Incorporating Polygenic Risk Scores and Nongenetic Risk Factors for Breast Cancer Risk Prediction Among Asian Women.
Incorporating Polygenic Risk Scores and Nongenetic Risk Factors for Breast Cancer Risk Prediction Among Asian Women.
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DOI：
10.1001/jamanetworkopen.2021.49030
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发表时间：
2022-03-01
影响因子：
13.8
通讯作者：
Zheng W
中科院分区：
文献类型：
--
作者：
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
结合多基因风险评分 (PRS) 和非遗传风险因素的乳腺癌风险预测模型对亚洲女性的效果如何？在这项针对 126-894 名女性的诊断研究中，开发了包含 111 种遗传变异的 PRS，并使用前瞻性队列研究的数据进行了测试。 PRS 与乳腺癌风险显着相关，添加 7 个非遗传风险因素提高了模型的准确性。这些发现支持预测模型在识别亚洲女性乳腺癌高风险方面的实用性。这项诊断研究开发并测试了亚洲女性乳腺癌风险预测模型，其中纳入了多基因风险评分 (PRS) 和非遗传风险因素。多基因风险评分（PRS）在乳腺癌风险预测中显示出前景；然而，针对亚洲女性进行的研究有限。结合 PRS 和非遗传风险因素，为亚洲女性开发乳腺癌风险预测模型。这项诊断研究包括来自亚洲乳腺癌联盟的亚洲血统女性。 PRS 是使用 123 041 名亚洲血统女性（包括 18 650 名乳腺癌女性）进行的乳腺癌全基因组关联研究 (GWAS) 数据开发的，采用 3 种方法：(1) 报告欧洲血统女性的 PRS； (2) 通过 GWAS 确定的风险位点精细作图来确定乳腺癌相关的单核苷酸变异 (SNV)； (3)全基因组风险预测算法。使用来自前瞻性队列研究的 416 名病例参与者和 1558 名对照参与者的数据建立了非遗传风险评分 (NGRS)，其中包括 7 个公认的非遗传风险因素。 PRS 最初在包括 1426 名病例参与者和 1323 名对照参与者的独立数据集中进行了验证，并与 NGRS 一起在第二个数据集中（包括 368 名病例参与者和 736 名对照参与者）在前瞻性队列研究中进行了进一步评估。 Logistic 回归用于检查风险评分与乳腺癌风险的关联，以估计 95% CI 的比值比 (OR) 和受试者工作特征曲线下面积 (AUC)。总共包括 126 894 名亚洲血统女性； 20 444 人（16.1%）患有乳腺癌。在提供人口统计特征的研究中，病例参与者的平均 (SD) 年龄范围为 49.1 (10.8) 至 54.4 (10.4) 岁，对照参与者的平均 (SD) 年龄范围为 50.6 (9.5) 至 54.0 (7.4) 岁。在前瞻性队列中，使用精细作图方法 (PRS111) 开发的具有 111 个 SNV 的 PRS 显示出与包含超过 855000 个 SNV 的全基因组 PRS 相当的预测性能。 PRS111 评分每 SD 增加的 OR 为 1.67（95% CI，1.46-1.92），AUC 为 0.639（95% CI，0.604-0.674）。 NGRS 的预测能力有限（AUC，0.565；95% CI，0.529-0.601）。与平均风险组（第 40-60 个百分位）相比，PRS111 和 NGRS 前 5% 的女性患乳腺癌的风险分别高出 3.84 倍（95% CI，2.30-6.46）和 2.10 倍（95% CI，1.22-3.62）。包含 PRS111 和 NGRS 的预测模型实现了最高的预测精度（AUC，0.648；95% CI，0.613-0.682）。在这项研究中，使用乳腺癌风险相关 SNV 衍生的 PRS 在亚洲和欧洲女性中具有相似的预测性能。将非遗传风险因素纳入模型进一步提高了预测准确性。这些发现支持这些模型在制定个性化筛查和预防策略方面的实用性。
How well do breast cancer risk prediction models that incorporate polygenic risk scores (PRSs) and nongenetic risk factors perform for Asian women? In this diagnostic study of 126 894 women, a PRS including 111 genetic variants was developed and tested using data from a prospective cohort study. The PRS was significantly associated with breast cancer risk, and adding 7 nongenetic risk factors improved the model’s accuracy. These findings support the utility of prediction models in identifying Asian women with high risk of breast cancer. This diagnostic study develops and tests breast cancer risk prediction models for Asian women, incorporating polygenic risk scores (PRSs) and nongenetic risk factors. Polygenic risk scores (PRSs) have shown promise in breast cancer risk prediction; however, limited studies have been conducted among Asian women. To develop breast cancer risk prediction models for Asian women incorporating PRSs and nongenetic risk factors. 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. 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). 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). 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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影响因子：
4.5
作者：
Long J;Cai Q;Shu XO;Qu S;Li C;Zheng Y;Gu K;Wang W;Xiang YB;Cheng J;Chen K;Zhang L;Zheng H;Shen CY;Huang CS;Hou MF;Shen H;Hu Z;Wang F;Deming SL;Kelley MC;Shrubsole MJ;Khoo US;Chan KY;Chan SY;Haiman CA;Henderson BE;Le Marchand L;Iwasaki M;Kasuga Y;Tsugane S;Matsuo K;Tajima K;Iwata H;Huang B;Shi J;Li G;Wen W;Gao YT;Lu W;Zheng W
通讯作者：
Zheng W
影响因子：
64.8
作者：
Michailidou K;Lindström S;Dennis J;Beesley J;Hui S;Kar S;Lemaçon A;Soucy P;Glubb D;Rostamianfar A;Bolla MK;Wang Q;Tyrer J;Dicks E;Lee A;Wang Z;Allen J;Keeman R;Eilber U;French JD;Qing Chen X;Fachal L;McCue K;McCart Reed AE;Ghoussaini M;Carroll JS;Jiang X;Finucane H;Adams M;Adank MA;Ahsan H;Aittomäki K;Anton-Culver H;Antonenkova NN;Arndt V;Aronson KJ;Arun B;Auer PL;Bacot F;Barrdahl M;Baynes C;Beckmann MW;Behrens S;Benitez J;Bermisheva M;Bernstein L;Blomqvist C;Bogdanova NV;Bojesen SE;Bonanni B;Børresen-Dale AL;Brand JS;Brauch H;Brennan P;Brenner H;Brinton L;Broberg P;Brock IW;Broeks A;Brooks-Wilson A;Brucker SY;Brüning T;Burwinkel B;Butterbach K;Cai Q;Cai H;Caldés T;Canzian F;Carracedo A;Carter BD;Castelao JE;Chan TL;David Cheng TY;Seng Chia K;Choi JY;Christiansen H;Clarke CL;NBCS Collaborators;Collée M;Conroy DM;Cordina-Duverger E;Cornelissen S;Cox DG;Cox A;Cross SS;Cunningham JM;Czene K;Daly MB;Devilee P;Doheny KF;Dörk T;Dos-Santos-Silva I;Dumont M;Durcan L;Dwek M;Eccles DM;Ekici AB;Eliassen AH;Ellberg C;Elvira M;Engel C;Eriksson M;Fasching PA;Figueroa J;Flesch-Janys D;Fletcher O;Flyger H;Fritschi L;Gaborieau V;Gabrielson M;Gago-Dominguez M;Gao YT;Gapstur SM;García-Sáenz JA;Gaudet MM;Georgoulias V;Giles GG;Glendon G;Goldberg MS;Goldgar DE;González-Neira A;Grenaker Alnæs GI;Grip M;Gronwald J;Grundy A;Guénel P;Haeberle L;Hahnen E;Haiman CA;Håkansson N;Hamann U;Hamel N;Hankinson S;Harrington P;Hart SN;Hartikainen JM;Hartman M;Hein A;Heyworth J;Hicks B;Hillemanns P;Ho DN;Hollestelle A;Hooning MJ;Hoover RN;Hopper JL;Hou MF;Hsiung CN;Huang G;Humphreys K;Ishiguro J;Ito H;Iwasaki M;Iwata H;Jakubowska A;Janni W;John EM;Johnson N;Jones K;Jones M;Jukkola-Vuorinen A;Kaaks R;Kabisch M;Kaczmarek K;Kang D;Kasuga Y;Kerin MJ;Khan S;Khusnutdinova E;Kiiski JI;Kim SW;Knight JA;Kosma VM;Kristensen VN;Krüger U;Kwong A;Lambrechts D;Le Marchand L;Lee E;Lee MH;Lee JW;Neng Lee C;Lejbkowicz F;Li J;Lilyquist J;Lindblom A;Lissowska J;Lo WY;Loibl S;Long J;Lophatananon A;Lubinski J;Luccarini C;Lux MP;Ma ESK;MacInnis RJ;Maishman T;Makalic E;Malone KE;Kostovska IM;Mannermaa A;Manoukian S;Manson JE;Margolin S;Mariapun S;Martinez ME;Matsuo K;Mavroudis D;McKay J;McLean C;Meijers-Heijboer H;Meindl A;Menéndez P;Menon U;Meyer J;Miao H;Miller N;Taib NAM;Muir K;Mulligan AM;Mulot C;Neuhausen SL;Nevanlinna H;Neven P;Nielsen SF;Noh DY;Nordestgaard BG;Norman A;Olopade OI;Olson JE;Olsson H;Olswold C;Orr N;Pankratz VS;Park SK;Park-Simon TW;Lloyd R;Perez JIA;Peterlongo P;Peto J;Phillips KA;Pinchev M;Plaseska-Karanfilska D;Prentice R;Presneau N;Prokofyeva D;Pugh E;Pylkäs K;Rack B;Radice P;Rahman N;Rennert G;Rennert HS;Rhenius V;Romero A;Romm J;Ruddy KJ;Rüdiger T;Rudolph A;Ruebner M;Rutgers EJT;Saloustros E;Sandler DP;Sangrajrang S;Sawyer EJ;Schmidt DF;Schmutzler RK;Schneeweiss A;Schoemaker MJ;Schumacher F;Schürmann P;Scott RJ;Scott C;Seal S;Seynaeve C;Shah M;Sharma P;Shen CY;Sheng G;Sherman ME;Shrubsole MJ;Shu XO;Smeets A;Sohn C;Southey MC;Spinelli JJ;Stegmaier C;Stewart-Brown S;Stone J;Stram DO;Surowy H;Swerdlow A;Tamimi R;Taylor JA;Tengström M;Teo SH;Beth Terry M;Tessier DC;Thanasitthichai S;Thöne K;Tollenaar RAEM;Tomlinson I;Tong L;Torres D;Truong T;Tseng CC;Tsugane S;Ulmer HU;Ursin G;Untch M;Vachon C;van Asperen CJ;Van Den Berg D;van den Ouweland AMW;van der Kolk L;van der Luijt RB;Vincent D;Vollenweider J;Waisfisz Q;Wang-Gohrke S;Weinberg CR;Wendt C;Whittemore AS;Wildiers H;Willett W;Winqvist R;Wolk A;Wu AH;Xia L;Yamaji T;Yang XR;Har Yip C;Yoo KY;Yu JC;Zheng W;Zheng Y;Zhu B;Ziogas A;Ziv E;ABCTB Investigators;ConFab/AOCS Investigators;Lakhani SR;Antoniou AC;Droit A;Andrulis IL;Amos CI;Couch FJ;Pharoah PDP;Chang-Claude J;Hall P;Hunter DJ;Milne RL;García-Closas M;Schmidt MK;Chanock SJ;Dunning AM;Edwards SL;Bader GD;Chenevix-Trench G;Simard J;Kraft P;Easton DF
通讯作者：
Easton DF
东亚人的全基因组关联研究确定了乳腺癌的新易感位点
影响因子：
4.5
作者：
Long J;Cai Q;Sung H;Shi J;Zhang B;Choi JY;Wen W;Delahanty RJ;Lu W;Gao YT;Shen H;Park SK;Chen K;Shen CY;Ren Z;Haiman CA;Matsuo K;Kim MK;Khoo US;Iwasaki M;Zheng Y;Xiang YB;Gu K;Rothman N;Wang W;Hu Z;Liu Y;Yoo KY;Noh DY;Han BG;Lee MH;Zheng H;Zhang L;Wu PE;Shieh YL;Chan SY;Wang S;Xie X;Kim SW;Henderson BE;Le Marchand L;Ito H;Kasuga Y;Ahn SH;Kang HS;Chan KY;Iwata H;Tsugane S;Li C;Shu XO;Kang DH;Zheng W
通讯作者：
Zheng W
影响因子：
7.7
作者：
Rudolph, Anja;Song, Minsun;Garcia-Closas, Montserrat
通讯作者：
Garcia-Closas, Montserrat
影响因子：
9.8
作者：
Kramer, Iris;Hooning, Maartje J.;Schmidt, Marjanka K.
通讯作者：
Schmidt, Marjanka K.