Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer.
Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer.
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DOI：
10.1038/s41391-020-00311-2
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发表时间：
2021-06
影响因子：
4.8
通讯作者：
PRACTICAL Consortium
中科院分区：
文献类型：
--
作者：
Karunamuni RA;Huynh-Le MP;Fan CC;Thompson W;Eeles RA;Kote-Jarai Z;Muir K;Lophatananon A;UKGPCS collaborators;Schleutker J;Pashayan N;Batra J;APCB BioResource (Australian Prostate Cancer BioResource);Grönberg H;Walsh EI;Turner EL;Lane A;Martin RM;Neal DE;Donovan JL;Hamdy FC;Nordestgaard BG;Tangen CM;MacInnis RJ;Wolk A;Albanes D;Haiman CA;Travis RC;Stanford JL;Mucci LA;West CML;Nielsen SF;Kibel AS;Wiklund F;Cussenot O;Berndt SI;Koutros S;Sørensen KD;Cybulski C;Grindedal EM;Park JY;Ingles SA;Maier C;Hamilton RJ;Rosenstein BS;Vega A;IMPACT Study Steering Committee and Collaborators;Kogevinas M;Penney KL;Teixeira MR;Brenner H;John EM;Kaneva R;Logothetis CJ;Neuhausen SL;Razack A;Newcomb LF;Canary PASS Investigators;Gamulin M;Usmani N;Claessens F;Gago-Dominguez M;Townsend PA;Roobol MJ;Zheng W;Profile Study Steering Committee;Mills IG;Andreassen OA;Dale AM;Seibert TM;PRACTICAL Consortium
多基因危险评分（PHS）可以识别前列腺癌风险增加的个体。我们估计了额外的SNP对先前验证的PHS（PHS 46）性能的益处。180个先前显示与前列腺癌相关的SNP被用于在具有欧洲血统的男性中开发PHS模型。使用机器学习方法LASSO正则化考克斯回归来选择SNP并估计其在训练集（75，596名男性）中的系数。在测试/验证集（6，411名男性）中使用两个指标评价了所得模型的性能：（1）风险比（HR）和（2）前列腺特异性抗原（PSA）测试的阳性预测值（PPV）。估计PHS在前5%至中间40%（HR 95/50）、前20%至后20%（HR 80/20）和后20%至中间40%（HR 20/50）的个体之间的HR。计算PHS前20%（PPV 80）和前5%（PPV 95）的PPV，作为PSA升高且在活检中被诊断患有临床显着前列腺癌的个体比例。在考克斯模型（PHS 166）中，166个SNP具有非零系数。与PHS 46相比，PHS 166的所有HR指标均显示出显著改善：HR 95/50从3.72增加至5.09，HR 80/20从6.12增加至9.45，HR 20/50从0.41降低至0.34。相比之下，在临床显著前列腺癌的PSA检测的PPV中未观察到显著差异。增加120个SNP（PHS 166 vs PHS 46）显著改善了前列腺癌的HR，而PSA检测的PPV保持不变。
Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46). 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy. 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer. Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.
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影响因子：
1.3
作者：
Therneau, TM;Li, HZ
通讯作者：
Li, HZ
DOI：
10.1038/s41431-020-0664-2
发表时间：
2020-10
期刊：
European journal of human genetics : EJHG
影响因子：
--
作者：
Karunamuni RA;Huynh-Le MP;Fan CC;Eeles RA;Easton DF;Kote-Jarai Z;Amin Al Olama A;Benlloch Garcia S;Muir K;Gronberg H;Wiklund F;Aly M;Schleutker J;Sipeky C;Tammela TLJ;Nordestgaard BG;Key TJ;Travis RC;Neal DE;Donovan JL;Hamdy FC;Pharoah P;Pashayan N;Khaw KT;Thibodeau SN;McDonnell SK;Schaid DJ;Maier C;Vogel W;Luedeke M;Herkommer K;Kibel AS;Cybulski C;Wokolorczyk D;Kluzniak W;Cannon-Albright L;Brenner H;Schöttker B;Holleczek B;Park JY;Sellers TA;Lin HY;Slavov C;Kaneva R;Mitev V;Batra J;Clements JA;Spurdle A;Australian Prostate Cancer BioResource (APCB);Teixeira MR;Paulo P;Maia S;Pandha H;Michael A;Mills IG;Andreassen OA;Dale AM;Seibert TM;PRACTICAL Consortium
通讯作者：
PRACTICAL Consortium
DOI：
10.1158/1055-9965.epi-16-0106
发表时间：
2017-01
期刊：
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
影响因子：
--
作者：
Amos CI;Dennis J;Wang Z;Byun J;Schumacher FR;Gayther SA;Casey G;Hunter DJ;Sellers TA;Gruber SB;Dunning AM;Michailidou K;Fachal L;Doheny K;Spurdle AB;Li Y;Xiao X;Romm J;Pugh E;Coetzee GA;Hazelett DJ;Bojesen SE;Caga-Anan C;Haiman CA;Kamal A;Luccarini C;Tessier D;Vincent D;Bacot F;Van Den Berg DJ;Nelson S;Demetriades S;Goldgar DE;Couch FJ;Forman JL;Giles GG;Conti DV;Bickeböller H;Risch A;Waldenberger M;Brüske-Hohlfeld I;Hicks BD;Ling H;McGuffog L;Lee A;Kuchenbaecker K;Soucy P;Manz J;Cunningham JM;Butterbach K;Kote-Jarai Z;Kraft P;FitzGerald L;Lindström S;Adams M;McKay JD;Phelan CM;Benlloch S;Kelemen LE;Brennan P;Riggan M;O'Mara TA;Shen H;Shi Y;Thompson DJ;Goodman MT;Nielsen SF;Berchuck A;Laboissiere S;Schmit SL;Shelford T;Edlund CK;Taylor JA;Field JK;Park SK;Offit K;Thomassen M;Schmutzler R;Ottini L;Hung RJ;Marchini J;Amin Al Olama A;Peters U;Eeles RA;Seldin MF;Gillanders E;Seminara D;Antoniou AC;Pharoah PD;Chenevix-Trench G;Chanock SJ;Simard J;Easton DF
通讯作者：
Easton DF
影响因子：
30.8
作者：
Eeles, Rosalind A.;Al Olama, Ali Amin;Benlloch, Sara;Saunders, Edward J.;Leongamornlert, Daniel A.;Tymrakiewicz, Malgorzata;Ghoussaini, Maya;Luccarini, Craig;Dennis, Joe;Jugurnauth-Little, Sarah;Dadaev, Tokhir;Neal, David E.;Hamdy, Freddie C.;Donovan, Jenny L.;Muir, Ken;Giles, Graham G.;Severi, Gianluca;Wiklund, Fredrik;Gronberg, Henrik;Haiman, Christopher A.;Schumacher, Fredrick;Henderson, Brian E.;Le Marchand, Loic;Lindstrom, Sara;Kraft, Peter;Hunter, David J.;Gapstur, Susan;Chanock, Stephen J.;Berndt, Sonja I.;Albanes, Demetrius;Andriole, Gerald;Schleutker, Johanna;Weischer, Maren;Canzian, Federico;Riboli, Elio;Key, Tim J.;Travis, Ruth C.;Campa, Daniele;Ingles, Sue A.;John, Esther M.;Hayes, Richard B.;Pharoah, Paul D. P.;Pashayan, Nora;Khaw, Kay-Tee;Stanford, Janet L.;Ostrander, Elaine A.;Signorello, Lisa B.;Thibodeau, Stephen N.;Schaid, Dan;Maier, Christiane;Vogel, Walther;Kibel, Adam S.;Cybulski, Cezary;Lubhiski, Jan;Cannon-Albright, Lisa;Brenner, Hermann;Park, Jong Y.;Kaneva, Radka;Batra, Jyotsna;Spurdle, Amanda B.;Clements, Judith A.;Teixeira, Manuel R.;Dicks, Ed;Lee, Andrew;Dunning, Alison M.;Baynes, Caroline;Conroy, Don;Maranian, Melanie J.;Ahmed, Shahana;Govindasami, Koveela;Guy, Michelle;Wilkinson, Rosemary A.;Sawyer, Emma J.;Morgan, Angela;Dearnaley, David P.;Horwich, Alan;Huddart, Robert A.;Khoo, Vincent S.;Parker, Christopher C.;Van As, Nicholas J.;Woodhouse, Christopher J.;Thompson, Alan;Dudderidge, Tim;Ogden, Chris;Cooper, Colin S.;Lophatananon, Artitaya;Cox, Angela;Southey, Melissa C.;Hopper, John L.;English, Dallas R.;Aly, Markus;Adolfsson, Jan;Xu, Jiangfeng;Zheng, Siqun L.;Yeager, Meredith;Kaaks, Rudolf;Diver, W. Ryan;Gaudet, Mia M.;Stern, Mariana C.;Corral, Roman;Joshi, Amit D.;Shahabi, Ahva;Wahlfors, Tiina;Tammela, Teuvo L. J.;Auvinen, Anssi;Virtamo, Jarmo;Klarskov, Peter;Nordestgaard, Borge G.;Roder, M. Andreas;Nielsen, Sune F.;Bojesen, Stig E.;Siddiq, Afshan;FitzGerald, Liesel M.;Kolb, Suzanne;Kwon, Erika M.;Karyadi, Danielle M.;Blot, William J.;Zheng, Wei;Cai, Qiuyin;McDonnell, Shannon K.;Rinckleb, Antje E.;Drake, Bettina;Colditz, Graham;Wokolorczyk, Dominika;Stephenson, Robert A.;Teerlink, Craig;Muller, Heiko;Rothenbacher, Dietrich;Sellers, Thomas A.;Lin, Hui-Yi;Slavov, Chavdar;Mitev, Vanio;Lose, Felicity;Srinivasan, Srilakshmi;Maia, Sofia;Paulo, Paula;Lange, Ethan;Cooney, Kathleen A.;Antoniou, Antonis C.;Vincent, Daniel;Bacot, Francois;Tessier, Daniel C.;Kote-Jarai, Zsofia;Easton, Douglas F.
通讯作者：
Easton, Douglas F.
影响因子：
2.1
作者：
Heller, Glenn
通讯作者：
Heller, Glenn