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. 2019 Jan 3;104(1):21-34.
doi: 10.1016/j.ajhg.2018.11.002. Epub 2018 Dec 13.

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

Nasim Mavaddat  1 Kyriaki Michailidou  2 Joe Dennis  3 Michael Lush  3 Laura Fachal  4 Andrew Lee  3 Jonathan P Tyrer  4 Ting-Huei Chen  5 Qin Wang  3 Manjeet K Bolla  3 Xin Yang  3 Muriel A Adank  6 Thomas Ahearn  7 Kristiina Aittomäki  8 Jamie Allen  3 Irene L Andrulis  9 Hoda Anton-Culver  10 Natalia N Antonenkova  11 Volker Arndt  12 Kristan J Aronson  13 Paul L Auer  14 Päivi Auvinen  15 Myrto Barrdahl  16 Laura E Beane Freeman  7 Matthias W Beckmann  17 Sabine Behrens  16 Javier Benitez  18 Marina Bermisheva  19 Leslie Bernstein  20 Carl Blomqvist  21 Natalia V Bogdanova  22 Stig E Bojesen  23 Bernardo Bonanni  24 Anne-Lise Børresen-Dale  25 Hiltrud Brauch  26 Michael Bremer  27 Hermann Brenner  28 Adam Brentnall  29 Ian W Brock  30 Angela Brooks-Wilson  31 Sara Y Brucker  32 Thomas Brüning  33 Barbara Burwinkel  34 Daniele Campa  35 Brian D Carter  36 Jose E Castelao  37 Stephen J Chanock  7 Rowan Chlebowski  38 Hans Christiansen  27 Christine L Clarke  39 J Margriet Collée  40 Emilie Cordina-Duverger  41 Sten Cornelissen  42 Fergus J Couch  43 Angela Cox  30 Simon S Cross  44 Kamila Czene  45 Mary B Daly  46 Peter Devilee  47 Thilo Dörk  48 Isabel Dos-Santos-Silva  49 Martine Dumont  50 Lorraine Durcan  51 Miriam Dwek  52 Diana M Eccles  53 Arif B Ekici  54 A Heather Eliassen  55 Carolina Ellberg  56 Christoph Engel  57 Mikael Eriksson  45 D Gareth Evans  58 Peter A Fasching  59 Jonine Figueroa  60 Olivia Fletcher  61 Henrik Flyger  62 Asta Försti  63 Lin Fritschi  64 Marike Gabrielson  45 Manuela Gago-Dominguez  65 Susan M Gapstur  36 José A García-Sáenz  66 Mia M Gaudet  36 Vassilios Georgoulias  67 Graham G Giles  68 Irina R Gilyazova  69 Gord Glendon  70 Mark S Goldberg  71 David E Goldgar  72 Anna González-Neira  73 Grethe I Grenaker Alnæs  74 Mervi Grip  75 Jacek Gronwald  76 Anne Grundy  77 Pascal Guénel  41 Lothar Haeberle  17 Eric Hahnen  78 Christopher A Haiman  79 Niclas Håkansson  80 Ute Hamann  81 Susan E Hankinson  82 Elaine F Harkness  83 Steven N Hart  84 Wei He  45 Alexander Hein  17 Jane Heyworth  85 Peter Hillemanns  48 Antoinette Hollestelle  86 Maartje J Hooning  86 Robert N Hoover  7 John L Hopper  87 Anthony Howell  88 Guanmengqian Huang  81 Keith Humphreys  45 David J Hunter  89 Milena Jakimovska  90 Anna Jakubowska  91 Wolfgang Janni  92 Esther M John  93 Nichola Johnson  61 Michael E Jones  94 Arja Jukkola-Vuorinen  95 Audrey Jung  16 Rudolf Kaaks  16 Katarzyna Kaczmarek  76 Vesa Kataja  96 Renske Keeman  42 Michael J Kerin  97 Elza Khusnutdinova  69 Johanna I Kiiski  98 Julia A Knight  99 Yon-Dschun Ko  100 Veli-Matti Kosma  101 Stella Koutros  7 Vessela N Kristensen  25 Ute Krüger  56 Tabea Kühl  102 Diether Lambrechts  103 Loic Le Marchand  104 Eunjung Lee  79 Flavio Lejbkowicz  105 Jenna Lilyquist  84 Annika Lindblom  106 Sara Lindström  107 Jolanta Lissowska  108 Wing-Yee Lo  109 Sibylle Loibl  110 Jirong Long  111 Jan Lubiński  76 Michael P Lux  17 Robert J MacInnis  112 Tom Maishman  51 Enes Makalic  87 Ivana Maleva Kostovska  90 Arto Mannermaa  101 Siranoush Manoukian  113 Sara Margolin  114 John W M Martens  86 Maria Elena Martinez  115 Dimitrios Mavroudis  67 Catriona McLean  116 Alfons Meindl  117 Usha Menon  118 Pooja Middha  119 Nicola Miller  97 Fernando Moreno  66 Anna Marie Mulligan  120 Claire Mulot  121 Victor M Muñoz-Garzon  122 Susan L Neuhausen  20 Heli Nevanlinna  98 Patrick Neven  123 William G Newman  58 Sune F Nielsen  124 Børge G Nordestgaard  23 Aaron Norman  84 Kenneth Offit  125 Janet E Olson  84 Håkan Olsson  56 Nick Orr  126 V Shane Pankratz  127 Tjoung-Won Park-Simon  48 Jose I A Perez  128 Clara Pérez-Barrios  129 Paolo Peterlongo  130 Julian Peto  49 Mila Pinchev  105 Dijana Plaseska-Karanfilska  90 Eric C Polley  84 Ross Prentice  131 Nadege Presneau  52 Darya Prokofyeva  132 Kristen Purrington  133 Katri Pylkäs  134 Brigitte Rack  92 Paolo Radice  135 Rohini Rau-Murthy  136 Gad Rennert  105 Hedy S Rennert  105 Valerie Rhenius  4 Mark Robson  136 Atocha Romero  129 Kathryn J Ruddy  137 Matthias Ruebner  17 Emmanouil Saloustros  138 Dale P Sandler  139 Elinor J Sawyer  140 Daniel F Schmidt  141 Rita K Schmutzler  78 Andreas Schneeweiss  142 Minouk J Schoemaker  94 Fredrick Schumacher  143 Peter Schürmann  48 Lukas Schwentner  92 Christopher Scott  84 Rodney J Scott  144 Caroline Seynaeve  86 Mitul Shah  4 Mark E Sherman  145 Martha J Shrubsole  111 Xiao-Ou Shu  111 Susan Slager  84 Ann Smeets  123 Christof Sohn  142 Penny Soucy  50 Melissa C Southey  146 John J Spinelli  147 Christa Stegmaier  148 Jennifer Stone  149 Anthony J Swerdlow  150 Rulla M Tamimi  151 William J Tapper  152 Jack A Taylor  153 Mary Beth Terry  154 Kathrin Thöne  102 Rob A E M Tollenaar  155 Ian Tomlinson  156 Thérèse Truong  41 Maria Tzardi  157 Hans-Ulrich Ulmer  158 Michael Untch  159 Celine M Vachon  84 Elke M van Veen  58 Joseph Vijai  125 Clarice R Weinberg  160 Camilla Wendt  114 Alice S Whittemore  161 Hans Wildiers  123 Walter Willett  162 Robert Winqvist  134 Alicja Wolk  163 Xiaohong R Yang  7 Drakoulis Yannoukakos  164 Yan Zhang  12 Wei Zheng  111 Argyrios Ziogas  10 ABCTB InvestigatorskConFab/AOCS InvestigatorsNBCS CollaboratorsAlison M Dunning  4 Deborah J Thompson  3 Georgia Chenevix-Trench  165 Jenny Chang-Claude  166 Marjanka K Schmidt  167 Per Hall  168 Roger L Milne  169 Paul D P Pharoah  170 Antonis C Antoniou  3 Nilanjan Chatterjee  171 Peter Kraft  172 Montserrat García-Closas  7 Jacques Simard  50 Douglas F Easton  170
Affiliations
Free PMC article

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

Nasim Mavaddat et al. Am J Hum Genet. .
Free PMC article

Abstract

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.

Keywords: breast; cancer; epidemiology; genetic; polygenic; prediction; risk; score; screening; stratification.

Figures

Figure 1
Figure 1
Association between the 313 SNP Polygenic Risk Score and Breast Cancer Risk Association between the 313 SNP polygenic risk score (PRS) and breast cancer risk in women of European origin for (A) overall breast cancers, (B) estrogen receptor (ER)-positive disease, and (C) ER-negative disease, in the validation (dashed line) and test (solid line) sets. Odds ratios are for different quantiles of the PRS relative to the mean PRS. Odds ratios and 95% confidence intervals are shown.
Figure 2
Figure 2
Prospective Validation for the 313 SNP Polygenic Risk Score Prospective validation for the 313 SNP polygenic risk score (PRS) by study for (A) overall breast cancer, (B) ER-positive disease, and (C) ER-negative disease. Association between the 313 SNP PRS and breast cancer risk in women of European origin. Odds ratios and 95% confidence intervals are shown. I-squared and p value for heterogeneity were calculated using fixed effect meta-analysis.
Figure 3
Figure 3
Cumulative and 10-Year Absolute Risk of Developing Breast Cancer Cumulative and 10-year absolute risk of developing breast cancer for (A) overall breast cancer, (B) ER-positive disease, and (C) ER-negative disease by percentiles of the 313 SNP polygenic risk scores (PRSs). Note different scales and PRS categories in the different panels. The red line shows the 2.6% risk threshold corresponding to the mean risk for women aged 47 years. Absolute risks were calculated based on UK incidence and mortality data and using the PRS relative risks estimated as described in the Material and Methods.

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