PALB2, CHEK2 and ATM rare variants and cancer risk: data from COGS

ORIGINAL ARTICLE PALB2,CHEK2 and ATM rare variants and cancer risk: data from COGS ▸Additional material is published online only. To view please visit the journal online (http://dx.doi.org/10.1136/ jmedgenet-2016-103839). For numbered affiliations see end of article. Correspondence to Professor Melissa C. Southey, Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Melbourne, Victoria 3010, Australia; msouthe[email protected] Received 29 March 2016 Revised 1 June 2016 Accepted 21 June 2016 Published Online First 5 September 2016 To cite: Southey MC, Goldgar DE, Winqvist R, et al.J Med Genet 2016;53:800–811. Melissa C Southey, 1 David E Goldgar, 2 Robert Winqvist, 3 Katri Pylkäs, 3 Fergus Couch, 4 Marc Tischkowitz, 5 William D Foulkes, 6 Joe Dennis, 7 Kyriaki Michailidou, 7 Elizabeth J van Rensburg, 8 Tuomas Heikkinen, 9 Heli Nevanlinna, 9 John L Hopper, 10 Thilo Dörk, 11 Kathleen BM Claes, 12 Jorge Reis-Filho, 13 Zhi Ling Teo, 1 Paolo Radice, 14 Irene Catucci, 15 Paolo Peterlongo, 15 Helen Tsimiklis, 1 Fabrice A Odefrey, 1 James G Dowty, 10 Marjanka K Schmidt, 16 Annegien Broeks, 16 Frans B Hogervorst, 16 Senno Verhoef, 16 Jane Carpenter, 17 Christine Clarke, 18 Rodney J Scott, 19 Peter A Fasching, 20,21 Lothar Haeberle, 20,22 Arif B Ekici, 23 Matthias W Beckmann, 20 Julian Peto, 24 Isabel dos-Santos-Silva, 24 Olivia Fletcher, 25 Nichola Johnson, 25 Manjeet K Bolla, 7 Elinor J Sawyer, 26 Ian Tomlinson, 27 Michael J Kerin, 28 Nicola Miller, 28 Federik Marme, 29,30 Barbara Burwinkel, 29,31 Rongxi Yang, 29,31 Pascal Guénel, 32,33 Thérèse Truong, 32,33 Florence Menegaux, 32,33 Marie Sanchez, 32,33 Stig Bojesen, 34,35 Sune F Nielsen, 34,35 Henrik Flyger, 36 Javier Benitez, 37,38 M Pilar Zamora, 39 Jose Ignacio Arias Perez, 40 Primitiva Menéndez, 41 Hoda Anton-Culver, 42 Susan Neuhausen, 43 Argyrios Ziogas, 44 Christina A Clarke, 45 Hermann Brenner, 46,47,48 Volker Arndt, 46 Christa Stegmaier, 49 Hiltrud Brauch, 48,50,51 Thomas Brüning, 52 Yon-Dschun Ko, 53 Taru A Muranen, 54 Kristiina Aittomäki, 55 Carl Blomqvist, 56 Natalia V Bogdanova, 11,57 Natalia N Antonenkova, 58 Annika Lindblom, 59 Sara Margolin, 60 Arto Mannermaa, 61,62 Vesa Kataja, 63,64 Veli-Matti Kosma, 61,62 Jaana M Hartikainen, 61,62 Amanda B Spurdle, 65 kConFab Investigators, 66 Australian Ovarian Cancer Study Group 65,66 Els Wauters, 67,68 Dominiek Smeets, 67,68 Benoit Beuselinck, 69 Giuseppe Floris, 69 Jenny Chang-Claude, 70 Anja Rudolph, 70 Petra Seibold, 70 Dieter Flesch-Janys, 71 Janet E Olson, 72 Celine Vachon, 72 Vernon S Pankratz, 72 Catriona McLean, 73 Christopher A Haiman, 74 Brian E Henderson, 74 Fredrick Schumacher, 74 Loic Le Marchand, 75 Vessela Kristensen, 76,77 Grethe Grenaker Alnæs, 76 Wei Zheng, 78 David J Hunter, 79,80 Sara Lindstrom, 79,80 Susan E Hankinson, 80,81 Peter Kraft, 79,80 Irene Andrulis, 82,83 Julia A Knight, 84,85 Gord Glendon, 82 Anna Marie Mulligan, 86,87 Arja Jukkola-Vuorinen, 88 Mervi Grip, 89 Saila Kauppila, 90 Peter Devilee, 91 Robert A E M Tollenaar, 91 Caroline Seynaeve, 92,98 Antoinette Hollestelle, 92,98 Montserrat Garcia-Closas, 93 Jonine Figueroa, 94 Stephen J Chanock, 94 Jolanta Lissowska, 95 Kamila Czene, 96 Hatef Darabi, 96 Mikael Eriksson, 96 Diana M Eccles, 97 Sajjad Rafiq, 97 William J Tapper, 97 Sue M Gerty, 97 Maartje J Hooning, 98 John W M Martens, 98 J Margriet Collée, 99 Madeleine Tilanus-Linthorst, 100 Per Hall, 101 Jingmei Li, 102 Judith S Brand, 101 Keith Humphreys, 101 Angela Cox, 103 Malcolm W R Reed, 103 Craig Luccarini, 104 Caroline Baynes, 104 Alison M Dunning, 104 Ute Hamann, 105 Diana Torres, 105,106 Hans Ulrich Ulmer, 107 Thomas Rüdiger, 108 Anna Jakubowska, 109 Jan Lubinski, 109 Katarzyna Jaworska, 109,110 Katarzyna Durda, 109 Susan Slager, 72 Amanda E Toland, 111 Christine B Ambrosone, 112 Drakoulis Yannoukakos, 113 Anthony Swerdlow, 114,115 Alan Ashworth, 93 Nick Orr, 93 Michael Jones, 114 Anna González-Neira, 37 Guillermo Pita, 37 M Rosario Alonso, 37 Nuria Álvarez, 37 Daniel Herrero, 37 800 Southey MC, et al.J Med Genet 2016;53:800–811. doi:10.1136/jmedgenet-2016-103839 Cancer genetics group.bmj.com on November 29, 2016 - Published by http://jmg.bmj.com/Downloaded from Daniel C Tessier, 116 Daniel Vincent, 117 Francois Bacot, 117 Jacques Simard, 118 Martine Dumont, 118 Penny Soucy, 118 Rosalind Eeles, 119,120 Kenneth Muir, 121 Fredrik Wiklund, 122 Henrik Gronberg, 122 Johanna Schleutker, 123,124 Børge G Nordestgaard, 125 Maren Weischer, 126 Ruth C Travis, 127 David Neal, 128 Jenny L Donovan, 129 Freddie C Hamdy, 130 Kay-Tee Khaw, 131 Janet L Stanford, 132,133 William J Blot, 134 Stephen Thibodeau, 4 Daniel J Schaid, 72 Joseph L Kelley, 135 Christiane Maier, 136,137 Adam S Kibel, 138,139 Cezary Cybulski, 140 Lisa Cannon-Albright, 141 Katja Butterbach, 46 Jong Park, 142 Radka Kaneva, 143 Jyotsna Batra, 144 Manuel R Teixeira, 145 Zsofia Kote-Jarai, 119 Ali Amin Al Olama, 7 Sara Benlloch, 7 Stefan P Renner, 146 Arndt Hartmann, 147 Alexander Hein, 146 Matthias Ruebner, 146 Diether Lambrechts, 148,149 Els Van Nieuwenhuysen, 150 Ignace Vergote, 150 Sandrina Lambretchs, 150 Jennifer A Doherty, 151 Mary Anne Rossing, 152,153 Stefan Nickels, 154 Ursula Eilber, 154 Shan Wang-Gohrke, 155 Kunle Odunsi, 156 Lara E Sucheston-Campbell, 156 Grace Friel, 156 Galina Lurie, 157 Jeffrey L Killeen, 158 Lynne R Wilkens, 157 Marc T Goodman, 159,160 Ingo Runnebaum, 161 Peter A Hillemanns, 162 Liisa M Pelttari, 9 Ralf Butzow, 163 Francesmary Modugno, 164,165 Robert P Edwards, 135 Roberta B Ness, 166 Kirsten B Moysich, 167 Andreas du Bois, 168,169 Florian Heitz, 168,169 Philipp Harter, 168,169 Stefan Kommoss, 169,170 Beth Y Karlan, 171 Christine Walsh, 171 Jenny Lester, 171 Allan Jensen, 172 Susanne Krüger Kjaer, 172,173 Estrid Høgdall, 172,174 Bernard Peissel, 175 Bernardo Bonanni, 176 Loris Bernard, 177 Ellen L Goode, 72 Brooke L Fridley, 178 Robert A Vierkant, 72 Julie M Cunningham, 4 Melissa C Larson, 72 Zachary C Fogarty, 72 Kimberly R Kalli, 179 Dong Liang, 180 Karen H Lu, 181 Michelle A T Hildebrandt, 182 Xifeng Wu, 182 Douglas A Levine, 183 Fanny Dao, 183 Maria Bisogna, 183 Andrew Berchuck, 184 Edwin S Iversen, 185 Jeffrey R Marks, 186 Lucy Akushevich, 187 Daniel W Cramer, 188 Joellen Schildkraut, 187 Kathryn L Terry, 188 Elizabeth M Poole, 189,190 Meir Stampfer, 80,189 Shelley S Tworoger, 189,190 Elisa V Bandera, 191 Irene Orlow, 192 Sara H Olson, 192 Line Bjorge, 193,194 Helga B Salvesen, 193,194 Anne M van Altena, 195 Katja K H Aben, 196,197,198 Lambertus A Kiemeney, 196 Leon F A G Massuger, 195 Tanja Pejovic, 199 Yukie Bean, 199 Angela Brooks-Wilson, 200,201 Linda E Kelemen, 202,203 Linda S Cook, 204 Nhu D Le, 205 Bohdan Górski, 206 Jacek Gronwald, 206 Janusz Menkiszak, 207 Claus K Høgdall, 173 Lene Lundvall, 208 Lotte Nedergaard, 209 Svend Aage Engelholm, 210 Ed Dicks, 211 Jonathan Tyrer, 211 Ian Campbell, 212 Iain McNeish, 213 James Paul, 214 Nadeem Siddiqui, 215 Rosalind Glasspool, 215 Alice S Whittemore, 216 Joseph H Rothstein, 216 Valerie McGuire, 216 Weiva Sieh, 216 Hui Cai, 78 Xiao-Ou Shu, 78 Rachel T Teten, 217 Rebecca Sutphen, 217 John R McLaughlin, 218 Steven A Narod, 219 Catherine M Phelan, 220 Alvaro N Monteiro, 220 David Fenstermacher, 221 Hui-Yi Lin, 221 Jennifer B Permuth, 220 Thomas A Sellers, 220 Y Ann Chen, 221 Ya-Yu Tsai, 220 Zhihua Chen, 221 Aleksandra Gentry-Maharaj, 222 Simon A Gayther, 223 Susan J Ramus, 223 Usha Menon, 222 Anna H Wu, 223 Celeste L Pearce, 223 David Van Den Berg, 223 Malcolm C Pike, 223,224 Agnieszka Dansonka-Mieszkowska, 225 Joanna Plisiecka-Halasa, 225 Joanna Moes-Sosnowska, 225 Jolanta Kupryjanczyk, 225 Paul DP Pharoah, 211 Honglin Song, 211 Ingrid Winship, 226,227 Georgia Chenevix-Trench, 65 Graham G Giles, 10,228 Sean V Tavtigian, 2 Doug F Easton, 7 Roger L Milne 10,228 ABSTRACT Background The rarity of mutations in PALB2,CHEK2 and ATM make it difficult to estimate precisely associated cancer risks. Population-based family studies have provided evidence that at least some of these mutations are associated with breast cancer risk as high as those associated with rare BRCA2 mutations. We aimed to estimate the relative risks associated with specific rare variants in PALB2, CHEK2 and ATM via a multicentre case-control study. Methods We genotyped 10 rare mutations using the custom iCOGS array: PALB2 c.1592delT, c.2816T>G and c.3113G>A, CHEK2 c.349A>G, c.538C>T, c.715G>A, c.1036C>T, c.1312G>T, and c.1343T>G and ATM c.7271T>G. We assessed associations with breast cancer risk (42 671 cases and 42 164 controls), as well as prostate (22 301 cases and 22 320 controls) and ovarian (14 542 cases and 23 491 controls) cancer risk, for each variant. Results For European women, strong evidence of association with breast cancer risk was observed for PALB2 c.1592delT OR 3.44 (95% CI 1.39 to 8.52, p=7.1×10 −5 ), PALB2 c.3113G>A OR 4.21 (95% CI 1.84 to 9.60, p=6.9×10 −8 ) and ATM c.7271T>G OR 11.0 (95% CI 1.42 to 85.7, p=0.0012). We also found evidence of association with breast cancer risk for three variants in CHEK2, c.349A>G OR 2.26 (95% CI 1.29 to 3.95), c.1036C>T OR 5.06 (95% CI 1.09 to 23.5) and c.538C>T OR 1.33 (95% CI 1.05 to 1.67) (p≤0.017). Evidence for prostate cancer risk was observed for CHEK2 c.1343T>G OR 3.03 (95% CI 1.53 to 6.03, p=0.0006) for African men and CHEK2 c.1312G>T OR 2.21 (95% CI 1.06 to 4.63, p=0.030) for European Southey MC, et al.J Med Genet 2016;53:800–811. doi:10.1136/jmedgenet-2016-103839 801 Cancer genetics group.bmj.com on November 29, 2016 - Published by http://jmg.bmj.com/Downloaded from men. No evidence of association with ovarian cancer was found for any of these variants. Conclusions This report adds to accumulating evidence that at least some variants in these genes are associated with an increased risk of breast cancer that is clinically important. INTRODUCTION The rapid introduction of massive parallel sequencing (MPS) into clinical genetics services is enabling the screening of multiple breast cancer susceptibility genes in one assay at reduced cost for women who are at increased risk of breast (and other) cancer. These gene panels now typically include the so-called ‘moderate-risk’breast cancer susceptibility genes, including PALB2,CHEK2 and ATM. 1–3 However, mutations in these genes are individually extremely rare and limited data are available with which to accurately estimate the risk of cancer associated with them. Estimation of the age-specific cumulative risk (penetrance) of breast cancer associated with specific mutations in these three genes has been limited to those that have been observed more frequently, such as PALB2 c.1592delT (a Finnish founder mutation), PALB2 c.3113G>A and ATM c.7271T>G. These mutations have been estimated to be associated with a 40% (95% CI 17% to 77%), 91% (95% CI 44% to 100%) and 52% (95% CI 28% to 80%) cumulative risk of breast cancer to the age of 70 years, respectively. 4–7 These findings, based on segregation analyses in families of population-based case series, indicate that at least some mutations in these ‘moderate-risk’genes are associated with a breast cancer risk comparable to that of the average pathogenic mutation in BRCA2: 45% (95% CI 31% to 56%). 8 However, such estimates are imprecise and, moreover, may be confounded by modifying genetic variants or other familial risk factors. Case-control studies provide an alternative approach to estimating cancer risks associated with specific variants. This design can estimate the relative risk directly, without making assumptions about the modifying effects of other risk factors. However, because these variants are rare, such studies need to be extremely large to provide precise estimates. The clearest evidence for association, and the most precise breast cancer risk estimates, for rare variants in PALB2, CHEK2 and ATM relate to protein truncating and splice-junction variants. 910 However, studies based on mutation screening in casecontrol studies, combined with stratification of variants by their evolutionary likelihood suggest that at least some evolutionarily unlikely missense substitutions are associated with a similar risk to those conferred by truncating mutations. 11–13 For example, Tavtigian et al 12 estimated an OR of 2.85 (95% CI 0.83 to 4.86) for evolutionarily unlikely missense substitutions in the 30 third of ATM, which is comparable to that for truncating variants. Specifically, ATM c.7271C>G has been associated with a more substantial breast cancer risk in several studies. 713 Le Calvez-Kelm et al, 11 estimated that the ORs associated with rare mutations in CHEK2 from similarly designed studies were 6.18 (95% CI 1.76 to 21.8) for rare protein-truncating and splicejunction variants and 8.75 (95% CI 1.06 to 72.2) for evolutionarily unlikely missense substitutions. 11 It is plausible that monoallelic mutations in PALB2,CHEK2 and ATM could be associated with increased risk of cancers other than breast cancer, as has been observed for BRCA1 and BRCA2 and both ovarian and prostate cancers. 14–17 However, with the exception of pancreatic cancer in PALB2 carriers, there is little evidence to support or refute the existence of such associations, although a few individually striking pedigrees have been observed. 4818–20 In this study we selected rare genetic variants on the basis that they had been observed in breast cancer candidate gene case-control screening projects involving PALB2,CHEK2 or ATM. These included three rare variants in PALB2: the protein truncating variants c.1592delT (p.Leu531Cysfs) 4 and c.3113 G>A (p.Trp1038*) 6 and the missense variant c.2816T>G, (p. Leu939Trp), six rare missense variants in CHEK2: c.349A>G (p.Arg117Gly) and c.1036C>T (p.Arg346Cys) predicted to be deleterious on the basis of evolutionary conservation, 11 c.538C>T (p.Arg180Cys), c.715G>A (p.Glu239Lys), c.1312G>T (p.Asp438Tyr) and c.1343T>G (p.Ile448Ser) and ATM c.7271T>G (p.Val2424Gly). 7 We assessed the association of these variants with breast, ovarian and prostate risk by casecontrol analyses in three large consortia participating in the Collaborative Oncological Gene-environment Study. 21 22 METHODS Participants Participants were drawn from studies participating in three consortia as follows: The Breast Cancer Association Consortium (BCAC), involving a total of 48 studies: 37 of women from populations with predominantly European ancestry (42 671 cases and 42 164 controls), 9 of Asian women (5795 cases and 6624 controls) and 2 of African-American women (1046 cases and 932 controls). All cases had invasive breast cancer. The majority of studies were population-based or hospital-based case-control studies, but some studies of European women oversampled cases with a family history or with bilateral disease (see online supplementary table S1). Overall, 79% of BCAC cases with known Estrogen Recptor (ER) status (23% missing) are ERpositive. The proportion of cases selected by family history that are ER-positive is 78% (38% missing). The Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) involving a total of 26 studies: 25 included men with European ancestry (22 301 cases and 22 320 controls) and 3 included AfricanAmerican men (623 cases and 569 controls). The majority of studies were population-based or hospital-based case-control studies (see online supplementary table S2). The Ovarian Cancer Association Consortium (OCAC), involving a total of 46 studies. Some studies were case-only and their data were combined with case-control studies from the same geographical region (leaving 36 study groupings). Of these groupings, 33 included women from populations with predominantly European ancestry (16 287 cases (14 542 with invasive disease) and 23 491 controls), 25 included Asian women (813 cases (720 with invasive disease) and 1574 controls), 17 included African-American women (186 cases (150 with invasive disease) and 200 controls) and 29 included women of other ethnic origin (893 cases (709 with invasive disease) and 864 controls). The majority of studies were population-based or hospital-based casecontrol studies (see online supplementary table S3). Details regarding sample quality control have been published previously. 22 23 All study participants gave informed consent and all studies were approved by the corresponding local ethics committees (see online supplementary tables S1–S3). Variant selection We selected for genotyping 13 rare mutations that had been observed in population-based case-control mutation screening studies. These variants were PALB2 (c.1592delT, p. 802 Southey MC, et al.J Med Genet 2016;53:800–811. doi:10.1136/jmedgenet-2016-103839 Cancer genetics group.bmj.com on November 29, 2016 - Published by http://jmg.bmj.com/Downloaded from Leu531Cysfs; 4510 c.2323C>T p.Gln775*; 20 c.2816T>G, p. Leu939Trp; 220 c.3113G>A, p.Trp1038*; 2620 c.3116delA, p. Asn1039IIefs; 2620 c.3549C>G, p.Tyr1183* 2 ), CHEK2 (c.349A>G, p.ArgR117Gly; c.538C>T, p.Arg180Cys; c.715G>A p.Glu239Lys; c.1036C>T, p.Arg346Cys; c.1312G>T, p.Asp438Tyr; c.1343T>G, p.Ile448Ser) 11 and ATM (c.7271T>G, p.Val2424Gly) 71324 see table 1. A DNA sample carrying each of these variants was included in a plate of control DNAs that was distributed to each genotyping centre to assist with quality control and genotype calling. Genotyping Three PALB2 variants c.2323C>T (p.Gln775*), c.3116delA (p.Asn1039IIefs) and c.3549C>G (p.Tyr1183*) were unable to be designed for measurement on the custom Illumina iSelect genotyping array and were not considered further (table 1). Genotyping was conducted using a custom Illumina Infinium array (iCOGS) in four centres, as part of a multiconsortia collaboration as described previously. 22 Genotypes were called using Illumina’s proprietary GenCall algorithm and then, for the data generated from the rare variant probes, manually confirmed with reference to the positive control sample. Two per cent of samples were provided in duplicate by all studies and 270 HapMap2 samples were genotyped in all four genotyping centres. Subjects with an overall call rate <95% were excluded. Plates with call rates <90% were excluded on a variant-byvariant basis. Cluster plots generated for all of the 10 rare variants were manually checked to confirm automated calls (see online supplementary figure S1). Statistical methods The association of each variant with breast, prostate and ovarian cancer risk was assessed using unconditional logistic regression to estimate ORs for carriers versus non-carriers, adjusting for study (categorical). p Values were determined by the likelihood ratio test comparing models with and without carrier status as a covariate. We also applied conditional logistic regression, defining risk sets by study, and found that this made no difference to the OR estimates, CIs or p values to two significant figures; since model convergence was a problem for this latter regression analysis, all subsequent analyses were based on unconditional logistic regression. For the main analyses of breast cancer risk in European women, we also included as covariates the first six principal components, together with a seventh component specific to one study (Leuven Multidisciplinary Breast Centre (LMBC)) for which there was substantial inflation not accounted for by the components derived from the analysis of all studies. Addition of further principal components did not reduce inflation further. Data from all breast cancer studies were included to assess statistical significance. Data from cases selected for inclusion based on personal or family history of breast cancer were excluded in order to obtain unbiased OR estimates for the general population of white European women (leaving 37 039 cases and 38 260 controls from 32 studies). Multiple testing was adjusted for using the Benjamini-Hochberg procedure to control the false discovery rate, with a significance threshold of 0.05. 25 Reported p values are unadjusted unless otherwise stated. Reported CIs are all nominal. We included two race-specific principal components in each of the main breast cancer analyses of Asian and African-American women. Similar analyses were conducted using the data from PRACTICAL and OCAC, consistent with those used previously. 23 26 All analyses were carried out using Stata: Release V.10 (StataCorp, 2008). RESULTS PALB2 In BCAC, PALB2 c.1592delT (Leu531Cysfs) was only observed in 35 cases and 6 controls, all from four studies from Sweden and Finland (Helsinki Breast Cancer Study (HEBCS), Kuopio Breast Cancer Project (KBCP), Oulu Breast Cancer Study (OBCS) and Karolinska Mammography Project for Risk Prediction Breast Cancer (pKARMA); see online supplementary Table 1 Rare genetic variants included in the iCOGS array. Gene Variant* Amino acid* dbSNP rs Breast cancer risk estimates Align-GVGD Reference(s) Designed‡GenotypedOR (95% CI) Penetrance† (95% CI) PALB2 c.1592delT p.Leu531Cysfs rs180177102 3.94 (1.5-12.1)§ 40% (17–77) na 4,5,10 Yes Yes c.2323C>T p.Gln775* rs180177111 na 25,26 No No c.2816T>G p.Leu939Trp rs45478192 C55 20 Yes Yes c.3113G>A p.Trp1038* rs180177132 95% (44–100) na 2,6,20 Yes Yes c.3116delA p.Asn1039Ilefs rs180177133 na 2 No No c.3549C>G p.Tyr1183* rs118203998 na 2 No No CHEK2 c.349A>G p.Arg117Gly rs28909982 8.75 (1.06–72.2)¶ C65 11 Yes Yes c.538C>T p.Arg180Cys rs77130927 2.47 (0.45–13.49)** C25 11 Yes Yes c.715G>A p.Glu239Lys rs121908702 1.82 (0.62–5.34)†† C15 11 Yes Yes c.1036C>T p.Arg346Cys na 8.75 (1.06–72.2)¶ C65 11 Yes Yes c.1312G>T p.Asp438Tyr na 2.47 (0.45–13.49)** C25 11 Yes Yes c.1343T>G p.Ile448Ser rs17886163 1.82 (0.62–5.34)†† C15 11 Yes Yes ATM c.7271T>G p.Val2424Gly rs28904921 52% (28–80) C65 7,13,23,27 Yes Yes *Human Genome Variation Society (HGVS); reference sequences PALB2, NM_024675.3, NP_078951.2; CHEK2, NM_007194.3, NP_009125.1; ATM, NM_000051.3, NP_000042.3. †Age-specific cumulative risk of breast cancer to age 70 years. 5–7 ‡Able to be designed for measurement on the custom Illumina iSelect genotyping array. 21 22 §Breast cancer cases unselected for family history of breast cancer. 4 ¶OR estimated in a combined group of C65 CHEK2 variants. 11 **OR estimated in a combined group of C25 CHEK2 variants. 11 ††OR estimated in a combined group of C15 CHEK2 variants. 11 na, not available. Southey MC, et al.J Med Genet 2016;53:800–811. doi:10.1136/jmedgenet-2016-103839 803 Cancer genetics group.bmj.com on November 29, 2016 - Published by http://jmg.bmj.com/Downloaded from table S1), giving strong evidence of association with breast cancer risk (p=7.1×10 −5 ); the OR estimate was 4.52 (95% CI 1.90 to 10.8) based on all studies and 3.44 (95% CI 1.39 to 8.52) based on unselected cases and controls (table 2). We also found evidence of heterogeneity by ER status (p=0.0023), the association being stronger for ER-negative disease (OR 6.49 (95% CI 2.17 to 19.4) versus 2.24 (95% CI 1.05 to 7.24) for ER-positive disease). PALB2 c.3113G>A (p.Trp1038*) was identified in 44 cases and 8 controls from nine BCAC studies. Only one carrier of the variant was of non-European origin. Strong evidence of association with breast cancer risk was observed (p=6.9×10 −8 ), with an estimated OR of 5.93 (95% CI 2.77 to 12.7) based on all studies and 4.21 (95% CI 1.85 to 9.61) based on unselected cases and controls. There was no evidence of a differential association by ER status (p=0.15). Based on unselected cases, the estimated OR associated with carrying either of these PALB2 variants (c.1592delT or c.3113G>A) was 3.85 (95% CI 2.09 to 7.09). PALB2 c.2816T>G (p.Leu939Trp) was identified in 150 cases and 145 controls and there was no evidence of association with risk of breast cancer. There was no evidence of association with risk of prostate or ovarian cancer for any of the three PALB2 variants (see tables 3 and 4). Table 2 Summary results from Breast Cancer Association Consortium studies of white Europeans (42 671 invasive breast cancer cases and 42 164 controls) Variant Frequency* Controls Frequency* Cases OR (95% CI) LRT p Value OR†(95% CI) LRT p Value† PALB2§ c.1592delT (p.Leu531Cysfs) 0.00014 0.00082 4.52 (1.90 to 10.8) 7.1×10 −5 3.44 (1.39 to 8.52) 0.003 c.2816T>G (p.Leu939Trp) 0.00342 0.00352 1.05 (0.83 to 1.32) 0.70 1.03 (0.80 to 1.32) 0.82 c.3113G>A (p.Trp1038*) 0.00019 0.00101 5.93 (2.77 to 12.7) 6.9×10 −8 4.21 (1.84 to 9.60) 1.2×10 −4 CHEK2 c.349A>G (p.Arg117Gly) 0.00043 0.00103 2.26 (1.29 to 3.95) 0.003 2.03 (1.10 to 3.73) 0.020 c.538C>T (p.Arg180Cys) 0.00337 0.00370 1.33 (1.05 to 1.67) 0.016 1.34 (1.06 to 1.70) 0.015 c.715G>A (p.Glu239Lys) 0.00021 0.00035 1.70 (0.73 to 3.93) 0.210 1.47 (0.60 to 3.64) 0.40 c.1036C>T (p.Arg346Cys) 0.00005 0.00021 5.06 (1.09 to 23.5) 0.017 3.39 (0.68 to 16.9) 0.11 c.1312G>T (p.Asp438Tyr) 0.00078 0.00082 1.03 (0.62 to 1.71) 0.910 0.87 (0.49 to 1.52) 0.62 c.1343T>G (p.Ile448Ser)‡0.00002 0 –––– ATM c.7271T>G (p.Val2424Gly) 0.00002 0.00028 11.6 (1.50 to 89.9) 0.0012 11.0 (1.42 to 85.7) 0.0019 *Proportion of subjects carrying the variant. †Excluding women from five studies that selected all cases based on family history or bilateral disease and the subset of selected cases from other studies (based on 34 488 unselected cases and 34 059 controls). ‡CHEK2 c.1343T>G (p.Ile448Ser) was only observed in one control and no cases of white European origin. §PALB2 c.3113G>A (p.Trp1038*) only observed in the UK, Australia, the USA and Canada. PALB2 c.1592delT (p.Leu531Cysfs) only observed in Finland and Sweden. LRT, likelihood ratio test; OR, OR for carriers of the variant versus common-allele homozygotes, adjusted for study and seven principal components. Table 3 Summary results from the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome studies for white European men* (22 301 prostate cancer cases and 22 320 controls) Variant Frequency† Controls Frequency† Cases OR (95% CI) LRT p Value PALB2 c.1592delT (p.Leu531Cysfs) 0.00018 0.00031 2.06 (0.59 to 7.11) 0.24 c.2816T>G (p.Leu939Trp) 0.00354 0.00381 0.95 (0.69 to 1.29) 0.73 c.3113G>A (p.Trp1038*) 0.00045 0.00027 0.49 (0.18 to 1.36) 0.16 CHEK2‡ c.349A>G (p.Arg117Gly) 0.00063 0.00081 1.46 (0.71 to 3.02) 0.30 c.538C>T (p.Arg180Cys) 0.00341 0.00296 1.02 (0.73 to 1.44) 0.90 c.715G>A (p.Glu239Lys) 0.00018 0.00027 1.47 (0.41 to 5.35) 0.55 c.1036C>T (p.Arg346Cys) 0.00018 0.00022 1.07 (0.28 to 4.07) 0.93 c.1312G>T (p.Asp438Tyr) 0.00049 0.00103 2.21 (1.06 to 4.63) 0.03 c.1343T>G (p.Ile448Ser) 0 0.00009 –– c.1343T>G (Africans§) 0.019 0.057 3.03 (1.53 to 6.03) 0.001 ATM c.7271T>G (p.Val2424Gly) 0.00004 0.00027 4.37 (0.52 to 36.4) 0.17 *For white European men, unless otherwise indicated. †Proportion of subjects carrying the variant. ‡CHEK2 c.1343T>G (p.Ile448Ser) was the only CHEK2 variant observed in African men and was identified in two cases and no controls of white European origin. §Based on data from 623 and 569 African-American cases and controls, respectively. LRT, likelihood ratio test; OR, OR for carriers of the variant versus common-allele homozygotes, adjusted for study and seven principal components. 804 Southey MC, et al.J Med Genet 2016;53:800–811. doi:10.1136/jmedgenet-2016-103839 Cancer genetics group.bmj.com on November 29, 2016 - Published by http://jmg.bmj.com/Downloaded from CHEK2 CHEK2 c.349A>G (p.Arg117Gly) was identified in 44 cases and 18 controls in studies participating in BCAC; all of these women were of European origin. We found evidence of association with breast cancer (p=0.003), with little change in the OR after excluding selected cases (OR 2.03 (95% CI 1.10 to 3.73)). CHEK2 c.538C>T (p.Arg180Cys) was identified in 158 breast cancer cases and 142 controls in studies of white Europeans. Evidence of association with breast cancer risk (p=0.016) was observed, with an unbiased OR estimate of 1.34 (95% CI 1.06 to 1.70). A consistent OR estimate was observed for Asian women, based on 45 case and 45 control carriers (OR 1.16 (95% CI 0.75 to 1.76)). CHEK2 c.715G>A (p.Glu239Lys) mutations were identified in 15 cases and 9 controls, all European women participating in BCAC and no evidence of association with risk of breast cancer was observed (p=0.21). CHEK2 c.1036C>T (p.Arg346Cys) was identified in nine cases from seven studies and two controls from two different studies in BCAC (neither control carrier was from a study that had case carriers), all of European origin. We found evidence of association with breast cancer risk (p=0.017) with reduced OR estimate of 3.39 (95% CI 0.68 to 16.9) after excluding selected cases. None of the above four CHEK2 variants (CHEK2 c.349A>G (p.Arg117Gly); c.538C>T (p.Arg180Cys); c.715G>A (p. Glu239Lys) and c.1036C>T (p.Arg346Cys)) were found to be associated with an increased risk of prostate or ovarian cancer (tables 3 and 4). CHEK2 variant c.1312G>T (p.Asp438Tyr) was not associated with risk of breast cancer for European women (p=0.91). Variant c.1343T>G (p.Ile448Ser) was not observed in any breast cancer cases of European or Asian origin. It was detected in 48 cases and 29 controls of African origin, giving weak evidence of association (OR 1.52 (95% CI 0.95 to 2.43, p=0.083)). CHEK2 c.1312G>T (p.Asp438Tyr) was identified in 23 cases and 11 controls from PRACTICAL, all European, providing evidence of association with prostate cancer risk (OR 2.21 (95% CI 1.06 to 4.63, p=0.030)). CHEK2 c.1343T>G (p. Ile448Ser) was observed in 35 cases and 11 controls, all African, participating in PRACTICAL and was also associated with an increased risk of prostate cancer (OR 3.03 (95% CI 1.53 to 6.03, p=0.00059)). There was no evidence that these CHEK2 variants were associated with risk of ovarian cancer (table 4). ATM ATM c.7271T>G (p.Val2424Gly) was identified in 12 cases and 1 control in studies participating in BCAC, all of European origin, giving evidence of association with breast cancer risk (p=0.0012). The OR estimate based on unselected studies was 11.0 (95% CI 1.42 to 85.7). There was no evidence of association of this variant with prostate or ovarian cancer risk (see tables 3 and 4). DISCUSSION The present report adds to an accumulating body of evidence that at least some rare variants in so-called ‘moderate-risk’genes are associated with an increased risk of breast cancer that is of clinical relevance. These findings are presented at a time when detailed information about variants in these genes is becoming more readily available via the translation of diagnostic genetic testing from Sanger sequencing-based testing platforms to MPS platforms that test panels of genes in single assays. 27–29 The vast majority of information about PALB2,CHEK2 and ATM, variants generated from these new testing platforms is not being used in clinical genetics services due to lack of reliable estimates of the cancer risk associated with individual variants, or groups of variants, in each gene. Previous analyses have been largely based on selected families, relying on data on the segregation of the variant. The present study is by far the largest to take a case-control approach. Consistent with previous reports, 5–7911–13 PALB2 c.3113G>A (p.Trp1038*), PALB2 c.1592delT (p.Leu531Cysfs) and ATM c.7271T>G (p.Val2424Gly) were found to be associated with substantially increased risk of breast cancer all with associated relative risk estimates of 3.44 or greater. The estimates for the two loss-of-function PALB2 variants (c.1592delT and c.3113G<A) were consistent with each other and with estimates based on segregation analysis. 569 We found no evidence of association with breast cancer for PALB2 c.2816T>G (p.Leu939Trp), with an upper 95% confidence limit excluding an OR >1.5 which is notable given the Table 4 Summary results from the Ovarian Cancer Association Consortium studies for white European women (14 542 invasive ovarian cancer cases and 23 491 controls) Variant Frequency* Controls Frequency* Cases OR (95% CI) LRT p Value PALB2 c.1592delT (p.Leu531Cysfs) 0.00004 0.00012 2.50 (0.21 to 29.1) 0.45 c.2816T>G (p.Leu939Trp) 0.00413 0.00399 0.96 (0.69 to 1.34) 0.81 c.3113G>A (p.Trp1038*) 0.00034 0.00031 1.34 (0.36 to 4.97) 0.66 CHEK2 c.349A>G (p.Arg117Gly) 0.00038 0.00031 1.07 (0.32 to 3.60) 0.92 c.538C>T (p.Arg180Cys) 0.00128 0.00160 1.49 (0.83 to 2.67) 0.18 c.715G>A (p.Glu239Lys) 0.00021 0.00037 1.47 (0.42 to 5.22) 0.54 c.1036C>T (p.Arg346Cys)‡00–– c.1312G>T (p.Asp438Tyr) 0.00081 0.00074 0.92 (0.42 to 1.99) 0.83 c.1343T>G (p.Ile448Ser) 0.00009 0 –– ATM c.7271T>G (p.Val2424Gly) 0 0.00012 –– *Proportion of subjects carrying the variant. ‡c.1036C>T (p.Arg346Cys) was not observed in any sample. LRT, likelihood ratio test; OR, OR for carriers of the variant versus common-allele homozygotes, adjusted for study and seven principal components. Southey MC, et al.J Med Genet 2016;53:800–811. doi:10.1136/jmedgenet-2016-103839 805 Cancer genetics group.bmj.com on November 29, 2016 - Published by http://jmg.bmj.com/Downloaded from Align-Grantham Variation Granthan Deviation (Align-GVGD) score and the observed impact on protein function. 30 The estimate for ATM c.7271T>G (p.Val2424Gly) was also consistent with that found by segregation analysis. 713 The substantial increased risk of breast cancer associated with ATM c.7271T>G (p.Val2424Gly) could be due to the reduction in kinase activity (with near-normal protein levels) observed for ATM p.Val2424Gly, 31 thus this variant is likely to be acting as a dominant negative mutation. 32 In contrast, we found no evidence of an association with risk of prostate or ovarian cancer with any of these three variants: however, the confidence limits were wide; based on the upper 95% confidence limit we could exclude an OR of >1.4 for prostate cancer for the loss-of-function PALB2 c.3113G>A and 1.9 for c.1592delT and c.3113G>A combined. We analysed six rare missense variants in CHEK2. Two of these (CHEK2 c.349A>G (p.Arg117Gly; rs28909982) and c.1036C>T (p.Arg346Cys)) had evidence of a significant impact on the protein based on in silico prediction. We proposed these variants for inclusion in the iCOGS design as they had been identified in 3/1242 cases and 1/1089 controls and 3/1242 cases and 0/1089 controls, respectively, in a populationbased case-control mutation screening study of CHEK2. 11 In that study, Le Calvez-Kelm et al, estimated an OR of 8.75 (95% CI 1.06 to 72.2) for variants with an Align-GVGD score C65 (based on nine cases and one control). The current analysis provides confirmatory evidence of this association in a much larger sample (OR 2.18 (95% CI 1.23 to 3.85)) including 40 unselected case and 18 control carriers. The evidence that CHEK2 is a breast cancer susceptibility gene is largely based on studies of protein truncating variants, in particular CHEK2 1100delC. 33 Reports of the association of the missense variant I157T, (C15) and breast cancer risk have been conflicting but a large meta-analysis involving 15 985 breast cancer cases and 18 609 controls estimated a modest OR of 1.58 (95% CI 1.42 to 1.75). 34 We also found evidence (p=0.015) of an association for c.538C>T (Align-GVGD C25); OR 1.34 (95% CI 1.06 to 1.70), a risk comparable to I157T. The p values reported above have not been adjusted for multiple testing. This was not considered appropriate for the associations with breast cancer risk of PALB2 c.1592delT, c.3113G>A and ATM c.7271T>G because these associations had previously been reported; our aim was to more precisely estimate the associated relative risks. All three associations with breast cancer risk reported for CHEK2 variants remained statistically significant after adjusting for the other tests conducted in relation to breast cancer risk, but not after correcting for all tests for all cancers. Nevertheless, the findings for CHEK2 c.349A>G and c.1036C>T confirmed those reported previously, although collectively. The association observed with CHEK2 c.538C>T requires independent replication. Do this approach and new data have an impact on clinical recommendations for women and families carrying these rare genetic variants? Although age-specific cumulate risks for cancer are more informative for genetic counselling and clinical management of carriers, our study provides information that is relevant to clinical recommendations. As discussed in Easton et al, 35 a relative risk of 4 will place a woman in a ‘high-risk’category (in the absence of any other risk factor) and a relative risk between 2 and 4 will place a woman in this category if other risk factors are present. Thus, several of the variants included in this report (PALB2 c.1592delT; c.3113G>A ATM c.7271T>G) would place the carrier in a high-risk group, especially if other risk factors, such as a family history, are present. The high level of breast cancer risk associated with PALB2 c.1592delT and c.3113G>A reported here is consistent with the penetrance estimate reported for a group of loss-of-function mutations in PALB2 9 and has an advantage in terms of clinical utility that the estimates in this study have been made at a mutation-specific level. Therefore, this work provides important information for risk reduction recommendations (such as prophylactic mastectomy and potentially salpingo-oophorectomy) for carriers of these variants. However, further prospective research is required to characterise these risks and to understand the potential of other risk-reducing strategies such as salpingo-oophorectomy and chemoprevention. The consistency of the relative risk estimates with those derived through family based studies supports the hypothesis that these variants combine multiplicatively with other genetic loci and familial risk factors; this information is critical for deriving comprehensive risk models. Even with very large sample sizes such as those studied here, however, it is still only possible to derive individual risk estimates for a limited set of variants, and even for these variants the estimates are still imprecise. This internationally collaborative approach also has limited capacity to improve risk estimates for rare variants that are only observed in specificpopulations. Inevitably, therefore, risk models will depend on combining data across multiple variants, using improved in silico predictions and potentially biochemical/functional evidence to synthesise these estimates efficiently. It will also be necessary develop counselling and patient management strategies that can accommodate a multifactorial approach to variant classification. Author affiliations 1 Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Melbourne, Australia 2 Huntsman Cancer Institute, Salt Lake City, UT, USA 3 Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit and Biocenter Oulu, University of Oulu, Nordlab Oulu, Oulu, Finland 4 Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA 5 Department of Medical Genetics and National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, and the Department of Clinical Genetics, East Anglian Regional Genetics Service, Addenbrooke’s Hospital 6 Program in Cancer Genetics, Department of Human Genetics and Oncology, Lady Davis Institute, and Research Institute, McGill University Health Centre, McGill University, Montreal, Canada, 7 Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge, UK 8 Department of Genetics, University of Pretoria, South Africa 9 Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland 10 Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, Australia, 11 Gynaecology Research Unit, Hannover Medical School, Hannover, Germany 12 Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium, 13 Department of Pathology and Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, New York, USA 14 Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Preventive and Predictive Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy 15 IFOM, the FIRC Institute of Molecular Oncology, Milan, Italy 16 Netherlands Cancer Institute, Antoni van Leeuwenhoek hospital, Amsterdam, The Netherlands 17 Australian Breast Cancer Tissue Bank, University of Sydney at the Westmead Institute for Medical Research, NSW, Australia 18 Centre for Cancer Research, University of Sydney at the Westmead Institute for Medical Research, NSW, Australia 19 Division of Molecular Medicine, Pathology North, Newcastle and University of Newcastle, NSW, Australia 20 University Breast Center Franconia, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany 806 Southey MC, et al.J Med Genet 2016;53:800–811. doi:10.1136/jmedgenet-2016-103839 Cancer genetics group.bmj.com on November 29, 2016 - Published by http://jmg.bmj.com/Downloaded from 21 David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California at Los Angeles, CA, USA 22 Unit of Biostatistics, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany 23 Institute of Human Genetics, University Hospital Erlangen, Friedrich Alexander University Erlangen-Nuremberg, Erlangen, Germany 24 Non-communicable Disease Epidemiology Department, London School of Hygiene and Tropical Medicine, London, UK 25 Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, UK 26 Division of Cancer Studies, NIHR Comprehensive Biomedical Research Centre, Guy’s & St. Thomas’NHS Foundation Trust in partnership with King’s College London, London, UK 27 Wellcome Trust Centre for Human Genetics and Oxford Biomedical Research Centre, University of Oxford, UK and Oxford NIHR Biomedical Research Centre, Headington, OX3 7LE 28 Surgery, Lambe Institute for Translational Science, NUIGalway, University Hospital Galway, Galway, Ireland 29 Department of Obstetrics and Gynecology, University of Heidelberg, Heidelberg, Germany 30 National Center for Tumor Diseases, University of Heidelberg, Heidelberg, Germany 31 Molecular Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany 32 Inserm (National Institute of Health and Medical Research), CESP (Center for Research in Epidemiology and Population Health), U1018, Environmental Epidemiology of Cancer, Villejuif, France 33 University Paris-Sud, UMRS 1018, Villejuif, France 34 Copenhagen General Population Study, Herlev Hospital, Copenhagen University Hospital, University of Copenhagen, Copenhagen, Denmark 35 Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, University of Copenhagen, Copenhagen, Denmark 36 Department of Breast Surgery, Herlev Hospital, Copenhagen University Hospital, Copenhagen, Denmark 37 Human Genetics Group, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), Madrid, Spain 38 Centro de Investigación en Red de Enfermedades Raras (CIBERER), Valencia, Spain 39 Servicio de Oncología Médica, Hospital Universitario La Paz, Madrid, Spain 40 Servicio de Cirugía General y Especialidades, Hospital Monte Naranco, Oviedo, Spain 41 Servicio de Anatomía Patológica, Hospital Monte Naranco, Oviedo, Spain 42 Department of Epidemiology, University of California Irvine, Irvine, California, USA 43 Beckman Research Institute of City of Hope, Duarte, California, USA 44 Department of Epidemiology, University of California Irvine, Irvine, California, USA 45 Cancer Prevention Institute of California, Fremont, California, USA 46 Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany 47 Division of Preventive Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany 48 German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany 49 Saarland Cancer Registry, Saarbrücken, Germany 50 Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart 51 University of Tübingen, Tübingen, Germany 52 Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University, Bochum (IPA), Germany 53 Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany 54 Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland 55 Department of Clinical Genetics, Helsinki University Central Hospital, Helsinki, Finland 56 Department of Oncology, Helsinki University Central Hospital, Helsinki, Finland 57 Department of Radiation Oncology, Hannover Medical School, Hannover, Germany 58 N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus 59 Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden 60 Department of Oncology –Pathology, Karolinska Institutet, Stockholm, Sweden 61 School of Medicine, Institute of Clinical Medicine, Pathology and Forensic Medicine, and Cancer Center of Eastern Finland, University of Eastern Finland, Kuopio, Finland 62 Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, Kuopio, Finland 63 School of Medicine, Institute of Clinical Medicine, Oncology, University of Eastern Finland, Kuopio, Finland 64 Biocenter Kuopio, Cancer Center of Eastern Finland, Kuopio University Hospital, Kuopio, Finland 65 QIMR Berghofer Medical Research Institute, Brisbane, Australia 66 Research Department, Peter MacCallum Cancer Centre and The Sir Peter MacCallum Department of Oncology, University of Melbourne, Victoria, Australia 67 Vesalius Research Center (VRC), VIB, Leuven, Belgium 68 Laboratory for Translational Genetics, Department of Oncology, University of Leuven, Leuven, Belgium 69 University Hospital Gasthuisberg, Leuven, Belgium 70 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany 71 Department of Cancer Epidemiology/Clinical Cancer Registry and Institute for Medical Biometrics and Epidemiology, University Clinic Hamburg-Eppendorf, Hamburg, Germany 72 Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA 73 Anatomical Pathology, The Alfred Hospital, Melbourne, Australia 74 Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA 75 Epidemiology Program, Cancer Research Center, University of Hawaii, Honolulu, HI, USA 76 Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Radiumhospitalet, Oslo, Norway 77 Faculty of Medicine (Faculty Division Ahus), University of Oslo (UiO), Norway 78 Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA 79 Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, MA, USA 80 Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA 81 Channing Laboratory, Department of Medicine, Brigham and Women’sHospitaland Harvard Medical School, Boston, MA, USA 82 Ontario Cancer Genetics Network, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada 83 Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada 84 Prosserman Centre for Health Research, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada 85 Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada 86 Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada 87 Laboratory Medicine Program, University Health Network, Toronto, Ontario; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada 88 Department of Oncology, Oulu University Hospital, University of Oulu, Oulu, Finland 89 Department of Surgery, Oulu University Hospital, University of Oulu, Oulu, Finland 90 Department of Pathology, Oulu University Hospital, University of Oulu, Oulu, Finland 91 Department of Surgical Oncology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands 92 Family Cancer Clinic, Department of Medical Oncology, Erasmus MC-Daniel den Hoed Cancer Centre, Rotterdam, The Netherlands 93 The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, SW3 6JB, UK 94 Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA 95 Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Memorial Cancer Center & Institute of Oncology, Warsaw, Poland 96 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17177, Sweden 97 Faculty of Medicine, University of Southampton (UoS), Southampton UK 98 Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands 99 Department of Clinical Genetics, Family Cancer Clinic, Erasmus University Medical Center, Rotterdam, The Netherlands 100 Department of Surgical Oncology, Family Cancer Clinic, Erasmus University Medical Center, Rotterdam, The Netherlands 101 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17177, Sweden 102 Human Genetics Division, Genome Institute of Singapore, Singapore 138672, Singapore 103 Sheffield Cancer Research, Department of Oncology, University of Sheffield, Sheffield, UK 104 Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK 105 Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany 106 Institute of Human Genetics, Pontificia Universidad Javeriana, Bogota, Colombia 107 Frauenklinik der Stadtklinik Baden-Baden, Baden-Baden, Germany 108 Institute of Pathology, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany 109 Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland 110 Postgraduate School of Molecular Medicine, Warsaw Medical University, Warsaw, Poland Southey MC, et al.J Med Genet 2016;53:800–811. doi:10.1136/jmedgenet-2016-103839 807 Cancer genetics group.bmj.com on November 29, 2016 - Published by http://jmg.bmj.com/Downloaded from 111 Department of Molecular Virology, Immunology and Medical Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA 112 Roswell Park Cancer Institute, Buffalo, New York, USA 113 Molecular Diagnostics Laboratory, IRRP, National Centre for Scientific Research "Demokritos", Aghia Paraskevi Attikis, Athens, Greece 114 Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK 115 Division of Breast Cancer Research, Institute of Cancer Research, London, UK 116 Centre d’innovation Genome Quebec et University McGill Montreal Quebec, Canada 117 McGill University, Montreal, Quebec, Canada 118 Cancer Genomics Laboratory, Centre Hospitalier Universitaire de Quebec Research Center. Laval University, Quebec, Canada 119 The Institute of Cancer Research, London, SM2 5NG, UK 120 Royal Marsden NHS Foundation Trust, Fulham, London, SW3 6JJ, UK 121 University of Warwick, Coventry, UK 122 Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden 123 Department of Medical Biochemistry and Genetics, University of Turku, and Tyks Microbiology and Genetics, Department of Medical Genetics, Turku University Hospital, Turku, Finland 124 Institute of Biomedical Technology/BioMediTech, University of Tampere, Tampere, Finland 125 Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev Ringvej 75, DK-2730 Herlev, Denmark 126 Department of Human Genetics University of Utah, Salt Lake City, UT, USA and Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, University of Copenhagen, Copenhagen, Denmark 127 Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK 128 Surgical Oncology (Uro-Oncology: S4), University of Cambridge, Box 279, Addenbrooke’s Hospital, Hills Road, Cambridge, UK and Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK 129 Professor of Social Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS 130 Nuffield Department of Surgical Sciences, Old Road Campus Research Building (off Roosevelt Drive), University of Oxford, Headington, Oxford, OX3 7DQ 131 Cambridge Institute of Public Health, University of Cambridge, Forvie Site, Robinson Way, Cambridge CB2 0SR 132 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA 133 Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington, USA 134 International Epidemiology Institute, 1455 Research Blvd., Suite 550, Rockville, MD 20850 135 Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA 136 Department of Urology, University Hospital Ulm, Germany 137 Institute of Human Genetics University Hospital Ulm, Germany 138 Brigham and Women’s Hospital/Dana-Farber Cancer Institute, 45 Francis StreetASB II-3, Boston, MA 02115 139 Washington University, St Louis, Missouri 140 International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland 141 Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine 142 Division of Cancer Prevention and Control, H. Lee Moffitt Cancer Center, 12902 Magnolia Dr., Tampa, Florida, USA 143 Molecular Medicine Center and Department of Medical Chemistry and Biochemistry, Medical University –Sofia, 2 Zdrave St, 1431, Sofia, Bulgaria 144 Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and Schools of Life Science and Public Health, Queensland University of Technology, Brisbane, Australia 145 Department of Genetics, Portuguese Oncology Institute, Porto, Portugal and Biomedical Sciences Institute (ICBAS), Porto University, Porto, Portugal 146 University Hospital Erlangen, Department of Gynecology and Obstetrics, FriedrichAlexander-University Erlangen-Nuremberg, Comprehensive Cancer Center ErlangenEMN, Universitaetsstrasse 21-23, 91054 Erlangen, Germany 147 University Hospital Erlangen, Institute of Pathology, Friedrich-Alexander-University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Universitaetsstrasse 21-23, 91054 Erlangen, German 148 Vesalius Research Center, VIB, Leuven, Belgium 149 Laboratory for Translational Genetics, Department of Oncology, University of Leuven, Belgium 150 Department of Epidemiology, The Geisel School of Medicine at Dartmouth, Lebanon, NH, USA 151 Department of Epidemiology, The Geisel School of Medicine at Dartmouth, Hannover, NH, USA 152 Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA 153 Department of Epidemiology, University of Washington, Seattle, WA, USA 154 German Cancer Research Center, Division of Cancer Epidemiology, Heidelberg, Germany 155 Department of Obstetrics and Gynecology, University of Ulm, Ulm, Germany 156 Department of Gynecological Oncology, Roswell Park Cancer Institute, Buffalo, NY 157 Cancer Epidemiology Program, University of Hawaii Cancer Center, Hawaii, USA 158 Department of Pathology, Kapiolani Medical Center for Women and Children, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii 96826, USA 159 Cancer Prevention and Control, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA 160 Community and Population Health Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA 161 Department of Gynecology and Obstetrics, Friedrich Schiller University, Jena University Hospital, Jena, Germany 162 Clinics of Obstetrics and Gynaecology, Hannover Medical School, Hannover, Germany 163 Department of Pathology, Helsinki University Central Hospital, Helsinki, 00029 HUS, Finland 164 University of Pittsburgh Department of Obstetrics, Gynecology and Reproductive Sciences and Ovarian Cancer Center of Excellence Pittsburgh PA USA 165 University of Pittsburgh Department of Epidemiology, University of Pittsburgh Graduate School of Public Health and Womens Cancer Research Program, MageeWomens Research Institute and University of Pittsburgh Cancer Institute Pittsburgh PA USA 166 The University of Texas School of Public Health, Houston, TX, USA 167 Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY 168 Department of Gynecology and Gynecologic Oncology, Kliniken Essen-Mitte/ Evang. Huyssens-Stiftung/ Knappschaft GmbH, Essen, Germany 169 Department of Gynecology and Gynecologic Oncology, Dr. Horst Schmidt Kliniken Wiesbaden, Wiesbaden, Germany 170 Tuebingen University Hospital, Department of Women’sHealth,Tuebingen, Germany 171 Women’s Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California 172 Department of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark 173 Department of Obstetrics and Gynecology, Rigshospitalet, Copenhagen, Denmark 174 Molecular Unit, Department of Pathology, Herlev Hospital, University of Copenhagen, Copenhagen, Denmark 175 Unit of Medical Genetics, Department of Preventive and Predictive Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy 176 Division of Cancer Prevention and Genetics, Istituto Europeo di Oncologia (IEO), Milan, Italy 177 Department of Experimental Oncology, Istituto Europeo di Oncologia (IEO), Milan, Italy and Cogentech Cancer Genetic Test Laboratory, Milan, Italy 178 University of Kansas Medical Center, Kansas City, KS, USA 179 Department of Medical Oncology, Mayo Clinic, Rochester, Minnesota, USA 180 College of Pharmacy and Health Sciences, Texas Southern University, Houston, Texas, USA 181 Department of Gynecologic Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA 182 Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA 183 Gynecology Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA 184 Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, North Carolina, USA 185 Department of Statistical Science, Duke University, Durham, North Carolina, USA 186 Department of Surgery, Duke University Medical Center, Durham, North Carolina, USA 187 Cancer Prevention, Detection & Control Research Program, Duke Cancer Institute, Durham, North Carolina, USA 188 Obstetrics and Gynecology Epidemiology Center, Brigham and Women’sHospital, Boston, Massachusetts, USA 189 Channing Division of Network Medicine, Brigham and Women’sHospitaland Harvard Medical School 190 Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA 191 Cancer Prevention and Control Program, Rutgers Cancer Institute of New Jersey, The State University of New Jersey, New Brunswick, NJ, USA 192 Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA 193 Department of Gynecology and Obstetrics, Haukeland University Horpital, Bergen, Norway 194 Centre for Cancer Biomarkers, Department of Clinical Sciences, University of Bergen, Bergen, Norway 808 Southey MC, et al.J Med Genet 2016;53:800–811. doi:10.1136/jmedgenet-2016-103839 Cancer genetics group.bmj.com on November 29, 2016 - Published by http://jmg.bmj.com/Downloaded from