Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women
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 perf...
| Main Authors: | , , , , , |
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BMC
2022-04-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-022-09425-3 |
| _version_ | 1811272604504293376 |
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| author | Can Hou Bin Xu Yu Hao Daowen Yang Huan Song Jiayuan Li |
| author_facet | Can Hou Bin Xu Yu Hao Daowen Yang Huan Song Jiayuan Li |
| author_sort | Can Hou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T22:43:20Z |
| format | Article |
| id | doaj.art-0ea7c02240ad459d936f2632467c31d6 |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-04-12T22:43:20Z |
| publishDate | 2022-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj.art-0ea7c02240ad459d936f2632467c31d62022-12-22T03:13:37ZengBMCBMC Cancer1471-24072022-04-0122111310.1186/s12885-022-09425-3Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese womenCan Hou0Bin Xu1Yu Hao2Daowen Yang3Huan Song4Jiayuan Li5West China Biomedical Big Data Center, West China Hospital, Sichuan UniversityDepartment of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan UniversityDepartment of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan UniversityRobot Perception and Control Joint Lab, Sichuan University & AisonoWest China Biomedical Big Data Center, West China Hospital, Sichuan UniversityDepartment of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan UniversityAbstract 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.https://doi.org/10.1186/s12885-022-09425-3Breast cancerPolygenic risk scoreSingle nucleotide polymorphismsArtificial neural networkEstrogen receptor-negative breast cancer |
| spellingShingle | Can Hou Bin Xu Yu Hao Daowen Yang Huan Song Jiayuan Li Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women BMC Cancer Breast cancer Polygenic risk score Single nucleotide polymorphisms Artificial neural network Estrogen receptor-negative breast cancer |
| title | Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women |
| title_full | Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women |
| title_fullStr | Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women |
| title_full_unstemmed | Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women |
| title_short | Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women |
| title_sort | development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in chinese women |
| topic | Breast cancer Polygenic risk score Single nucleotide polymorphisms Artificial neural network Estrogen receptor-negative breast cancer |
| url | https://doi.org/10.1186/s12885-022-09425-3 |
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