Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women.
Development and validation of polygenic risk scores for prediction of breast cancer and breast cancer subtypes in Chinese women.
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用于预测中国女性乳腺癌和乳腺癌亚型的多基因风险评分的开发和验证
DOI：
10.1186/s12885-022-09425-3
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发表时间：
2022-04-08
期刊：
影响因子：
3.8
通讯作者：
Li J
中科院分区：
文献类型：
--
作者：
Hou C;Xu B;Hao Y;Yang D;Song H;Li J
关于中国女性乳腺癌多基因风险评分（PRS）的研究很少。本研究的目的是开发和验证可用于对中国女性总体和亚型特异性乳腺癌风险分层的PRS，并评估新提出的基于人工神经网络（ANN）的PRS构建方法的性能。使用全基因组关联研究（GWAS）的数据集构建PRS，并在独立的病例对照研究中进行验证。采用重复逻辑回归（RLR）、逻辑岭回归（LRR）和人工神经网络（ANN）3种方法，基于24个单核苷酸多态性（SNPs）构建乳腺癌总体和亚型特异性的PRS。在未调整和调整Gail-2模型5年风险或经典乳腺癌风险因素的情况下评价PRS的预测性能和校准。主要PRSANN和PRSLRR均显示出对总体乳腺癌的适度预测能力（对照组PRS每四分位数范围增加的比值比[IQ-OR] 1.76 vs 1.58;受试者工作特征曲线下面积[AUC] 0.601 vs 0.598），调整后仍具有预测性。虽然雌激素受体阴性（ER-）乳腺癌的预测效果很差，但仅对ER-乳腺癌病例进行训练的ER-PRS在预测ER-乳腺癌方面有了实质性的改善。基于24个SNPs的PRS可以提供额外的风险信息，以帮助在中国一般人群中进行乳腺癌风险分层。新提出的人工神经网络方法的PRS建设有可能取代传统的方法，但需要更多的研究来验证和调查其性能。在线版本包含补充材料，可通过10.1186/s12885-022-09425-3获得。
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. 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. 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. 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. The online version contains supplementary material available at 10.1186/s12885-022-09425-3.
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影响因子：
8.8
作者：
Beral, V;Hamajima, N;Hirose, K;Rohan, T;Calle, EE;Heath, CW;Coates, RJ;Liff, JM;Talamini, R;Chantarakul, N;Koetsawang, S;Rachawat, D;Morabia, A;Schuman, L;Stewart, W;Szklo, M;Bain, C;Schofield, F;Siskind, V;Band, P;Coldman, AJ;Gallagher, RP;Hislop, TG;Yang, P;Kolonel, LM;Nomura, AMY;Hu, J;Johnson, KC;Mao, Y;De Sanjose, S;Lee, N;Marchbanks, P;Ory, HW;Peterson, HB;Wilson, HG;Wingo, PA;Ebeling, K;Kunde, D;Nishan, P;Hopper, JL;Colditz, G;Gajalakshmi, V;Martin, N;Pardthaisong, T;Solpisornkosol, S;Theetranont, C;Boosiri, B;Chutivongse, S;Jimakorn, P;Virutamasen, P;Wongsrichanalai, C;Ewertz, M;Adami, HO;Bergkvist, L;Magnusson, C;Persson, I;Chang-Claude, J;Paul, C;Skegg, DCG;Spears, GFS;Boyle, P;Evstifeeva, T;Daling, JR;Hutchinson, WB;Malone, K;Noonan, EA;Stanford, JL;Thomas, DB;Weiss, NS;White, E;Andrieu, N;Brêmond, A;Clavel, F;Gairard, B;Lansac, J;Piana, L;Renaud, R;Izquierdo, A;Viladiu, P;Cuevas, HR;Ontiveros, P;Palet, A;Salazar, SB;Arsitizabal, N;Cuadros, A;Tryggvadottir, L;Tulinius, H;Bachelot, A;Lê, MG;Peto, J;Franceschi, S;Lubin, F;Modan, B;Ron, E;Wax, Y;Friedman, GD;Hiatt, RA;Levi, F;Bishop, T;Kosmelj, K;Primic-Zakelj, M;Ravnihar, B;Stare, J;Beeson, WL;Fraser, G;Bulbrook, RD;Cuzick, J;Duffy, SW;Fentiman, IS;Hayward, JL;Wang, DY;McMichael, AJ;McPherson, K;Hanson, RL;Leske, MC;Mahoney, MC;Nasca, PC;Varma, AO;Weinstein, AL;Moller, TR;Olsson, H;Ranstam, J;Goldbohm, RA;van den Brandt, PA;Apelo, RA;Baens, J;de la Cruz, JR;Javier, B;Lacaya, LB;Ngelangel, CA;La Vecchia, C;Negri, E;Marubini, E;Ferraroni, M;Gerber, M;Richardson, S;Segala, C;Gatei, D;Kenya, P;Kungu, A;Mati, JG;Brinton, LA;Hoover, R;Schairer, C;Spirtas, R;Lee, HP;Rookus, MA;van Leeuwen, FE;Schoenberg, JA;McCredie, M;Gammon, MD;Clarke, EA;Jones, L;Neil, A;Vessey, M;Yeates, D;Appleby, P;Banks, E;Bull, D;Crossley, B;Goodill, A;Green, J;Hermon, C;Key, T;Langston, N;Lewis, C;Reeves, G;Collins, R;Doll, R;Peto, R;Mabuchi, K;Preston, D;Hannaford, P;Kay, C;Rosero-Bixby, L;Gao, YT;Jin, F;Yuan, JM;Wei, HY;Yun, T;Zhiheng, C;Berry, G;Cooper Booth, J;Jelihovsky, T;MacLennan, R;Shearman, R;Wang, QS;Baines, CJ;Miller, AB;Wall, C;Lund, E;Stalsberg, H;Shu, XO;Zheng, W;Katsouyanni, K;Trichopoulou, A;Trichopoulos, D;Dabancens, A;Martinez, L;Molina, R;Salas, O;Alexander, XE;Anderson, K;Folsom, AR;Hulka, BS;Bernstein, L;Enger, S;Haile, RW;Paganini-Hill, A;Pike, MC;Ross, RK;Ursin, G;Yu, MC;Longnecker, MP;Newcomb, P;Bergkvist, L;Kalache, A;Farley, TMM;Holck, S;Meirik, O
通讯作者：
Meirik, O
影响因子：
9.2
作者：
Chang CC;Chow CC;Tellier LC;Vattikuti S;Purcell SM;Lee JJ
通讯作者：
Lee JJ
DOI：
10.1093/jnci/91.11.943
发表时间：
1999-06-02
期刊：
JOURNAL OF THE NATIONAL CANCER INSTITUTE
影响因子：
--
作者：
Peto, J;Collins, N;Stratton, MR
通讯作者：
Stratton, MR
DOI：
10.1097/gme.0b013e3181aac32b
发表时间：
2009-11
期刊：
Menopause (New York, N.Y.)
影响因子：
--
作者：
Dorjgochoo T;Gu K;Kallianpur A;Zheng Y;Zheng W;Chen Z;Lu W;Shu XO
通讯作者：
Shu XO
DOI：
10.1016/s1470-2045(12)70425-4
发表时间：
2012-11
期刊：
The Lancet. Oncology
影响因子：
--
作者：
Collaborative Group on Hormonal Factors in Breast Cancer
通讯作者：
Collaborative Group on Hormonal Factors in Breast Cancer