Inherited variants in the inner centromere protein (INCENP) gene of the chromosomal passenger...

For Peer Review

Inherited variants in the inner centromere protein

(INCENP) gene of the chromosomal passenger complex contribute to the susceptibility of ER negative breast cancer

Journal: Carcinogenesis

Manuscript ID: CARCIN-2014-00711.R1

Manuscript Type: Original Manuscript

Date Submitted by the Author: 05-Dec-2014

Complete List of Authors: Kabisch, Maria; German Cancer Research Center (DKFZ), Molecular

Genetics of Breast Cancer Bermejo, Justo; University Hospital Heidelberg, Institute of Medical Biometry and Informatics (IMBI) Dünnebier, Thomas; German Cancer Research Center (DKFZ), Molecular Genetics of Breast Cancer Ying, Shibo; German Cancer Research Center (DKFZ), Molecular Genetics of Breast Cancer Hamann, Ute; German Cancer Research Center (DKFZ), Molecular Genetics of Breast Cancer

Keywords: inherited susceptibility, ER negative breast cancer, INCENP, chromosomal passenger complex

Carcinogenesis Carcinogenesis Advance Access published January 13, 2015 at C

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Inherited variants in the inner centromere protein (INCENP) gene of the chromosomal passenger 1 complex contribute to the susceptibility of ER negative breast cancer 2 3 4 5 Authors: Maria Kabisch

1#¤, Justo Lorenzo Bermejo

2#¤, Thomas Dünnebier

1¤, Shibo Ying

1, Kyriaki 6

Michailidou3, Manjeet K. Bolla

3, Qin Wang

3, Joe Dennis

3, Mitul Shah

4, Barbara J. Perkins

4, Kamila 7

Czene5, Hatef Darabi

5, Mikael Eriksson

5, Stig E. Bojesen

6,7, Børge G. Nordestgaard

6,7, Sune F. 8

Nielsen6,7

, Henrik Flyger8, Diether Lambrechts

9,10, Patrick Neven

11, Stephanie Peeters

11, Caroline 9

Weltens11

, Fergus J. Couch12

, Janet E. Olson13

, Xianshu Wang12

, Kristen Purrington14

, Jenny 10 Chang-Claude

15, Anja Rudolph

15, Petra Seibold

15, Dieter Flesch-Janys

16, Julian Peto

17, Isabel dos-11

Santos-Silva17

, Nichola Johnson18

, Olivia Fletcher18

, Heli Nevanlinna19

, Taru A. Muranen19

, Kristiina 12 Aittomäki

20, Carl Blomqvist

21, Marjanka K. Schmidt

22, Annegien Broeks

22, Sten Cornelissen

22, 13

Frans B. L. Hogervorst22

, Jingmei Li23

, Judith S. Brand5, Keith Humphreys

5, Pascal Guénel

24,25, 14

Thérèse Truong24,25

, Florence Menegaux24,25

, Marie Sanchez24,25

, Barbara Burwinkel26,27

, Frederik 15 Marmé

26,28, Rongxi Yang

26,27, Peter Bugert

29, Anna González-Neira

30, Javier Benitez

30,31,32, M. Pilar 16

Zamora33

, Jose I. Arias Perez34

, Angela Cox35

, Simon S. Cross36

, Malcolm W. R. Reed35

, Irene L. 17 Andrulis

37,38, Julia A. Knight

39,40, Gord Glendon

41, Sandrine Tchatchou

37, Elinor J. Sawyer

42, Ian 18

Tomlinson43

, Michael J. Kerin44

, Nicola Miller44

, kConFab Investigators45

, Australian Ovarian 19 Cancer Study Group

45,46, Christopher A. Haiman

47, Fredrick Schumacher

47, Brian E. Henderson

47, 20

Loic Le Marchand48

, Annika Lindblom49

, Sara Margolin50

, Maartje J. Hooning51

, Antoinette 21 Hollestelle

51, Mieke Kriege

51, Linetta B. Koppert

52, John L. Hopper

53, Melissa C. Southey

54, Helen 22

Tsimiklis54

, Carmel Apicella53

, Seth Slettedahl13

, Amanda E. Toland55

, Celine Vachon13

, Drakoulis 23 Yannoukakos

56, Graham G. Giles

53,57, Roger L. Milne

53,57, Catriona McLean

58, Peter A. 24

Fasching59,60,61

, Matthias Ruebner59,60

, Arif B. Ekici60,62

, Matthias W. Beckmann59,60

, Hermann 25 Brenner

63,64, Aida K. Dieffenbach

63,64, Volker Arndt

63, Christa Stegmaier

65, Alan Ashworth

18, 26

Nicholas Orr18

, Minouk J. Schoemaker66

, Anthony Swerdlow66,67

, Montserrat García-Closas18,66

, 27 Jonine Figueroa

68, Stephen J. Chanock

68, Jolanta Lissowska

69, Mark S. Goldberg

70,71, France 28

Labrèche72

, Martine Dumont73

, Robert Winqvist74

, Katri Pylkäs74

, Arja Jukkola-Vuorinen75

, Mervi 29 Grip

76, Hiltrud Brauch

77,78,79, Thomas Brüning

80, Yon-Dschun Ko

81, The GENICA 30

Network1,77,78,79,80,81,82,83

, Paolo Radice84

, Paolo Peterlongo85

, Giulietta Scuvera86

, Stefano 31 Fortuzzi

85,87, Natalia Bogdanova

88, Thilo Dörk

89, Arto Mannermaa

90,91,92, Vesa Kataja

93,94,95, Veli-Matti 32

Kosma90,91,92

, Jaana M. Hartikainen90,91,92

, Peter Devilee96

, Robert A. E. M. Tollenaar97

, Caroline 33 Seynaeve

51, Christi J. Van Asperen

98, Anna Jakubowska

99, Jan Lubinski

99, Katarzyna Jaworska-34

Bieniek99

, Katarzyna Durda99

, Wei Zheng100

, Martha J. Shrubsole100

, Qiuyin Cai100

, Diana Torres1, 101

, 35 Hoda Anton-Culver

102, Vessela Kristensen

103,104,105, François Bacot

106, Daniel C. Tessier

106, Daniel 36

Vincent106

, Craig Luccarini4, Caroline Baynes

4, Shahana Ahmed

4, Mel Maranian

4, Jacques 37

Simard73

, Georgia Chenevix-Trench107

, Per Hall5, Paul D.P. Pharoah

34, Alison M. Dunning

4, Douglas 38

F. Easton34

and Ute Hamann1¤+ 39

40 Affiliations:

1Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), 69120 41

Heidelberg, Germany; 2Institute of Medical Biometry and Informatics (IMBI), University Hospital 42

Heidelberg, 69120 Heidelberg, Germany; 3Centre for Cancer Genetic Epidemiology, Department of Public 43

Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, UK; 4Centre for Cancer 44

Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, CB1 8RN, UK; 45 5Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-17177, 46

Sweden; 6Copenhagen General Population Study, Herlev Hospital, Copenhagen University Hospital, 47

2730 Herlev, Denmark; 7Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University 48

Hospital, 2730 Herlev, Denmark; 8Department of Breast Surgery, Herlev Hospital, Copenhagen University 49

Hospital, 2730 Herlev, Denmark; 9Vesalius Research Center (VRC), VIB, 3000 Leuven, Belgium; 50

10Laboratory for Translational Genetics, Department of Oncology, University of Leuven, 3000 Leuven, 51

Belgium; 11

KU Leuven (University of Leuven), Department of Oncology, Multidisciplinary Breast Center, 52 University Hospitals Leuven, 3000 Leuven, Belgium;

12Department of Laboratory Medicine and Pathology, 53

Mayo Clinic, Rochester, MN 55905, USA; 13

Department of Health Sciences Research, Mayo Clinic, 54 Rochester, MN 55905, USA;

14Karmanos Cancer Institute, Detroit, MI 48201, USA;

15Division of Cancer 55

Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; 16

Department of 56 Cancer Epidemiology/Clinical Cancer Registry and Institute for Medical Biometrics and Epidemiology, 57 University Clinic Hamburg-Eppendorf, 20246 Hamburg, Germany;

17Department of Non-Communicable 58

Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK; 59 18

Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, SW3 6JB, 60

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UK; 19

Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Central 61 Hospital, FI-00029 Helsinki, Finland;

20Department of Clinical Genetics, University of Helsinki and Helsinki 62

University Central Hospital, FI-00029 Helsinki, Finland; 21

Department of Oncology, University of Helsinki 63 and Helsinki University Central Hospital, FI-00029 Helsinki, Finland;

22Netherlands Cancer Institute, 64

Antoni van Leeuwenhoek Hospital, 1066 CX Amsterdam, The Netherlands; 23

Human Genetics Division, 65 Genome Institute of Singapore, Singapore 138672;

24Inserm (National Institute of Health and Medical 66

Research), CESP (Center for Research in Epidemiology and Population Health), Environmental 67 Epidemiology of Cancer, 94807 Villejuif, France;

25University Paris-Sud, 94807 Villejuif, France; 68

26Department of Obstetrics and Gynecology, University of Heidelberg, 69120 Heidelberg, Germany; 69

27Molecular Epidemiology Group, German Cancer Research Center (DKFZ), 69120 Heidelberg, 70

Germany; 28

National Center for Tumor Diseases, University of Heidelberg, 69120 Heidelberg, Germany; 71 29

Institute of Transfusion Medicine and Immunology, Medical Faculty Mannheim, Heidelberg University, 72 68167 Mannheim, Germany;

30Human Genotyping-CEGEN Unit, Human Cancer Genetics Program, 73

Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain; 31

Human Genetics Group, 74 Human Cancer Genetics Program, Spanish National Cancer Research Centre (CNIO), 28029 Madrid, 75 Spain;

32Centro de Investigación en Red de Enfermedades Raras (CIBERER), 46010 Valencia, Spain; 76

33Servicio de Oncología Médica, Hospital Universitario La Paz, 28046 Madrid, Spain;

34Servicio de 77

Cirugía General y Especialidades, Hospital Monte Naranco, 33012 Oviedo, Spain; 35

Department of 78 Oncology, University of Sheffield, Sheffield, S10 2RX, UK;

36Academic Unit of Pathology, Department of 79

Neuroscience, University of Sheffield, Sheffield, S10 2HQ, UK; 37

Lunenfeld-Tanenbaum Research 80 Institute of Mount Sinai Hospital, Toronto, ON, M5G 1X5, Canada;

38Department of Molecular Genetics, 81

University of Toronto, Toronto, ON, M5S 1A8, Canada; 39

Prosserman Centre for Health Research, 82 Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, M5G 1X5, Canada; 83 40

Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, M5T 84 3M7, Canada;

41ON Cancer Genetics Network, Lunenfeld-Tanenbaum Research Institute of Mount Sinai 85

Hospital, Toronto, ON, M5G 1X5, Canada; 42

Research Oncology, Division of Cancer Studies, King's 86 College London, Guy’s Hospital, London, SE1 9RT, UK;

43Wellcome Trust Centre for Human Genetics 87

and Oxford Biomedical Research Centre, University of Oxford, OX3 7BN, UK; 44

Clinical Science Institute, 88 University Hospital Galway, Galway, Ireland;

45Peter MacCallum Cancer Center, Melbourne, Victoria 89

3002, Australia; 46

QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia; 90 47

Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los 91 Angeles, CA 90033, USA;

48Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI 92

96813, USA; 49

Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm SE-93 17177, Sweden;

50Department of Oncology - Pathology, Karolinska Institutet, Stockholm SE-17177, 94

Sweden; 51

Department of Medical Oncology, Erasmus MC Cancer Institute, 3008 AE Rotterdam, The 95 Netherlands;

52Department of Surgical Oncology, Erasmus MC Cancer Institute, 3008 AE Rotterdam, The 96

Netherlands; 53

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global 97 Health, The University of Melbourne, Melbourne, Victoria 3010, Australia;

54Department of Pathology, The 98

University of Melbourne, Melbourne, Victoria 3010, Australia; 55

Department of Molecular Virology, 99 Immunology and Medical Genetics, Comprehensive Cancer Center, The Ohio State University, 100 Columbus, OH 43210, USA;

56Molecular Diagnostics Laboratory, IRRP, National Centre for Scientific 101

Research "Demokritos", Aghia Paraskevi Attikis, 153 10 Athens, Greece; 57

Cancer Epidemiology Centre, 102 Cancer Council Victoria, Melbourne, Victoria 3053, Australia;

58Anatomical Pathology, The Alfred 103

Hospital, Melbourne, Victoria 3004, Australia; 59

University Breast Center Franconia, Department of 104 Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-105 Nuremberg, 91054 Erlangen, Germany;

60Comprehensive Cancer Center Erlangen-EMN, 91054 106

Erlangen, Germany; 61

David Geffen School of Medicine, Department of Medicine Division of Hematology 107 and Oncology, University of California at Los Angeles, Los Angeles, CA 90095, USA;

62Institute of Human 108

Genetics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, 91054 109 Erlangen, Germany;

63Division of Clinical Epidemiology and Aging Research, German Cancer Research 110

Center (DKFZ), 69120 Heidelberg, Germany; 64

German Cancer Consortium (DKTK), 69120 Heidelberg, 111 Germany;

65Saarland Cancer Registry, 66119 Saarbrücken, Germany;

66Division of Genetics and 112

Epidemiology, Institute of Cancer Research, London, SM2 5NG, UK; 67

Division of Breast Cancer 113 Research, Institute of Cancer Research, London, SM2 5NG, UK;

68Division of Cancer Epidemiology and 114

Genetics, National Cancer Institute, Rockville, MD 20850, USA; 69

Department of Cancer Epidemiology 115 and Prevention, M. Sklodowska-Curie Memorial Cancer Center & Institute of Oncology, 02-781 Warsaw, 116 Poland;

70Department of Medicine, McGill University, Montreal, QC, H3G 2M1, Canada;

71Division of 117

Clinical Epidemiology, McGill University Health Centre, Royal Victoria Hospital, Montreal, QC, H3G 2M1, 118 Canada;

72Département de santé environnementale et santé au travail, Département de médecine 119

sociale et preventive, École de santé publique, Université de Montréal, Montreal, QC, H3T 1A8, Canada; 120

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73Centre Hospitalier Universitaire de Québec Research Center and Laval University, QC, G1V 4G2, 121

Canada; 74

Laboratory of Cancer Genetics and Tumor Biology, Department of Clinical Chemistry and 122 Biocenter Oulu, University of Oulu, NordLab Oulu/Oulu University Hospital, FI-90220 Oulu, Finland; 123 75

Department of Oncology, Oulu University Hospital, University of Oulu, FI-90220 Oulu, Finland; 124 76

Department of Surgery, Oulu University Hospital, University of Oulu, FI-90220 Oulu, Finland; 77

Dr. 125 Margarete Fischer-Bosch-Institute of Clinical Pharmacology, 70376 Stuttgart, Germany;

78University of 126

Tübingen, 72074 Tübingen, Germany; 79

German Cancer Consortium (DKTK) and German Cancer 127 Research Center (DKFZ), 69120 Heidelberg, Germany

80Institute for Prevention and Occupational 128

Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), 44789 129 Bochum, Germany;

81Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter 130

Krankenhaus, 53113 Bonn, Germany; 82

Institute of Pathology, Medical Faculty of the University of Bonn, 131 53127 Bonn, Germany;

83Institute of Occupational Medicine and Maritime Medicine, University Medical 132

Center Hamburg-Eppendorf, 20246 Hamburg, Germany; 84

Unit of Molecular Bases of Genetic Risk and 133 Genetic Testing, Department of Preventive and Predictive Medicine, Fondazione IRCCS Istituto 134 Nazionale dei Tumori (INT), 20133 Milan, Italy;

85IFOM, Fondazione Istituto FIRC di Oncologia 135

Molecolare, 20139 Milan, Italy; 86

Unit of Medical Genetics, Department of Preventive and Predictive 136 Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), 20133 Milan, Italy;

87Cogentech Cancer 137

Genetic Test Laboratory, 20139 Milan, Italy; 88

Department of Radiation Oncology, Hannover Medical 138 School, 30625 Hannover, Germany;

89Department of Obstetrics and Gynaecology, Hannover Medical 139

School, 30625 Hannover, Germany; 90

School of Medicine, Institute of Clinical Medicine, Pathology and 140 Forensic Medicine, University of Eastern Finland, FI-70211 Kuopio, Finland;

91Cancer Center of Eastern 141

Finland, University of Eastern Finland, FI-70211 Kuopio, Finland; 92

Imaging Center, Department of 142 Clinical Pathology, FI-70211 Kuopio University Hospital, Kuopio, Finland;

93School of Medicine, Institute 143

of Clinical Medicine, Oncology, University of Eastern Finland, FI-70211 Kuopio, Finland; 94

Cancer Center, 144 Kuopio University Hospital, FI-70210 Kuopio, Finland;

95Jyväskylä Central Hospital, FI-40620 Jyväskylä, 145

Finland; 96

Department of Human Genetics & Department of Pathology, Leiden University Medical Center, 146 2333 ZC Leiden, The Netherlands;

97Department of Surgical Oncology, Leiden University Medical Center, 147

2333 ZC Leiden, The Netherlands; 98

Department of Clinical Genetics, Leiden University Medical Center, 148 2333 ZC Leiden, The Netherlands;

99Department of Genetics and Pathology, Pomeranian Medical 149

University, 70-115 Szczecin, Poland; 100

Division of Epidemiology, Department of Medicine, Vanderbilt 150 Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, 151 Nashville, TN 37203, USA;

101Institute of Human Genetics, Pontificia Universidad Javeriana, 11001000 152

Bogotá, Colombia; 102

Department of Epidemiology, University of California Irvine, Irvine, CA 92697, USA; 153 103

Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Radiumhospitalet, N-154 0310 Oslo, Norway;

104Institute of Clinical Medicine, Faculty of Medicine, University of Oslo (UiO), N-0450 155

Oslo, Norway; 105

Department of Clinical Molecular Biology (EpiGen), University of Oslo (UiO), N-0450 156 Oslo, Norway;

106McGill University and Génome Québec Innovation Centre, Montréal, QC, H3A 0G1, 157

Canada; 107

Department of Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, 158 Australia 159 160 # The first 2 authors contributed equally to this work. 161

¤ Manuscript writing group 162

+ Corresponding author 163

164

165

Corresponding Author: 166

Ute Hamann, PhD, Professor 167

German Cancer Research Center (DKFZ) 168

Molecular Genetics of Breast Cancer (B072) 169

Im Neuenheimer Feld 580 170

69120 Heidelberg, Germany 171

Tel.: 0049/6221/42-2344 172

Fax: 0049/6221/42-4721 173

Email: [email protected] 174

175


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Summary 176

This is the first study investigating the contribution of inherited variants in core genes of the chromosomal 177

passenger complex to breast cancer susceptibility. It was found that several INCENP variants are 178

associated with the risk of ER negative breast cancer in the European population. 179

180

181

Keywords: Inherited susceptibility, ER negative breast cancer, INCENP, chromosomal passenger 182

complex 183

184

185

Article category: Cancer Biomarkers and Molecular Epidemiology 186

187

188

Abstract 189

The chromosomal passenger complex (CPC) plays a pivotal role in the regulation of cell division. 190

Therefore, inherited CPC variability could influence tumor development. The present candidate gene 191

approach investigates the relationship between single nucleotide polymorphisms (SNPs) in genes 192

encoding key CPC components and breast cancer risk. Fifteen SNPs in four CPC genes (INCENP, 193

AURKB, BIRC5 and CDCA8) were genotyped in 88,911 European women from 39 case-control studies of 194

the Breast Cancer Association Consortium. Possible associations were investigated in fixed-effects meta-195

analyses. The synonymous SNP rs1675126 in exon 7 of INCENP was associated with overall breast 196

cancer risk (per A allele OR 0.95, 95% CI 0.92-0.98, p=0.007) and particularly with ER negative breast 197

tumors (per A allele OR 0.89, 95% CI 0.83-0.95, p=0.0005). SNPs not directly genotyped were imputed 198

based on 1000 Genomes. The SNPs rs1047739 in the 3’UTR and rs144045115 downstream of INCENP 199

showed the strongest association signals for overall (per T allele OR 1.03, 95% CI 1.00-1.06, p=0.0009) 200

and ER negative breast cancer risk (per A allele OR 1.06, 95% CI 1.02-1.10, p=0.0002). Two genotyped 201

SNPs in BIRC5 were associated with familial breast cancer risk (top SNP rs2071214: per G allele OR 202

1.12, 95% CI 1.04-1.21, p=0.002). The data suggest that INCENP in the CPC pathway contributes to ER 203

negative breast cancer susceptibility in the European population. In spite of a modest contribution of CPC 204

inherited variants to the total burden of sporadic and familial breast cancer, their potential as novel targets 205

for breast cancer treatment should be further investigated. 206

207

Abstract word count: 250 208

Manuscript word count: 4426 209

210


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Introduction 211

Breast cancer is the most commonly occurring epithelial malignancy among women, with an estimated 212

1.4 million new cases and over 450,000 deaths worldwide [1]. Familial aggregation and twin studies have 213

shown the substantial contribution of inherited susceptibility to breast cancer [2;3]. Many genetic loci have 214

been identified that contribute to this familial risk [4], including genes with high-penetrance mutations, 215

notably BRCA1 and BRCA2, moderate penetrance genes including ATM, BRIP1, CHEK2 and PALB2 and 216

common lower penetrance alleles, of which more than 80 have been identified so far. In total these loci 217

explain around 35% of the familial risk of breast cancer [5] leaving a large portion of the observed familial 218

clustering of the disease unexplained [6]. 219

The chromosomal passenger complex (CPC) is a key regulator of mitosis and is essential for 220

maintenance of genomic stability through its control of multiple processes during both nuclear and 221

cytoplasmic division (cytokinesis) [7;8]. 222

The core CPC is composed of the three non-enzymatic subunits, the microtubule-binding inner 223

centromere protein (INCENP), survivin (baculoviral IAP repeat containing 5, BIRC5) and borealin (cell 224

division cycle associated 8, CDCA8), which regulate the activity, localization and stability of the CPC’s 225

catalytic subunit, Aurora kinase B (AURKB) [9]. INCENP is the platform on which the CPC assembles. 226

The INCENP N-terminus forms a triple-helix bundle with the C-terminus of survivin and N-terminus of 227

borealin [9] that is required for CPC localization to the centromere, anaphase spindle midzone and 228

telophase midbody [9-12]. AURKB binds to a conserved region (IN box) at the INCENP C-terminus [13]. 229

Strict localization of AURKB by CPC ensures that the kinase, which has more than 50 substrates [8], 230

phosphorylates the correct targets at the proper steps in cell cycle progression. 231

Loss of CPC function results in lagging chromosomes during metaphase, leading to segregation 232

errors, and in addition cleavage furrows fail to maintain ingression, resulting in cytokinesis failures [14-233

18]. Moreover, lagging chromosomes can secondarily cause cytokinesis failures during telophase. 234

Furthermore, analyses of point mutations in CPC proteins reveal independent roles of these proteins in 235

the initiation of cytokinesis [19-21]. Disturbed CPC function may also be caused by overexpression of 236

CPC subunits and by deregulation of its regulatory kinases and phosphatases. Indeed, high expression 237

levels of INCENP were observed in colorectal cancer cell lines [22], while high expression levels of 238

survivin [23] and AURKB [24;25] have been found in various cancers including breast cancer and shown 239

to be associated with poor prognosis [26;27]. 240


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Given the key role of the CPC in maintaining genomic stability and the facts that chromosome 241

segregation error [28;29] and overexpression of CPC components are frequently seen in human cancers, 242

we hypothesize that genetic variants in the core CPC genes INCENP, AURKB, BIRC5 and CDCA8 affect 243

breast cancer susceptibility. Thus, the primary aim of the present investigation was to assess possible 244

associations between selected tag SNPs and potentially functional SNPs in four CPC genes and breast 245

cancer risk. Subsequently, in silico analyses of SNP function and gene expression were carried out to 246

provide supportive evidence of the identified risk variants. The secondary aim was to explore genetic 247

associations with the survival of breast cancer patients. 248

249

Material and Methods 250

Study participants 251

Study subjects were 88,911 women of European ancestry from 39 case-control studies participating in 252

the Breast Cancer Association Consortium (BCAC). All BCAC studies had local ethical approvals and all 253

included individuals gave informed consent [5]. Seventeen SNPs were selected for genotyping including 254

twelve tag SNPs for INCENP, three potentially functional SNPs in AURKB, BIRC5 and CDCA8, one SNP 255

in AURKB previously reported to be associated with familial breast cancer risk [30] and one SNP in 256

BIRC5 previously reported to be correlated with survivin expression [31]. SNP genotyping in the BCAC 257

samples was conducted using a custom Illumina Infinium array (iCOGS) in four centres, as part of a multi-258

consortia collaboration (the Collaborative Oncological Gene-environment Study COGS) [5]. Genotypes 259

were called using Illumina’s proprietary GenCall algorithm. Quality control included checks on call rate, 260

heterozygosity and Hardy-Weinberg equilibrium. Details on BCAC studies and the SNP selection 261

approach can be found in Supplementary Material and Methods and in Supplementary Tables S1 and S2. 262

Statistical analyses 263

Single SNP association analysis 264

The BCAC provided genotype data along with the first seven genetic principal components to allow 265

adjustment for population stratification (the first six components based on ~37,000 uncorrelated 266

polymorphisms plus a seventh specifically derived for the LMBC study). Available phenotype data 267

included disease status, hormone receptor status, family history and survival information. The association 268

between genotypes and overall breast cancer risk was investigated in an individual-based fixed-effects 269

model meta-analysis comprising 88,911 study subjects. Per allele odds ratios (ORs) and corresponding 270


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95% confidence intervals (CIs) were estimated by logistic regression in a model that incorporated study 271

and the first seven principal genetic components as fixed effects. Study heterogeneity was assessed by 272

the I2 index. Forest plots were generated and the nearest neighbor method based on the medians of the 273

seven first genetic principal components was used to cluster studies according to genetic similarity. 274

To assess the familial breast cancer risk, cases with a family history of breast cancer in a first 275

degree relative were compared to all controls using an additive logistic regression model adjusted for 276

study and the first seven principal genetic components. 277

A case-only analysis was carried out to explore whether SNPs in CPC genes are associated with 278

the hormone receptor status of the tumor (estrogen receptor (ER) positive/negative, progesterone 279

receptor (PR) positive/negative and human epidermal growth factor receptor 2 (HER2) positive/negative). 280

Survival information was available for only approximately 65% of all cases. The relationship 281

between genotype and overall survival (OS), breast cancer specific survival (BCSS) and relapse-free 282

survival (RFS) was investigated in cases, which did not represent with distant metastases at diagnosis. 283

OS was defined as the time between breast cancer diagnosis and death or last follow-up, whichever 284

occurred first. For BCSS only deaths from breast cancer according to ICD10 code counted as events, 285

whereas deaths from any other cause were censored. RFS was defined as the time between breast 286

cancer diagnosis and locoregional relapse or relapse of distant metastasis after a period of remission, 287

whichever occurred first. Survival times were censored after 15 years (for OS and BCSS) and 10 years 288

(for RFS). If cases were diagnosed before study entry, survival times were left-truncated. Survival 289

analyses were performed by Cox regression models incorporating study and the seven genetic principal 290

components as fixed effects. Per allele hazard ratios (HRs) and corresponding 95% CIs were reported. In 291

addition Kaplan-Meier estimates of survival were plotted stratified by genotype, and genotype-specific 292

estimated 10-year (for OS and BCCS) and 5-year (for RFS) survival rates were reported. 293

If an association with the hormone receptor status of the tumor was detected, the subtype-294

specific disease risk and survival was investigated as well. 295

Haplotype analysis 296

Pairwise linkage disequilibrium (LD) between INCENP SNPs was measured by r2 and a LD heat map was 297

generated. Based on different combinations of SNPs and taking the LD block structure into account, we 298

inferred haplotypes using the expectation-maximization (EM) algorithm. Haplotype frequencies were 299

calculated. Subsequently the association between most frequent haplotypes and overall breast cancer 300


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risk was analyzed by a logistic regression model adjusted for study and the seven principal components. 301

The model fit was evaluated by Akaike’s information criterion (AIC) in order to identify the optimal SNP 302

combination, where the smallest AIC value represents the best model. Haplotype-specific ORs and 303

corresponding 95% CIs were also estimated. 304

Interaction and pathway analysis 305

In order to assess the interaction effects of SNPs in different CPC genes on the risk of breast cancer, we 306

carried out a SNP-SNP interaction analysis. The multiplicative interaction index (MII) and the interaction 307

contrast ratio (ICR) were calculated and deviation from multiplicativity and additivity was tested based on 308

Wilcoxon signed-rank tests. 95% CIs were computed by bootstrapping with 10,000 simulations. To 309

investigate whether SNPs in genes of the CPC pathway are jointly associated with overall breast cancer 310

risk, p-values from single SNP analyses were summarized into one combined p-value using Fisher’s 311

method for independent tests. 312

Imputation of genotypes 313

Multiple imputation of genotypes was performed based on all genotyped INCENP SNPs, to detect 314

associations with not directly genotyped but potentially causal SNPs. The European subpopulations from 315

the 1000 Genomes Project phase 1 were used as reference panel [32]. Genotypes of only single 316

nucleotide polymorphisms were imputed in a region centered on the INCENP gene. The extent of this 317

region was identified by visual inspection of recombination rates and, spanned at least ±150 kb starting 318

from the first and last reference SNP. A logistic regression model adjusted for study and the seven 319

genetic principal components was applied in subsequent association analyses for imputed genotypes 320

summarized by minus the logarithm of the p-value. A gene map of the investigated region was created 321

together with a LD map relying on pairwise r2 values for imputed and reference SNPs. 322

Population attributable fraction and familial risk 323

The population attributable fraction (PAF) was calculated for the SNP showing the strongest association 324

with overall breast cancer risk in order to quantify the proportion of sporadic cases related to the risk 325

variant. Also the familial relative risk (FRR), which reflects the attributable proportion of familial cases, 326

was estimated. The calculation of PAF and FRR relied on the estimated ORs and minor allele frequencies 327

(MAF), together with an assumed prevalence of breast cancer in the general population equal to 7.8% 328

until the age of 74 [33;34]. Subsequently the obtained PAF and FRR of INCENP were compared to the 329

PAFs and FRRs of previously identified breast cancer susceptibility variants. Considered susceptibility 330


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variants included high penetrance mutations in BRCA1 and BRCA2 [6], moderate penetrance variants in 331

ATM, BRIP1, CHEK2, and PALB2 [6;35] as well as 80 low penetrance variants throughout the genome 332

[4]. 333

eQTL analysis 334

We examined whether identified risk variants influence gene expression. Information from HapMap, 335

NCBI’s Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) was exploited [36;37]. 336

For 60 unrelated individuals of the CEU population, genotype data were available from the International 337

HapMap project. Expression data derived from EBV transformed lymphoblastoid cell lines of the same 338

individuals have been made public through GEO. The breast cancer study (BRCA) from TCGA provides 339

germline DNA genotypes as well as expression data for tumor and matched normal breast tissue 340

samples. All eQTL analyses involved a two-sided Kruskal-Wallis test. Differences in expression levels 341

between normal and tumor breast tissue samples were analyzed with a two-sided Wilcoxon-Mann-342

Whitney test. 343

Functional SNP analysis 344

In order to explore the functional significance of identified risk variants, the R package FunciSNP was 345

used to examine in silico annotations with chromatin features available in ENCODE [38;39]. The list of 346

examined functional characteristics included 5 built-in biofeatures (CTCF binding sites, DNaseI 347

hypersensitivity sites (HS), Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE) signals and 348

known promoter regions) across several cell lines as well as 57 biofeatures (DNaseI HS sites, FAIRE 349

signals, transcription factor binding sites (TFBS), methylation sites, Chromatin State Segmentation by 350

HMM (ChromHMM) and histone modifications by Chip-seq) specifically downloaded for HMEC normal 351

mammary epithelial cells and breast cancer cell lines MCF7 and T47D. Functional SNP analyses were 352

carried out for variants in a 300 kb window centered on the SNP with the strongest association. 353

354

Results 355

Eight tag SNPs (including three surrogates) out of twelve originally selected tag SNPs for INCENP were 356

genotyped by the BCAC. Additional genotype data were provided for one upstream and one downstream 357

SNP of INCENP. Five SNPs (including one surrogate) out of five originally selected SNPs for AURKB, 358

BIRC5 and CDCA8 were genotyped. A description of all 15 SNPs genotyped for INCENP, AURKB, 359

BIRC5 and CDCA8 can be found in Supplementary Table S2. 360


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Associations of SNPs in CPC genes with breast cancer risk and survival 361

88,911 European women (46,450 cases and 42,461 controls) from 39 BCAC studies were included in 362

association analyses. Reported probability values were not adjusted for multiplicity, they should be 363

interpreted considering that four genes and 15 partially linked variants were simultaneously investigated. 364

Five INCENP SNPs (top SNP rs1675126) were associated with a decreased overall breast 365

cancer risk. Women with variant rs1675126 showed the largest breast cancer risk reduction (per minor A 366

allele OR 0.95, 95% CI 0.92-0.98, p=0.007). The two SNPs rs4963459 and rs4963471, located 367

respectively upstream and downstream of INCENP, were associated with overall breast cancer risk as 368

well (rs4963459: per minor A allele OR 1.02, 95% CI 1.00-1.04, p=0.021; rs4963471: per minor G allele 369

OR 1.03, 95% CI 1.01-1.05, p=0.003). Association results for overall breast cancer risk of all SNPs in 370

INCENP are displayed in Table 1. There was also a weak association of rs2306625 in CDCA8 with 371

overall breast cancer risk (per minor A allele OR 0.97, 95% CI 0.95-0.99, p=0.040) (Table 2). 372

Figure 1A represents the clustering of studies by genetic similarity based on the first genetic 373

principal components. The forest plot on the association with overall breast cancer risk for the top SNP 374

rs1675126 is shown in Figure 1B. The reflection of the geographical study distribution was evident. 375

However, a regional clustering of OR estimates was not obvious. Study heterogeneity was not apparent 376

(I2=0%). 377

The familial breast cancer risk was increased for women with variants rs2071214 and rs3764384 in 378

BIRC5 (rs2071214: per minor G allele OR 1.12, 95% CI 1.04-1.21, p=0.002; rs3764384: per minor A 379

allele OR 1.04, 95% CI 1.00-1.08, p=0.043) (Supplementary Table S3). 380

Case-only analysis revealed that four INCENP SNPs, which were associated with overall breast 381

cancer risk, showed differential association according to ER (top SNP rs1675126: per minor A allele OR 382

1.09, 95% CI 1.01-1.16, p=0.012), but not to PR or HER2 tumor status (Table 3). Subsequent analysis of 383

subtype-specific disease risk revealed that five INCENP SNPs (top SNP rs1675126) showed stronger 384

associations with risk of ER negative breast tumors than with overall breast cancer risk. Women with 385

variant rs1675126 showed the largest reduction in risk of developing ER negative tumors (per minor A 386

allele OR 0.89, 95% CI 0.83-0.95, p=0.0005). This observed association was the strongest among all 387

breast cancer risk analyses and remained statistically significant after correction for multiple testing. The 388

Bonferroni adjusted p-value was p=0.04 (0.0005*75 – considering 15 tests on overall breast cancer risk, 389

15 tests on familial breast cancer and 15 tests for each of the three hormone receptors). The large 390


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sample size of the present association study provided sufficient statistical power to detect small 391

differences between cases and controls in allele frequencies. Table 1 displays association results for all 392

INCENP SNPs stratified by ER status. The forest plot on the association with ER negative breast cancer 393

risk for the top SNP rs1675126 is shown in Figure 1C. The CDCA8 SNP rs2306625 was associated with 394

HER2 (per minor A OR 0.95, 95% CI 0.91-0.99, p=0.033), but not with ER or PR tumor status (Table 3). 395

No association of rs2306625 with risk of HER2 positive or negative breast tumors was observed 396

(Supplementary Table S4). 397

No survival association – either with overall, breast cancer specific or relapse-free survival – was 398

observed for the SNPs in INCENP, AURKB and BIRC5. The investigated SNP in CDCA8 was associated 399

with relapse-free survival. Patients with variant rs2306625 showed an increased risk of relapse (per minor 400

A allele HR 1.17, 95% CI 1.05-1.31, p=0.004, 89% of the survival times were censored). The 401

5-year RFS rate was 0.90 (95% CI 0.89-0.91) for patients homozygous for the common allele, 0.89 (95% 402

CI 0.88-0.91) for heterozygotes and 0.88 (95% CI 0.83-0.91) for patients homozygous for the minor allele. 403

The association of rs2306625 with relapse-free survival was stronger when cases with a HER2 positive 404

tumor were compared to all controls (per minor A allele HR 1.56, 95% CI 1.12-2.17, p=0.008, 84% of the 405

survival times were censored). The results from survival analysis of all SNPs in CPC genes are displayed 406

in Supplementary Tables S5-S8. The relapse-free survival stratified by CDCA8 rs2306625 genotype is 407

shown in Supplementary Figure S1. 408

Associations of INCENP haplotypes with overall breast cancer risk 409

The five INCENP SNPs that were singly associated with a decreased overall breast cancer risk were in 410

high LD (r2>0.8) and located in two LD blocks comprising a region of approximately 12 kb (Supplementary 411

Figure S2). Haplotypes were estimated for these SNPs in order to assess their synergistic effect on 412

breast cancer risk. First, the SNPs were ordered according to their p-values obtained from overall breast 413

cancer risk analysis. Haplotypes were then inferred for (i) the top two SNPs, (ii) the top three SNPs and 414

(iii) all five SNPs. Among all assessed SNP combinations, the model fit was best for the combination of 415

the top three SNPs (AIC=238222.1), but did not improve the model fit for rs1675126 alone 416

(AIC=119150.0). The haplotype frequencies and haplotype-specific estimates for all assessed SNP 417

combinations are displayed in Supplementary Table S9. 418

Results from interaction and pathway analyses are presented in the Supporting Results section. 419

Genotype imputation of untyped SNPs in the INCENP region 420


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Since several genotyped INCENP SNPs were associated with overall and ER negative breast cancer risk, 421

association mapping was refined by imputing additional variants. The reference panel for imputation 422

comprised 379 individuals of the European subpopulations CEU (Utah residents with Northern and 423

Western European ancestry from the CEPH collection), TSI (Toscani in Italia), GBR (British from England 424

and Scotland), FIN (Finnish from Finland) and IBS (Iberian populations from Spain) from the 1000 425

Genomes Project. After visual inspection of recombination rates, an approximately 465 kb region (from 426

61735132 to 62201016 of chromosome 11, NCBI build 37) centered on INCENP and comprising 6,282 427

SNPs was selected for genotype imputation. 5,078 SNPs fulfilled genotype heterozygosity and were 428

imputed with high accuracy (99.1% median average certainty of best-guess genotypes). Subsequent 429

association analysis revealed that the strongest signal for the association with overall breast cancer risk 430

was obtained for rs1047739 (per minor T allele OR 1.03, 95% CI 1.00-1.06, p=0.0009) (Figure 2A). A 431

marginal differential association according to ER tumor status was detected for rs1047739 (per minor T 432

allele OR 1.04, 95% CI 1.00-1.08, p=0.005), but rs144045115 showed the strongest association signal for 433

the association with ER negative breast cancer risk (per minor A allele OR 1.06, 95% CI 1.02-1.10, 434

p=0.0002) (Figure 2B). The two variants are located in the 3’ UTR and downstream of INCENP. A gene 435

map, recombination rates, and LD in the investigated INCENP region are represented in Figures 2C, D, 436

E, respectively. 437

PAF and FRR related to the top INCENP SNP associated with overall breast cancer risk 438

PAFs and FRRs for INCENP SNP rs1047739 compared with previously identified susceptibility variants 439

are displayed in Figure 3. Rs1047739 showed a per allele OR of 1.03 for the association with overall 440

breast cancer risk and a MAF of 0.24. Assuming a cumulative risk of breast cancer in the European Union 441

of 7.8% until the age of 74, rs1047739 results in a PAF of 1.4% and a FRR of 1.0. In comparison to other 442

susceptibility variants, the INCENP SNP rs1047739 contributed to a higher PAF than any rare variant in 443

BRCA1, BRCA2, ATM, BRIP1, CHEK2, or PALB2. Most recently identified common susceptibility variants 444

showed larger PAFs and FRRs than rs1047739, where FGFR2 rs2981579 showed the second highest 445

PAF after PTHLH rs10771399 and the highest FRR among all common variants. The updated list of 446

breast cancer susceptibility variants along with the corresponding ORs, MAFs, PAFs and FRRs are listed 447

in Supplementary Table S10. 448

Associations of INCENP SNPs with gene expression 449


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All variants located between the upstream SNP rs4963459 and the downstream SNP rs4963471 of 450

INCENP, including 103 SNPs available for 60 HapMap individuals (expression in lymphoblastoid cells) 451

and 34 SNPs available for 447 TCGA individuals (expression in 60 normal breast tissue samples and 387 452

tumor breast tissue samples), were examined regarding their impact on gene expression. The mean 453

expression level was -2.84 (± 0.54) in the complete set of normal breast tissue samples and -1.88 (± 454

0.80) in the complete set of tumor breast tissue samples (p<0.0001). Two SNPs (expected five 455

(103x0.05)) were associated with gene expression in lymphoblastoid cells and one SNP (expected two 456

(34x0.05)) was associated with gene expression in normal breast tissue. All of these three SNPs were 457

also associated with overall and ER negative breast cancer risk. Nine SNPs (expected two (34x0.05)) 458

were associated with gene expression in tumor breast tissue. However, none of these were associated 459

with risk of breast cancer. Distribution of INCENP expression levels per SNP genotypes is displayed in 460

Supplementary Figure S3. 461

Potential functional INCENP SNPs 462

The INCENP SNP rs1047739 was annotated with three histone modifications by H3K27me3, H3K36me3 463

and H4K20me1, indicating an actively transcribed and accessible chromatin region that marks RNA 464

polymerase II elongation and a silenced promoter. Moreover, rs1047739 overlapped with the chromatin 465

state of transcriptional elongation. Altogether, the fifteen variants tightly linked (r2≥0.8) to SNP rs1047739 466

showed features consistent to open chromatin, promoter silencing, blocked enhancer activity and 467

repressed gene expression. Detailed information is presented in Supplementary Table S11. 468

469

Discussion 470

This is the first study that investigates whether genetic variability in genes of the core CPC including 471

INCENP, AURKB, BIRC5 and CDCA8 may affect primarily the overall, familial and subtype-specific 472

breast cancer risk and secondarily the survival. 473

The INCENP protein of the CPC is a scaffold protein that comprises two functional subunits: The 474

N-terminus binds to BIRC5 and CDCA8, which is required for the localization of the complex to the 475

centromeres of chromosomes, while the conserved C-terminus binds AURKB partly activating the kinase. 476

This allows AURKB to phosphorylate a C-terminal Thr-Ser-Ser motif in INCENP and a Thr in the T-loop of 477

its kinase domain, resulting in full AURKB activation [40;41]. INCENP is not only phosphorylated by 478

AURKB, but also by Cdk1, which is involved in Polo-like kinase 1 recruitment to the kinetochores but also 479


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in the progression from metaphase to anaphase [42]. Yet, the molecular mechanisms by which SNPs in 480

INCENP and other CPC genes influence breast cancer risk are unknown. 481

We found that several genotyped and imputed SNPs within and downstream of INCENP were 482

associated with overall and particularly with ER negative breast cancer risk. The SNP rs1675126 showed 483

the strongest association signal with overall and ER negative breast cancer risk among all genotyped 484

INCENP SNPs. The association with ER negative breast cancer risk is of particular interest. Only 20 to 485

25% of all breast tumors are ER negative. ER negative breast cancer is often diagnosed at an earlier age 486

and has a worse prognosis than ER positive breast cancer. So far, seven loci specifically associated to 487

ER negative breast cancer susceptibility have been identified [43]. The imputed SNP that showed the 488

strongest association signal in the overall breast cancer risk analysis was rs1047739 located in the 3’ 489

UTR. Even though rs1047739 showed also a differentiated association regarding ER tumor status, 490

imputed rs144045115 downstream of INCENP showed the strongest association with ER negative breast 491

cancer risk. In silico analyses indicated that rs1047739 is located in an accessible chromatin region 492

actively transcribed and that three miRNAs (has-miR-346, has-miR-632 and has-miR-654-3P predicted 493

by Targetscan and MicroCosm Targets 5) bind to the rs1047739 containing region, suggesting that it may 494

be the causal variant. The exact molecular mechanisms of how rs1047739 influences INCENP 495

transcription should be further investigated in vitro. It has been previously reported that expression levels 496

of INCENP are increased in tumor cells [22]. This is in line with our finding that INCENP expression was 497

increased in tumor breast tissue samples compared to normal breast tissue samples based on data from 498

TCGA. 499

In a previous publication, rs2241909 in AURKB was associated with familial breast cancer risk in 500

a German study population [30]. We could not replicate this association in our present large data set from 501

several European study populations. Instead we observed that the two SNPs rs3764384 and rs2071214 502

in BIRC5 were associated with familial breast cancer risk. It was also observed that rs2306625 in CDCA8 503

was particularly associated with relapse-free survival. Therefore rs2306625 may eventually influence both 504

the risk of disease onset and in case of tumor development the pathological characteristics of the tumor 505

and/or its response to treatment. The per G allele increased-risk and better-prognosis finding would 506

contrast with BRCA1/2 mutations in breast cancer 44], but would mimic on the other hand mutations of 507

mismatch repair genes in colorectal cancer [45]. 508


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Low recombination rates downstream of INCENP allowed for genotype imputation in the region of 509

six secretoglobin genes (SCGBs) taking ten genotyped INCENP SNPs as reference. SCGBs are 510

members of a supergene family and most of them are localized in a dense cluster on chromosome 11q13 511

including SCGB1D1, SCGB1D2, SCGB2A1, SCGB2A2, SCGB1D4 and SCGB1A1 [46]. The SCGBs 512

encode small secretory proteins and seem to play a role in the modulation of inflammation, tissue repair 513

and tumorigenesis. Some SCBGs are overexpressed in breast cancer [47-49] and are more frequently 514

associated with ER positive tumors [50; 51]. Interestingly, imputation results indicated variants in the 515

region of SCGB1D1 throughout SCGB1A1, which were associated with overall breast cancer risk (top 516

SNP rs3781965 (located in intron 2 of SCGB1D1): per minor T allele OR 1.02, 95% CI 1.00-1.04, 517

p=0.001), whereas associations with ER negative tumors were detected only for the upstream and gene 518

region of SCGB1D1 (top SNP rs2232935 (located upstream of SCGB1D1): per minor T allele OR 1.05, 519

95% CI 1.02-1.09, p=0.0003). Even though the biological functions of SCGB products are still poorly 520

understood and variants in the 3’ region of INCENP showed stronger association signals in breast cancer 521

risk analysis, initial results on the possible association between inherited variation in SCGBs and breast 522

cancer susceptibility should be explored based on directly typed variants in future consortial work. 523

In conclusion, taking advantage of BCAC and COGS efforts that translated into a homogeneous, 524

high-quality genotyping of 88,911 women from 39 European studies, we were able to identify potential 525

novel variants in the INCENP gene which associated with a 3% per allele increased risk of breast cancer, 526

and with a 6% per allele increased risk of ER negative breast tumors. The present study demonstrates 527

the benefit of scientific collaborations leading to large sample collections in order to identify low 528

penetrance variants, in particular for disease subtypes. It is likely that next generation sequencing in 529

combination with the integration of information on additional layers of genetic variability will refine marker 530

association signals and unravel increasing proportions of sporadic and familial cases of disease. In 531

parallel, the identification of new susceptibility variants may point to novel drug targets. Due to the 532

established involvement in the regulation of cell division, this is probably the most relevant aspect of the 533

identified associations between CPC variants and breast cancer. 534

535

Acknowledgements 536

We acknowledge the financial support by Deutsche Forschungsgemeinschaft and the Ruprecht-Karls-537 Universität Heidelberg within the funding programme Open Access Publishing. The results shown in this 538 article are in part based upon data generated by the TCGA Research Network: 539 http://cancergenome.nih.gov, the ENCODE Consortium and the ENCODE production laboratory(s): 540


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http://genome.ucsc.edu/ENCODE, the GEO contributors: http://www.ncbi.nlm.nih.gov/geo and the 1000 541 Genomes Project Consortium: http://www.1000genomes.org/home. BCAC We thank all the individuals 542 who took part in these studies and all the researchers, clinicians, technicians and administrative staff who 543 have enabled this work to be carried out. COGS This study would not have been possible without the 544 contributions of the following: PH (COGS); DFE, PP, KM, MKB, QW (BCAC), Andrew Berchuck (OCAC), 545 Rosalind A. Eeles, DFE, Ali Amin Al Olama, Zsofia Kote-Jarai, Sara Benlloch (PRACTICAL), GCT, 546 Antonis Antoniou, Lesley McGuffog, FJC and Ken Offit (CIMBA), JD, AMD, Andrew Lee, and Ed Dicks, 547 CL and the staff of the Centre for Genetic Epidemiology Laboratory, JB, AGN and the staff of the CNIO 548 genotyping unit, JS and DCT, FB, DV, Sylvie LaBoissière and Frederic Robidoux and the staff of the 549 McGill University and Génome Québec Innovation Centre, SEB, SFN, BGN, and the staff of the 550 Copenhagen DNA laboratory, and Julie M. Cunningham, Sharon A. Windebank, Christopher A. Hilker, 551 Jeffrey Meyer and the staff of Mayo Clinic Genotyping Core Facility. SEARCH The SEARCH and EPIC 552 teams. pKARMA The Swedish Medical Research Counsel. CGPS Staff and participants of the 553 Copenhagen General Population Study. For the excellent technical assistance: Dorthe Uldall Andersen, 554 Maria Birna Arnadottir, Anne Bank, Dorthe Kjeldgård Hansen. The Danish Breast Cancer Group (DBCG) 555 is acknowledged for the tumor information. LMBC Gilian Peuteman, Dominiek Smeets, Thomas Van 556 Brussel and Kathleen Corthouts. MARIE Judith Heinz, Nadia Obi, Alina Vrieling, Sabine Behrens, Ursula 557 Eilber, Muhabbet Celik, Til Olchers, Stefan Nickels. BBCS Eileen Williams, Elaine Ryder-Mills, Kara 558 Sargus. HEBCS Kirsimari Aaltonen, Karl von Smitten, Sofia Khan, Tuomas Heikkinen, Irja Erkkilä. ABCS 559 Sten Cornelissen, Richard van Hien, Linde Braaf, FBLH, Senno Verhoef, Laura van 't Veer, Emiel 560 Rutgers, Ellen van der Schoot, Femke Atsma. SASBAC The Swedish Medical Research Counsel. 561 BSUCH Medical Faculty Mannheim, Germany (PB). CNIO-BCS Guillermo Pita, Charo Alonso, Daniel 562 Herrero, Nuria Álvarez, MPZ, Primitiva Menendez, the Human Genotyping-CEGEN Unit (CNIO). SBCS 563 Sue Higham, Helen Cramp, Ian Brock, Sabapathy Balasubramanian and Dan Connley. OFBCR Teresa 564 Selander, Nayana Weerasooriya. BIGGS Niall McInerney, Gabrielle Colleran, Andrew Rowan, Angela 565 Jones. kConFab/AOCS We wish to thank Heather Thorne, Eveline Niedermayr, all the kConFab 566 research nurses and staff, the heads and staff of the Family Cancer Clinics, and the Clinical Follow Up 567 Study (which has received funding from the NHMRC, the National Breast Cancer Foundation, Cancer 568 Australia, and the National Institute of Health (USA)) for their contributions to this resource, and the many 569 families who contribute to kConFab. RBCS Petra Bos, Jannet Blom, Ellen Crepin, Anja Nieuwlaat, 570 Annette Heemskerk, the Erasmus MC Family Cancer Clinic. ABCFS Maggie Angelakos, Judi Maskiell, 571 Gillian Dite. TNBCC Robert Pilarski and Charles Shapiro were instrumental in the formation of the OSU 572 Breast Cancer Tissue Bank. We thank the Human Genetics Sample Bank for processing of samples and 573 providing OSU Columbus area control samples. BBCC Silke Landrith, Alexander Hein, Sonja Oeser, 574 Michael Schneider. ESTHER Hartwig Ziegler, Sonja Wolf, Volker Hermann. UKBGS We thank 575 Breakthrough Breast Cancer and the Institute of Cancer Research for support and funding of the 576 Breakthrough Generations Study, and the study participants, study staff, and the doctors, nurses and 577 other health care providers and health information sources who have contributed to the study. We 578 acknowledge NHS funding to the Royal Marsden/ICR NIHR Biomedical Research Centre. PBCS Louise 579 Brinton, Mark Sherman, Neonila Szeszenia-Dabrowska, Beata Peplonska, Witold Zatonski, Pei Chao, 580 Michael Stagner. MTLGEBCS We would like to Martine Tranchant (CHU de Québec Research Center), 581 Marie-France Valois, Annie Turgeon and Lea Heguy (McGill University Health Center, Royal Victoria 582 Hospital; McGill University) for DNA extraction, sample management and skillful technical assistance. JS 583 is Chairholder of the Canada Research Chair in Oncogenetics. OBCS Meeri Otsukka, Kari Mononen. 584 GENICA The GENICA Network: Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, 585 Stuttgart, and University of Tübingen, Germany; (HBra, Wing-Yee Lo, Christina Justenhoven), 586 Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, 587 Germany (YDK, Christian Baisch), Institute of Pathology, University of Bonn, Germany (Hans-Peter 588 Fischer), Molecular Genetics of Breast Cancer, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, 589 Germany (UH) and Institute for Prevention and Occupational Medicine of the German Social Accident 590 Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, Germany (TB, Beate Pesch, Sylvia 591 Rabstein, Anne Lotz); Institute of Occupational Medicine and Maritime Medicine, University Medical 592 Center Hamburg-Eppendorf, Germany (Volker Harth). MBCSG Siranoush Manoukian, Bernard Peissel 593 and Daniela Zaffaroni of the Fondazione IRCCS Istituto Nazionale dei Tumori (INT); Bernardo 594 Bonanni,Monica Barile and Irene Feroce of the Istituto Europeo di Oncologia (IEO) and Loris Bernard the 595 personnel of the Cogentech Cancer Genetic Test Laboratory. HMBCS Peter Hillemanns, Hans 596 Christiansen and Johann H. Karstens. KBCP Eija Myöhänen, Helena Kemiläinen. ORIGO We thank E. 597 Krol-Warmerdam, and J. Blom for patient accrual, administering questionnaires, and managing clinical 598 information. The LUMC survival data were retrieved from the Leiden hospital-based cancer registry 599 system (ONCDOC) with the help of Dr. J. Molenaar. NBHS We thank study partcipants and research staff 600


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for their contributions and commitment to this study. SKKDKFZS We thank all study participants, 601 clinicians, family doctors, researchers and technicians for their contributions and commitment to this 602 study. CTS The CTS Steering Committee includes Leslie Bernstein, Susan Neuhausen, James Lacey, 603 Sophia Wang, Huiyan Ma, Yani Lu, and Jessica Clague DeHart at the Beckman Research Institute of City 604 of Hope, Dennis Deapen, Rich Pinder, Eunjung Lee, and Fred Schumacher at the University of Southern 605 California, Pam Horn-Ross, Peggy Reynolds, Christina Clarke Dur and David Nelson at the Cancer 606 Prevention Institute of California, and HAC, Argyrios Ziogas, and Hannah Park at the University of 607 California Irvine. NBCS NBCS includes the following clinical collaborators: Prof. Per Eystein Lønning, MD 608 (Section of Oncology, Institute of Medicine, University of Bergen and Department of Oncology, Haukeland 609 University Hospital, Bergen, Norway), Prof. Em. Sophie D. Fosså, MD (National Resource Centre for 610 Long-term Studies after Cancer, Rikshospitalet-Radiumhospitalet Cancer Clinic Montebello, Oslo, 611 Norway), Head physician Tone Ikdahl, MD (Department of Oncology, Oslo University Hospital, Oslo, 612 Norway), Dr. Lars Ottestad, MD (Department of Genetics and Department of Oncology, Oslo University 613 Hospital Radiumhospitalet), Dr. Marit Muri Holmen, MD (Department of Radiology, Oslo University 614 Hospital Radiumhospitalet, Oslo, Norway), Dr. Vilde Haakensen, MD (Department of Genetics and 615 Department of Oncology, Oslo University Hospital Radiumhospitalet and Institute for Clinical Medicine, 616 Faculty of Medicine, University of Oslo, Oslo, Norway), Prof. Bjørn Naume, MD (Division of Cancer 617 Medicine and Radiotherapy, Department of Oncology, Oslo University Hospital Radiumhospitalet, Oslo, 618 Norway), Assoc. Prof. Åslaug Helland, MD (Department of Genetics, Institute for Cancer Research and 619 Department of Oncology, Oslo University Hospital Radiumhospitalet, Oslo, Norway and Institute of 620 Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway), Prof. Inger Torhild Gram, MD 621 (Department of Community Medicine, Faculty of Health Sciences, University of Tromsø and Norwegian 622 Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Tromsø, Norway), 623 Prof. Em. Rolf Kåresen, MD (Department of Breast and Endocrine Surgery, Institute for Clinical Medicine, 624 Ullevaal Hospital, Oslo University Hospital and Institute of Clinical Medicine, Faculty of Medicine, 625 University of Oslo, Oslo, Norway), Dr. Ellen Schlichting, MD (Department for Breast and Endocrine 626 Surgery, Oslo University Hospital Ullevaal, Oslo, Norway), Prof. Toril Sauer, MD (Department of 627 Pathology at Akershus University hospital, Lørenskog, Norway), Dr. Olav Engebråten, MD (Institute for 628 Clinical Medicine, Faculty of Medicine, University of Oslo and Department of Oncology, Oslo University 629 Hospital, Oslo, Norway), Dr. Margit Riis, MD (Department of Surgery, Akershus University Hospital and 630 Department of Clinical Molecular Biology (EpiGen), Institute of Clinical Medicine, Akershus University 631 Hospital, University of Oslo, Lørenskog, Norway). 632 This work was supported by the following: BCAC is funded by Cancer Research UK [C1287/A10118, 633 C1287/A12014] and by the European Community´s Seventh Framework Programme under grant 634 agreement number 223175 (grant number HEALTH-F2-2009-223175) (COGS). Funding for the iCOGS 635 infrastructure came from: the European Community's Seventh Framework Programme under grant 636 agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, 637 C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692), the 638 National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 639 CA148065 and 1U19 CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-640 0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast 641 Cancer, Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian 642 Cancer Research Fund. SEARCH is funded by a programme grant from Cancer Research UK 643 [C490/A10124] and supported by the UK National Institute for Health Research Biomedical Research 644 Centre at the University of Cambridge. The pKARMA study was supported by Märit and Hans Rausings 645 Initiative Against Breast Cancer. The CGPS was supported by the Chief Physician Johan Boserup and 646 Lise Boserup Fund, the Danish Medical Research Council and Herlev Hospital. LMBC is supported by 647 the 'Stichting tegen Kanker' (232-2008 and 196-2010). Diether Lambrechts is supported by the FWO and 648 the KULPFV/10/016-SymBioSysII. The MCBCS was supported by the NIH grants CA128978, CA116167, 649 CA176785 an NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer [CA116201], 650 and the Breast Cancer Research Foundation and a generous gift from the David F. and Margaret T. 651 Grohne Family Foundation and the Ting Tsung and Wei Fong Chao Foundation. The MARIE study was 652 supported by the Deutsche Krebshilfe e.V. [70-2892-BR I], the Hamburg Cancer Society, the German 653 Cancer Research Center and the genotype work in part by the Federal Ministry of Education and 654 Research (BMBF) Germany [01KH0402]. The BBCS is funded by Cancer Research UK and 655 Breakthrough Breast Cancer and acknowledges NHS funding to the NIHR Biomedical Research Centre, 656 and the National Cancer Research Network (NCRN). The HEBCS was financially supported by the 657 Helsinki University Central Hospital Research Fund, Academy of Finland (266528), the Finnish Cancer 658 Society, The Nordic Cancer Union and the Sigrid Juselius Foundation. The ABCS study was supported 659 by the Dutch Cancer Society [grants NKI 2007-3839; 2009 4363]; BBMRI-NL, which is a Research 660


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Infrastructure financed by the Dutch government (NWO 184.021.007); and the Dutch National Genomics 661 Initiative. The SASBAC study was supported by funding from the Agency for Science, Technology and 662 Research of Singapore (A*STAR), the US National Institute of Health (NIH) and the Susan G. Komen 663 Breast Cancer Foundation. The CECILE study was funded by Fondation de France, Institut National du 664 Cancer (INCa), Ligue Nationale contre le Cancer, Ligue contre le Cancer Grand Ouest, Agence Nationale 665 de Sécurité Sanitaire (ANSES), Agence Nationale de la Recherche (ANR). The BSUCH study was 666 supported by the Dietmar-Hopp Foundation, the Helmholtz Society and the German Cancer Research 667 Center (DKFZ). The CNIO-BCS was supported by the Genome Spain Foundation, the Red Temática de 668 Investigación Cooperativa en Cáncer and grants from the Asociación Española Contra el Cáncer and the 669 Fondo de Investigación Sanitario (PI11/00923 and PI081120). The Human Genotyping-CEGEN Unit 670 (CNIO) is supported by the Instituto de Salud Carlos III. The SBCS was supported by Yorkshire Cancer 671 Research S295, S299, S305PA and Sheffield Exeperimental Cancer Medicine Centre. The Ontario 672 Familial Breast Cancer Registry (OFBCR) was supported by grant UM1 CA164920 from the National 673 Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of 674 the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry 675 (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by 676 the USA Government or the BCFR. BIGGS ES is supported by NIHR Comprehensive Biomedical 677 Research Centre, Guy's & St. Thomas' NHS Foundation Trust in partnership with King's College London, 678 United Kingdom. IT is supported by the Oxford Biomedical Research Centre. kConFab is supported by a 679 grant from the National Breast Cancer Foundation, and previously by the National Health and Medical 680 Research Council (NHMRC), the Queensland Cancer Fund, the Cancer Councils of New South Wales, 681 Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia. The MEC was 682 support by NIH grants CA63464, CA54281, CA098758 and CA132839. Financial support for KARBAC 683 was provided through the regional agreement on medical training and clinical research (ALF) between 684 Stockholm County Council and Karolinska Institutet, the Swedish Cancer Society, The Gustav V Jubilee 685 foundation and Bert von Kantzows foundation. The RBCS was funded by the Dutch Cancer Society 686 (DDHK 2004-3124, DDHK 2009-4318). The Australian Breast Cancer Family Study (ABCFS) was 687 supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this 688 manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the 689 collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, 690 commercial products, or organizations imply endorsement by the USA Government or the BCFR. The 691 ABCFS was also supported by the National Health and Medical Research Council of Australia, the New 692 South Wales Cancer Council, the Victorian Health Promotion Foundation (Australia) and the Victorian 693 Breast Cancer Research Consortium. J.L.H. is a National Health and Medical Research Council 694 (NHMRC) Australia Fellow and a Victorian Breast Cancer Research Consortium Group Leader. M.C.S. is 695 a NHMRC Senior Research Fellow and a Victorian Breast Cancer Research Consortium Group Leader. 696 The TNBCC was supported by: a Specialized Program of Research Excellence (SPORE) in Breast 697 Cancer (CA116201), a grant from the Breast Cancer Research Foundation, a generous gift from the 698 David F. and Margaret T. Grohne Family Foundation and the Ting Tsung and Wei Fong Chao 699 Foundation, the Stefanie Spielman Breast Cancer fund and the OSU Comprehensive Cancer Center, 700 DBBR (a CCSG Share Resource by National Institutes of Health Grant P30 CA016056), the Hellenic 701 Cooperative Oncology Group research grant (HR R_BG/04) and the Greek General Secretary for 702 Research and Technology (GSRT) Program, Research Excellence II, the European Union (European 703 Social Fund – ESF), and Greek national funds through the Operational Program "Education and Lifelong 704 Learning" of the National Strategic Reference Framework (NSRF) - ARISTEIA. MCCS cohort recruitment 705 was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian 706 NHMRC grants 209057, 251553 and 504711 and by infrastructure provided by Cancer Council Victoria. 707 The work of the BBCC was partly funded by ELAN-Fond of the University Hospital of Erlangen. The 708 ESTHER study was supportd by a grant from the Baden Württemberg Ministry of Science, Research and 709 Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant 710 from the German Cancer Aid (Deutsche Krebshilfe). The UKBGS is funded by Breakthrough Breast 711 Cancer and the Institute of Cancer Research (ICR), London. ICR acknowledges NHS funding to the NIHR 712 Biomedical Research Centre. The PBCS was funded by Intramural Research Funds of the National 713 Cancer Institute, Department of Health and Human Services, USA. The work of MTLGEBCS was 714 supported by the Quebec Breast Cancer Foundation, the Canadian Institutes of Health Research for the 715 “CIHR Team in Familial Risks of Breast Cancer” program – grant # CRN-87521 and the Ministry of 716 Economic Development, Innovation and Export Trade – grant # PSR-SIIRI-701. The OBCS was 717 supported by research grants from the Finnish Cancer Foundation, the Academy of Finland (grant 718 number 250083, 122715 and Center of Excellence grant number 251314), the Finnish Cancer 719 Foundation, the Sigrid Juselius Foundation, the University of Oulu, the University of Oulu Support 720


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Foundation and the special Governmental EVO funds for Oulu University Hospital-based research 721 activities. The GENICA was funded by the Federal Ministry of Education and Research (BMBF) Germany 722 grants 01KW9975/5, 01KW9976/8, 01KW9977/0 and 01KW0114, the Robert Bosch Foundation, 723 Stuttgart, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, the Institute for Prevention and 724 Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum 725 (IPA), Bochum, as well as the Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, 726 Johanniter Krankenhaus, Bonn, Germany. MBCSG is supported by grants from the Italian Association for 727 Cancer Research (AIRC) and by funds from the Italian citizens who allocated the 5/1000 share of their tax 728 payment in support of the Fondazione IRCCS Istituto Nazionale Tumori, according to Italian laws (INT-729 Institutional strategic projects “5x1000”). The HMBCS was supported by a grant from the Friends of 730 Hannover Medical School and by the Rudolf Bartling Foundation. The KBCP was financially supported by 731 the special Government Funding (EVO) of Kuopio University Hospital grants, Cancer Fund of North Savo, 732 the Finnish Cancer Organizations, and by the strategic funding of the University of Eastern Finland. The 733 ORIGO study was supported by the Dutch Cancer Society (RUL 1997-1505) and the Biobanking and 734 Biomolecular Resources Research Infrastructure (BBMRI-NL CP16). The SZBCS was supported by 735 Grant PBZ_KBN_122/P05/2004. The NBHS was supported by NIH grant R01CA100374. Biological 736 sample preparation was conducted the Survey and Biospecimen Shared Resource, which is supported by 737 P30 CA68485. SKKDKFZS is supported by the DKFZ. The CTS was initially supported by the California 738 Breast Cancer Act of 1993 and the California Breast Cancer Research Fund (contract 97-10500) and is 739 currently funded through the National Institutes of Health (R01 CA77398). Collection of cancer incidence 740 data was supported by the California Department of Public Health as part of the statewide cancer 741 reporting program mandated by California Health and Safety Code Section 103885. HAC receives 742 support from the Lon V Smith Foundation (LVS39420). The NBCS was supported by grants from the 743 Norwegian Research council, 155218/V40, 175240/S10 to ALBD, FUGE-NFR 181600/V11 to VNK and a 744 Swizz Bridge Award to ALBD. 745 746

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Table 1: Association between SNPs in INCENP and breast cancer risk

Overall breast cancer risk ER negative breast cancer risk ER positive breast cancer risk

SNP Genotype Controls N (%)

Cases N (%)

Per allele ORa

(95% CI) p-value

b Cases

N (%) Per allele OR

a

(95% CI) p-value

b Cases

N (%) Per allele OR

a

(95% CI) p-value

b

rs4963459 GG 13,479 (31.8) 14,552 (31.3) 1.02 (1.00-1.04) 0.021 2,346 (31.7) 1.02 (0.98-1.06) 0.175 8,557 (31.6) 1.01 (0.99-1.03) 0.242

GA 20,934 (49.3) 22,926 (49.4) 3,639 (49.1) 13,322 (49.2)

AA 8,043 (18.9) 8,957 (19.3) 1,426 (19.2) 5,185 (19.2)

rs17707648 GG 38,500 (90.7) 41,992 (90.4) 1.00 (0.96-1.05) 0.752 6,727 (90.8) 0.98 (0.90-1.07) 0.674 24,464 (90.4) 1.01 (0.96-1.06) 0.566

GA 3,861 (9.1) 4,340 (9.3) 666 (9.0) 2,541 (9.4)

AA 100 (0.2) 118 (0.3) 20 (0.3) 69 (0.3)

rs1628349 GG 37,684 (88.8) 41,313 (88.9) 0.95 (0.91-0.99) 0.037 6,646 (89.7) 0.88 (0.81-0.96) 0.004 23,951 (88.5) 0.98 (0.93-1.02) 0.401

GA 4,609 (10.9) 4,960 (10.7) 743 (10.0) 3,008 (11.1)

AA 167 (0.4) 176 (0.4) 24 (0.3) 114 (0.4)

rs1792949 CC 18,911 (44.9) 20,932 (45.4) 0.97 (0.95-0.99) 0.038 3,401 (46.3) 0.94 (0.90-0.97) 0.002 12,024 (44.7) 0.99 (0.97-1.02) 0.799

CA 18,550 (44.0) 20,081 (43.6) 3,190 (43.4) 11,756 (43.7)

AA 4,699 (11.2) 5,100 (11.1) 761 (10.4) 3,097 (11.5)

rs1675063 AA 19,396 (45.7) 21,512 (46.3) 0.97 (0.95-0.99) 0.029 3,473 (46.9) 0.94 (0.90-0.98) 0.003 12,390 (45.8) 0.99 (0.97-1.01) 0.574

AG 18,594 (43.8) 20,074 (43.2) 3,208 (43.3) 11,769 (43.5)

GG 4,464 (10.5) 4,851 (10.5) 729 (9.8) 2,908 (10.7)

rs1675126 GG 34,763 (81.9) 38,102 (82.0) 0.95 (0.92-0.98) 0.007 6,161 (83.1) 0.89 (0.83-0.95) 5x10-4

21,979 (81.2) 0.97 (0.94-1.01) 0.210

GA 7,213 (17.0) 7,856 (16.9) 1,184 (16.0) 4,772 (17.6)

AA 482 (1.1) 488 (1.1) 68 (0.9) 319 (1.2)

rs7129085 AA 16,264 (38.3) 18,062 (38.9) 0.97 (0.95-0.99) 0.017 2,952 (39.8) 0.93 (0.90-0.97) 9x10-4

10,411 (38.5) 0.98 (0.96-1.01) 0.340

AC 19,943 (47.0) 21,601 (46.5) 3,440 (46.4) 12,588 (64.5)

CC 6,240 (14.7) 6,771 (14.6) 1,017 (13.7) 4,066 (15.0)

rs3781969 AA 26,025 (61.3) 28,660 (61.7) 0.99 (0.97-1.01) 0.641 4,589 (61.9) 0.97 (0.93-1.02) 0.313 16,723 (61.8) 1.00 (0.97-1.02) 0.994

AC 14,401 (33.9) 15,611 (33.6) 2,499 (33.7) 9,076 (33.5)

CC 2,026 (4.8) 2,168 (4.7) 324 (4.4) 1,266 (4.7)

rs11230934 AA 22,687 (53.4) 24,937 (53.7) 0.99 (0.97-1.01) 0.514 4,031 (54.4) 0.96 (0.92-1.00) 0.074 14,572 (53.8) 1.00 (0.97-1.02) 0.987

AC 16,638 (39.2) 18,148 (39.1) 2,878 (38.8) 10,498 (38.8)

CC 3,129 (7.4) 3,359 (7.2) 503 (6.8) 2,000 (7.4)

rs4963471 AA 23,064 (54.3) 24,865 (53.5) 1.03 (1.01-1.05) 0.003 3,949 (53.3) 1.05 (1.01-1.09) 0.012 14,609 (54.0) 1.02 (1.00-1.05) 0.028

AG 16,365 (38.5) 18,265 (39.3) 2,915 (39.3) 10,519 (38.9)

GG 3,032 (7.1) 3,314 (7.1) 547 (7.4) 1,942 (7.2)

Probability values under 5% are shown in bold aOdds ratio (OR) adjusted for a fixed study effect and the first seven principal components

bProbability value based on logistic regression and an additive model


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Table 2: Association between SNPs in AURKB, BIRC5 and CDCA8 and breast cancer risk Overall breast cancer risk

Gene SNP Genotype Controls N (%)

Cases N (%)

Per allele ORa

(95% CI) p-value

b

AURKB rs1059476 GG 33,953 (80.0) 37,051 (79.8) 0.94 (0.97-1.00) 0.154

AG 7,966 (18.7) 8,818 (19.0)

AA 523 (1.2) 568 (1.2)

rs2241909 AA 18,882 (44.5) 20,753 (44.7) 0.98 (0.96-1.00) 0.196

AG 18,844 (44.4) 20,508 (44.2)

GG 4,687 (11.1) 5,135 (11.1)

BIRC5 rs2071214 AA 37,965 (89.4) 41,512 (89.4) 1.02 (0.98-1.06) 0.293

AG 4,356 (10.3) 4,792 (10.3)

GG 139 (0.3) 145 (0.3)

rs3764384 GG 19,315 (45.5) 20,958 (45.1) 1.00 (0.98-1.02) 0.602

AG 18,690 (44.0) 20,492 (44.1)

AA 4,449 (10.5) 4,991 (10.8)

CDCA8 rs2306625 GG 28,220 (66.5) 31,003 (66.8) 0.97 (0.95-0.99) 0.040

AG 12,698 (29.9) 13,821 (29.8)

AA 1,526 (3.6) 1,590 (3.4)


bProbability value based on logistic regression and an additive mode


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Table 3: Association between SNPs in CPC genes and hormone receptor status ER status PR status HER2 status

Gene SNP Genotype Cases N (%)

Per allele ORa

(95% CI) p-value

b Per allele OR

a

(95% CI) p-value

b Per allele OR

a

(95% CI) p-value

b

INCENP rs4963459 GG 14,552 (31.3) 0.99 (0.95-1.03) 0.656 1.00 (0.96-1.04) 0.831 0.95 (0.89-1.01) 0.127

GA 22,926 (49.4)

AA 8,957 (19.3)

rs17707648 GG 41,992 (90.4) 1.05 (0.96-1.15) 0.234 1.02 (0.94-1.11) 0.558 0.98 (0.85-1.12) 0.797

GA 4,340 (9.3)

AA 118 (0.3)

rs1628349 GG 41,313 (88.9) 1.09 (1.00-1.18) 0.046 1.04 (0.97-1.13) 0.215 1.07 (0.94-1.21) 0.281

GA 4,960 (10.7)

AA 176 (0.4)

rs1792949 CC 20,932 (45.4) 1.04 (1.00-1.09) 0.025 1.03 (0.99-1.07) 0.111 1.01 (0.95-1.08) 0.607

CA 20,081 (43.6)

AA 5,100 (11.1)

rs1675063 AA 21,512 (46.3) 1.03 (0.99-1.08) 0.074 1.02 (0.98-1.06) 0.229 1.03 (0.96-1.09) 0.362

AG 20,074 (43.2)

GG 4,851 (10.5)

rs1675126 GG 38,102 (82.0) 1.09 (1.01-1.16) 0.012 1.05 (0.99-1.12) 0.064 1.03 (0.93-1.14) 0.569

GA 7,856 (16.9)

AA 488 (1.1)

rs7129085 AA 18,062 (38.9) 1.04 (1.00-1.08) 0.028 1.02 (0.98-1.06) 0.243 1.01 (0.95-1.08) 0.594

AC 21,601 (46.5)

CC 6,771 (14.6)

rs3781969 AA 28,660 (61.7) 1.00 (0.96-1.05) 0.706 1.00 (0.96-1.05) 0.706 1.00 (0.93-1.08) 0.827

AC 15,611 (33.6)

CC 2,168 (4.7)

rs11230934 AA 24,937 (53.7) 1.02 (0.98-1.07) 0.218 1.01 (0.97-1.05) 0.469 1.06 (0.81-1.38) 0.654

AC 18,148 (39.1)

CC 3,359 (7.2)

rs4963471 AA 24,865 (53.5) 0.97 (0.93-1.02) 0.273 0.98 (0.94-1.02) 0.528 0.99 (0.92-1.06) 0.775

AG 18,265 (39.3)

GG 3,314 (7.1)

AURKB rs1059476 GG 34,056 (80.0) 1.04 (1.00-1.09) 0.052 1.03 (0.99-1.08) 0.071 0.99 (0.94-1.05) 0.873

AG 7,999 (18.8)

AA 526 (1.2)

rs2241909 AA 18,934 (44.5) 1.00 (0.97-1.03) 0.721 0.98 (0.96-1.01) 0.423 0.98 (0.94-1.01) 0.306


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AG 18,907 (44.4)

GG 4,711 (11.1)

BIRC5 rs2071214 AA 38,090 (89.4) 1.03 (0.97-1.09) 0.316 1.04 (0.98-1.10) 0.115 0.98 (0.91-1.05) 0.593

AG 4,370 (10.3)

GG 139 (0.3)

rs3764384 GG 19,384 (45.5) 0.97 (0.95-1.00) 0.110 0.97 (0.94-1.00) 0.067 0.98 (0.95-1.01) 0.374

AG 18,748 (44.0)

AA 4,461 (10.5)

CDCA8 rs2306625 GG 28,308 (66.5) 1.00 (0.96-1.03) 0.845 0.98 (0.95-1.02) 0.508 0.95 (0.91-0.99) 0.033

AG 12,740 (29.9)

AA 1,535 (3.6)




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Figure legends

Fig. 1. (A) Clustering of studies based on the first genetic principal components. The distance between merged

studies reflects their genetic similarity. (B) Forest plot for the association between rs1675126 and overall breast

cancer risk. (C) Forest plot for the association between rs1675126 and ER negative breast cancer risk.

Fig. 2. (A) Association between overall breast cancer risk and genotyped (black) and imputed (grey) SNPs in the

greater INCENP region. (B) Association between ER negative breast cancer risk and genotyped (black) and imputed

(grey) SNPs in the greater INCENP region. Both plots show the –log10 p-values based on logistic regression adjusted

for study and seven principal components. Only imputed SNPs with MAF>0.01 are depicted. The imputed SNPs with

the smallest p-value (rs1047739 and rs144045115) are shown as grey triangles. (C) Gene map including all genes in

the investigated region. (D) Recombination rates in the investigated region. Chromosomal positions refer to NCBI

build 37. (E) LD heatmap based on genotype data retrieved from the European subpopulations from HapMap phase 3

showing pairwise r2 values (from 0 (white) to 1 (black)).

Fig. 3. PAFs vs. FRRs for all breast cancer susceptibility variants of low, moderate and high penetrance (grey dots).

The top imputed INCENP SNP rs1047739 is shown as a black dot.


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Figure 1

218x164mm (300 x 300 DPI)


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Figure 2

190x254mm (300 x 300 DPI)


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Figure 3

185x176mm (300 x 300 DPI)


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Supplementary Material and Methods

Selection and description of SNPs for genotyping

A detailed description of selected SNPs in CPC genes is provided in Supplementary Table S2. For

INCENP, a SNP tagging approach was carried out. To define the set of tag SNPs we used genotype

data of 60 individuals from HapMap’s phase 3 CEU population (Utah residents with Northern and

Western European ancestry from the CEPH collection) [1]. The analyzed sequence spans the region

from position 61644021 to 61677211 (NCBI build 36) of chromosome 11 including the INCENP coding

and a 4 kb putative promoter region. Considering SNPs with a minor allele frequency (MAF) equal or

greater 0.01, twelve tag SNPs captured 100% of 46 alleles with r2 equal or greater 0.75. INCENP tag

SNPs comprised five SNPs located in the putative promoter region (rs11230910, rs1675127,

rs7122395, rs12418839, rs1792949), five intronic SNPs (rs922138, rs1675123, rs3781969,

rs3890102, rs11230934), one synonymous SNP (rs1675126, N396N) and one non-synonymous SNP

(rs7129085, E644D).

Further, three in silico predicted potentially functional SNPs located in the coding or putative

promoter regions of AURKB (rs1059476, M298T), BIRC5 (rs2071214, E129K) and CDCA8

(rs2306625, putative promoter) were selected as well as one synonymous SNP (rs2241909, S295S) in

AURKB previously shown to be associated with familial breast cancer risk [2] and one SNP located in

the 5’ untranslated region of BIRC5 (rs9904341) previously reported to be correlated with survivin

expression [3].

Software and tools

The Haploview software was used to calculate pairwise LD, to identify LD blocks, to create LD heat

maps and to select tag SNPs [4;5]. Association analyses of breast cancer risk and survival, haplotype

inference, clustering of studies by genetic similarity, interaction and pathway analyses, eQTL analysis

and the calculation of the population attributable fraction and the familial risk were conducted with SAS

software, version 9.2 (SAS Institute Inc., Cary, NC, USA). The R packages meta and rmeta were used

to calculate the I2

index and to generate forest plots [6;7]. Genotypes were imputed with IMPUTE v2

[8] and subsequent association testing was performed with SNPTEST v2 [9]. To plot imputation results

and recombination rates R 3.0.1 (http://www.R-project.org) was used. The genes located in the

imputation region were mapped with Locuszoom [10].


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Supplementary Results

Description of SNPs for association testing

A description of genotyped SNPs in CPC genes is provided in Supplementary Table S2. For INCENP,

five tag SNPs (rs1792949, rs1675126, rs7129085, rs3781969, rs11230934) were directly genotyped.

Six tag SNPs failed quality control, but for three of these SNPs surrogates were genotyped

(rs1628349, rs17707648, rs1675063). One tag SNP (rs7122395) was excluded from iCOGS. In total,

genotype data was available for eight out of twelve originally selected INCENP tag SNPs covering

74% of all alleles. The two genotyped SNPs rs4963459 and rs4963459, respectively located upstream

and downstream of INCENP, were additionally provided by BCAC.

Four out of five originally selected SNPs in AURKB, BIRC5 and CDCA8 were directly

genotyped (rs2241909, rs1059476, rs2071214, rs2306625). One BIRC5 SNP failed quality control, but

was replaced by a surrogate (rs3764384).

Interaction and combined association of SNPs in genes of the CPC pathway regarding overall

breast cancer risk

A SNP-SNP interaction analysis was carried out for the top INCENP SNP rs1675126 and the

CDCA8 SNP rs2306625. A departure from both – multiplicative (MII=0.994, 95% CI 0.993-0.995,

p<0.0001) and additive (ICR=-0.005, 95% CI -0.006-(-0.004), p<0.0001) interaction effects – was

detected.

Combining the p-values 0.007 (top INCENP SNP rs1675126), 0.154 (top AURKB SNP

rs1059476), 0.293 (top BIRC5 SNP rs2071214) and 0.040 (CDCA8 rs2306625) from single SNP

analysis, resulted in one global p-value (p=0.004) indicating that SNPs in genes of the CPC pathway

are jointly associated with overall breast cancer risk.

Potential functional INCENP SNPs

Fifteen SNPs overlapped with in total 12 different biofeatures. For all of these SNPs, including two

surrogate SNPs of rs1047739 (r2=1.00) located in intron 15 of INCENP and 28 kb upstream of

SCGB1D1, histone modifications (H3K27me3, H3K36me3 and H4K20me1) and three different

chromatin states (transcriptional elongation, heterochromatin and repressed) were found in HMEC

cells and partly in MCF7 cells (H3K27me3). Open chromatin by FAIRE signals across several cell

lines and a CTCF binding site in MCF7 cells were annotated for rs3867138, which was highly

correlated with rs1047739 (r2=0.98) and located in intron 11 of INCENP. Several SNPs upstream of

SCGB1D1 were correlated with rs1047739 (r2=0.84-0.87) and additionally presented with open


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chromatin by DNaseI HS sites across several cell lines and in the two breast cancer cell lines MCF7

and T47D as well as known promoter regions across several cell lines, EZH2 binding sites in HMEC

cells and a CTCF binding site in MCF7 cells.

Associations of INCENP SNPs with gene expression

The top two imputed SNPs rs1047739 and rs144045115 were not available in HapMap or TCGA. The

top genotyped SNP rs1675126 was not associated with the expression of INCENP – neither in

lymphoblastoid cells, nor in normal or tumor breast tissue. The imputed intronic SNP rs3890102

(p=0.03) and the genotyped downstream SNP rs4963471 were associated with INCENP expression in

lymphoblastoid cells, but rs4963471 showing a weaker association signal than rs3890102. The

median expression level was 6.34 (range 6.14-6.51) for genotype AA of rs3890102, 6.31 (range 6.16-

6.45) for AG and 6.22 (range 6.22-6.22) for GG. Another imputed intronic SNP (rs11230928) was

associated with INCENP expression in normal breast tissue (p=0.01). The median expression level

was -2.99 (range -3.56-(-0.11)) for genotype CC of rs11230928, -2.95 (range -3.78-(-1.95)) for CT and

-2.50 (range -3.02-(-1.69)) for TT. The SNPs associated with gene expression in lymphoblastoid cells

and in normal breast tissue were also associated with overall breast cancer risk (rs3890102: per minor

allele OR 1.02, 95% CI 1.00-1.04, p=0.001; rs11230928: per minor allele OR 1.02, 95% CI 1.00-1.04,

p=0.01) and ER negative breast tumors (rs3890102: per minor allele OR 1.04, 95% CI 1.00-1.08,

p=0.005; rs11230928: per minor allele OR 1.06, 95% CI 1.02-1.10, p=0.0005).


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Table S1: Studies of European women

Sample size

Study Country (ISO 3166) All Cases Controls

ABCFS Australia (AUS) 1,341 790 551

ABCS Netherlands (NLD) 2,754 1,325 1,429

BBCC Germany (DEU) 1,022 564 458

BBCS United Kingdom (GBR) 2,951 1,554 1,397

BIGGS Ireland (IRL) 1,555 836 719

BSUCH Germany (DEU) 1,806 852 954

CECILE France (FRA) 2,018 1,019 999

CGPS Denmark (DNK) 6,987 2,901 4,086

CNIO-BCS Spain (ESP) 1,778 902 876

CTSa United States (USA) 139 68 71

ESTHER Germany (DEU) 980 478 502

GENICAb Germany (DEU) 892 465 427

HEBCS Finland (FIN) 2,898 1,664 1,234

HMBCS Belarus (BLR) 820 690 130

KARBAC Sweden (SWE) 1,384 722 662

KBCP Finland (FIN) 696 445 251

LMBC Belgium (BEL) 4,059 2,671 1,388

MARIE Germany (DEU) 3,596 1,818 1,778

MBCSG Italy (ITA) 888 488 400

MCBCS United States (USA) 3,793 1,862 1,931

MCCS Australia (AUS) 1,125 614 511

MEC United States (USA) 1,472 731 741

MTLGEBCS Canada (CAN) 925 489 436

NBCSa Norway (NOR) 92 22 70

NBHS United States (USA) 243 125 118

OBCS Finland (FIN) 921 507 414

OFBCR Canada (CAN) 1,686 1,175 511

ORIGO Netherlands (NLD) 684 357 327

PBCS Poland (POL) 943 519 424

RBCS Netherlands (NLD) 1,363 664 699

SASBAC Sweden (SWE) 2,541 1,163 1,378

SBCS United Kingdom (GBR) 1,691 843 848

SEARCH United Kingdom (GBR) 17,416 9,347 8,069

SKKDKFZSa Germany (DEU) 165 136 29

SZBCS Poland (POL) 680 365 315

TNBCCc DEMOKRITOS: Greece (GRC),

NBHS_TN: United States (USA), OSU: United States (USA), RPCI: United States (USA)

1,180 756 424

UKBGS United Kingdom (GBR) 946 476 470

kConFab/AOCS Australia (AUS) 1,510 613 897

pKARMA Sweden (SWE) 10,971 5,434 5,537 aCTS, NBCS and SKKDKFZS are studies in BCAC but genotyped as part of the Triple

Negative Breast Cancer Consortium (TNBCC) bPart of GENICA was also genotyped as part of TNBCC

cDEMOKRITOS, NBHS_TN, CSU and RPCI were additional non-BCAC studies

included as part of the TNBCC


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Table S2: Selected and genotyped SNPs in CPC genes

Selected SNPs for genotyping Further genotyped SNPs

Gene dbSNP Positiona MAF

b DNA

changeb

Location Protein

change

Functional consequence Genotyping

status

dbSNP Positiona MAF

b DNA

changeb

Location Protein

change

SNP information

Inner centromere

protein gene

(INCENP)

rs11230910 chr11:61644172 (0.37) C>T upstream - nk, 1/12 tag SNPs failed QC

rs1675127 chr11:61647944 (0.05) C>G upstream - nk, 1/12 tag SNPs failed QC rs1628349 chr11:61651058 (0.05) G>A intron - surrogate for rs1675127

rs7122395 chr11:61647947 (0.42) C>T upstream - nk, 1/12 tag SNPs excluded

rs12418839 chr11:61651467 (0.01) A>G upstream - nk, 1/12 tag SNPs failed QC rs17707648 chr11:61647388 (0.05) G>A upstream - surrogate for rs12418839

rs1792949 chr11:61651569 (0.40) G>T upstream - nk, 1/12 tag SNPs genotyped

rs922138 chr11:61657590 (0.36) G>C intron - nk, 1/12 tag SNPs failed QC rs1675063 chr11:61657316 (0.35) A>G intron - surrogate for rs922138

rs1675126 chr11:61662950 (0.08) C>T exon N396N nk, 1/12 tag SNPs genotyped

rs1675123 chr11:61665248 (0.14) A>G intron - nk, 1/12 tag SNPs failed QC

rs7129085 chr11:61669772 (0.41) T>G exon E644D nk, 1/12 tag SNPs genotyped

rs3781969 chr11:61670187 (0.26) T>G intron - nk, 1/12 tag SNPs genotyped

rs3890102 chr11:61672399 (0.22) A>G intron - nk, 1/12 tag SNPs failed QC

rs11230934 chr11:61675493 (0.32) T>G intron - nk, 1/12 tag SNPs genotyped

rs4963459 chr11:61633713 (0.36) G>A upstream - additional provided SNP

rs4963471 chr11:61698506 (0.25) T>C downstream - additional provided SNP

Aurora kinase B

(AURKB)

rs2241909 chr17:8049064 (0.34) A>G exon S295S previously reported to be

associated with familial breast

cancer risk [2]

genotyped

rs1059476 chr17:8049056 (0.10) G>A exon M298T may impact protein function ([2]

and in silico analysis

(unpublished))

genotyped

Survivin

(baculoviral IAP

repeat containing 5,

BIRC5)

rs9904341 chr17:73721962 (0.32) G>C 5’UTR - previously reported to be

correlated with survivin

expression [3]

failed QC rs3764384 chr17:73719323 (0.28) C>T upstream - surrogate for rs9904341

rs2071214 chr17:73731186 (0.05) A>G exon E129K may impact protein function (in

silico analysis (unpublished))

genotyped

Borealin

(cell division cycle

associated 8,

CDCA8)

rs2306625 chr1:37930798 (0.23) C>T upstream - located in the predicted

promoter region (in silico

analysis (unpublished)).

genotyped

nk, not known aBased on NCBI build 36

bBased on 60 CEU individuals from HapMap phase 3


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Table S3: Association between SNPs in CPC genes and familial breast cancer risk

Familial breast cancer risk


Cases N (%)

Per allele ORa

(95% CI) p-value

b

INCENP rs4963459 GG 13,479 (31.8) 2,567 (31.1) 1.02 (0.98-1.06) 0.193

GA 20,934 (49.3) 4,099 (49.7)

AA 8,043 (18.9) 1,577 (19.1)

rs17707648 GG 38,500 (90.7) 7,462 (90.5) 0.99 (0.91-1.08) 0.845

GA 3,861 (9.1) 770 (9.3)

AA 100 (0.2) 14 (0.2)

rs1628349 GG 37,684 (88.8) 7,359 (89.2) 0.93 (0.86-1.01) 0.080

GA 4,609 (10.9) 858 (10.4)

AA 167 (0.4) 29 (0.4)

rs1792949 CC 18,911 (44.9) 3,675 (44.9) 0.98 (0.94-1.01) 0.311

CA 18,550 (44.0) 3,634 (44.4)

AA 4,699 (11.2) 876 (10.7)

rs1675063 AA 19,396 (45.7) 3,795 (46.0) 0.97 (0.93-1.01) 0.175

AG 18,594 (43.8) 3,613 (43.8)

GG 4,464 (10.5) 837 (10.2)

rs1675126 GG 34,763 (81.9) 6,768 (82.1) 0.96 (0.90-1.02) 0.216

GA 7,213 (17.0) 1,399 (17.0)

AA 482 (1.1) 79 (1.0)

rs7129085 AA 16,264 (38.3) 3,203 (38.9) 0.96 (0.93-1.00) 0.093

AC 19,943 (47.0) 3,872 (47.0)

CC 6,240 (14.7) 1,168 (14.2)

rs3781969 AA 26,025 (61.3) 5,077 (61.6) 0.99 (0.95-1.03) 0.656

AC 14,401 (33.9) 2,777 (33.7)

CC 2,026 (4.8) 392 (4.8)

rs11230934 AA 22,687 (53.4) 4,434 (53.8) 0.98 (0.95-1.03) 0.442

AC 16,638 (39.2) 3,213 (39.0)

CC 3,129 (7.4) 599 (7.3)

rs4963471 AA 23,064 (54.3) 4,414 (53.5) 1.03 (0.98-1.07) 0.165

AG 16,365 (38.5) 3,252 (39.4)

GG 3,032 (7.1) 580 (7.0)

AURKB rs1059476 GG 33,953 (80.0) 6,506 (78.9) 1.01 (0.95-1.07) 0.714

AG 7,966 (18.7) 1,625 (19.7)

AA 523 (1.2) 115 (1.4)

rs2241909 AA 18,882 (44.5) 3,631 (44.1) 0.99 (0.95-1.03) 0.660

AG 18,844 (44.4) 3,662 (44.5)

GG 4,687 (11.1) 946 (11.5)

BIRC5 rs2071214 AA 37,965 (89.4) 7,292 (88.4) 1.12 (1.04-1.21) 0.002

AG 4,356 (10.3) 919 (11.1)

GG 139 (0.3) 35 (0.4)

rs3764384 GG 19,315 (45.5) 3,690 (44.8) 1.04 (1.00-1.08) 0.043

AG 18,690 (44.0) 3,648 (44.2)

AA 4,449 (10.5) 907 (11.0)

CDCA8 rs2306625 GG 28,220 (66.5) 5,463 (66.3) 1.02 (0.97-1.06) 0.382

GA 12,698 (29.9) 2,503 (30.4)

AA 1,526 (3.6) 276 (3.4)

Probability values under 5% are shown in bold

aOdds ratio (OR) adjusted for a fixed study effect and the first seven principal components



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Table S4: Association between rs2306625 in CDCA8 and breast cancer risk stratified by HER2 status

HER2 negative breast cancer HER2 positive breast cancer


Cases N (%)

Per allele ORa

(95% CI) p-value

b Cases

N (%) Per allele OR

a

(95% CI) p-value

b

CDCA8 rs2306625 GG 28,220 (66.5) 9,484 (67.4) 0.96 (0.93-1.00) 0.109 1,764 (68.0) 0.96 (0.88-1.03) 0.295

AG 12,698 (29.9) 4,113 (29.2) 747 (28.8)

AA 1,526 (3.6) 474 (3.4) 83 (3.2)

aOdds ratio (OR) adjusted for a fixed study effect and the first seven principal components



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Table S5: Association between SNPs in CPC genes and overall survival

Overall survival


Events N (%)

10-year survival (95% CI)

Per allele HRa

(95% CI) p-value

b

INCENP rs4963459 GG 9,515 (31.4) 991 (31.0) 0.83 (0.82-0.84) 1.00 (0.95-1.05) 0.941

GA 15,024 (49.6) 1,599 (49.9) 0.84 (0.83-0.84)

AA 5,784 (19.1) 612 (19.1) 0.83 (0.82-0.85)

rs17707648 GG 27,490 (90.6) 2,901 (90.6) 0.83 (0.83-0.84) 0.99 (.89-1.11) 0.907

GA 2,773 (9.1) 298 (9.3) 0.83 (0.81-0.85)

AA 74 (0.2) 4 (0.1) c

rs1628349 GG 27,030 (89.1) 2,895 (90.4) 0.83 (0.82-0.84) 0.93 (0.83-1.04) 0.189

GA 3,193 (10.5) 303 (9.5) 0.86 (0.84-0.87)

AA 113 (0.4) 5 (0.2) c

rs1792949 CC 13,613 (45.2) 1,429 (44.9) 0.83 (0.82-0.84) 1.03 (0.98-1.09) 0.247

CA 13,079 (43.4) 1,389 (43.7) 0.84 (0.83-0.84)

AA 3,427 (11.4) 362 (11.4) 0.82 (0.80-0.84)

rs1675063 AA 13,998 (46.2) 1,474 (46.1) 0.83 (0.82-0.84) 1.02 (0.97-1.08) 0.403

AG 13,100 (43.2) 1,384 (43.2) 0.84 (0.83-0.85)

GG 3,228 (10.6) 343 (10.7) 0.82 (0.80-0.84)

rs1675126 GG 24,953 (82.3) 2,657 (83.0) 0.83 (0.83-0.84) 1.01 (0.92-1.10) 0.860

GA 5,075 (16.7) 524 (16.4) 0.84 (0.82-0.85)

AA 306 (1.0) 22 (0.7) 0.86 (0.78-0.91)

rs7129085 AA 11,787 (38.9) 1,271 (39.7) 0.83 (0.82-0.84) 0.99 (0.94-1.04) 0.727

AC 14,059 (46.4) 1,470 (45.9) 0.84 (0.83-0.84)

CC 4,476 (14.8) 460 (14.4) 0.83 (0.81-0.84)

rs3781969 AA 18,574 (61.3) 1,940 (60.6) 0.84 (0.83-0.84) 1.01 (0.95-1.07) 0.712

AC 10,267 (33.9) 1,096 (34.3) 0.83 (0.82-0.84)

CC 1,486 (4.9) 164 (5.1) 0.81 (0.78-0.84)

rs11230934 AA 16,220 (53.5) 1,702 (53.1) 0.84 (0.83-0.84) 1.00 (0.95-1.06) 0.973

AC 11,840 (39.0) 1,260 (39.3) 0.83 (0.82-0.84)

CC 2,274 (7.5) 241 (7.5) 0.82 (0.79-0.84)

rs4963471 AA 16,276 (53.7) 1,706 (53.3) 0.83 (0.82-0.84) 1.02 (0.97-1.08) 0.417

AG 11,899 (39.2) 1,235 (38.6) 0.84 (0.83-0.85)

GG 2,157 (7.1) 262 (8.2) 0.81 (0.78-0.83)

AURKB rs1059476 GG 24,353 (80.3) 2,587 (80.8) 0.83 (0.83-0.84) 0.96 (0.89-1.04) 0.340

AG 5,622 (18.5) 568 (17.8) 0.84 (0.82-0.85)

AA 354 (1.2) 45 (1.4) 0.78 (0.70-0.84)

rs2241909 AA 13,593 (44.9) 1,423 (44.5) 0.83 (0.82-0.84) 0.98 (0.93-1.04) 0.534


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AG 13,391 (44.2) 1,410 (44.1) 0.83 (0.82-0.84)

GG 3,315 (10.9) 362 (11.3) 0.83 (0.82-0.85)

BIRC5 rs2071214 AA 26,964 (88.9) 2,877 (89.9) 0.83 (0.83-0.84) 0.90 (0.81-1.00) 0.061

AG 3,286 (10.8) 312 (9.7) 0.85 (0.83-0.87)

GG 86 (0.3) 13 (0.4) 0.69 (0.48-0.83)

rs3764384 GG 13,721 (45.2) 1,437 (44.9) 0.83 (0.82-0.84) 1.02 (0.97-1.07) 0.530

GA 13,390 (44.1) 1,444 (45.1) 0.83 (0.82-0.84)

AA 3,221 (10.6) 321 (10.0) 0.83 (0.81-0.85)

CDCA8 rs2306625 GG 20,238 (66.8) 2,142 (67.0) 0.84 (0.83-0.84) 1.04 (0.98-1.11) 0.179

AG 9,050 (29.9) 931 (29.1) 0.83 (0.82-0.85)

AA 1,017 (3.4) 125 (3.9) 0.80 (0.76-0.83) aHazard ratio (HR) adjusted for a fixed study effect and the first seven principal components

bProbability value based on Cox regression and an additive model

c not calculated due to low frequency of events


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Table S6: Association between SNPs in CPC genes and breast cancer specific survival

Breast cancer specific survival


Events N (%)


Per allele HRa

(95% CI) p-value

b

INCENP rs4963459 GG 9,515 (31.4) 503 (31.5) 0.91 (0.90-0.92) 0.98 (0.91-1.05) 0.550

GA 15,024 (49.6) 788 (49.4) 0.92 (0.91-0.92)

AA 5,784 (19.1) 305 (19.1) 0.91 (0.90-0.92)

rs17707648 GG 27,490 (90.6) 1,443 (90.4) 0.92 (0.91-0.92) 0.99 (0.85-1.17) 0.933

GA 2,773 (9.1) 150 (9.4) 0.91 (0.89-0.93)

AA 74 (0.2) 3 (0.2) c

rs1628349 GG 27,030 (89.1) 1,456 (91.2) 0.91 (0.91-0.92) 0.86 (0.73-1.02) 0.087

GA 3,193 (10.5) 139 (8.7) 0.93 (0.91-0.94)

AA 113 (0.4) 1 (0.1) c

rs1792949 CC 13,613 (45.2) 718 (45.4) 0.92 (0.91-0.92) 1.04 (0.97-1.12) 0.255

CA 13,079 (43.3) 682 (43.1) 0.92 (0.91-0.92)

AA 3,427 (11.4) 183 (11.6) 0.91 (0.89-0.92)

rs1675063 AA 13,998 (46.2) 732 (45.9) 0.92 (0.91-0.92) 1.03 (0.96-1.11) 0.418

AG 13,100 (43.2) 698 (43.8) 0.91 (0.91-0.92)

GG 3,228 (10.6) 165 (10.3) 0.91 (0.89-0.92)

rs1675126 GG 24,953 (82.3) 1,324 (83.0) 0.92 (0.91-0.92) 1.05 (0.93-1.19) 0.430

GA 5,075 (16.7) 262 (16.4) 0.91 (0.90-0.92)

AA 306 (1.0) 10 (0.6) 0.91 (0.83-0.96)

rs7129085 AA 11,787 (38.9) 634 (39.7) 0.92 (0.91-0.92) 1.00 (0.93-1.07) 0.947

AC 14,059 (46.4) 734 (46.0) 0.92 (0.91-0.92)

CC 4,476 (14.8) 228 (14.3) 0.91 (0.90-0.92)

rs3781969 AA 18,574 (61.3) 962 (60.4) 0.92 (0.91-0.92) 1.00 (0.92-1.09) 0.954

AC 10,267 (33.9) 557 (35.0) 0.91 (0.90-0.92)

CC 1,486 (4.9) 74 (4.7) 0.92 (0.89-0.93)

rs11230934 AA 16,220 (53.5) 849 (53.2) 0.92 (0.91-0.92) 0.99 (0.91-1.07) 0.727

AC 11,840 (39.0) 630 (39.5) 0.91 (0.91-0.92)

CC 2,274 (7.5) 117 (7.3) 0.91 (0.89-0.93)

rs4963471 AA 16,276 (53.7) 867 (54.3) 0.91 (0.91-0.92) 0.98 (0.90-1.06) 0.538

AG 11,899 (39.2) 603 (37.8) 0.92 (0.91-0.93)

GG 2,157 (7.1) 126 (7.9) 0.90 (0.88-0.92)

AURKB rs1059476 GG 24,353 (80.3) 1,300 (81.5) 0.91 (0.91-0.92) 0.92 (0.82-1.04) 0.168

AG 5,622 (18.5) 267 (16.7) 0.92 (0.91-0.93)

AA 354 (1.2) 28 (1.8) 0.87 (0.80-0.91)

rs2241909 AA 13,593 (44.9) 711 (44.7) 0.91 (0.90-0.92) 0.98 (0.91-1.06) 0.606


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AG 13,391 (44.2) 695 (43.7) 0.92 (0.91-0.92)

GG 3,315 (10.9) 186 (11.7) 0.92 (0.90-0.93)

BIRC5 rs2071214 AA 26,964 (88.9) 1,434 (89.9) 0.91 (0.91-0.92) 0.90 (0.77-1.06) 0.205

AG 3,286 (10.8) 154 (9.7) 0.93 (0.91-0.94)

GG 86 (0.3) 7 (0.4) c

rs3764384 GG 13,721 (45.2) 705 (44.2) 0.92 (0.91-0.92) 1.06 (0.99-1.14) 0.110

GA 13,390 (44.1) 716 (44.9) 0.92 (0.91-0.92)

AA 3,221 (10.6) 175 (11.0) 0.91 (0.90-0.93)

CDCA8 rs2306625 GG 20,238 (66.8) 1,072 (67.2) 0.92 (0.91-0.92) 1.06 (0.97-1.16) 0.192

AG 9,050 (29.9) 469 (29.4) 0.91 (0.90-0.92)

AA 1,017 (3.4) 55 (3.5) 0.90 (0.87-0.93) aHazard ratio (HR) adjusted for a fixed study effect and the first seven principal components




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Table S7: Association between SNPs in CPC genes and relapse-free survival

Relapse-free survival


Events N (%)


Per allele HRa

(95% CI) p-value

b

INCENP rs4963459 GG 3,013 (30.9) 367 (32.8) 0.89 (0.88-0.90) 1.05 (0.96-1.14) 0.295

GA 4,840 (49.7) 512 (45.8) 0.91 (0.90-0.92)

AA 1,885 (19.4) 240 (21.5) 0.88 (0.87-0.90)

rs17707648 GG 8,855 (90.8) 1,022 (91.3) 0.90 (0.89-0.90) 0.98 (0.80-1.20) 0.835

GA 864 (8.9) 96 (8.6) 0.91 (0.88-0.92)

AA 31 (0.3) 1 (0.1) c

rs1628349 GG 8,662 (88.9) 1,010 (90.3) 0.90 (0.89-0.91) 0.97 (0.80-1.17) 0.731

GA 1,048 (10.8) 108 (9.7) 0.90 (0.88-0.92)

AA 39 (0.4) 1 (0.1) c

rs1792949 CC 4,386 (45.4) 516 (46.4) 0.89 (0.88-0.90) 0.99 (0.91-1.09) 0.907

CA 4,158 (43.0) 457 (41.1) 0.90 (0.89-0.91)

AA 1,120 (11.6) 140 (12.6) 0.89 (0.86-0.91)

rs1675063 AA 4,484 (46.0) 528 (47.2) 0.89 (0.88-0.90) 0.96 (0.88-1.05) 0.383

AG 4,210 (43.2) 475 (42.5) 0.90 (0.89-0.91)

GG 1,047 (10.8) 115 (10.3) 0.90 (0.88-0.92)

rs1675126 GG 7,962 (81.7) 942 (84.2) 0.89 (0.88-0.90) 0.96 (0.83-1.12) 0.624

GA 1,683 (17.3) 167 (14.9) 0.91 (0.89-0.92)

AA 103 (1.06) 10 (0.9) 0.93 (0.83-0.97)

rs7129085 AA 3,799 (39.0) 454 (40.6) 0.89 (0.88-0.90) 0.95 (0.87-1.03) 0.210

AC 4,501 (46.2) 523 (46.7) 0.90 (0.89-0.91)

CC 1,439 (14.8) 142 (12.7) 0.91 (0.90-0.93)

rs3781969 AA 6,004 (61.6) 687 (61.5) 0.90 (0.89-0.91) 0.96 (0.87-1.06) 0.393

AC 3,247 (33.3) 374 (33.5) 0.90 (0.89-0.91)

CC 490 (5.0) 56 (5.0) 0.89 (0.86-0.92)

rs11230934 AA 5,282 (54.2) 604 (54.0) 0.90 (0.89-0.90) 0.95 (0.86-1.04) 0.267

AC 3,708 (38.0) 444 (39.7) 0.90 (0.89-0.91)

CC 757 7.8 71 (6.3) 0.91 (0.89-0.93)

rs4963471 AA 5,190 (53.3) 601 (53.8) 0.90 (0.89-0.91) 1.03 (0.94-1.13) 0.507

AG 3,848 (39.5) 409 (36.6) 0.90 (0.89-0.91)

GG 708 (7.3) 107 (9.6) 0.86 (0.82-0.88)

AURKB rs1059476 GG 7,899 (81.0) 915 (81.8) 0.90 (0.89-0.90) 0.91 (0.79-1.05) 0.182

AG 1,754 (18.0) 192 (17.2) 0.90 (0.88-0.91)

AA 96 (1.0) 12 (1.1) 0.90 (0.80-0.95)

rs2241909 AA 4,434 (45.6) 484 (43.5) 0.90 (0.89-0.91) 1.03 (0.95-1.13) 0.468


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AG 4,290 (44.1) 502 (45.1) 0.89 (0.88-0.90)

GG 1,004 (10.3) 128 (11.5) 0.88 (0.85-0.90)

BIRC5 rs2071214 AA 8,691 (89.2) 1,016 (90.9) 0.90 (0.89-0.90) 0.90 (0.74-1.10) 0.287

AG 1,024 (10.5) 101 (9.0) 0.91 (0.89-0.93)

GG 34 (0.4) 1 (0.1) c

rs3764384 GG 4,370 (44.8) 501 (44.8) 0.90 (0.89-0.91) 0.98 (0.90-1.07) 0.653

GA 4,317 (44.3) 493 (44.1) 0.90 (0.89-0.91)

AA 1,061 (10.9) 125 (11.2) 0.90 (0.88-0.92)

CDCA8 rs2306625 GG 6,410 (65.9) 729 (65.3) 0.90 (0.89-0.91) 1.17 (1.05-1.31) 0.004

AG 2,992 (30.8) 344 (30.8) 0.89 (0.88-0.91)

AA 324 (3.3) 44 (4.0) 0.88 (0.83-0.91)

Probability values under 5% are shown in bold type aHazard ratio (HR) adjusted for a fixed study effect and the first seven principal components




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Table S8: Association between rs2306625 in CDCA8 and relapse-free survival stratified by HER2 status

HER2 negative breast cancer HER2 positive breast cancer


Events N (%)


Per allele HRa

(95% CI) p-value

b Cases

N (%) Events N (%)


Per allele HRa

(95% CI) p-value

b

CDCA8 rs2306625 GG 2,563 (66.2) 317 (65.9) 0.88 (0.87-0.89) 1.13 (0.96-1.34) 0.138 490 (66.0) 69 (56.6) 0.85 (0.81-0.88) 1.56 (1.12-2.17) 0.008

AG 1,175 (30.4) 149 (31.0) 0.87 (0.84-0.89) 229 (30.8) 47 (38.5) 0.78 (0.71-0.83)

AA 133 (3.4) 15 (3.1) 0.88 (0.79-93) 24 (3.2) 6 (4.9) 0.68 (0.40-0.85)

Probability values under 5% are shown in bold type aHazard ratio (HR) adjusted for a fixed study effect and the first seven principal components



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Table S9: Association between INCENP haplotypes and breast cancer risk

Overall breast cancer risk

SNP(s) Haplotype

a

Controls N (%)

Cases N (%)

ORb (95% CI) p-value

c AIC

rs1675126, rs7129085 C-C 57,066 (67.0) 62,542 (67.3) Reference 0.059 238225.1 C-M 23,178 (27.2) 25,044 (27.0) 0.98 (0.96-1.00) M-M 4,932 (5.8) 5,297 (5.7) 0.95 (0.91-0.99)

rs1675126, rs7129085, rs1675063 C-C-C 52,285 (61.4) 57,292 (61.7) Reference 0.032 238222.1 C-C-M 5,237 (6.2) 5,746 (6.2) 0.98 (0.94-1.02) M-C-M 19,124 (22.5) 20,623 (22.2) 0.98 (0.96-1.01) M-M-M 8,113 (9.5) 8,705 (9.4) 0.95 (0.91-0.98)

rs1675126, rs7129085, rs1675063, rs1628349, rs1792949 C-C-C-C-C 50,277 (59.0) 55,125 (59.3) Reference 0.127 238227.8 C-C-C-C-M 5,211 (6.1) 5,733 (6.2) 0.98 (0.94-1.02) C-M-M-C-M 17,616 (20.7) 19,026 (20.5) 0.98 (0.96-1.01) M-M-M-M-M 4,592 (5.4) 4,884 (5.3) 0.94 (0.90-0.98)

C, common allele; M, minor allele

Probability values under 5% are shown in bold aOnly haplotypes with frequency >5% are displayed

bOdds ratio (OR) adjusted for a fixed study effect and the first seven principal components

cProbability value based on logistic regression and a haplotype specific model


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Table S10: Breast cancer susceptibility variants of high, moderate and low penetrance and their estimated PAFs and

FRRs

Locus Gene Variant OR

a MAF Inheritance PAF (%) FRR Reference

17q21

13q12.3

BRCA1 9.00 0.0004 dominant 0.64 1.0252 [11]

13q12.3 BRCA2 6.30 0.0005 dominant 0.53 1.0139 [11]

11q22.3 ATM 2.40 0.003 dominant 0.83 1.0057 [11;12]

17q22-q24 BRIP1 2.00 0.001 dominant 0.20 1.0010 [11;12]

22q12.1 CHEK2 2.00 0.002 dominant 0.40 1.0020 [11]

16p12.1 PALB2 2.30 0.005 dominant 1.28 1.0081 [12]

1p11 NOTCH2/FCGR1B rs11249433 1.16 0.39 additive 11.10 1.0048 [13]

1p13 TPN22/BCL2L15 rs11552449 1.07 0.17 additive 2.32 1.0007 [13]

1p36 PEX14 rs616488 1.06 0.67 additive 8.01 1.0008 [13]

1q32 LGR6 rs6678914 1.10 0.59 additive 6.79 1.0033 [13]

1q32 MDM4 rs4245739 1.14 0.26 additive 10.55 1.0019 [13]

2p24 desert rs12710696 1.10 0.36 additive 6.72 1.0020 [13]

2q14 None rs4849887 1.10 0.90 additive 15.25 1.0006 [13]

2q31 CDCA7 rs1550623 1.06 0.84 additive 5.41 1.0007 [13]

2q31 METAP1D rs2016394 1.05 0.52 additive 9.84 1.0005 [13]

2q33 CASP8 rs1045485 1.14 0.87 additive 19.02 1.0014 [13]

2q33 CASP8 rs10931936 1.14 0.74 additive 16.65 1.0024 [13]

2q35 DIRC3 rs16857609 1.08 0.26 additive 3.99 1.0011 [13]

2q35 IGFBP2, IGFBP5, TPN2 rs13387042 1.20 0.49 additive 16.39 1.0070 [13]

3p24 SLC4A7/NEK10 rs4973768 1.11 0.46 additive 9.19 1.0025 [13]

3p24 TGFBR2 rs12493607 1.06 0.35 additive 4.03 1.0008 [13]

3p26 ITPR1/EGOT rs6762644 1.07 0.40 additive 5.30 1.0011 [13]

4q24 TET2 rs9790517 1.05 0.23 additive 2.25 1.0004 [13]

4q34 ADAM29 rs6828523 1.11 0.87 additive 16.07 1.0010 [13]

5p12 MRPS30/HCN1 rs9790879 1.10 0.40 additive 7.41 1.0021 [13]

5p12 MRPS30/HCN1 rs10941679 1.19 0.25 additive 8.68 1.0056 [13]

5p15 TERT/CLPTM1L rs10069690 1.18 0.30 additive 9.75 1.0055 [13]

5q11 MAP3K1/MEIR3 rs889312 1.13 0.28 additive 6.79 1.0030 [13]

5q11 PDE4D rs1353747 1.09 0.90 additive 13.27 1.0005 [13]

5q11 RAB3C rs10472076 1.05 0.38 additive 3.66 1.0005 [13]

5q33 EBF1 rs1432679 1.07 0.43 additive 5.68 1.0011 [13]

6p23 RANBP9 rs204247 1.05 0.43 additive 4.12 1.0006 [13]

6p25 FOXQ1 rs11242675 1.06 0.61 additive 7.35 1.0009 [13]


6q25 ESR1 rs2046210 1.11 0.34 additive 6.96 1.0024 [13]

6q25 ESR1 rs3757318 1.21 0.07 additive 2.86 1.0027 [13]

7q35 ARHGEF5/NOBOX rs720475 1.06 0.75 additive 8.88 1.0007 [13]

8p12 None rs9693444 1.07 0.32 additive 4.29 1.0010 [13]

8q21 HNF4G rs2943559 1.13 0.07 additive 1.79 1.0011 [13]


8q24 MYC rs1562430 1.17 0.40 additive 11.9 1.0054 [13]

8q24 MYC rs13281615 1.08 0.40 additive 6.027 1.0014 [13]

8q24 MIR1208 rs11780156 1.07 0.16 additive 2.19 1.0006 [13]

9p21 CDKN2A/B rs1011970 1.09 0.17 additive 2.97 1.0011 [13]

9q31 KLF4/RAD23B rs865686 1.12 0.61 additive 13.23 1.0028 [13]


10p12 DNAJC1 rs11814448 1.26 0.02 additive 1.03 1.0013 [13]

10p12 MLLT10/DNAJC1 rs7072776 1.07 0.29 additive 3.90 1.0009 [13]

10p15 ANKRD16 rs2380205 1.06 0.57 additive 6.90 1.0009 [13]

10q21 ZNF365 rs10995190 1.16 0.85 additive 21.91 1.0021 [13]

10q22 ZMIZ1 rs704010 1.07 0.39 additive 5.18 1.0010 [13]

10q25 TCF7L2 rs7904519 1.06 0.46 additive 5.23 1.0008 [13]

10q26 FGFR2 rs2981579 1.43 0.42 additive 26.54 1.0243 [13]

10q26 FGFR2 rs2981582 1.26 0.38 additive 16.50 1.0111 [13]



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11p15 LSP1/H19 rs909116 1.17 0.30 additive 9.26 1.0050 [13]

11p15 LSP1/H19 rs3817198 1.07 0.30 additive 4.03 1.0009 [13]

11q12.3 INCENP rs1047739 1.03 0.24 additive 1.42 1.0002

11q13 CCND1/FGFs rs614367 1.15 0.15 additive 4.31 1.0026 [13]

11q13 OVOL1 rs3903072 1.05 0.53 additive 5.51 1.0007 [13]


12p11 PTHLH rs10771399 1.27 0.90 additive 32.30 1.0029 [13]

12p13 None rs12422552 1.05 0.26 additive 2.53 1.0005 [13]

12q22 NTN4 rs17356907 1.10 0.70 additive 12.28 1.0016 [13]

12q24 TBX3/MAPKAP5 rs1292011 1.09 0.59 additive 9.12 1.0014 [13]

13q13 BRCA2 rs11571833 1.26 0.01 additive 0.52 1.0007 [13]

14q13 PAX9/SLC25A21 rs2236007 1.08 0.79 additive 10.59 1.0007 [13]

14q24 RAD51B rs999737 1.06 0.76 additive 8.99 1.0006 [13]

14q24 RAD51B rs8009944 1.14 0.76 additive 17.03 1.0023 [13]

14q24 RAD51L1 rs2588809 1.08 0.16 additive 2.50 1.0008 [13]

14q32 CCDC88C rs941764 1.06 0.34 additive 3.92 1.0007 [13]

16q12 MIR1972-2-FTO rs17817449 1.08 0.60 additive 8.26 1.0011 [13]

16q12 TOX3/LOC643714 rs3803662 1.20 0.26 additive 9.42 1.0063 [13]

16q12 TOX3/LOC643714 rs12443621 1.11 0.46 additive 9.19 1.0025 [13]

16q23 CDYL2 rs13329835 1.08 0.22 additive 3.40 1.0010 [13]

16q22 FTO rs11075995 1.07 0.24 additive 3.25 1.0008 [13]

17q23 STXBP4/COX11 rs6504950 1.05 0.73 additive 7.43 1.0005 [13]

18q11 CHST9 rs1436904 1.04 0.60 additive 4.58 1.0003 [13]


19p13 MERIT40 rs8170 1.26 0.18 additive 8.56 1.0083 [13]

19p13 MERIT40 rs2363956 1.19 0.50 additive 15.97 1.0064 [13]

19q13 KCNN4/ZNF283 rs3760982 1.06 0.46 additive 5.23 1.0008 [13]

19p13 SSBP4/ISYNA1/ELL rs4808801 1.08 0.65 additive 8.88 1.0011 [13]

20q11 RALY rs2284378 1.16 0.35 additive 10.07 1.0047 [13]

21q21 NRIP1 rs2823093 1.06 0.73 additive 8.67 1.0007 [13]

22q12 EMID1/RHBDD3 rs132390 1.12 0.04 additive 0.95 1.0005 [13]

22q13 MKL1 rs6001930 1.12 0.11 additive 2.57 1.0013 [13] aPer allele OR, when mode of inheritance is additive


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Table S11: FunciSNP analysis of variants tightly linked (r2≥0.8) to INCENP rs1047739.

dbSNP Position

a r

2 Nearest TSS Location Promoters/TFBS DNaseI HS FAIRE Methylation ChromHMM

b Histone modifications

c

rs3867138 chr11:61911422 0.98 INCENP intron M (CTCF) C H (State10) H (1,2,3) rs3890102 chr11:61915823 1.00 INCENP intron H (State10) H (1,2,3) rs2868774 chr11:61929242 1.00 SCGB1D1 upstream H (State13) H (1,2) rs7127472 chr11:61937658 0.84 SCGB1D1 upstream T H (State13) H (1) rs4963471 chr11:61941930 0.86 SCGB1D1 upstream M (CTCF) H (State13) H (1), M (1) rs4963289 chr11:61942541 0.87 SCGB1D1 upstream M C H (State13) H (1) rs61893702 chr11:61942898 0.87 SCGB1D1 upstream H (State13) H (1) rs12789264 chr11:61947706 0.87 SCGB1D1 upstream C, T H (State13) H (1) rs35664814 chr11:61948310 0.87 SCGB1D1 upstream H (State13) H (1) rs4963473 chr11:61949286 0.87 SCGB1D1 upstream H (State13) H (1) rs7940224 chr11:61952855 0.85 SCGB1D1 upstream H (State13) H (1) rs3825033 chr11:61956010 0.84 SCGB1D1 upstream H (State13) H (1) rs2232931 chr11:61957070 0.86 SCGB1D1 upstream C, H (EZH2) H (State13) H (1) rs2232936 chr11:61957539 0.86 SCGB1D1 upstream C, H (EZH2) M H (State12) H (1) rs12800538 chr11:61958043 0.87 SCGB1D1 intron H (EZH2) H (State12) H (1)

TSS, transcription start site; TFBS, transcription factor binding site; HS, hypersensitivity site; C, several cell lines; H, HMEC; M, MCF7; T, T47D aBased on NCBI build 37

bState10:=transcriptional elongation; State12:=polycomb-repressed; State13:=heterochromatin, low signal

c1:=H3K27me3; 2:=H3K36me3; 3:=H4K20me1


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References

1. Frazer,K. et al. (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature, 449, 851-861.

2. Tchatchou,S. et al. (2007) Aurora kinases A and B and familial breast cancer risk. Cancer Lett., 247, 266-272.

3. Xu,Y. et al. (2004) A mutation found in the promoter region of the human survivin gene is correlated to overexpression of survivin in cancer cells. DNA and Cell Biology, 100, 527-537.

4. de Bakker,P.I.W. et al. (2005) Efficiency and power in genetic association studies. Nature genetics, 37, 1217-1223.

5. Barrett,J.C. et al. (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics (Oxford, England), 21, 263-265.

6. Schwarzer, G. (2013) meta: Meta-Analysis with R. Computer Program

7. Lumley, T. (2012) rmeta: Meta-Analysis. Computer Program 8. Howie,B.N. et al. (2009) A flexible and accurate genotype imputation method for the next generation of

genome-wide association studies. PLoS genetics, 5, e1000529.

9. Marchini, J. (2010) SNPTEST v2. Computer Program 10. Pruim,R.J. et al. (2010) LocusZoom: regional visualization of genome-wide association scan results.

Bioinformatics (Oxford, England), 26, 2336-2337.

11. Hemminki,K. et al. (2008) Etiologic impact of known cancer susceptibility genes. Mutation Research/Reviews in Mutation Research, 658, 42-54.

12. Mavaddat,N. et al. (2010) Genetic susceptibility to breast cancer. Molecular Oncology, 4, 174-191.

13. Ghoussaini,M. et al. (2013) Inherited Genetic Susceptibility to Breast Cancer: The Beginning of the End or the End of the Beginning? The American Journal of Pathology, 183, 1038-1051.


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Supplementary figure legends

Fig. S1. Kaplan-Meier estimates of relapse-free survival stratified by CDCA8 rs2306625 genotypes CC, CT and TT.

Censored observations are displayed as crosses.

Fig. S2. LD heatmap with LD blocks based on genotypes of ten INCENP SNPs from 88,911 BCAC subjects showing

pairwise r2 values (from 0 (white) to 1 (black)).

Fig. S3. (A) Boxplot of INCENP expression levels in lymphoblastoid cells, (B1) normal breast and (B2) tumor breast

tissue stratified by SNP genotypes. Expression data were retrieved from HapMap’s CEU population through GEO and

from the TCGA’s BRCA study.


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Figure S1

197x175mm (300 x 300 DPI)


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Figure S2

127x103mm (300 x 300 DPI)


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Figure S3

157x91mm (300 x 300 DPI)


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Inherited variants in the inner centromere protein (INCENP) gene of the chromosomal passenger...

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