| 黄育北,宋丰举,陈可欣.全基因组关联研究在乳腺癌筛查中的应用价值初探[J].Chinese journal of Epidemiology,2019,40(6):713-718 |
| 全基因组关联研究在乳腺癌筛查中的应用价值初探 |
| Application values of genome-wide association studies in screening for breast cancer |
| Received:November 21, 2018 |
| DOI：10.3760/cma.j.issn.0254-6450.2019.06.021 |
| KeyWord: 乳腺肿瘤 全基因组关联研究 单核苷酸多态性 |
| English Key Word: Breast neoplasms Genome-wide association study Single-nucleotide polymorphism |
| FundProject:国家自然科学基金（81502476）；国家重点研发计划（2018YFC1315600） |
| Author Name | Affiliation | E-mail | | Huang Yubei | Department of Epidemiology and Biostatistics, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin Medical University Cancer Hospital, Tianjin 300060, China | | | Song Fengju | Department of Epidemiology and Biostatistics, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin Medical University Cancer Hospital, Tianjin 300060, China | songfengju@163.com | | Chen Kexin | Department of Epidemiology and Biostatistics, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin Medical University Cancer Hospital, Tianjin 300060, China | |
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| Abstract: |
| 目的 探索全基因组关联研究（GWAS）发现的单核苷酸多态性位点（SNP）在乳腺癌筛查中的潜在应用价值。方法 基于我国女性2013年的年龄构成、年龄别的乳腺癌发病率，以及明确的乳腺癌传统危险因素分布情况，对中国200万35～69岁女性人群进行模拟。进一步模拟GWAS发现的23个与我国女性乳腺癌风险相关的SNP位点的分布情况。依据SNP的遗传风险解释程度及风险再分类准确性的改善程度，初筛出可用于预测乳腺癌高危人群的目标SNP，并进一步探索目标SNP对乳腺癌检出率、乳腺癌风险预测曲线下面积（AUC）、高危人群中乳腺癌发病风险的影响。结果 共发现12个SNP可用于预测乳腺癌高危人群。如果将预测风险位于P95及更高风险的人群定义为高危人群，并在此类人群中进行筛查，采用目标SNP预测的高危人群中的乳腺癌检出率（146.99/10万）明显低于采用传统危险因素预测的高危人群中的乳腺癌检出率（177.46/10万）（P<0.001）。在传统危险因素基础上，加上目标SNP进行高危人群预测，高危人群中乳腺癌检出率（229.00/10万）提高29.0%（P<0.001）。同时乳腺癌风险预测的AUC从64.4%上升至67.8%（P<0.001），高危人群中乳腺癌发病风险OR值从3.32上升至4.33。结论 GWAS筛选出的目标SNP可提高乳腺癌检出率、乳腺癌总体风险预测准确性，并有助于在乳腺癌筛查前发现潜在的乳腺癌高危人群。 |
| English Abstract: |
| Objective To investigate the potential application values of screening on breast cancer, using the single-nucleotide polymorphisms (SNPs) that were identified from the genome-wide association studies (GWASs). Methods Two million Chinese women aged 35-69 years were simulated, based on both age distributions, age-specific incidence rates of breast cancer and the distribution of known risk factors, in 2013. Twenty-three SNPs identified from GWAS were further simulated. Both genetic-related risks explained by each SNPs and the improvement on the risks under reclassification, were used to select SNPs for the prediction on breast cancer among the targeted high-risk population. Further analyses were conducted to investigate the following items as:improvements on detection rates of breast cancer among the high-risk populations, areas under the curve (AUC) and the odds ratio (OR) among women at high risk. Results A total of 12 SNPs were eligible for targeting the high-risk population of breast cancer. When high-risk populations were defined as women whose predicted risks were higher than the 95th predicted risk of the whole population, the detection rate (146.99/100 000) among high-risked women predicted by 12 SNPs would be significantly lower than 177.46/100 000, which was predicted by the known risk factors (P<0.001), among the high-risked women. Among those women at high risk, the detection rate (229.00/100 000) predicted by integrating known risk factors and 12 SNPs was significantly higher than that predicted by known risk factors (P<0.001). Also, the AUC increased from 64.4% to 67.8% (P<0.001), and the OR of increased from 3.32 to 4.33, predicted by integrating known risk factors and 12 SNPs, for women at high risk on breast cancer. Conclusion Targeted SNPs that were identified from genome-wide association studies could be used to improve the detection rates as well as the overall accuracy of risk prediction so as to identify the potential high-risk women on breast cancer before carrying on the screening program. |
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