3 16-bp duplication polymorphisms on the breast cancer risk in Chinese women, especially those from eastern China. Methods Blood samples collected from 254 breast cancer patients and 252 healthy female individuals were investigated. Genotypes of the two polymorphisms were determined by direct sequencing and conventional PCR, respectively. Results Heterozygous Arg/Pro and homozygous Del/Del were the most frequent genotypes of the two polymorphisms, respectively. Heterozygous Arg/Pro had a higher prevalence in breast cancer cases (Padj = 0.10; ORadj = 1.43, 95% CI 0.93–2.18), and no homozygous 16-bp duplication (Ins/Ins) genotype was found in the whole 506 clinical samples. For the distributions of allele and haplotype frequencies, no statistically significant difference was observed between the two groups when multiple (additive, dominant and recessive) genetic models were utilized in the analysis (Padj > 0.05). Conclusion The results suggested that the two TP53 polymorphisms did not affect breast cancer risk in Chinese Han women, but the heterozygous Arg/Pro may exist as the possible risk genotype of the codon 72 polymorphism in contrast to the homozygous Arg/Arg and Pro/Pro. Electronic supplementary material The online version of this article (10.1186/s40001-018-0345-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Weiming Hao
- Institute of Life Sciences, Jiangsu University, Zhenjiang, China.,Pathogen Diagnostic Center, Institut Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai, China
| | - Xia Xu
- Department of Chemotherapy, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, 210009, Jiangsu, People's Republic of China
| | - Haifeng Shi
- Institute of Life Sciences, Jiangsu University, Zhenjiang, China
| | - Chiyu Zhang
- Pathogen Diagnostic Center, Institut Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai, China
| | - Xiaoxiang Chen
- Department of Gynecologic Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, 42# Baiziting Street, Nanjing, 210009, Jiangsu, People's Republic of China.
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Xin J, Chu H, Ben S, Ge Y, Shao W, Zhao Y, Wei Y, Ma G, Li S, Gu D, Zhang Z, Du M, Wang M. Evaluating the effect of multiple genetic risk score models on colorectal cancer risk prediction. Gene 2018; 673:174-180. [PMID: 29908285 DOI: 10.1016/j.gene.2018.06.035] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Revised: 05/25/2018] [Accepted: 06/12/2018] [Indexed: 12/29/2022]
Abstract
Currently, genetic risk score (GRS) model has been a widely used method to evaluate the genetic effect of cancer risk prediction, but seldom studies investigated their discriminatory power, especially for colorectal cancer (CRC) risk prediction. In this study, we applied both simulation and real data to comprehensively compare the discriminability of different GRS models. The GRS models were fitted by logistic regression with three scenarios, including simple count GRS (SC-GRS), logistic regression weighted GRS (LR-GRS, including DL-GRS and OR-GRS) and explained variance weighted GRS (EV-GRS, including EV_DL-GRS and EV_OR-GRS) models. The model performance was evaluated by receiver operating characteristic (ROC) curves and area under curves (AUC) metric, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). In real data analysis, as DL-GRS and EV_DL-GRS models were carried with serious over-fitting, the other three models were kept for further comparison. Compared to unweighted SC-GRS model, reclassification was significantly decreased in OR-GRS model (NRI = -0.082, IDI = -0.002, P < 0.05), while EV_OR-GRS model showed negative NRI and IDI (NRI = -0.077, IDI = -5.54E-04, P < 0.05) compared to OR-GRS model. Besides, traditional model with smoking status (AUC = 0.523) performed lower discriminability compared to the combined model (AUC = 0.607) including genetic (i.e., SC-GRS) and smoking factors. Similarly, the findings from simulation were all consistent to real data results. It is plausible that SC-GRS model could be optimal for predicting genetic risk of CRC. Moreover, the addition of more significant genetic variants to traditional model could further improve predictive power on CRC risk prediction.
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Affiliation(s)
- Junyi Xin
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Haiyan Chu
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Shuai Ben
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yuqiu Ge
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Wei Shao
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yang Zhao
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Yongyue Wei
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Gaoxiang Ma
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Shuwei Li
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Dongying Gu
- Department of Oncology, The Affiliated Nanjing Hospital of Nanjing Medical University, Nanjing, China
| | - Zhengdong Zhang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China.
| | - Mulong Du
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China.
| | - Meilin Wang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China.
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Thanh NTN, Lan NTT, Phat PT, Giang NDT, Hue NT. Two polymorphisms, rs2046210 and rs3803662, are associated with breast cancer risk in a Vietnamese case-control cohort. Genes Genet Syst 2018; 93:101-109. [DOI: 10.1266/ggs.17-00053] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Affiliation(s)
- Nguyen Thi Ngoc Thanh
- Department of Biology and Biotechnology, University of Science, Vietnam National University
| | - Nguyen Thi Tuyet Lan
- Department of Biotechnology, International University, Vietnam National University
| | - Phan Thanh Phat
- Department of Biology and Biotechnology, University of Science, Vietnam National University
| | | | - Nguyen Thi Hue
- Department of Biology and Biotechnology, University of Science, Vietnam National University
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Association of multiple genetic variants with breast cancer susceptibility in the Han Chinese population. Oncotarget 2018; 7:85483-85491. [PMID: 27863437 PMCID: PMC5356751 DOI: 10.18632/oncotarget.13402] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Accepted: 10/19/2016] [Indexed: 12/22/2022] Open
Abstract
We selected 13 tag single nucleotide polymorphisms (tSNPs) to investigate whether they were associated with breast cancer risk in the Chinese Han population. Upon statistical analyses of clinical data from 551 patients and 577 controls, we found that six of the 13 SNPs were associated with breast cancer; namely, rs4973768(Odds ratio (OR) = 1.30, 95% confidence interval (CI) =1.01-1.67), rs981782(OR =1.30, 95% CI=1.01-1.66), rs1432679(OR =0.84, 95% CI=0.70-0.99), rs10759243(OR=1.30, 95%CI=1.09-1.55), rs10822013(OR =1.18, 95% CI=1.00-1.39) and rs704010(OR =1.63, 95% CI=1.04-2.56). When stratified based on breast cancer subtype, our analyses revealed that three SNPs (rs981782, rs10759243 and rs704010) correlated with ER+ breast cancer, while another three (rs4973768, rs1432679 and rs10822013) correlated with ER- breast cancer. We obtained similar results while investigating the correlation of SNPs with PR status or clinical stage. Our results suggest that associations identified between SNPs and breast cancer through genome-wide association studies (GWAS) may not always be generalizable across races.
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Jiang C, Yu S, Qian P, Guo R, Zhang R, Ao Z, Li Q, Wu G, Chen Y, Li J, Wang C, Yao W, Xu J, Qian G, Ji F. The breast cancer susceptibility-related polymorphisms at the TOX3/LOC643714 locus associated with lung cancer risk in a Han Chinese population. Oncotarget 2018; 7:59742-59753. [PMID: 27486757 PMCID: PMC5312345 DOI: 10.18632/oncotarget.10874] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Accepted: 07/09/2016] [Indexed: 11/25/2022] Open
Abstract
It has been well established that besides environmental factors, genetic factors are also associated with lung cancer risk. However, to date, the prior identified genetic variants and loci only explain a small fraction of the familial risk of lung cancer. Hence it is vital to investigate the remaining missing heritability to understand the development and process of lung cancer. In the study, to test our hypothesis that the previously identified breast cancer risk-associated genetic polymorphisms at the TOX3/LOC643714 locus might contribute to lung cancer risk, 16 SNPs at the TOX3/LOC643714 locus were evaluated in a Han Chinese population based on a case-control study. Pearson's chi-square test or Fisher's exact test revealed that rs9933638, rs12443621, and rs3104746 were significantly associated with lung cancer risk (P < 0.001, P < 0.001, and P = 0.005, respectively). Logistic regression analyses displayed that lung cancer risk of individuals with rs9933638(GG+GA) were 1.89 times higher than that of rs9933638AA carriers (OR = 1.893, 95% CI = 1.308-2.741, P = 0.001). Similar findings were manifested for rs12443621 (OR = 1.824, 95% CI = 1.272-2.616, P = 0.001, rs12443621(GG+GA) carriers vs. rs12443621AA carriers) and rs3104746 (OR = 1.665, 95% CI = 1.243-2.230, P = 0.001, rs3104746TT carriers vs. rs3104746(TA+AA) carriers). The study discovered for the first time that three SNPs (rs9933638, rs12443621, and rs3104746) at the TOX3/LOC643714 locus contributed to lung cancer risk, providing new evidences that lung cancer and breast cancer are linked at the molecular and genetic level to a certain extent.
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Affiliation(s)
- Chaowen Jiang
- Institute of Human Respiratory Disease, Xinqiao Hospital, The Third Military Medical University, Chongqing 400037, China
| | - Shilong Yu
- Institute of Human Respiratory Disease, Xinqiao Hospital, The Third Military Medical University, Chongqing 400037, China
| | - Pin Qian
- Institute of Human Respiratory Disease, Xinqiao Hospital, The Third Military Medical University, Chongqing 400037, China
| | - Ruiling Guo
- Department of Respiratory Diseases, 324th Hospital of People's Liberation Army (No.324 Hospital of PLA), Chongqing 400020, China
| | - Ruijie Zhang
- Department of Respiratory Medicine, The Third Xiangya Hospital, Central South University, Changsha, Hunan 410013, China
| | - Zhi Ao
- Institute of Human Respiratory Disease, Xinqiao Hospital, The Third Military Medical University, Chongqing 400037, China
| | - Qi Li
- Institute of Human Respiratory Disease, Xinqiao Hospital, The Third Military Medical University, Chongqing 400037, China
| | - Guoming Wu
- Institute of Human Respiratory Disease, Xinqiao Hospital, The Third Military Medical University, Chongqing 400037, China
| | - Yan Chen
- Institute of Human Respiratory Disease, Xinqiao Hospital, The Third Military Medical University, Chongqing 400037, China
| | - Jin Li
- Institute of Human Respiratory Disease, Xinqiao Hospital, The Third Military Medical University, Chongqing 400037, China
| | - Changzheng Wang
- Institute of Human Respiratory Disease, Xinqiao Hospital, The Third Military Medical University, Chongqing 400037, China
| | - Wei Yao
- Institute of Human Respiratory Disease, Xinqiao Hospital, The Third Military Medical University, Chongqing 400037, China
| | - Jiancheng Xu
- Institute of Human Respiratory Disease, Xinqiao Hospital, The Third Military Medical University, Chongqing 400037, China
| | - Guisheng Qian
- Institute of Human Respiratory Disease, Xinqiao Hospital, The Third Military Medical University, Chongqing 400037, China
| | - Fuyun Ji
- Institute of Human Respiratory Disease, Xinqiao Hospital, The Third Military Medical University, Chongqing 400037, China
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Evaluation of three polygenic risk score models for the prediction of breast cancer risk in Singapore Chinese. Oncotarget 2018; 9:12796-12804. [PMID: 29560110 PMCID: PMC5849174 DOI: 10.18632/oncotarget.24374] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 01/25/2018] [Indexed: 11/25/2022] Open
Abstract
Genome-wide association studies (GWAS) have proven highly successful in identifying single nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk. The majority of these studies are on European populations, with limited SNP association data in other populations. We genotyped 51 GWAS-identified SNPs in two independent cohorts of Singaporean Chinese. Cohort 1 comprised 1294 BC cases and 885 controls and was used to determine odds ratios (ORs); Cohort 2 had 301 BC cases and 243 controls for deriving polygenic risk scores (PRS). After age-adjustment, 11 SNPs were found to be significantly associated with BC risk. Five SNPs were present in <1% of Cohort 1 and were excluded from further PRS analysis. To assess the cumulative effect of the remaining 46 SNPs on BC risk, we generated three PRS models: Model-1 included 46 SNPs; Model-2 included 11 statistically significant SNPs; and Model-3 included the SNPs in Model-2 but excluded SNPs that were in strong linkage disequilibrium with the others. Across Models-1, -2 and -3, women in the highest PRS quartile had the greatest ORs of 1.894 (95% CI = 1.157–3.100), 2.013 (95% CI = 1.227–3.302) and 1.751 (95% CI = 1.073–2.856) respectively, suggesting a direct correlation between PRS and BC risk. Given the potential of PRS in BC risk stratification, our findings suggest the need to tailor the selection of SNPs to be included in an ethnic-specific PRS model.
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Dong H, Huang Y, Song F, Dai H, Liu P, Zhu Y, Wang P, Han J, Hao X, Chen K. Improved Performance of Adjunctive Ultrasonography After Mammography Screening for Breast Cancer Among Chinese Females. Clin Breast Cancer 2017; 18:e353-e361. [PMID: 28887010 DOI: 10.1016/j.clbc.2017.07.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Revised: 07/13/2017] [Accepted: 07/17/2017] [Indexed: 12/31/2022]
Abstract
INTRODUCTION Until now, no studies have investigated whether women other than those with dense breasts are suitable for adjunctive ultrasonography after negative mammography, and investigated whether all women with negative mammography are suitable for adjunctive ultrasonography. METHODS Based on the Multi-modality Independent Screening Trial in China, a total of 31,918 women aged 45 to 65 years underwent both ultrasonography and mammography. Physicians performed ultrasonography and mammography separately and were blinded to each other's findings until their interpretations had been recorded. For both ultrasonography and mammography, suspicious results and those highly suggestive of a malignancy were confirmed by pathologic examination, whereas other results were confirmed by 1-year follow-up after initial screening. RESULTS Based on Breast Imaging Reporting and Data System (BIRADS) assessments, 84 (84.8%) of 99 cancers were identified on mammography (detection rate, 2.6/1000), and 61 (61.6%) of 99 cancers were identified on ultrasonography (detection rate, 1.9/1000). Integrated mammography with ultrasonography identified 94 (95.0%) of 99 cancers, with an increment of 11.9% in cancer detection rate (from 2.6/1000 to 2.9/1000) (P < .05). Moreover, among women with BIRADS 3, adjunctive ultrasonography detected no cancers. All 10 additional cancers detected by adjunctive ultrasonography were from women with BIRADS 0 to 2, at a cost of 207 women with false positives. Additionally, dense breasts and benign breast disease were significantly associated with positive ultrasonography after BIRADS 0 to 2 (all P values < .05). CONCLUSIONS After negative mammography, adjunctive ultrasonography should only be recommended for BIRADS 0 to 2 but not BIRADS 3, especially for women with dense breasts or benign breast disease.
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Affiliation(s)
- Henglei Dong
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Yubei Huang
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Fengju Song
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Hongji Dai
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Peifang Liu
- Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Ying Zhu
- Department of Breast Imaging, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Peishan Wang
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Jiali Han
- Department of Epidemiology, Fairbanks School of Public Health, Simon Cancer Center, Indiana University, Indianapolis, IN
| | - Xishan Hao
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China; Chinese Anti-Cancer Association, Tianjin, China.
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
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Genetic and environmental factors and serum hormones, and risk of estrogen receptor-positive breast cancer in pre- and postmenopausal Japanese women. Oncotarget 2017; 8:65759-65769. [PMID: 29029469 PMCID: PMC5630369 DOI: 10.18632/oncotarget.20182] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 08/04/2017] [Indexed: 12/22/2022] Open
Abstract
Breast cancer incidence in Japanese women has more than tripled over the past two decades. We have previously shown that this marked increase is mostly due to an increase in the estrogen receptor (ER)-positive, HER2-negative subtype. We conducted a case-control study; ER-positive, HER2-negative breast cancer patients who were diagnosed since 2011 and women without disease were recruited. Environmental factors, serum levels of testosterone and 25-hydroxyvitamin D, and common genetic variants reported as predictors of ER-positive breast cancer or found in Asian women were evaluated between patients and controls in pre- and postmenopausal women. To identify important risk predictors, risk prediction models were created by logistic regression models. In premenopausal women, two environmental factors (history of breastfeeding, and history of benign breast disease) and four genetic variants (TOX3-rs3803662, ESR1-rs2046210, 8q24-rs13281615, and SLC4A7-rs4973768) were considered to be risk predictors, whereas three environmental factors (body mass index, history of breastfeeding, and hyperlipidemia), serum levels of testosterone and 25-hydroxyvitamin D, and two genetic variants (TOX3-rs3803662 and ESR1-rs2046210) were identified as risk predictors. Inclusion of common genetic variants and serum hormone measurements as well as environmental factors improved risk assessment models. The decline in the birthrate according to recent changes of lifestyle might be the main cause of the recent notable increase in the incidence of ER-positive breast cancer in Japanese women.
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Hsieh YC, Tu SH, Su CT, Cho EC, Wu CH, Hsieh MC, Lin SY, Liu YR, Hung CS, Chiou HY. A polygenic risk score for breast cancer risk in a Taiwanese population. Breast Cancer Res Treat 2017; 163:131-138. [PMID: 28205043 DOI: 10.1007/s10549-017-4144-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Accepted: 02/06/2017] [Indexed: 10/20/2022]
Abstract
BACKGROUND Multiple common variants identified by genome-wide association studies showed limited evidence of the risk of breast cancer in Taiwan. In this study, we analyzed the breast cancer risk in relation to 13 individual single-nucleotide polymorphisms (SNPs) identified by a GWAS in an Asian population. METHODS In total, 446 breast cancer patients and 514 healthy controls were recruited for this case-control study. In addition, we developed a polygenic risk score (PRS) including those variants significantly associated with breast cancer risk, and also evaluated the contribution of PRS and clinical risk factors to breast cancer using receiver operating characteristic curve (AUC). RESULTS Logistic regression results showed that nine individual SNPs were significantly associated with breast cancer risk after multiple testing. Among all SNPs, six variants, namely FGFR2 (rs2981582), HCN1 (rs981782), MAP3K1 (rs889312), TOX3 (rs3803662), ZNF365 (rs10822013), and RAD51B (rs3784099), were selected to create PRS model. A dose-response association was observed between breast cancer risk and the PRS. Women in the highest quartile of PRS had a significantly increased risk compared to women in the lowest quartile (odds ratio 2.26; 95% confidence interval 1.51-3.38). The AUC for a model which contained the PRS in addition to clinical risk factors was 66.52%, whereas that for a model which with established risk factors only was 63.38%. CONCLUSIONS Our data identified a genetic risk predictor of breast cancer in Taiwanese population and suggest that risk models including PRS and clinical risk factors are useful in discriminating women at high risk of breast cancer from those at low risk.
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Affiliation(s)
- Yi-Chen Hsieh
- Graduate Institute of Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Shih-Hsin Tu
- Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, 252 Wu-Hsing St., Taipei, Taiwan.,Taipei Cancer Center, Taipei Medical University, Taipei, Taiwan.,Breast Medical Center, Taipei Medical University Hospital, Taipei, Taiwan
| | - Chien-Tien Su
- School of Public Health, College of Public Health and Nutrition, Taipei Medical University, Taipei, Taiwan.,Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Er-Chieh Cho
- Department of Clinical Pharmacy, School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | - Chih-Hsiung Wu
- Department of Surgery, Taipei Medical University-Shuang Ho Hospital, Taipei, Taiwan
| | - Mao-Chih Hsieh
- Department of Surgery, Taipei Medical University-Wan Fang Hospital, Taipei, Taiwan
| | - Shiyng-Yu Lin
- Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yun-Ru Liu
- Joint Biobank, Office of Human Research, Taipei Medical University, Taipei, Taiwan
| | - Chin-Sheng Hung
- Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, 252 Wu-Hsing St., Taipei, Taiwan. .,Taipei Cancer Center, Taipei Medical University, Taipei, Taiwan. .,Breast Medical Center, Taipei Medical University Hospital, Taipei, Taiwan.
| | - Hung-Yi Chiou
- School of Public Health, College of Public Health and Nutrition, Taipei Medical University, 250 Wu-Hsing St., Taipei, Taiwan.
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Wen W, Shu XO, Guo X, Cai Q, Long J, Bolla MK, Michailidou K, Dennis J, Wang Q, Gao YT, Zheng Y, Dunning AM, García-Closas M, Brennan P, Chen ST, Choi JY, Hartman M, Ito H, Lophatananon A, Matsuo K, Miao H, Muir K, Sangrajrang S, Shen CY, Teo SH, Tseng CC, Wu AH, Yip CH, Simard J, Pharoah PDP, Hall P, Kang D, Xiang Y, Easton DF, Zheng W. Prediction of breast cancer risk based on common genetic variants in women of East Asian ancestry. Breast Cancer Res 2016; 18:124. [PMID: 27931260 PMCID: PMC5146840 DOI: 10.1186/s13058-016-0786-1] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 11/23/2016] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Approximately 100 common breast cancer susceptibility alleles have been identified in genome-wide association studies (GWAS). The utility of these variants in breast cancer risk prediction models has not been evaluated adequately in women of Asian ancestry. METHODS We evaluated 88 breast cancer risk variants that were identified previously by GWAS in 11,760 cases and 11,612 controls of Asian ancestry. SNPs confirmed to be associated with breast cancer risk in Asian women were used to construct a polygenic risk score (PRS). The relative and absolute risks of breast cancer by the PRS percentiles were estimated based on the PRS distribution, and were used to stratify women into different levels of breast cancer risk. RESULTS We confirmed significant associations with breast cancer risk for SNPs in 44 of the 78 previously reported loci at P < 0.05. Compared with women in the middle quintile of the PRS, women in the top 1% group had a 2.70-fold elevated risk of breast cancer (95% CI: 2.15-3.40). The risk prediction model with the PRS had an area under the receiver operating characteristic curve of 0.606. The lifetime risk of breast cancer for Shanghai Chinese women in the lowest and highest 1% of the PRS was 1.35% and 10.06%, respectively. CONCLUSION Approximately one-half of GWAS-identified breast cancer risk variants can be directly replicated in East Asian women. Collectively, common genetic variants are important predictors for breast cancer risk. Using common genetic variants for breast cancer could help identify women at high risk of breast cancer.
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Affiliation(s)
- Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN USA
- Vanderbilt Epidemiology Center and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, 2525 West End Avenue, 8th Floor, Nashville, TN 37203-1738 USA
| | - Xiao-ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN USA
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN USA
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN USA
| | - Manjeet K. Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Yu-Tang Gao
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China
| | - Ying Zheng
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
| | - Alison M. Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Montserrat García-Closas
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD USA
| | - Paul Brennan
- International Agency for Research on Cancer, Lyon, France
| | - Shou-Tung Chen
- Department of Surgery, Changhua Christian Hospital, Changhua, Taiwan
| | - Ji-Yeob Choi
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea
- Cancer Research Institute, Seoul National University, Seoul, Korea
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Department of Surgery, National University Health System, Singapore, Singapore
| | - Hidemi Ito
- Division of Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan
| | - Artitaya Lophatananon
- Division of Health Sciences, Warwick Medical School, Warwick University, Coventry, UK
| | - Keitaro Matsuo
- Division of Molecular Medicine, Aichi Cancer Center Research Institute, Nagoya, Japan
| | - Hui Miao
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Kenneth Muir
- Division of Health Sciences, Warwick Medical School, Warwick University, Coventry, UK
- Institute of Population Health, University of Manchester, Manchester, UK
| | | | - Chen-Yang Shen
- School of Public Health, China Medical University, Taichung, Taiwan
- Taiwan Biobank, Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Soo H. Teo
- Cancer Research Initiatives Foundation, Subang Jaya, Selangor, Malaysia
- Breast Cancer Research Unit, Cancer Research Institute, University Malaya Medical Centre, Kuala Lumpur, Malaysia
| | - Chiu-chen Tseng
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Anna H. Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Cheng Har Yip
- Breast Cancer Research Unit, Cancer Research Institute, University Malaya Medical Centre, Kuala Lumpur, Malaysia
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec Research Center, Laval University, Québec City, Canada
| | - Paul D. P. Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Daehee Kang
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Yongbing Xiang
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China
| | - Douglas F. Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN USA
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Cheng Y, Jiang T, Zhu M, Li Z, Zhang J, Wang Y, Geng L, Liu J, Shen W, Wang C, Hu Z, Jin G, Ma H, Shen H, Dai J. Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations. Oncotarget 2016; 8:53959-53967. [PMID: 28903315 PMCID: PMC5589554 DOI: 10.18632/oncotarget.10403] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 06/04/2016] [Indexed: 11/25/2022] Open
Abstract
In the past ten years, great successes have been accumulated by taking advantage of both candidate-gene studies and genome-wide association studies. However, limited studies were available to systematically evaluate the genetic effects for lung cancer risk with large-scale and different ethnic populations. We systematically reviewed relevant literatures and filtered out 241 important genetic variants identified in 124 articles. A two-stage case-control study within specific subgroups was performed to assess the effects [Training set: 2,331 cases vs. 3,077 controls (Chinese population); testing set: 1,937 cases vs. 1,984 controls (European population)]. Variable selection and model development were used LASSO penalized regression and genetic risk score (GRS) system. Further change in area under the receiver operator characteristic curves (AUC) made by the epidemiologic model with and without GRS was used to compare predictions. It kept 38 genetic variants in our study and the ratios of lung cancer risk for subjects in the upper quartile GRS was three times higher compared to that in the low quartile (odds ratio: 4.64, 95% CI: 3.87–5.56). In addition, we found that adding genetic predictors to smoking risk factor-only model improved lung cancer predictive value greatly: AUC, 0.610 versus 0.697 (P < 0.001). Similar performance was derived in European population and the combined two data sets. Our findings suggested that genetic predictors could improve the predictive ability of risk model for lung cancer and highlighted the application among different populations, indicating that the lung cancer risk assessment model will be a promising tool for high risk population screening and prediction.
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Affiliation(s)
- Yang Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Tao Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Zhihua Li
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Jiahui Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Yuzhuo Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Liguo Geng
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Jia Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Wei Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Cheng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Guangfu Jin
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Juncheng Dai
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
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Han MR, Long J, Choi JY, Low SK, Kweon SS, Zheng Y, Cai Q, Shi J, Guo X, Matsuo K, Iwasaki M, Shen CY, Kim MK, Wen W, Li B, Takahashi A, Shin MH, Xiang YB, Ito H, Kasuga Y, Noh DY, Matsuda K, Park MH, Gao YT, Iwata H, Tsugane S, Park SK, Kubo M, Shu XO, Kang D, Zheng W. Genome-wide association study in East Asians identifies two novel breast cancer susceptibility loci. Hum Mol Genet 2016; 25:3361-3371. [PMID: 27354352 DOI: 10.1093/hmg/ddw164] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Revised: 05/04/2016] [Accepted: 05/20/2016] [Indexed: 12/16/2022] Open
Abstract
Breast cancer is one of the most common malignancies among women worldwide. Genetic factors have been shown to play an important role in breast cancer aetiology. We conducted a two-stage genome-wide association study (GWAS) including 14 224 cases and 14 829 controls of East Asian women to search for novel genetic susceptibility loci for breast cancer. Single nucleotide polymorphisms (SNPs) in two loci were found to be associated with breast cancer risk at the genome-wide significance level. The first locus, represented by rs12118297 at 1p22.3 (near the LMO4 gene), was associated with breast cancer risk with odds ratio (OR) and (95% confidence interval (CI)) of 0.91 (0.88-0.94) and a P-value of 4.48 × 10- 8 This association was replicated in another study, DRIVE GAME-ON Consortium, including 16 003 cases and 41 335 controls of European ancestry (OR = 0.95, 95% CI = 0.91-0.99, P-value = 0.019). The second locus, rs16992204 at 21q22.12 (near the LINC00160 gene), was associated with breast cancer risk with OR (95% CI) of 1.13 (1.07-1.18) and a P-value of 4.63 × 10 - 8 The risk allele frequency for this SNP is zero in European-ancestry populations in 1000 Genomes Project and thus its association with breast cancer risk cannot be assessed in DRIVE GAME-ON Consortium. Functional annotation using the ENCODE data indicates that rs12118297 might be located in a repressed element and locus 21q22.12 may affect breast cancer risk through regulating LINC00160 expressions and interaction with oestrogen receptor signalling. Our findings provide additional insights into the genetics of breast cancer.
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Affiliation(s)
- Mi-Ryung Han
- Department of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Jirong Long
- Department of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Ji-Yeob Choi
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, South Korea.,Cancer Research Institute, Seoul National University College of Medicine, Seoul 03080, South Korea
| | - Siew-Kee Low
- Laboratory for Statistical Analysis, Center for Integrative Medical Sciences, RIKEN, Yokohama 351-0198, Japan
| | - Sun-Seog Kweon
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju 61469, South Korea.,Jeonnam Regional Cancer Center, Chonnam National University Hwasun Hospital, Hwasun 58128, South Korea
| | - Ying Zheng
- Shanghai Municipal Center for Disease Control and Prevention, Shanghai 200336, China
| | - Qiuyin Cai
- Department of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Jiajun Shi
- Department of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Xingyi Guo
- Department of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Keitaro Matsuo
- Division of Molecular Medicine, Aichi Cancer Center Research Institute, Nagoya 464-8681, Japan.,Department of Epidemiology, Nagoya University Graduates School of Medicine, Nagoya 464-8681, Japan
| | - Motoki Iwasaki
- Epidemiology Division, Research Center for Cancer Prevention and Screening, National Cancer Center, Tokyo 104-0045, Japan
| | - Chen-Yang Shen
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan.,Taiwan Biobank, Academia Sinica, Taipei 115, Taiwan.,College of Public Health, China Medical University, Taichung 404, Taiwan
| | - Mi Kyung Kim
- Division of Cancer Epidemiology and Management, National Cancer Center, Gyeonggi-do 10408, South Korea
| | - Wanqing Wen
- Department of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Atsushi Takahashi
- Laboratory for Statistical Analysis, Center for Integrative Medical Sciences, RIKEN, Yokohama 351-0198, Japan
| | - Min-Ho Shin
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju 61469, South Korea
| | - Yong-Bing Xiang
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai 200032, China
| | - Hidemi Ito
- Division of Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya 464-8681, Japan
| | - Yoshio Kasuga
- Department of Surgery, Nagano Matsushiro General Hospital, Nagano 381-1231, Japan
| | - Dong-Young Noh
- Cancer Research Institute, Seoul National University College of Medicine, Seoul 03080, South Korea.,Department of Surgery, Seoul National University College of Medicine, Seoul 03080, South Korea
| | - Koichi Matsuda
- Laboratory of Molecular Medicine, Human Genome Center, Institute of Medical Science, the University of Tokyo, Tokyo 108-8639, Japan
| | - Min Ho Park
- Department of Surgery, Chonnam National University Medical School, Gwangju 61469, South Korea
| | - Yu-Tang Gao
- Department of Epidemiology, Shanghai Cancer Institute, Shanghai 200032, China
| | - Hiroji Iwata
- Department of Breast Oncology, Aichi Cancer Center Central Hospital, Nagoya 464-8681, Japan
| | - Shoichiro Tsugane
- Research Center for Cancer Prevention and Screening, National Cancer Center, Tokyo 104-0045, Japan
| | - Sue K Park
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, South Korea.,Cancer Research Institute, Seoul National University College of Medicine, Seoul 03080, South Korea.,Department of Preventive Medicine, Seoul National University College of Medicine, Seoul 03080, South Korea
| | - Michiaki Kubo
- Laboratory for Genotyping Development, Center for Integrative Medical Sciences, RIKEN, Yokohama 351-0198, Japan
| | - Xiao-Ou Shu
- Department of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
| | - Daehee Kang
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, South Korea.,Cancer Research Institute, Seoul National University College of Medicine, Seoul 03080, South Korea.,Department of Preventive Medicine, Seoul National University College of Medicine, Seoul 03080, South Korea
| | - Wei Zheng
- Department of Medicine, Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN 37203, USA
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Associations of Genetic Variants at Nongenic Susceptibility Loci with Breast Cancer Risk and Heterogeneity by Tumor Subtype in Southern Han Chinese Women. BIOMED RESEARCH INTERNATIONAL 2016; 2016:3065493. [PMID: 27022606 PMCID: PMC4789034 DOI: 10.1155/2016/3065493] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Revised: 01/06/2016] [Accepted: 02/04/2016] [Indexed: 12/05/2022]
Abstract
Current understanding of cancer genomes is mainly “gene centric.” However, GWAS have identified some nongenic breast cancer susceptibility loci. Validation studies showed inconsistent results among different populations. To further explore this inconsistency and to investigate associations by intrinsic subtype (Luminal-A, Luminal-B, ER−&PR−&HER2+, and triple negative) among Southern Han Chinese women, we genotyped five nongenic polymorphisms (2q35: rs13387042, 5p12: rs981782 and rs4415084, and 8q24: rs1562430 and rs13281615) using MassARRAY IPLEX platform in 609 patients and 882 controls. Significant associations with breast cancer were observed for rs13387042 and rs4415084 with OR (95% CI) per-allele 1.29 (1.00–1.66) and 0.83 (0.71–0.97), respectively. In subtype specific analysis, rs13387042 (per-allele adjusted OR = 1.36, 95% CI = 1.00–1.87) and rs4415084 (per-allele adjusted OR = 0.82, 95% CI = 0.66–1.00) showed slightly significant association with Luminal-A subtype; however, only rs13387042 was associated with ER−&PR−&HER2+ tumors (per-allele adjusted OR = 1.55, 95% CI = 1.00–2.40), and none of them were linked to Luminal-B and triple negative subtype. Collectively, nongenic SNPs were heterogeneous according to the intrinsic subtype. Further studies with larger datasets along with intrinsic subtype categorization should explore and confirm the role of these variants in increasing breast cancer risk.
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38
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He Y, Liu H, Chen Q, Sun X, Liu C, Shao Y. Relationship between five GWAS-identified single nucleotide polymorphisms and female breast cancer in the Chinese Han population. Tumour Biol 2016; 37:9739-44. [DOI: 10.1007/s13277-016-4795-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Accepted: 01/06/2016] [Indexed: 01/22/2023] Open
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39
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Haryono SJ, Datasena IGB, Santosa WB, Mulyarahardja R, Sari K. A pilot genome-wide association study of breast cancer susceptibility loci in Indonesia. Asian Pac J Cancer Prev 2016; 16:2231-5. [PMID: 25824743 DOI: 10.7314/apjcp.2015.16.6.2231] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Genome-wide association studies (GWASs) of the entire genome provide a systematic approach for revealing novel genetic susceptibility loci for breast cancer. However, genetic association studies have hitherto been primarily conducted in women of European ancestry. Therefofre we here performed a pilot GWAS with a single nucleotide polymorphism (SNP) array 5.0 platform from Affymetrix® that contains 443,813 SNPs to search for new genetic risk factors in 89 breast cancer cases and 46 healthy women of Indonesian ancestry. The case-control association of the GWAS finding set was evaluated using PLINK. The strengths of allelic and genotypic associations were assessed using logistic regression analysis and reported as odds ratios (ORs) and P values; P values less than 1.00x10(-8) and 5.00x10(-5) were required for significant association and suggestive association, respectively. After analyzing 292,887 SNPs, we recognized 11 chromosome loci that possessed suggestive associations with breast cancer risk. Of these, however, there were only four chromosome loci with identified genes: chromosome 2p.12 with the CTNNA2 gene [Odds ratio (OR)=1.20, 95% confidence interval (CI)=1.13-1.33, P=1.08x10(-7)]; chromosome 18p11.2 with the SOGA2 gene (OR=1.32, 95%CI=1.17-1.44, P=6.88x10(-6)); chromosome 5q14.1 with the SSBP2 gene (OR=1.22, 95%CI=1.11-1.34, P=4.00x10(-5)); and chromosome 9q31.1 with the TEX10 gene (OR=1.24, 95%CI=1.12-1.35, P=4.68x10(-5)). This study identified 11 chromosome loci which exhibited suggestive associations with the risk of breast cancer among Indonesian women.
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Affiliation(s)
- Samuel J Haryono
- Department of Oncology, MRCCC Siloam Hospital Semanggi, Jakarta, Indonesia E-mail :
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Lee CPL, Choi H, Soo KC, Tan MH, Chay WY, Chia KS, Liu J, Li J, Hartman M. Mammographic Breast Density and Common Genetic Variants in Breast Cancer Risk Prediction. PLoS One 2015; 10:e0136650. [PMID: 26401662 PMCID: PMC4581713 DOI: 10.1371/journal.pone.0136650] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2014] [Accepted: 08/06/2015] [Indexed: 01/25/2023] Open
Abstract
Introduction Known prediction models for breast cancer can potentially by improved by the addition of mammographic density and common genetic variants identified in genome-wide associations studies known to be associated with risk of the disease. We evaluated the benefit of including mammographic density and the cumulative effect of genetic variants in breast cancer risk prediction among women in a Singapore population. Methods We estimated the risk of breast cancer using a prospective cohort of 24,161 women aged 50 to 64 from Singapore with available mammograms and known risk factors for breast cancer who were recruited between 1994 and 1997. We measured mammographic density using the medio-lateral oblique views of both breasts. Each woman’s genotype for 75 SNPs was simulated based on the genotype frequency obtained from the Breast Cancer Association Consortium data and the cumulative effect was summarized by a genetic risk score (GRS). Any improvement in the performance of our proposed prediction model versus one containing only variables from the Gail model was assessed by changes in receiver-operating characteristic and predictive values. Results During 17 years of follow-up, 680 breast cancer cases were diagnosed. The multivariate-adjusted hazard ratios (95% confidence intervals) were 1.60 (1.22–2.10), 2.20 (1.65–2.92), 2.33 (1.71–3.20), 2.12 (1.43–3.14), and 3.27 (2.24–4.76) for the corresponding mammographic density categories: 11-20cm2, 21-30cm2, 31-40cm2, 41-50cm2, 51-60cm2, and 1.10 (1.03–1.16) for GRS. At the predicted absolute 10-year risk thresholds of 2.5% and 3.0%, a model with mammographic density and GRS could correctly identify 0.9% and 0.5% more women who would develop the disease compared to a model using only the Gail variables, respectively. Conclusion Mammographic density and common genetic variants can improve the discriminatory power of an established breast cancer risk prediction model among females in Singapore.
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Affiliation(s)
- Charmaine Pei Ling Lee
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Hyungwon Choi
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | | | - Min-Han Tan
- National Cancer Centre, Singapore, Singapore
| | | | - Kee Seng Chia
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Jenny Liu
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Jingmei Li
- Human Genetics, Genome Institute of Singapore, Singapore, Singapore
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
- Department of Surgery, National University Hospital, Singapore, Singapore
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- * E-mail:
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Heterogeneity of Breast Cancer Associations with Common Genetic Variants in FGFR2 according to the Intrinsic Subtypes in Southern Han Chinese Women. BIOMED RESEARCH INTERNATIONAL 2015; 2015:626948. [PMID: 26421298 PMCID: PMC4573424 DOI: 10.1155/2015/626948] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2015] [Revised: 08/03/2015] [Accepted: 08/18/2015] [Indexed: 12/15/2022]
Abstract
GWAS have identified variation in the FGFR2 locus as risk factors for breast cancer. Validation studies, however, have shown inconsistent results by ethnics and pathological characteristics. To further explore this inconsistency and investigate the associations of FGFR2 variants with breast cancer according to intrinsic subtype (Luminal-A, Luminal-B, ER-&PR-&HER2+, and triple negative) among Southern Han Chinese women, we genotyped rs1078806, rs1219648, rs2420946, rs2981579, and rs2981582 polymorphisms in 609 patients and 882 controls. Significant associations with breast cancer risk were observed for rs2420946, rs2981579, and rs2981582 with OR (95% CI) per risk allele of 1.19 (1.03-1.39), 1.24 (1.07-1.43), and 1.17 (1.01-1.36), respectively. In subtype specific analysis, above three SNPs were significantly associated with increased Luminal-A risk in a dose-dependent manner (P trend < 0.01); however, only rs2981579 was associated with Luminal-B, and none were linked to ER-&PR- subtypes (ER-&PR-&HER2+ and triple negative). Haplotype analyses also identified common haplotypes significantly associated with luminal-like subtypes (Luminal-A and Luminal-B), but not with ER-&PR- subtypes. Our results suggest that associations of FGFR2 SNPs with breast cancer were heterogeneous according to intrinsic subtype. Future studies stratifying patients by their intrinsic subtypes will provide new insights into the complex genetic mechanisms underlying breast cancer.
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Association of three SNPs in TOX3 and breast cancer risk: Evidence from 97275 cases and 128686 controls. Sci Rep 2015; 5:12773. [PMID: 26239137 PMCID: PMC4523945 DOI: 10.1038/srep12773] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2014] [Accepted: 07/09/2015] [Indexed: 11/21/2022] Open
Abstract
The associations of SNPs in TOX3 gene with breast cancer risk were investigated by some Genome-wide association studies and epidemiological studies, but the study results were contradictory. To derive a more precise estimate of the associations, we conducted a meta-analysis. ORs with 95% CI were used to assess the strength of association between TOX3 polymorphisms and breast cancer risk in fixed or random effect model. A total of 37 publications with 97275 cases and 128686 controls were identified. We observed that the rs3803662 C > T, rs12443621 A > G and rs8051542 C > T were all correlated with increased risk of breast cancer. In the stratified analyses by ethnicity, significantly elevated risk was detected for all genetic models of the three SNPs in Caucasians. In Asian populations, there were significant associations of rs3803662 and rs8051542 with breast cancer risk. Whereas there was no evidence for statistical significant association between the three SNPs and breast cancer risk in Africans. Additionally, we observed different associations of rs3803662 with breast cancer risk based on different ER subtype and BRCA1/BRCA2 mutation carriers. In conclusion, the meta-analysis suggested that three SNPs in TOX3 were significantly associated with breast cancer risk in different populations.
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Kim Y, Yoo KY, Goodman MT. Differences in Incidence, Mortality and Survival of Breast Cancer by Regions and Countries in Asia and Contributing Factors. Asian Pac J Cancer Prev 2015; 16:2857-70. [DOI: 10.7314/apjcp.2015.16.7.2857] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Molecular epidemiology, and possible real-world applications in breast cancer. Breast Cancer 2015; 23:33-38. [PMID: 25862066 DOI: 10.1007/s12282-015-0609-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Accepted: 04/02/2015] [Indexed: 10/23/2022]
Abstract
Gene-environment interaction, a key idea in molecular epidemiology, has enabled the development of personalized medicine. This concept includes personalized prevention. While genome-wide association studies have identified a number of genetic susceptibility loci in breast cancer risk, however, the application of this knowledge to practical prevention is still underway. Here, we briefly review the history of molecular epidemiology and its progress in breast cancer epidemiology. We then introduce our experience with the trial combination of GWAS-identified loci and well-established lifestyle and reproductive risk factors in the risk prediction of breast cancer. Finally, we report our exploration of the cumulative risk of breast cancer based on this risk prediction model as a potential tool for individual risk communication, including genetic risk factors and gene-environment interaction with obesity.
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Lee CPL, Irwanto A, Salim A, Yuan JM, Liu J, Koh WP, Hartman M. Breast cancer risk assessment using genetic variants and risk factors in a Singapore Chinese population. Breast Cancer Res 2014; 16:R64. [PMID: 24941967 PMCID: PMC4095592 DOI: 10.1186/bcr3678] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2013] [Accepted: 06/04/2014] [Indexed: 01/16/2023] Open
Abstract
Introduction Genetic variants for breast cancer risk identified in genome-wide association studies (GWAS) in Western populations require further testing in Asian populations. A risk assessment model incorporating both validated genetic variants and established risk factors may improve its performance in risk prediction of Asian women. Methods A nested case-control study of female breast cancer (411 cases and 1,212 controls) within the Singapore Chinese Health Study was conducted to investigate the effects of 51 genetic variants identified in previous GWAS on breast cancer risk. The independent effect of these genetic variants was assessed by creating a summed genetic risk score (GRS) after adjustment for body mass index and the Gail model risk factors for breast cancer. Results The GRS was an independent predictor of breast cancer risk in Chinese women. The multivariate-adjusted odds ratios (95% confidence intervals) of breast cancer for the second, third, and fourth quartiles of the GRS were 1.26 (0.90 to 1.76), 1.47 (1.06 to 2.04) and 1.75 (1.27 to 2.41) respectively (P for trend <0.001). In addition to established risk factors, the GRS improved the classification of 6.2% of women for their absolute risk of breast cancer in the next five years. Conclusions Genetic variants on top of conventional risk factors can improve the risk prediction of breast cancer in Chinese women.
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Scientific reporting is suboptimal for aspects that characterize genetic risk prediction studies: a review of published articles based on the Genetic RIsk Prediction Studies statement. J Clin Epidemiol 2014; 67:487-99. [DOI: 10.1016/j.jclinepi.2013.10.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2013] [Revised: 10/03/2013] [Accepted: 10/09/2013] [Indexed: 12/29/2022]
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Frequent mutation of rs13281615 and its association with PVT1 expression and cell proliferation in breast cancer. J Genet Genomics 2014; 41:187-95. [PMID: 24780616 DOI: 10.1016/j.jgg.2014.03.006] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2013] [Revised: 03/18/2014] [Accepted: 03/27/2014] [Indexed: 12/23/2022]
Abstract
The q24 band of chromosome 8 (8q24) is frequently amplified in human cancers including breast cancer, and several SNPs (single nucleotide polymorphisms) at 8q24, including rs13281615, have been identified for their association with cancer risks. These SNPs are in a "gene desert" region, and their functions in cancer development remain to be illustrated, although several of the SNPs appear to influence the genes in the "desert" in a long-range manner, including the v-myc avian myelocytomatosis viral oncogene homolog (MYC) and the non-protein coding plasmacytoma variant translocation 1 (PVT1), both of which have been implicated in human cancers. In the current study, we examined rs13281615 for its potential role in breast cancer using normal and cancer tissues from 121 Chinese women with breast cancer. In addition to confirming the association of the GG genotype of rs13281615 with breast cancer risk, we found that germline GG genotype was significantly associated with estrogen receptor (ER) positivity, higher tumor grade and higher proliferation index. We also found frequent somatic mutations (22/121 or 18.2%) of this SNP in breast cancer. Interestingly, the majority of the mutations (17/22 or 77%) involved a G→A change, resulting in a decrease in the number of cancers with the GG risk genotype and subsequent loss of GG association with higher tumor grade and proliferation index in cancers. Furthermore, PVT1 expression was increased in cancers, and the increase was associated with the GG genotype of rs13281615. These results suggest that the GG genotype of SNP rs13281615 plays a role in breast cancer likely by influencing PVT1 expression, and that during oncogenesis, "protective" mutations could occur.
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Zheng Q, Ye J, Wu H, Yu Q, Cao J. Association between mitogen-activated protein kinase kinase kinase 1 polymorphisms and breast cancer susceptibility: a meta-analysis of 20 case-control studies. PLoS One 2014; 9:e90771. [PMID: 24595411 PMCID: PMC3942489 DOI: 10.1371/journal.pone.0090771] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2013] [Accepted: 02/04/2014] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND The genome-wide single-nucleotide polymorphisms (SNPs) profiles can be used as diagnostic markers for human cancers. The associations between mitogen-activated protein kinase kinase kinase 1 (MAP3K1) SNPs rs889312 A>C, rs16886165 T>G and breast cancer risk have been widely evaluated, but the results were inconsistent. To derive a conclusive assessment of the associations, we performed a meta-analysis by combining data from all eligible case-control studies up to date. METHODS By searching PubMed, ISI web of knowledge, Embase and Cochrane databases, we identified all eligible studies published before September 2013. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of associations in fixed-effect or random-effect model. False-positive report probability (FPRP) was calculated to confirm the significance of the results. RESULTS A total of 59670 cases in 20 case-control studies were included in this meta-analysis. Significant associations with breast cancer risk were observed for SNPs rs889312 and rs16886165 polymorphisms with a per-allele OR of 1.11 (95% CI: 1.09-1.13) and 1.14 (95% CI: 1.09-1.20) respectively. For rs889312, in subgroup analysis by ethnicity, significant associations were identified in Europeans and Asians, but not in Africans. When stratified by estrogen receptor (ER) expression status, rs889312 was associated with both ER-positive and ER-negative breast cancers. Results from the FPRP analyses were consistent with and supportive to the above results. CONCLUSIONS The present meta-analysis suggests that rs889312-C allele and rs16886165-G allele might be risk factors for breast cancer, especially in Europeans and Asians.
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Affiliation(s)
- Qiaoli Zheng
- Clinical Research Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Jingjia Ye
- Clinical Research Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Haijian Wu
- Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Qing Yu
- Department of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
| | - Jiang Cao
- Clinical Research Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China
- * E-mail:
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Risk-association of five SNPs in TOX3/LOC643714 with breast cancer in southern China. Int J Mol Sci 2014; 15:2130-41. [PMID: 24481062 PMCID: PMC3958841 DOI: 10.3390/ijms15022130] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2013] [Revised: 01/16/2014] [Accepted: 01/21/2014] [Indexed: 11/17/2022] Open
Abstract
The specific mechanism by which low-risk genetic variants confer breast cancer risk is currently unclear, with contradictory evidence on the role of single nucleotide polymorphisms (SNPs) in TOX3/LOC643714 as a breast cancer susceptibility locus. Investigations of this locus using a Chinese population may indicate whether the findings initially identified in a European population are generalizable to other populations, and may provide new insight into the role of genetic variants in the etiology of breast cancer. In this case-control study, 623 Chinese female breast cancer patients and 620 cancer-free controls were recruited to investigate the role of five SNPs in TOX3/LOC643714 (rs8051542, rs12443621, rs3803662, rs4784227, and rs3112612); Linkage disequilibrium (LD) pattern analysis was performed. Additionally, we evaluated how these common SNPs influence the risk of specific types of breast cancer, as defined by estrogen receptor (ER) status, progesterone receptor (PR) status and human epidermal growth factor receptor 2 (HER2) status. Significant associations with breast cancer risk were observed for rs4784227 and rs8051542 with odds ratios (OR) of 1.31 ((95% confidence intervals (CI), 1.10-1.57)) and 1.26 (95% CI, 1.02-1.56), respectively, per T allele. The T-rs8051542 allele was significantly associated with ER-positive and HER2-negative carriers. No significant association existed between rs12443621, rs3803662, and rs3112612 polymorphisms and risk of breast cancer. Our results support the hypothesis that the applicability of a common susceptibility locus must be confirmed among genetically different populations, which may together explain an appreciable fraction of the genetic etiology of breast cancer.
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Chahil JK, Munretnam K, Samsudin N, Lye SH, Hashim NAN, Ramzi NH, Velapasamy S, Wee LL, Alex L. Genetic polymorphisms associated with breast cancer in malaysian cohort. Indian J Clin Biochem 2014; 30:134-9. [PMID: 25883419 DOI: 10.1007/s12291-013-0414-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2013] [Accepted: 12/11/2013] [Indexed: 10/25/2022]
Abstract
Genome-wide association studies have discovered multiple single nucleotide polymorphisms (SNPs) associated with the risk of common diseases. The objective of this study was to demonstrate the replication of previously published SNPs that showed statistical significance for breast cancer in the Malaysian population. In this case-control study, 80 subjects for each group were recruited from various hospitals in Malaysia. A total of 768 SNPs were genotyped and analyzed to distinguish risk and protective alleles. A total of three SNPs were found to be associated with increased risk of breast cancer while six SNPs showed protective effect. All nine were statistically significant SNPs (p ≤ 0.01), five SNPs from previous studies were successfully replicated in our study. Significant modifiable (diet) and non-modifiable (family history of breast cancer in first degree relative) risk factors were also observed. We identified nine SNPs from this study to be either conferring susceptibility or protection to breast cancer which may serve as potential markers in risk prediction.
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Affiliation(s)
- Jagdish Kaur Chahil
- Molecular Research and Services Laboratory, INFOVALLEY® Life Sciences Sdn. Bhd., Unit 101, Level 1, Block B, Mines Waterfront Business Park, No. 3, Jalan Tasik, 43300 MINES Resort City, Selangor Malaysia
| | - Khamsigan Munretnam
- Molecular Research and Services Laboratory, INFOVALLEY® Life Sciences Sdn. Bhd., Unit 101, Level 1, Block B, Mines Waterfront Business Park, No. 3, Jalan Tasik, 43300 MINES Resort City, Selangor Malaysia
| | - Nurulhafizah Samsudin
- Molecular Research and Services Laboratory, INFOVALLEY® Life Sciences Sdn. Bhd., Unit 101, Level 1, Block B, Mines Waterfront Business Park, No. 3, Jalan Tasik, 43300 MINES Resort City, Selangor Malaysia
| | - Say Hean Lye
- Molecular Research and Services Laboratory, INFOVALLEY® Life Sciences Sdn. Bhd., Unit 101, Level 1, Block B, Mines Waterfront Business Park, No. 3, Jalan Tasik, 43300 MINES Resort City, Selangor Malaysia
| | - Nikman Adli Nor Hashim
- Molecular Research and Services Laboratory, INFOVALLEY® Life Sciences Sdn. Bhd., Unit 101, Level 1, Block B, Mines Waterfront Business Park, No. 3, Jalan Tasik, 43300 MINES Resort City, Selangor Malaysia
| | - Nurul Hanis Ramzi
- Molecular Research and Services Laboratory, INFOVALLEY® Life Sciences Sdn. Bhd., Unit 101, Level 1, Block B, Mines Waterfront Business Park, No. 3, Jalan Tasik, 43300 MINES Resort City, Selangor Malaysia
| | - Sharmila Velapasamy
- Molecular Research and Services Laboratory, INFOVALLEY® Life Sciences Sdn. Bhd., Unit 101, Level 1, Block B, Mines Waterfront Business Park, No. 3, Jalan Tasik, 43300 MINES Resort City, Selangor Malaysia
| | - Ler Lian Wee
- Molecular Research and Services Laboratory, INFOVALLEY® Life Sciences Sdn. Bhd., Unit 101, Level 1, Block B, Mines Waterfront Business Park, No. 3, Jalan Tasik, 43300 MINES Resort City, Selangor Malaysia
| | - Livy Alex
- Molecular Research and Services Laboratory, INFOVALLEY® Life Sciences Sdn. Bhd., Unit 101, Level 1, Block B, Mines Waterfront Business Park, No. 3, Jalan Tasik, 43300 MINES Resort City, Selangor Malaysia
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