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PALB2, CHEK2 and ATM rare variants and cancer risk: data from COGS
  1. Melissa C Southey1,
  2. David E Goldgar2,
  3. Robert Winqvist3,
  4. Katri Pylkäs3,
  5. Fergus Couch4,
  6. Marc Tischkowitz5,
  7. William D Foulkes6,
  8. Joe Dennis7,
  9. Kyriaki Michailidou7,
  10. Elizabeth J van Rensburg8,
  11. Tuomas Heikkinen9,
  12. Heli Nevanlinna9,
  13. John L Hopper10,
  14. Thilo Dörk11,
  15. Kathleen BM Claes12,
  16. Jorge Reis-Filho13,
  17. Zhi Ling Teo1,
  18. Paolo Radice14,
  19. Irene Catucci15,
  20. Paolo Peterlongo15,
  21. Helen Tsimiklis1,
  22. Fabrice A Odefrey1,
  23. James G Dowty10,
  24. Marjanka K Schmidt16,
  25. Annegien Broeks16,
  26. Frans B Hogervorst16,
  27. Senno Verhoef16,
  28. Jane Carpenter17,
  29. Christine Clarke18,
  30. Rodney J Scott19,
  31. Peter A Fasching20,21,
  32. Lothar Haeberle20,22,
  33. Arif B Ekici23,
  34. Matthias W Beckmann20,
  35. Julian Peto24,
  36. Isabel dos-Santos-Silva24,
  37. Olivia Fletcher25,
  38. Nichola Johnson25,
  39. Manjeet K Bolla7,
  40. Elinor J Sawyer26,
  41. Ian Tomlinson27,
  42. Michael J Kerin28,
  43. Nicola Miller28,
  44. Federik Marme29,30,
  45. Barbara Burwinkel29,31,
  46. Rongxi Yang29,31,
  47. Pascal Guénel32,33,
  48. Thérèse Truong32,33,
  49. Florence Menegaux32,33,
  50. Marie Sanchez32,33,
  51. Stig Bojesen34,35,
  52. Sune F Nielsen34,35,
  53. Henrik Flyger36,
  54. Javier Benitez37,38,
  55. M Pilar Zamora39,
  56. Jose Ignacio Arias Perez40,
  57. Primitiva Menéndez41,
  58. Hoda Anton-Culver42,
  59. Susan Neuhausen43,
  60. Argyrios Ziogas44,
  61. Christina A Clarke45,
  62. Hermann Brenner46,47,48,
  63. Volker Arndt46,
  64. Christa Stegmaier49,
  65. Hiltrud Brauch48,50,51,
  66. Thomas Brüning52,
  67. Yon-Dschun Ko53,
  68. Taru A Muranen54,
  69. Kristiina Aittomäki55,
  70. Carl Blomqvist56,
  71. Natalia V Bogdanova11,57,
  72. Natalia N Antonenkova58,
  73. Annika Lindblom59,
  74. Sara Margolin60,
  75. Arto Mannermaa61,62,
  76. Vesa Kataja63,64,
  77. Veli-Matti Kosma61,62,
  78. Jaana M Hartikainen61,62,
  79. Amanda B Spurdle65,
  80. kConFab Investigators66,
  81. Australian Ovarian Cancer Study Group65,66,
  82. Els Wauters67,68,
  83. Dominiek Smeets67,68,
  84. Benoit Beuselinck69,
  85. Giuseppe Floris69,
  86. Jenny Chang-Claude70,
  87. Anja Rudolph70,
  88. Petra Seibold70,
  89. Dieter Flesch-Janys71,
  90. Janet E Olson72,
  91. Celine Vachon72,
  92. Vernon S Pankratz72,
  93. Catriona McLean73,
  94. Christopher A Haiman74,
  95. Brian E Henderson74,
  96. Fredrick Schumacher74,
  97. Loic Le Marchand75,
  98. Vessela Kristensen76,77,
  99. Grethe Grenaker Alnæs76,
  100. Wei Zheng78,
  101. David J Hunter79,80,
  102. Sara Lindstrom79,80,
  103. Susan E Hankinson80,81,
  104. Peter Kraft79,80,
  105. Irene Andrulis82,83,
  106. Julia A Knight84,85,
  107. Gord Glendon82,
  108. Anna Marie Mulligan86,87,
  109. Arja Jukkola-Vuorinen88,
  110. Mervi Grip89,
  111. Saila Kauppila90,
  112. Peter Devilee91,
  113. Robert A E M Tollenaar91,
  114. Caroline Seynaeve92,98,
  115. Antoinette Hollestelle92,98,
  116. Montserrat Garcia-Closas93,
  117. Jonine Figueroa94,
  118. Stephen J Chanock94,
  119. Jolanta Lissowska95,
  120. Kamila Czene96,
  121. Hatef Darabi96,
  122. Mikael Eriksson96,
  123. Diana M Eccles97,
  124. Sajjad Rafiq97,
  125. William J Tapper97,
  126. Sue M Gerty97,
  127. Maartje J Hooning98,
  128. John W M Martens98,
  129. J Margriet Collée99,
  130. Madeleine Tilanus-Linthorst100,
  131. Per Hall101,
  132. Jingmei Li102,
  133. Judith S Brand101,
  134. Keith Humphreys101,
  135. Angela Cox103,
  136. Malcolm W R Reed103,
  137. Craig Luccarini104,
  138. Caroline Baynes104,
  139. Alison M Dunning104,
  140. Ute Hamann105,
  141. Diana Torres105,106,
  142. Hans Ulrich Ulmer107,
  143. Thomas Rüdiger108,
  144. Anna Jakubowska109,
  145. Jan Lubinski109,
  146. Katarzyna Jaworska109,110,
  147. Katarzyna Durda109,
  148. Susan Slager72,
  149. Amanda E Toland111,
  150. Christine B Ambrosone112,
  151. Drakoulis Yannoukakos113,
  152. Anthony Swerdlow114,115,
  153. Alan Ashworth93,
  154. Nick Orr93,
  155. Michael Jones114,
  156. Anna González-Neira37,
  157. Guillermo Pita37,
  158. M Rosario Alonso37,
  159. Nuria Álvarez37,
  160. Daniel Herrero37,
  161. Daniel C Tessier116,
  162. Daniel Vincent117,
  163. Francois Bacot117,
  164. Jacques Simard118,
  165. Martine Dumont118,
  166. Penny Soucy118,
  167. Rosalind Eeles119,120,
  168. Kenneth Muir121,
  169. Fredrik Wiklund122,
  170. Henrik Gronberg122,
  171. Johanna Schleutker123,124,
  172. Børge G Nordestgaard125,
  173. Maren Weischer126,
  174. Ruth C Travis127,
  175. David Neal128,
  176. Jenny L Donovan129,
  177. Freddie C Hamdy130,
  178. Kay-Tee Khaw131,
  179. Janet L Stanford132,133,
  180. William J Blot134,
  181. Stephen Thibodeau4,
  182. Daniel J Schaid72,
  183. Joseph L Kelley135,
  184. Christiane Maier136,137,
  185. Adam S Kibel138,139,
  186. Cezary Cybulski140,
  187. Lisa Cannon-Albright141,
  188. Katja Butterbach46,
  189. Jong Park142,
  190. Radka Kaneva143,
  191. Jyotsna Batra144,
  192. Manuel R Teixeira145,
  193. Zsofia Kote-Jarai119,
  194. Ali Amin Al Olama7,
  195. Sara Benlloch7,
  196. Stefan P Renner146,
  197. Arndt Hartmann147,
  198. Alexander Hein146,
  199. Matthias Ruebner146,
  200. Diether Lambrechts148,149,
  201. Els Van Nieuwenhuysen150,
  202. Ignace Vergote150,
  203. Sandrina Lambretchs150,
  204. Jennifer A Doherty151,
  205. Mary Anne Rossing152,153,
  206. Stefan Nickels154,
  207. Ursula Eilber154,
  208. Shan Wang-Gohrke155,
  209. Kunle Odunsi156,
  210. Lara E Sucheston-Campbell156,
  211. Grace Friel156,
  212. Galina Lurie157,
  213. Jeffrey L Killeen158,
  214. Lynne R Wilkens157,
  215. Marc T Goodman159,160,
  216. Ingo Runnebaum161,
  217. Peter A Hillemanns162,
  218. Liisa M Pelttari9,
  219. Ralf Butzow163,
  220. Francesmary Modugno164,165,
  221. Robert P Edwards135,
  222. Roberta B Ness166,
  223. Kirsten B Moysich167,
  224. Andreas du Bois168,169,
  225. Florian Heitz168,169,
  226. Philipp Harter168,169,
  227. Stefan Kommoss169,170,
  228. Beth Y Karlan171,
  229. Christine Walsh171,
  230. Jenny Lester171,
  231. Allan Jensen172,
  232. Susanne Krüger Kjaer172,173,
  233. Estrid Høgdall172,174,
  234. Bernard Peissel175,
  235. Bernardo Bonanni176,
  236. Loris Bernard177,
  237. Ellen L Goode72,
  238. Brooke L Fridley178,
  239. Robert A Vierkant72,
  240. Julie M Cunningham4,
  241. Melissa C Larson72,
  242. Zachary C Fogarty72,
  243. Kimberly R Kalli179,
  244. Dong Liang180,
  245. Karen H Lu181,
  246. Michelle A T Hildebrandt182,
  247. Xifeng Wu182,
  248. Douglas A Levine183,
  249. Fanny Dao183,
  250. Maria Bisogna183,
  251. Andrew Berchuck184,
  252. Edwin S Iversen185,
  253. Jeffrey R Marks186,
  254. Lucy Akushevich187,
  255. Daniel W Cramer188,
  256. Joellen Schildkraut187,
  257. Kathryn L Terry188,
  258. Elizabeth M Poole189,190,
  259. Meir Stampfer80,189,
  260. Shelley S Tworoger189,190,
  261. Elisa V Bandera191,
  262. Irene Orlow192,
  263. Sara H Olson192,
  264. Line Bjorge193,194,
  265. Helga B Salvesen193,194,
  266. Anne M van Altena195,
  267. Katja K H Aben196,197,198,
  268. Lambertus A Kiemeney196,
  269. Leon F A G Massuger195,
  270. Tanja Pejovic199,
  271. Yukie Bean199,
  272. Angela Brooks-Wilson200,201,
  273. Linda E Kelemen202,203,
  274. Linda S Cook204,
  275. Nhu D Le205,
  276. Bohdan Górski206,
  277. Jacek Gronwald206,
  278. Janusz Menkiszak207,
  279. Claus K Høgdall173,
  280. Lene Lundvall208,
  281. Lotte Nedergaard209,
  282. Svend Aage Engelholm210,
  283. Ed Dicks211,
  284. Jonathan Tyrer211,
  285. Ian Campbell212,
  286. Iain McNeish213,
  287. James Paul214,
  288. Nadeem Siddiqui215,
  289. Rosalind Glasspool215,
  290. Alice S Whittemore216,
  291. Joseph H Rothstein216,
  292. Valerie McGuire216,
  293. Weiva Sieh216,
  294. Hui Cai78,
  295. Xiao-Ou Shu78,
  296. Rachel T Teten217,
  297. Rebecca Sutphen217,
  298. John R McLaughlin218,
  299. Steven A Narod219,
  300. Catherine M Phelan220,
  301. Alvaro N Monteiro220,
  302. David Fenstermacher221,
  303. Hui-Yi Lin221,
  304. Jennifer B Permuth220,
  305. Thomas A Sellers220,
  306. Y Ann Chen221,
  307. Ya-Yu Tsai220,
  308. Zhihua Chen221,
  309. Aleksandra Gentry-Maharaj222,
  310. Simon A Gayther223,
  311. Susan J Ramus223,
  312. Usha Menon222,
  313. Anna H Wu223,
  314. Celeste L Pearce223,
  315. David Van Den Berg223,
  316. Malcolm C Pike223,224,
  317. Agnieszka Dansonka-Mieszkowska225,
  318. Joanna Plisiecka-Halasa225,
  319. Joanna Moes-Sosnowska225,
  320. Jolanta Kupryjanczyk225,
  321. Paul DP Pharoah211,
  322. Honglin Song211,
  323. Ingrid Winship226,227,
  324. Georgia Chenevix-Trench65,
  325. Graham G Giles10,228,
  326. Sean V Tavtigian2,
  327. Doug F Easton7,
  328. Roger L Milne10,228
  1. 1Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Melbourne, Australia
  2. 2Huntsman Cancer Institute, Salt Lake City, UT, USA
  3. 3Laboratory of Cancer Genetics and Tumor Biology, Cancer and Translational Medicine Research Unit and Biocenter Oulu, University of Oulu, Nordlab Oulu, Oulu, Finland
  4. 4Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
  5. 5Department 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. 6Program in Cancer Genetics, Department of Human Genetics and Oncology, Lady Davis Institute, and Research Institute, McGill University Health Centre, McGill University, Montreal, Canada,
  7. 7Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Laboratory, Worts Causeway, Cambridge, UK
  8. 8Department of Genetics, University of Pretoria, South Africa
  9. 9Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
  10. 10Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, Australia,
  11. 11Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
  12. 12Center for Medical Genetics, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium,
  13. 13Department of Pathology and Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
  14. 14Unit 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. 15IFOM, the FIRC Institute of Molecular Oncology, Milan, Italy
  16. 16Netherlands Cancer Institute, Antoni van Leeuwenhoek hospital, Amsterdam, The Netherlands
  17. 17Australian Breast Cancer Tissue Bank, University of Sydney at the Westmead Institute for Medical Research, NSW, Australia
  18. 18Centre for Cancer Research, University of Sydney at the Westmead Institute for Medical Research, NSW, Australia
  19. 19Division of Molecular Medicine, Pathology North, Newcastle and University of Newcastle, NSW, Australia
  20. 20University Breast Center Franconia, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
  21. 21David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California at Los Angeles, CA, USA
  22. 22Unit of Biostatistics, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
  23. 23Institute of Human Genetics, University Hospital Erlangen, Friedrich Alexander University Erlangen-Nuremberg, Erlangen, Germany
  24. 24Non-communicable Disease Epidemiology Department, London School of Hygiene and Tropical Medicine, London, UK
  25. 25Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, UK
  26. 26Division 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. 27Wellcome Trust Centre for Human Genetics and Oxford Biomedical Research Centre, University of Oxford, UK and Oxford NIHR Biomedical Research Centre, Headington, OX3 7LE
  28. 28Surgery, Lambe Institute for Translational Science, NUIGalway, University Hospital Galway, Galway, Ireland
  29. 29Department of Obstetrics and Gynecology, University of Heidelberg, Heidelberg, Germany
  30. 30National Center for Tumor Diseases, University of Heidelberg, Heidelberg, Germany
  31. 31Molecular Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
  32. 32Inserm (National Institute of Health and Medical Research), CESP (Center for Research in Epidemiology and Population Health), U1018, Environmental Epidemiology of Cancer, Villejuif, France
  33. 33University Paris-Sud, UMRS 1018, Villejuif, France
  34. 34Copenhagen General Population Study, Herlev Hospital, Copenhagen University Hospital, University of Copenhagen, Copenhagen, Denmark
  35. 35Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, University of Copenhagen, Copenhagen, Denmark
  36. 36Department of Breast Surgery, Herlev Hospital, Copenhagen University Hospital, Copenhagen, Denmark
  37. 37Human Genetics Group, Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
  38. 38Centro de Investigación en Red de Enfermedades Raras (CIBERER), Valencia, Spain
  39. 39Servicio de Oncología Médica, Hospital Universitario La Paz, Madrid, Spain
  40. 40Servicio de Cirugía General y Especialidades, Hospital Monte Naranco, Oviedo, Spain
  41. 41Servicio de Anatomía Patológica, Hospital Monte Naranco, Oviedo, Spain
  42. 42Department of Epidemiology, University of California Irvine, Irvine, California, USA
  43. 43Beckman Research Institute of City of Hope, Duarte, California, USA
  44. 44Department of Epidemiology, University of California Irvine, Irvine, California, USA
  45. 45Cancer Prevention Institute of California, Fremont, California, USA
  46. 46Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
  47. 47Division of Preventive Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
  48. 48German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
  49. 49Saarland Cancer Registry, Saarbrücken, Germany
  50. 50Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart
  51. 51University of Tübingen, Tübingen, Germany
  52. 52Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University, Bochum (IPA), Germany
  53. 53Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany
  54. 54Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
  55. 55Department of Clinical Genetics, Helsinki University Central Hospital, Helsinki, Finland
  56. 56Department of Oncology, Helsinki University Central Hospital, Helsinki, Finland
  57. 57Department of Radiation Oncology, Hannover Medical School, Hannover, Germany
  58. 58N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus
  59. 59Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
  60. 60Department of Oncology – Pathology, Karolinska Institutet, Stockholm, Sweden
  61. 61School of Medicine, Institute of Clinical Medicine, Pathology and Forensic Medicine, and Cancer Center of Eastern Finland, University of Eastern Finland, Kuopio, Finland
  62. 62Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, Kuopio, Finland
  63. 63School of Medicine, Institute of Clinical Medicine, Oncology, University of Eastern Finland, Kuopio, Finland
  64. 64Biocenter Kuopio, Cancer Center of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
  65. 65QIMR Berghofer Medical Research Institute, Brisbane, Australia
  66. 66Research Department, Peter MacCallum Cancer Centre and The Sir Peter MacCallum Department of Oncology, University of Melbourne, Victoria, Australia
  67. 67Vesalius Research Center (VRC), VIB, Leuven, Belgium
  68. 68Laboratory for Translational Genetics, Department of Oncology, University of Leuven, Leuven, Belgium
  69. 69University Hospital Gasthuisberg, Leuven, Belgium
  70. 70Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
  71. 71Department of Cancer Epidemiology/Clinical Cancer Registry and Institute for Medical Biometrics and Epidemiology, University Clinic Hamburg-Eppendorf, Hamburg, Germany
  72. 72Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
  73. 73Anatomical Pathology, The Alfred Hospital, Melbourne, Australia
  74. 74Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
  75. 75Epidemiology Program, Cancer Research Center, University of Hawaii, Honolulu, HI, USA
  76. 76Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Radiumhospitalet, Oslo, Norway
  77. 77Faculty of Medicine (Faculty Division Ahus), University of Oslo (UiO), Norway
  78. 78Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
  79. 79Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, MA, USA
  80. 80Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
  81. 81Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
  82. 82Ontario Cancer Genetics Network, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada
  83. 83Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
  84. 84Prosserman Centre for Health Research, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada
  85. 85Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
  86. 86Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
  87. 87Laboratory Medicine Program, University Health Network, Toronto, Ontario; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
  88. 88Department of Oncology, Oulu University Hospital, University of Oulu, Oulu, Finland
  89. 89Department of Surgery, Oulu University Hospital, University of Oulu, Oulu, Finland
  90. 90Department of Pathology, Oulu University Hospital, University of Oulu, Oulu, Finland
  91. 91Department of Surgical Oncology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
  92. 92Family Cancer Clinic, Department of Medical Oncology, Erasmus MC-Daniel den Hoed Cancer Centre, Rotterdam, The Netherlands
  93. 93The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, SW3 6JB, UK
  94. 94Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA
  95. 95Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Memorial Cancer Center & Institute of Oncology, Warsaw, Poland
  96. 96Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17177, Sweden
  97. 97Faculty of Medicine, University of Southampton (UoS), Southampton UK
  98. 98Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
  99. 99Department of Clinical Genetics, Family Cancer Clinic, Erasmus University Medical Center, Rotterdam, The Netherlands
  100. 100Department of Surgical Oncology, Family Cancer Clinic, Erasmus University Medical Center, Rotterdam, The Netherlands
  101. 101Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm 17177, Sweden
  102. 102Human Genetics Division, Genome Institute of Singapore, Singapore 138672, Singapore
  103. 103Sheffield Cancer Research, Department of Oncology, University of Sheffield, Sheffield, UK
  104. 104Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
  105. 105Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
  106. 106Institute of Human Genetics, Pontificia Universidad Javeriana, Bogota, Colombia
  107. 107Frauenklinik der Stadtklinik Baden-Baden, Baden-Baden, Germany
  108. 108Institute of Pathology, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
  109. 109Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
  110. 110Postgraduate School of Molecular Medicine, Warsaw Medical University, Warsaw, Poland
  111. 111Department of Molecular Virology, Immunology and Medical Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
  112. 112Roswell Park Cancer Institute, Buffalo, New York, USA
  113. 113Molecular Diagnostics Laboratory, IRRP, National Centre for Scientific Research "Demokritos", Aghia Paraskevi Attikis, Athens, Greece
  114. 114Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
  115. 115Division of Breast Cancer Research, Institute of Cancer Research, London, UK
  116. 116Centre d'innovation Genome Quebec et University McGill Montreal Quebec, Canada
  117. 117McGill University, Montreal, Quebec, Canada
  118. 118Cancer Genomics Laboratory, Centre Hospitalier Universitaire de Quebec Research Center. Laval University, Quebec, Canada
  119. 119The Institute of Cancer Research, London, SM2 5NG, UK
  120. 120Royal Marsden NHS Foundation Trust, Fulham, London, SW3 6JJ, UK
  121. 121University of Warwick, Coventry, UK
  122. 122Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
  123. 123Department of Medical Biochemistry and Genetics, University of Turku, and Tyks Microbiology and Genetics, Department of Medical Genetics, Turku University Hospital, Turku, Finland
  124. 124Institute of Biomedical Technology/BioMediTech, University of Tampere, Tampere, Finland
  125. 125Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University Hospital, Herlev Ringvej 75, DK-2730 Herlev, Denmark
  126. 126Department 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. 127Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
  128. 128Surgical 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. 129Professor of Social Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS
  130. 130Nuffield Department of Surgical Sciences, Old Road Campus Research Building (off Roosevelt Drive), University of Oxford, Headington, Oxford, OX3 7DQ
  131. 131Cambridge Institute of Public Health, University of Cambridge, Forvie Site, Robinson Way, Cambridge CB2 0SR
  132. 132Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
  133. 133Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington, USA
  134. 134International Epidemiology Institute, 1455 Research Blvd., Suite 550, Rockville, MD 20850
  135. 135Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
  136. 136Department of Urology, University Hospital Ulm, Germany
  137. 137Institute of Human Genetics University Hospital Ulm, Germany
  138. 138Brigham and Women's Hospital/Dana-Farber Cancer Institute, 45 Francis Street- ASB II-3, Boston, MA 02115
  139. 139Washington University, St Louis, Missouri
  140. 140International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
  141. 141Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine
  142. 142Division of Cancer Prevention and Control, H. Lee Moffitt Cancer Center, 12902 Magnolia Dr., Tampa, Florida, USA
  143. 143Molecular Medicine Center and Department of Medical Chemistry and Biochemistry, Medical University – Sofia, 2 Zdrave St, 1431, Sofia, Bulgaria
  144. 144Australian 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. 145Department of Genetics, Portuguese Oncology Institute, Porto, Portugal and Biomedical Sciences Institute (ICBAS), Porto University, Porto, Portugal
  146. 146University Hospital Erlangen, Department of Gynecology and Obstetrics, Friedrich-Alexander-University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Universitaetsstrasse 21-23, 91054 Erlangen, Germany
  147. 147University Hospital Erlangen, Institute of Pathology, Friedrich-Alexander-University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Universitaetsstrasse 21-23, 91054 Erlangen, German
  148. 148Vesalius Research Center, VIB, Leuven, Belgium
  149. 149Laboratory for Translational Genetics, Department of Oncology, University of Leuven, Belgium
  150. 150Department of Epidemiology, The Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
  151. 151Department of Epidemiology, The Geisel School of Medicine at Dartmouth, Hannover, NH, USA
  152. 152Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
  153. 153Department of Epidemiology, University of Washington, Seattle, WA, USA
  154. 154German Cancer Research Center, Division of Cancer Epidemiology, Heidelberg, Germany
  155. 155Department of Obstetrics and Gynecology, University of Ulm, Ulm, Germany
  156. 156Department of Gynecological Oncology, Roswell Park Cancer Institute, Buffalo, NY
  157. 157Cancer Epidemiology Program, University of Hawaii Cancer Center, Hawaii, USA
  158. 158Department of Pathology, Kapiolani Medical Center for Women and Children, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii 96826, USA
  159. 159Cancer Prevention and Control, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
  160. 160Community and Population Health Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
  161. 161Department of Gynecology and Obstetrics, Friedrich Schiller University, Jena University Hospital, Jena, Germany
  162. 162Clinics of Obstetrics and Gynaecology, Hannover Medical School, Hannover, Germany
  163. 163Department of Pathology, Helsinki University Central Hospital, Helsinki, 00029 HUS, Finland
  164. 164University of Pittsburgh Department of Obstetrics, Gynecology and Reproductive Sciences and Ovarian Cancer Center of Excellence Pittsburgh PA USA
  165. 165University of Pittsburgh Department of Epidemiology, University of Pittsburgh Graduate School of Public Health and Womens Cancer Research Program, Magee-Womens Research Institute and University of Pittsburgh Cancer Institute Pittsburgh PA USA
  166. 166The University of Texas School of Public Health, Houston, TX, USA
  167. 167Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY
  168. 168Department of Gynecology and Gynecologic Oncology, Kliniken Essen-Mitte/ Evang. Huyssens-Stiftung/ Knappschaft GmbH, Essen, Germany
  169. 169Department of Gynecology and Gynecologic Oncology, Dr. Horst Schmidt Kliniken Wiesbaden, Wiesbaden, Germany
  170. 170Tuebingen University Hospital, Department of Women's Health, Tuebingen, Germany
  171. 171Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California
  172. 172Department of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark
  173. 173Department of Obstetrics and Gynecology, Rigshospitalet, Copenhagen, Denmark
  174. 174Molecular Unit, Department of Pathology, Herlev Hospital, University of Copenhagen, Copenhagen, Denmark
  175. 175Unit of Medical Genetics, Department of Preventive and Predictive Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
  176. 176Division of Cancer Prevention and Genetics, Istituto Europeo di Oncologia (IEO), Milan, Italy
  177. 177Department of Experimental Oncology, Istituto Europeo di Oncologia (IEO), Milan, Italy and Cogentech Cancer Genetic Test Laboratory, Milan, Italy
  178. 178University of Kansas Medical Center, Kansas City, KS, USA
  179. 179Department of Medical Oncology, Mayo Clinic, Rochester, Minnesota, USA
  180. 180College of Pharmacy and Health Sciences, Texas Southern University, Houston, Texas, USA
  181. 181Department of Gynecologic Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
  182. 182Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
  183. 183Gynecology Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
  184. 184Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, North Carolina, USA
  185. 185Department of Statistical Science, Duke University, Durham, North Carolina, USA
  186. 186Department of Surgery, Duke University Medical Center, Durham, North Carolina, USA
  187. 187Cancer Prevention, Detection & Control Research Program, Duke Cancer Institute, Durham, North Carolina, USA
  188. 188Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital, Boston, Massachusetts, USA
  189. 189Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School
  190. 190Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA
  191. 191Cancer Prevention and Control Program, Rutgers Cancer Institute of New Jersey, The State University of New Jersey, New Brunswick, NJ, USA
  192. 192Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
  193. 193Department of Gynecology and Obstetrics, Haukeland University Horpital, Bergen, Norway
  194. 194Centre for Cancer Biomarkers, Department of Clinical Sciences, University of Bergen, Bergen, Norway
  195. 195Radboud university medical center, Department of Gynaecology, Nijmegen, Netherlands 
  196. 196Radboud university medical centre, Radboud Institute for Health Sciences, Nijmegen, Netherlands 
  197. 197Netherlands Comprehensive Cancer Organisation, Utrecht, Netherlands
  198. 198Department of Obstetrcs & Gynecology, Oregon Health & Science University
  199. 199Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA
  200. 200Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, BC, Canada
  201. 201Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC Canada
  202. 202Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, SC, USA
  203. 203Hollings Cancer Center, Medical University of South Carolina, SC, USA
  204. 204Division of Epidemiology and Biostatistics, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico, USA
  205. 205Cancer Control Research, BC Cancer Agency, Vancouver, BC, Canada
  206. 206International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
  207. 207Department of Gynecological Surgery and Gynecological Oncology of Adults and Adolescents, Pomeranian Medical University, Szczecin, Poland
  208. 208Gyn Clinic, Rigshospitalet, University of Copenhagen, Denmark
  209. 209Department of Pathology, Rigshospitalet, University of Copenhagen, Denmark
  210. 210Department of Oncology, Rigshospitalet, University of Copenhagen, Denmark
  211. 211Department of Oncology, University of Cambridge, Strangeways Research laboratory, Cambridge, UK
  212. 212Cancer Genetics Laboratory, Research Division, Peter MacCallum Cancer Centre, St Andrews Place, East Melbourne
  213. 213Institute of Cancer Sciences, University of Glasgow, Wolfson Wohl Cancer Research Centre, Beatson Institute for Cancer Research, Glasgow, UK
  214. 214The Cancer Research UK Clinical Trials Unit, Beatson West of Scotland Cancer Centre, 1053 Great Western Road, Glasgow, G12 0YN
  215. 215Department of Gynaecological Oncology, Glasgow Royal Infirmary
  216. 216Department of Health Research and Policy - Epidemiology, Stanford University School of Medicine, Stanford CA, USA
  217. 217Epidemiology Center, College of Medicine, University of South Florida, Tampa, Florida, USA
  218. 218Public Health Ontario, Toronto, Canada
  219. 219Women's College Research Institute, University of Toronto, Toronto, Ontario, Canada
  220. 220Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
  221. 221Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
  222. 222Women's Cancer, Institute for Women's Health, UCL, London, United Kingdom
  223. 223Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, California, USA
  224. 224Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA
  225. 225Department of Pathology and Laboratory Diagnostics, The Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland
  226. 226Department of Medicine, The University of Melbourne Health, Australia,
  227. 227The Royal Melbourne Hospital, Victoria 3050, Australia
  228. 228Cancer Epidemiology Centre, Cancer Council Victoria, Victoria, Australia
  1. Correspondence to Professor Melissa C. Southey, Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Melbourne, Victoria 3010, Australia; msouthey{at}unimelb.edu.au

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 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.

  • Cancer: breast
  • Cancer: prostate
  • Genetics
  • Cancer: ovary
  • cancer predisposition

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/

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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.9 ,10 However, studies based on mutation screening in case-control 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 al12 estimated an OR of 2.85 (95% CI 0.83 to 4.86) for evolutionarily unlikely missense substitutions in the 3′ 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.7 ,13 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 splice-junction 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.4 ,8 ,18–20

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 case-control 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 ER-positive. 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 African-American 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 case-control 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.Leu531Cysfs;4 ,5 ,10 c.2323C>T p.Gln775*;20 c.2816T>G, p.Leu939Trp;2 ,20 c.3113G>A, p.Trp1038*;2 ,6 ,20 c.3116delA, p.Asn1039IIefs;2 ,6 ,20 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)7 ,13 ,24 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.

Table 1

Rare genetic variants included in the iCOGS array.

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-by-variant 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 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).

Table 2

Summary results from Breast Cancer Association Consortium studies of white Europeans (42 671 invasive breast cancer cases and 42 164 controls)

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 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)

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)

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,5–7 ,9 ,11–13 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.5 ,6 ,9 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 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.7 ,13 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 population-based 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 PALB29 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 specific populations. 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.

Acknowledgments

The authors thank the following for their contributions to this study: Qin Wang (BCAC), Lesley McGuffog, and Ken Offit (CIMBA), Andrew Lee, and Ed Dicks and the staff of the Centre for Genetic Epidemiology Laboratory, staff of the CNIO genotyping unit, Sylvie LaBoissière and Frederic Robidoux and the staff of the McGill University and Génome Québec Innovation Centre, staff of the Copenhagen DNA laboratory, and Sharon A Windebank, Christopher A Hilker, Jeffrey Meyer and the staff of Mayo Clinic Genotyping Core Facility. The authors also thank UM1 CA167552 (Willett) Cancer Epidemiology Cohort in Male Health Professionals, P01 CA87969 (Stampfer) Dietary and Hormonal Determinants of Cancer in Women, UM1 CA186107 (Stampfer) Long term multidisciplinary study of cancer in women: The Nurses’ Health Study, R01CA141298 (Stampfer) Growth Factors and Lethal Prostate Cancer Signature, NCI: K07-CA80668 DAMD17-02-1-0669 NIH/National Center for Research Resources/General Clinical Research Center grant MO1-RR000056, R01CA095023, P50-CA159981, NCI CCSG award P30-CA008748 (Memorial Sloan Kettering Cancer Center), Department of Defense (W81XWH-07-0449), National Health and Medical Research Council of Australia APP1029974 and APP1061177, Deutsche Krebshilfe (Maier), Wellcome Trust Centre for Human Genetics from the Wellcome Trust (090532/Z/09/Z). The authors thank the National Institute of Health Research for their support to The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, UK.

References

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Footnotes

  • Contributors All authors provided DNA samples and/or data and have participated in the Breast Cancer Association Consortium through attendance at regular meetings and planning of the iCOGS experiment. Each author has made substantial contribution through designing and coordinating the studies listed in the supplemental material and therefore have made substantial contributions to the conception or design of this work. Many authors played multiple roles across these activities. Specifically, MCS conceived this study, worked to include the rare variants on the iCOGS and drafted the manuscript. RLM led the statistical analysis and drafted the paper. Members of the PALB2 interest group, MCS, DEG, RW, KP, FC, MT, WF, JD, KM, EJvR, TH, HN, JLH, TD, KC, JR-F, ZLT, PR, IC, PP, HT, FAO, JGD contributed to the inclusion of the PALB2 rare variants on iCOGS. DFE coordinated the BCAC project and contributed to statistical analysis along with DEG. SVT contributed to the selection of CHEK2 rare variants. GC-T contributed to the selection of rare variants in ATM. AD, CL and JD made significant contribution to the data quality related to the calling of the rare genetics variants on iCOGS. MKS, AB, FBH, SV, JC, CC, RJS, PAF, LH, ABE, MWB, JP, IDSS, OF, NJ, MKB, EJS, IT, MJK, NM, FM, BB, RY, PG, TT, FM, MS, SB, SFN, HF, JB, MPZ, JIAP, PM, HAC, SN, AZ, CCD, HB, VA, CS, HB, TB, YDK, TAM, KA, CB, NVB, NNA, AL, SM, AM, VK, V-MK, JMH, AS, EW, DS, BB, GF, JCC, AR, PS, DFJ, JEO, CV, VSP, CM, CAH, BEH, FS, LLM, VK, GGA, WZ, DJH, SL, SEH, PK, IA, JAK, GG, AMM, AJV, MG, SK, PD, RAEMT, CS, AH, MGC, JF, SJC, JL, KC, HD, ME, DME, SR, WJT, SMG, MJH, JWMM, JMC, MTL, PH, JL, JSB, KH, AC, MWRR, CL, CB, AD, UH, DT, HUU, TR, AJ, JL, KJ, KD, SS, AET, CBA, DY, AS, AA, NO, MJ, AGN, GP, MRA, NA, DH, DCT, DV, FB, JS, MD, PS, RE, KM, FW, HG, JS, MW, BGN, RCT, DN, JLD, FCH, KTK, JLS, WJB, ST, DJS, JLK, CM, ASK, CC, LCA, KB, JP, RK, JB, MRT, ZKJ, AAAO, SB, SPR, AH, AH, MR, DL, EVN, IV, SL, JAD, MAR, SN, UE, SWG, KO, LES, GF, GL, JLK, LRW, MTG, IR, PAH, LMP, RB, FM, RPE, RBN, KBM, ADB, FH, PH, SK, BYK, CW, JL, AJ, SKK, EH, BP, BB, LB, ELG, BLF, RAV, JMC, MCL, ZCF, KRK, DL, KHL, MATH, XW, DAL, FD, MB, AB, ESI, JRM, LA, DWC, KLT, ELP, MS, SST, EVB, IO, SHO, LB, HBS, AMVA, KKHA, LAK, LFAGM, TP, YB, ABW, LEK, LSC, NDL, BG, JG, JM, CKH, LL, LN, SAE, ED, JT, IC, IN, JP, NS, RG, ASW, JHR, VG, WS, HC, XOS, RTT, RS, JRM, SAN, CP, ANM, DF, HYL, JPW, TAS, TAC, YYT, ZC, AGM, SAG, SJR, UM, AHW, CLP, DVDB, MCP, ADM, JPH, JMS, JK, PP, HS, IW, GC-T, GGG, SVT, DFE, RLM provided DNA samples and/or phenotypical data. All authors read and approved the final manuscript.

  • Funding Funding for the iCOGS infrastructure came from: the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C5047/A8384, C5047/A15007, C5047/A10692, CRUK C8197/A10123), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (No. 1 U19 CA 148537—the GAME-ON initiative), the Department of Defense (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, Komen Foundation for the Cure, the Breast Cancer Research Foundation, the Ovarian Cancer Research Fund and Susan G Komen (WF).

    Further information about the financial report received is outlined in the supplementary file online.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement This would vary for each study—each study is listed in the supplemental material.

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