Doctoral Programme in Biomedicine (DPBM)

Genetic modifiers of CHEK2-associated and familial breast cancer

Taru A. Muranen

Department of Obstetrics and Gynecology Helsinki University Hospital

Faculty of Medicine University of Helsinki Helsinki, Finland

Academic Dissertation To be discussed, with permission of the Faculty of Medicine, University of Helsinki, in Biomedicum 1, Lecture Hall 2, Haartmaninkatu 8, Helsinki on 2 November 2018, at 12 noon.

Helsinki 2018  Supervised by: Adjunct Professor Heli Nevanlinna, PhD Department of Obstetrics and Gynecology Helsinki University Hospital and University of Helsinki, Finland

Associate Professor Dario Greco, PhD Faculty of Medicine and Life Sciences Institute of Biosciences and Medical Technology University of Tampere, Finland

Reviewed by: Adjunct Professor Minna Tanner, MD, PhD Faculty of Medicine and Life Sciences University of Tampere, Finland

Professor Matti Nykter, PhD Faculty of Medicine and Life Sciences University of Tampere, Finland

Official Opponent: Associate Professor Ingrid Hedenfalk, PhD Division of Oncology and Pathology Department of Clinical Sciences Lund University, Sweden

Cover image: Three versions of the same pedigree overlaid: one colored by disease status (on the bottom), one colored by genotype of a moderate penetrance mutation (middle), and one colored by polygenic risk score (on the top).

Dissertationes Scholae Doctoralis Ad Sanitatem Investigandam Universitatis Helsinkiensis

ISBN 978-951-51-4503-1 (Paperback) ISBN 978-951-51-4504-8 (PDF) ISSN 2342-3161 (print) ISSN 2342-317X (online)

Unigrafia Helsinki 2018

2 Itseoppinut on ainoa oppinut. Muut ovat opetettuja. Erno Paasilinna

Ursalle, Elselle, Eerolle ja Urholle

3 Table of Contents

Table of Contents ...... 4 Abstract ...... 7 List of Original Publications ...... 9 Abbreviations ...... 10 Gene and protein names...... 11 1 Introduction ...... 12 2 Review of the Literature ...... 13 2.1 General cancer characteristics ...... 13 2.1.1 Cancer progression ...... 13 2.1.2 Cancer genes ...... 15 2.2 Breast cancer ...... 16 2.2.1 Mammary gland ...... 17 2.2.2 Breast cancer risk factors ...... 20 2.2.3 Breast cancer subtypes ...... 20 2.2.4 Origin of breast cancer...... 22 2.3 Breast cancer treatment ...... 23 2.3.1 Adjuvant endocrine therapy ...... 23 2.3.2 Other targeted biological therapies ...... 23 2.3.3 Adjuvant chemotherapy ...... 24 2.4 Genetic predisposition to breast cancer ...... 25 2.4.1 Breast cancer heritability ...... 25 2.4.2 High-risk genes ...... 28 2.4.3 Moderate-risk genes ...... 29 2.4.4 The Breast cancer pathway ...... 29 2.4.5 Common predisposing variants ...... 31 2.5 CHEK2...... 31 2.5.1 CHEK2 protein function ...... 31 2.5.2 CHEK2 mutations ...... 32 2.5.3 CHEK2 and breast cancer risk...... 33 2.5.4 CHEK2 in breast tumors ...... 34 3 Aims of the Study ...... 35 4 Materials and Methods ...... 36

4 4.1 Study subjects and data sources ...... 36 4.1.1 Breast tumors (I, II) ...... 36 4.1.2 Study subjects from the Breast Cancer Association Consortium (II, III) ...... 36 4.1.3 Study subjects of the Helsinki breast cancer study (IV) ...... 37 4.2 Methods...... 37 4.2.1 Microarray data processing and analyses (I, II) ...... 37 4.2.2 Permutation analysis (I: unpublished data)...... 39 4.2.3 Survival analyses (II) ...... 40 4.2.4 Tumor pathology analyses (II) ...... 40 4.2.5 The Polygenic risk score (III, IV) ...... 40 4.2.6 Risk association analyses (III, IV) ...... 40 4.2.7 Feature selection (III: unpublished data) ...... 40 4.2.8 In silico functional analysis (III: unpublished data) ...... 41 4.3 Ethics statement ...... 41 5 Results ...... 42 5.1 c.1100delC and p.(I157T) carrier tumors (I, II) ...... 42 5.1.1 c.1100delC-associated copy number aberrations (I) ...... 42 5.1.2 c.1100delC-associated differences in gene expression (I and unpublished data) ...... 42 5.1.3 Combined analysis of aCGH and GEX data (I and unpublished data) ...... 43 5.1.4 p.(I157T)-associated gene expression (II) ...... 45 5.1.5 Clinico-pathological characteristics (II) ...... 45 5.2 p.(I157T) or c.1100delC carrier survival (II) ...... 45 5.3 Genetic modifiers of c.1100delC-associated breast cancer risk (III)...... 45 5.3.1 Synergistic risk effect of common variants for c.1100delC carriers (III)...... 45 5.3.2 The sparse model (III: unpublished data) ...... 46 5.3.3 In silico functional characterization (III: unpublished data) ...... 46 5.4 Risk modifiers in breast cancer families (IV) ...... 47 6 Discussion ...... 48 6.1 CHEK2-associated breast cancer (I, II, III) ...... 48 6.1.1 Germline CHEK2 mutations are associated with ER-positive breast cancer (II) ...... 48 6.1.2 Genomic profiling elucidates the steps of CHEK2-related tumorigenesis (I, II) ...... 49 6.1.3 1p22 loss might complement CHEK2 deficiency in breast cancer progression (I) ...... 50 6.1.4 Elevated expression of olfactory receptors in c.1100delC carrier tumors (I) ...... 51 6.1.5 WNT pathway deregulation – typical for c.1100delC breast cancers (I,III)? ...... 52 6.1.6 Hypothetical model for c.1100delC-associated breast cancer progression (I, III) ...... 55

5 6.1.7 Is p.(I157T) ‘the first hit’ for germline mutation carriers (II)? ...... 55 6.2 Survival of breast cancer patients carrying germline CHEK2 mutations (II)...... 57 6.2.1 Increased mortality associated with c.1100delC ...... 57 6.2.2 CHEK2 mutations and increased risk of local recurrence or new primary tumors ...... 58 6.2.3 p.(I157T), lobular carcinoma, and patient survival warrant further research ...... 59 6.3 Common genetic variants in breast cancer risk prediction (III, IV) ...... 59 6.3.1 PRS could be used in risk stratification of c.1100delC carriers (III) ...... 59 6.3.2 No epistatic interaction exists between c.1100delC and the common variants (III) ...... 59 6.3.3 PRS explains part of the increased familial risk (IV) ...... 60 7 Summary and Conclusions ...... 62 8 Acknowledgments...... 64 References ...... 66 Appendix

6 Abstract

Aims CHEK2 (checkpoint kinase 2) is a moderate-risk breast cancer susceptibility gene. By definition, the CHEK2 susceptibility mutations do not segregate consistently with breast cancer within pedigrees, and other genetic factors have been proposed to modify the penetrance of the CHEK2 mutations. The primary purpose of this study was to identify the risk-modifying genetic factors using risk association analyses. Furthermore, genomic profiling of mutation carrier tumors could suggest candidate loci for further risk analyses, but more importantly shed light on the events that have led to tumor development, complementing the CHEK2 deficiency. CHEK2 c.1100delC has been suggested to be associated with poor prognosis after breast cancer diagnosis. We tested whether the same effect would be shared by p.(I157T), another recurrent CHEK2 mutation in the Finnish population. Additionally, breast cancer phenotypic features associated with the two mutations were examined in terms of pathological characteristics and differential gene expression. By now, collaborative international studies have identified a vast number of common variants associated with a modest increase in the risk of breast cancer. However, combining multiple variants into a polygenic risk score (PRS) has been assumed to have potential in breast cancer risk stratification. We assessed the applicability of the PRS in risk prediction for women at elevated baseline risk, namely carriers of c.1100delC and women with a positive family history of breast cancer. Essential methods Genomic copy number aberrations (CNA) associated with c.1100delC were analyzed using data from 26 c.1100delC carrier and 76 non-carrier tumors. Analyses were performed in R environment for statistical computing using Bioconductor packages CGHcall, CGHregions, and WECCA. Associations between CNA regions and c.1100delC were tested with Wilcoxon rank-sum test. C.1100delC-associated differential gene expression was examined using data from 13 c.1100delC carrier and 65 non-carrier tumors and p.(I157T)-associated gene expression with data from 10 p.(I157T) carrier and 162 non-carrier tumors. Analyses were performed with Bioconductor package limma. Functional enrichment of the differentially expressed genes was analyzed with DAVID functional annotation tool and Gene Set Enrichment Analysis (GSEA) using gene libraries available at mSigDB. Survival and tumor pathologic characteristics of breast cancer patients carrying CHEK2 mutations were studied in collaboration with the Breast Cancer Association Consortium (BCAC) in a dataset consisting of 25940 non-carriers, 590 p.(I157T) carriers, and 271 c.1100delC carriers. Survival analyses were performed with Cox regression and pathology analyses with Cochran-Mantel- Haenszel test. Risk effect associated with about 75 common variants was studied in a BCAC dataset of 78 354 non-carriers and 848 c.1100delC carriers as well as in a Finnish dataset consisting of 1 303 unselected cases, 378 additional familial index cases, 1 272 population controls, and 429 women from 52 breast cancer families. A polygenic risk was calculated as a product of per-variant log

7 odds ratios, and standardized according to healthy population controls. Risk association analyses were performed with logistic regression. Nested regression models were compared with likelihood-ratio test. Results We identified seven chromosomal locations, whose copy number aberrations were more frequent in c.1100delC carrier tumors than in non-carrier tumors. Functional in silico analysis of CNA regions and differentially expressed genes suggested that loss of GBP genes, elevated activity of olfactory receptors and deregulation of the WNT pathway could be recurrent driver events in c.1100delC carrier cancers. Gene expression analysis suggested that CDH1 inactivation is a frequent event in p.(I157T) carrier breast cancers, possibly accounting for most of the observed differences between p.(I157T) carrier and non-carrier breast cancers. Germline CHEK2 mutations c.1100delC and p.(I157T) differ in terms of their association with patient prognosis, c.1100delC being a marker for poor prognosis. Both mutations predispose to estrogen receptor (ER)-positive breast cancer. P.(I157T) is associated with lobular breast cancer, whereas c.1100delC is not associated with any specific breast cancer histological subtype. The breast cancer risk associated with the PRS was similar for c.1100delC carriers, women from breast cancer families, and unselected women. When accounting for the elevated background risk associated with c.1100delC, about 20% of mutation carriers with the highest PRS values were estimated to have higher than 30% lifetime risk. Furthermore, even though PRS explained part of the excess familial risk, the high PRS values retained predictive value in risk stratification of women with a positive family history of breast cancer. Conclusions The genomic analyses of CHEK2 mutation carrier tumors could lay a foundation for a model of c.1100delC-associated tumorigenesis. Furthermore, c.1100delC and p.(I157T) might have different roles in the origin and development of breast cancer. The findings from these hypothesis- generating studies could benefit future functional in vitro and in vivo studies on breast cancer etiology. The poor survival of breast cancer patients carrying c.1100delC warrants further examination. The data presented in this work indicate that the survival association is not shared by p.(I157T), emphasizing that findings based on a certain mutation cannot always be generalized to other mutations of the same gene. On a general population level, the usability of the current PRS is limited due to the very low proportion of unselected women stratified into the high-risk category by PRS alone. However, for women at elevated background risk as a consequence of an inherited moderate penetrance mutation, like c.1100delC, or positive family history, the PRS of about 75 variants could provide significant clinical benefit in identifying women at high lifetime risk. 

8 List of Original Publications This thesis is based on the following original publications, referred to in the text by their Roman numerals. In addition, unpublished data exploring Studies I and III further are included. I. Muranen TA, Greco D, Fagerholm R, Kilpivaara O, Kämpjärvi K, Aittomäki K, Blomqvist C, Heikkilä P, Borg Å, Nevanlinna H. Breast tumors from CHEK2 1100delC- mutation carriers: genomic landscape and clinical implications. Breast Cancer Res. 2011;13:R90. II. Muranen TA, Blomqvist C, Dörk T, Jakubowska A, Bojesen SE, Fagerholm R, Greco D, Aittomäki K, Shah M, Dunning AM, Rhenius V, Hall P, Czene K, Brand JS, Darabi H, Chang-Claude J, Rudolph A, Nordestgaard BG, Couch FJ, Hallberg E, Figueroa J, García-Closas M, Fasching PA, Beckmann MW, Li J, Liu J, Andrulis IL, Knight JA, Winqvist R, Pylkäs K, Mannermaa A, Kataja V, Lindblom A, Margolin S, Lubinski J, Dubrowinskaja N, Bolla MK, Dennis J, Michailidou K, Wang Q, Easton DF, Pharoah PDP, Schmidt MK, Nevanlinna H. Patient survival and tumor characteristics associated with CHEK2 I157T: findings from the Breast Cancer Association Consortium. Breast Cancer Res. 2016 Oct 3;18(1):98. III. Muranen TA, Greco D, Blomqvist C, Aittomäki K, Khan S, Hogervorst F, Verhoef S, Pharoah PDP, Dunning AM, Shah M, Luben R, Bojesen SE, Nordestgaard BG, Schoemaker M, Swerdlow A, García-Closas M, Figueroa J, Dörk T, Bogdanova NV, Hall P, Li J, Khusnutdinova E, Bermisheva M, Kristensen V, Borresen-Dale AL, Investigators N, Peto J, Dos Santos Silva I, Couch FJ, Olson JE, Hillemans P, Park-Simon TW, Brauch H, Hamann U, Burwinkel B, Marme F, Meindl A, Schmutzler RK, Cox A, Cross SS, Sawyer EJ, Tomlinson I, Lambrechts D, Moisse M, Lindblom A, Margolin S, Hollestelle A, Martens JWM, Fasching PA, Beckmann MW, Andrulis IL, Knight JA, Investigators K, Anton-Culver H, Ziogas A, Giles GG, Milne RL, Brenner H, Arndt V, Mannermaa A, Kosma VM, Chang-Claude J, Rudolph A, Devilee P, Seynaeve C, Hopper JL, Southey MC, John EM, Whittemore AS, Bolla MK, Wang Q, Michailidou K, Dennis J, Easton DF, Schmidt MK, Nevanlinna H. Genetic modifiers of CHEK2 c.1100delC- associated breast cancer risk. Genet Med. 2017 May;19(5):599-603. IV. Muranen TA, Mavaddat N, Khan S, Fagerholm R, Pelttari L, Blomqvist C, Aittomäki K, Easton DF, Nevanlinna H. Polygenic risk score is associated with increased disease risk in 52 Finnish breast cancer families. Breast Cancer Res Treat. 2016 Aug;158(3):463-9.

These publications are reprinted with the permission of their copyright holders.

9 Abbreviations aCGH Array comparative genomic hybridization BAC Bacterial artificial chromosome BCAC Breast Cancer Association Consortium CI Confidence interval

CMF Cyclophosphamide – methotrexate – 5-fluorouracil CNA Copy number aberration COGS Collaborative Oncological Gene-Environment Study ER Estrogen receptor FFPE Formalin-fixed paraffin-embedded FHA Fork head-associated FWER Family-wise error rate G1/G2 Gap 1/2 GEO Gene Expression Omnibus GEX Gene expression GSEA Gene set enrichment analysis GWAS Genome-wide association study HR Hazard ratio IC-NST Invasive carcinoma of no special type ILC Invasive lobular carcinoma KD Kinase domain M Metastasis MMitosis N Status of adjacent lymph nodes OR Odds ratio PgR Progesterone receptor PRS Polygenic risk score QGlutamine S; Ser Serine S Synthesis SCD SQ/TQ cluster domain SNP Single-nucleotide polymorphism T; Thr Threonine T Tumor size TCGA The Cancer Genome Atlas TEB Terminal end bud UTR Untranslated region

10 Gene and protein names

ACIII Adenylate cyclase III LHRH Luteinizing hormone releasing AKT AKT serine/threonine kinase 1 hormone ALDH Aldehyde dehydrogenase LRP1 LDL receptor related protein 1 ALG14 ALG14, UDP-N- LRRC8D Leucine rich repeat containing 8 acetylglucosaminyltransferase VRAC subunit D subunit MLH1 mutL homolog 1 ANKLE1 Ankyrin repeat and LEM domain MMP Matrix metalloproteinase containing 1 MRE11 MRE11 homolog, double strand APC APC, WNT signaling pathway break repair nuclease regulator MSH2 mutS homolog 2 ATE1 Arginyltransferase 1 MTOR Mechanistic target of rapamycin ATM ATM serine/threonine kinase kinase AURKA Aurora kinase A MYC MYC proto-oncogene, bHLH BARD1 BRCA1 associated RING domain 1 transcription factor BRCA1/2 BRCA1/2, DNA repair associated NBN Nibrin CALCOCO1 Calcium binding and coiled-coil NF1 Neurofibromin 1 domain 1 NQO1 NAD(P)H:quinone oxidoreductase CDC25A Cell division cycle 25 A OR Olfactory receptor CDH1 Cadherin 1 OR6C3 Olfactory receptor family 6 CDK1/2/4/6 Cyclin dependent kinase 1/2/4/6 subfamily C member 3 CHEK2 Checkpoint kinase 2 PALB2 Partner and localizer of BRCA2 CLCA1 Chloride channel accessory 1 PARP Poly(ADP-ribose) polymerase CSAD Cysteine sulfinic acid decarboxylase PIK3CB Phosphatidylinositol-4,5- bisphosphate 3-kinase catalytic EGFR Epidermal growth factor receptor subunit beta ELL Elongation factor for RNA PRKDC Protein kinase, DNA-activated, polymerase II catalytic polypeptide FANCM Fanconi anemia complementation PTEN Phosphatase and tensin homolog group M PVT1 Pvt1 oncogene FGF Fibroblast growth factor RAD50 RAD50 double strand break repair FGFR2 Fibroblast growth factor receptor 2 protein FTO FTO, alpha-ketoglutarate dependent RAD51 RAD51 recombinase dioxygenase RasGEF Ras-type guanine nucleotide FZD1 Frizzled class receptor 1 exchange factors GBP1-7 Guanylate binding protein 1-7 RB1 RB transcriptional corepressor 1 GNAL G protein subunit alpha L STAT Signal transducer and activator of HER2 Human epidermal growth factor transcription receptor 2 STK11 Serine/threonine kinase 11 IFN-Ȗ Interferon gamma TMED5 Transmembrane p24 trafficking IL-1ȕ Interleukin 1 beta protein 5 JAK Janus kinase TOP2A Topoisomerase II alpha KRT5 Keratin 5 TP53 Tumor protein p53 LEF1 Lymphoid enhancer binding factor 1

11 1 Introduction CHEK2 has been established as a moderate-penetrance breast cancer susceptibility gene. Two CHEK2 mutations, protein truncating c.1100delC and missense p.(I157T), are relatively common in the Finnish population, having carrier frequencies of 1.4% and 5.3%, respectively.1, 2 The relative risk associated with c.1100delC and other truncating CHEK2 mutations is two- to threefold, whereas the risk effect of p.(I157T) is considerably lower.3-5 C.1100delC predisposes to familial breast cancer. However, it does not segregate consistently with the disease within breast cancer families. Furthermore, the moderate-risk level associated with c.1100delC (odds ratio (OR): 2.26; [95% confidence interval (CI) 1.90-2.69]) has rendered its applicability in genetic counseling limited.1, 3 About 30% of breast cancer incidence has been estimated to be caused by genetic factors.6 High- and moderate-risk mutations account for about one-fifth of the heritability.7 However, they do not operate alone. The best genetic model explaining both familial clustering and population-level incidence of breast cancer consists of rare high-penetrance mutations and common variants with low effect sizes contributing together in a multiplicative fashion to increase the risk.8 The discovery of multiple risk-modifying variants in genome-wide association studies (GWAS) studies has paved the way for investigation of genetic variants modifying the risk associated with CHEK2 c.1100delC.9 In addition to risk-modifying effects, common genetic variation has been predicted to contribute to familial clustering of breast cancer.8 Furthermore, the multiplicative model suggests that the nominal risk effects associated with single variants could be combined in order to estimate risk of individual women. Recently, a polygenic risk score (PRS) was introduced for risk prediction on a population level,10 and we have investigated its applicability in breast cancer families. Hereditary cancer is typically caused by an inherited loss-of-function mutation in a tumor suppressor gene.11 The loss of the intact allele is assumed to be often the initiating event of a multistep path to cancer.12, 13 The later steps of tumorigenesis arise as a result of random somatic events. However, only those changes that endow a growth advantage in combination with the earlier events are selected during the course of tumor evolution. The final cancer phenotype reflects the accumulation of novel features associated with the driver events.14 Since the driver changes are specific for the cancer relative to adjacent healthy tissue, and since tumor growth is dependent on them, they represent an appealing target for cancer therapy. Genomic analyses of copy number aberrations and gene expression changes in tumor tissue can be used for characterization of the driver events leading to cancer in specific cancer subgroups.15, 16

12 2 Review of the Literature 2.1 General cancer characteristics 2.1.1 Cancer progression Cancer is a progressive disease in which the cancerous cells gradually lose their tissue-typical morphology, proliferate in an uncontrolled manner, invade the surrounding tissue, and eventually spread via lymphatic and blood vasculature to give rise to metastatic growths.14 Cancer progression is driven and accompanied by mutations, which can be considered as stochastic events whose probability is increased by two types of factors: those increasing the number of cell divisions and those causing DNA damage (Figure 1).17 Most of the neoplastic events, i.e. emergence of driver mutations, take place in progenitor/transit-amplifying cells, which have the capacity to dedifferentiate into stem cell state, but which also proliferate at a frequency high enough for accumulation of a sufficient number of malignant mutations. 18 However, the actual origin of a cancer could be any cell that retains proliferative capacity, ranging from stem cells to their more differentiated descendants, depending on tissue hierarchy and cell half-lives. 19 After the initiating event, the progeny of any pre-neoplastic cell may normally take their place and function in the tissue hierarchy or, alternatively, form a benign growth or even be erased by innate mechanisms controlling tissue homeostasis until further mutations endowing a growth advantage emerge. Thus, tumor progression is a cellular-level combination of stochastic events and Darwinian evolution.14, 18, 19

Healthy tissue Inherited mutations Age DNA replication Increasing number errors of stem cell Carcinogenic exposure Intrinsic divisions oxidative Inflammation, stress hormonal exposure etc. Increasing probability of tumor driver mutations

Cancer

Figure 1. Summary of factors increasing the probability of cancer progression.

The branching evolution characteristic for cancer development can be seen in genomic profiles of excised tumors and metastatic growths. The benign clonal cell populations co-exist with their more aggressive descendants. The different subpopulations can be distinguished by their mutation and gene expression profiles, highlighting the intrinsic heterogeneity of any single tumor.20

13 Sustaining Resisting proliferative cell death signaling

Genome Deregulating instability and cellular mutation energetics

Enabling replicative immortality

Activating invasion and metastasis

Figure 2. Cancer Hallmarks introduced by Hanahan and Weinberg can be categorized as changes taking place primarily in the neoplastic cell lineage (inner circle) and changes affecting the interactions between the cancer cells and their tissue environment (outer circle). 14, 21

Owing to the progressive nature of the disease, each cancer is unique. Hanahan and Weinberg summarized features shared by most cancers into six ‘Cancer Hallmarks’ and later refined the model by addition of two novel hallmarks and two enabling characteristics (Figure 2).14, 21 First of all, alterations in cellular and tissue level programs regulating cell proliferation and survival are divided into three hallmarks: sustaining proliferative signaling, evading the growth suppressors, and escaping the intrinsic apoptotic programs. The number of replicative cycles is restricted by telomere erosion in human cells. The rampantly dividing cells face a telomere crisis and need to reactivate the telomerase enzyme to gain replicative immortality, the fourth hallmark. Another restrictive mechanism for tumor growth is the shortage of oxygen and nutrients. Two further hallmarks enable cancer to overcome this challenge: inducing neo-vasculature and reshaping energy metabolism. The ultimate hallmark transforming cancer into a systemic disease is tissue invasion and metastasis, requiring changes in cell phenotype and adaptation to a foreign cellular environment. Finally, all the way through the tumor progression, the cancer cells must escape the surveillance of the immune system in order to triumph. In addition to these eight hallmarks, Hanahan and Weinberg named two features as specific cancer-enabling characteristics: genomic instability and chronic inflammation (Figure 2).14 The concept of cancer hallmarks is a simplified framework; all hallmarks cannot be considered to apply to all cancer cells at all times, not to all cancer stem cells, and not even to all cancers. Floor et al.22 suggested a four-layered hierarchical model for understanding carcinogenesis etiology

14 (Figure 3). In this model, the ‘Cancer Hallmarks’ form the highest hierarchical level, the general cancer characteristics. The hallmarks result from changes in cellular pathways arising from genetic, epigenetic, or lysogenic oncogenic events, which on the bottom level are caused by various intrinsic and environmental factors (Figure 1). Importantly, the hierarchical levels of the model are connected by complex one-to-many and many-to-one relations.22 This model emphasizes the need for a molecular-level understanding of cancer. A good start for this effort would be harvesting the oncogenic events, i.e. the acquired somatic and predisposing germline mutations in genetic studies. Elucidating how these changes affect the complex network of cellular pathways and differentiation programs giving rise to the cancer hallmarks will be the future goal of cancer research.

Figure 3. Hierarchical model of carcinogenesis etiology with potential simple and complex interactions at all levels of hierarchy. Adapted from Floor et al. 2012.22

2.1.2 Cancer genes Cancer-associated genes are categorized into two groups based on how their aberrations accelerate tumorigenesis: activated oncogenes and silenced tumor suppressor genes; both promote cancer progression. By definition, oncogenes are genes encoding proteins involved in cellular growth and differentiation programs that have lost important gene- or protein-level regulatory constraints. Cellular oncogenes have often been identified at characteristic chromosomal translocations due to sequence similarity to viral oncogenes.23 Oncogene activation is usually a somatic event, and mutations in proto-oncogenes are rarely associated with hereditary cancer.11, 21, 24, 25 The majority of tumor suppressor genes have been discovered in studies of cancer families.11, 26 Typically, inactivation of both alleles is required for cancer initiation, as suggested in Knudson’s ‘two-hit hypothesis’.12, 13 If one hit, i.e. a loss-of-function mutation of a tumor suppressor gene, is inherited, cancer probability is higher than if both hits were to be acquired as somatic changes. In addition to this simplistic model, haplo-insufficiency has been recognized as another mechanism for tumor suppressor-related cancer initiation; under certain environmental or intrinsic stress, expression of only one intact allele is not sufficient to protect the cell from additional neoplastic changes.17, 26

15 Tumor suppressor genes are further divided into three functional categories: gatekeepers, caretakers, and landscapers. Gatekeepers refer to the original idea of tumor suppressors as anti- oncogenes. Those are genes encoding proteins that limit cell cycle progression and entry into mitosis such as APC (APC, WNT signaling pathway regulator) in colorectal cancer and RB1 (RB transcriptional corepressor 1) in retinoblastoma. Caretakers include DNA repair genes, like BRCA1 and BRCA2 (BRCA1/2, DNA repair associated), whose mutations predispose to breast and ovarian cancer, or MSH2 (mutS homolog 2) and MLH1 (mutL homolog 1), which are associated with colorectal cancer.26 Landscaper genes are defined to encode proteins involved in regulating the tumor micro- environment. The tumor-initiating mutation of a landscaper gene might even take place in a stromal cell instead of the cancer cell lineage. Juvenile polyposis is a characteristic syndrome for a germline landscaper gene deficiency. It is manifested by multiple hamartomatous polyps of the colon at a young age. The abnormal growth of the epithelium has been concluded to be induced by a mutation in the surrounding stromal cells, causing sustained proliferative signaling. The elevated number of cell divisions in epithelial cells raises the probability of somatic neoplastic events, thus increasing the risk of carcinoma development.26, 27 Functional classification of tumor suppressor genes identifies the key features of cancer-associated genes. However, it is a rough simplification. One gene or protein may serve in different roles depending on the context. For example, TP53 (tumor protein p53) serves as both a gatekeeper and caretaker, connecting the surveillance of genomic integrity to apoptosis,26 and BRCA1 is involved in DNA repair, control of cell division, and regulation of differentiation via gene expression.28, 29 Furthermore, since even oncogene activation induces apoptotic programs, it is justified to shift the focus from single genes to the pathway level and consider cancer as a consequence of disturbed cellular programs.26 Molecular and cellular cancer research has focused in a reductionist fashion on characterization of cancer cell lineages like the transformed epithelial cells in carcinomas. However, cancer is not a cell-autonomous disease. Instead, the interactions with the microenvironment, extracellular matrix, stromal cells, and immune system contribute to cancer development. There is some experimental evidence implying that aneuploid cancerous cells could be normalized under regulation of a healthy cellular environment.30, 31 Furthermore, the risk of tumor spread has been suggested to depend more on immune response than on the features of the cancer cells themselves.32, 33 2.2 Breast cancer Breast cancer is the most common cancer and the leading cause of cancer-related death in women in developed countries.34 In Finland, the cumulative disease risk by the age of 75 years has increased steadily since the 1950s, reaching 9.9% in 2014. 35, 36 The high rank of breast cancer as a cause of mortality is partially misleading; it is mostly due to the high frequency of the disease itself. In general, breast cancer is a manageable disease, with a five-year survival rate of about 88%.35, 36 Male breast cancer is a rare disease, with an average of 25 diagnosed cases per year in Finland. 35, 36

16 Breast cancer covers a range of phenotypically different neoplastic diseases. However, here, the term ‘breast cancer’ is used to strictly refer to carcinomas originating from the epithelial cells of the mammary gland, distinct from connective tissue, lymphoid, or skin neoplasias occurring in the chest area.37

2.2.1 Mammary gland The human mammary gland is a tree-like structure, with primary, secondary, and tertiary ducts forming the stem and branches, and lobules forming the leaves. The hormone-independent early development of the mammary gland starts during embryogenesis, with formation of a bilateral mammary ridge, followed by development of placodes and rudimentary ductal trees. After birth, the gland remains in a quiescent stage until puberty, when the branching morphogenesis continues in response to estrogen stimulus.38, 39 At birth, the rudimentary ductal tree consists of short primitive ducts ending in terminal end buds (TEBs). Thereafter, the primary ducts are formed by bifurcation of the TEBs. While the primary ducts elongate, the secondary and tertiary ducts emerge as lateral appendages, with novel TEBs at each branch end. Eventually, the TEBs give rise to small ductules, which first develop into virginal lobules, i.e. terminal ductal lobular units, and later differentiate into milk secreting alveoli.38, 40 The tertiary ducts and the lobules are sensitive to oscillating exposure to ovarian and pituitary hormones, which induce a growth and differentiation pulse followed by a period of regression during each menstrual cycle. However, until the age of 35 years the regression never returns to the starting point, the net effect being cumulative growth and differentiation.38, 39 While the lobules develop, the number of ductules per lobule increases and the size of the ductules decreases. The full maturation of the ductal tree takes place during the first half of the first pregnancy, when the epithelial luminal cells differentiate in anticipation of lactation. Pregnancy, lactation, and weaning are followed by partial gland involution. Russo et al.38 classify the lobules into four types based on the number of ductules per lobule. The predominant lobule type for nulliparous women is ‘Lob1’ with about 11 ductules, whereas for parous women it is ‘Lob3’ with about 81 ductules representing the most abundant type. ‘Lob2’ is an intermediate structure and ‘Lob4’ refers to fully maturated structure during the latter half of pregnancy and lactation.38 In addition to structural changes, pregnancy induces epithelial cell differentiation by altering the balance of the activated cellular pathways, and by reducing the proliferative activity of the epithelial cells.41-43 Menopause is associated with major structural involution of the ductal tree, irrespective of parity. Thus, the mammary gland of menopausal parous and nulliparous women is structurally similar and occupied mainly by ‘Lob1’ lobules. However, on a cellular level there are significant differences in chromatin condensation and proliferative activity.42 The ducts are lined with two layers of epithelial cells: the luminal layer and the basal myoepithelial layer. Alveolar, milk-producing cells differentiate from dedicated alveolar precursors scattered around in the luminal layer.39 All of these epithelial cell lineages originate from the same precursors – the mammary stem cells, which dwell in the basal layer.40 The stem cell activity is restricted mainly to the period of embryonic development, and in the adult tissue cell regeneration is maintained by multiplication of luminal and basal lineage-specific precursors.44, 45 Basal lamina separates the epithelial cell layers from fibroblast-rich stroma, which surrounds the ductal tree amid the adipocytes of the mammary fat pad.39

17 Table 1. Regulators of mammary gland development.39, 46

Place of Gene/Protein Agonists Antagonists Targets Effects expression/ and action ligands

Systemic regulators Ovaries E1/E2 ESR1 pd: epithelial cell proliferation (Estrogens) preg: maintenance of alveolar cells Ovaries P PGR preg: tertiary branching (Progesterone) and alveologenesis Pituitary gland GH IGF1, ESR1 pd: epithelial cell proliferation (Growth hormone) Pituitary gland PRL PRLS preg: progesterone expression, (Prolactin) lobulo-alveolar development Liver IGF1 pd: epithelial cell proliferation (Insulin-like growth factor) Liver PLG KLK1 ECM iv: ECM breakdown, disruption of (Plasminogen) cell-cell contacts

Local regulators Epithelium WNT3, WNT6, WNT10B TBX3, LEF1/TCF ed: epithelial cell proliferation Wnt family members FGFR2B Mesenchyme WNT5A, WNT11 TGFB1 LEF1/TCF ed: epithelial cell proliferation Wnt family members pd: inhibition of ductal elongation

Epithelium WNT4 P LEF1/TCF preg: tertiary branching Wnt family member

Epithelium and LEF1/TCF WNT- Multiple mammary gland development mesenchyme lymphoid enhancer binding factor 1 / ligands transcriptional TCF family transcription factors level targets Mesenchyme TBX3 BMP4 WNT10B, ed: localization of developing gland T-box 3 BMP4 Epithelium BMP4 TBX3 BMPR1A, ed: localization of developing gland bone morphogenetic protein 4 TBX3 Mesenchyme BMPR1A PTH1R WNT ed: localization of developing gland, bone morphogenetic protein receptor signaling nipple formation type 1A Epithelium PTHLH PTH1R ed: branching and niplle formation parathyroid hormone like hormone Mesenchyme PTH1R PTHLH BMPR1A ed: branching and niplle formation parathyroid hormone 1 receptor Mammary line FGFR2 FGFs SPRY2 ed: placode placement Epithelium fibroblast growth factor receptor 2 pd: epithelial cell proliferation Somites FGF10 (and other FGF ligands) GLI3 FGFRs ed: placode placement Stroma fibroblast growth factor 10 pd: epithelial cell proliferation Somites GLI3 FGF10 ed: placode placement GLI family zinc finger 3 Mesenchyme NRG3 Integrins, ed: regulation of cell-cell interactions neuregulin 3 ECM, ERBB4 Epithelium ERBB4 NRG3 ed: regulation of cell-cell interactions erb-b2 receptor tyrosine kinase 4 Epithelium ERBB2 EGFR- pd: ductal morphogenesis erb-b2 receptor tyrosine kinase 2 coreceptor Stroma STX2 Metallo- CEBPB, pd: GH dependent branching syntaxin 2 / epimorphin enzymes MMP2, MMP3 Epithelium CEBPB STX2 Multiple pd: GH dependent branching CCAAT/enhancer binding protein beta transcriptional level targets Stroma IGF1 GH IGFBP5 IGF1R pd: epithelial cell proliferation insulin like growth factor 1 Stroma ESR1 E1/E2 AREG pd: epithelial cell proliferation and Luminal cells estrogen receptor 1 ductal elogation

18 Place of Gene/Protein Agonists Antagonists Targets Effects expression/ and action ligands Luminal cells AREG ESR1, HSPGs EGFR pd: proliferation of ER-negative cells amphiregulin ADAM17 Luminal cells ADAM17 PPCs TIMP3 AREG pd: epithelial cell proliferation a disintegrin and metalloproteinase 17 Stroma EGFR AREG, NRG3 pd: ductal elongation epidermal growth factor receptor and other EGF ligands Stroma GHR GH IGF pd: epithelial cell proliferation growth hormone receptor Epithelium TGFB1 TGFBR2 WNT pd: negative regulator of branching and Stroma transforming growth factor beta 1 signaling duct elongation Epithelium CSF1 CSF1R pd: macrophage recruitment colony stimulating factor 1 Stroma CCL11 CCR3 pd: eosinophil recruitment C-C motif chemokine ligand 11 Luminal cells PGR PWNT4,preg: tertiary branching progesterone receptor TNFSF11 and alveologenesis Luminal cells TNFSF11/RANKL PGR, JAK2 / TNFRSF11A preg: tertiary branching TNF superfamily member 11 / STAT5 and alveologenesis RANK ligand Luminal cells TNFRSF11A/RANK TNSF11 CCND1, preg: tertiary branching TNF receptor superfamily member 11a NFKB1 and alveologenesis Luminal cells PRLR PRL TNFS11, preg: tertiary branching prolactin receptor JAK2/STAT5 and alveologenesis Luminal cells Integrins ECM JAK2/STAT5 preg: tertiary branching and alveologenesis Luminal cells SIRPA ECM JAK2/STAT5 preg: tertiary branching signal regulatory protein Į and alveologenesis Luminal cells JAK2 / STAT5 PRLR, SOCS SOCS, preg: tertiary branching janus kinase 2 / Integrins, TNSF11 and alveologenesis signal transducer and activator of SIRPA transcription 5 Luminal cells SOCS family JAK2 / JAK2 / preg: tertiary branching suppressors of cytokine signaling STAT5 STAT5 and alveologenesis Epithelium LIF Milk stasis STAT3 iv: involution inducing signal leukemia inhibitory factor Luminal cells STAT3 LIF PI3 kinase, iv: inhibition of proliferative signals, signal transducer and activator of IGFBP5 apoptosis transcription 3 apoptotic programs Luminal cells IGFBP5 STAT3 IGF iv: inhibition of proliferative signals insulin-like growth factor binding protein-5 Epithelium KLK1 PLG iv: ECM breakdown, disruption of kallikrein 1 cell-cell contacts Stroma MMP2, MMP3 and MMP14 EGFR, TGFB, TIMPs 1-4, ECM, basal pd: duct elongation, secondary matrix metalloproteinases ESR1, STX2 TGFB lamina branching iv: disruption of basal lamina Stroma TIMPs 1-4 MMPs pd and iv: MMP regulation tissue inhibitor of metalloproteinases

Abbreviations: ed – embryonic development, pd – pubertal development, preg – pregnancy, iv - involution

The embryonic development of the mammary gland is independent of sex hormones and does not differ between women and men. It is regulated by reciprocal interactions between the epithelium and the underlying mesenchyme. Pubertal development is orchestrated by growth hormone secreted from the pituitary gland and ovarian estrogen, which together induce global and local downstream effects leading to mammary gland expansion (Table 1). Growth cycles associated

19 with the menstrual period rely mainly on ovarian progesterone stimulus, and the full maturation of the mammary gland taking place during pregnancy is driven by progesterone together with pi- tuitary gland prolactin. The key regulators in different developmental phases are listed in Table 1. In brief, WNT and FGF (fibroblast growth factor) pathways have an essential role in ductal growth and branching from embryogenesis until full maturation. The JAK/STAT (janus kinase/signal transducer and activator of transcription) pathway together with matrix metalloproteinases (MMPs) are involved in pubertal and antenatal development as well as in involution. Overall, the mammary gland development is a complex interplay between the epithelial cell layers, extracellular matrix, stromal fibroblasts, and recruited white blood cells and requires a strict control of cell proliferation and differentiation as well as matrix remodeling.39, 46 2.2.2 Breast cancer risk factors Breast cancer risk is increased by multiple factors related to life-style or to reproductive and medical history (Table 2).47-51 Pregnancy is associated with a transient increase in the risk of estrogen receptor (ER)-negative breast cancer. In the long run, pregnancy and breast feeding have a protective effect. However, if the time between the first menstrual period and the first full-term pregnancy exceeds 15 years, the protective effect fades away. The pregnancy-associated fluctuation in breast cancer risk has been proposed to reflect the intrinsic mammary gland biology. The periodically proliferating epithelial cells of a virgin gland are vulnerable to carcinogenic attack, whereas the full maturation of the gland entails protective changes in the form of increased chromatin condensation and lowered proliferative activity. However, gland expansion, accompanied by epithelial cell division and matrix remodeling, transiently raises the risk of malignant development during pregnancy.38, 42, 43, 46 Table 2. Breast cancer risk factors.

Life-style Reproductive history Medical history Tobacco smoking Nulliparity* Oral contraceptives* Alcohol consumption* High age at first full term pregnancy* Hormonal replacement therapy* Overweight* Early menarche* Chest area X-rays Lack of physical activity Late menopause* Mammographic density* Benign breast disease * Factors associated with increased exposure to estrogen.

Many of the risk factors are directly related to increased exposure to estrogen (Table 2).52 Estrogen contributes to breast cancer risk both by increasing the number of cell divisions and by inducing genotoxic stress; estrogen metabolite estradiol generates free oxygen radicals and forms DNA adducts, leading to depurination and increased risk of error-prone DNA repair.53 Oophorectomy or several years’ administration of antiestrogens halves the breast cancer risk of women at high familial risk.37, 48, 54 2.2.3 Breast cancer subtypes Breast cancers are categorized in many ways in order to assist in prognosis and in the choice of treatment. Grading is based on tumor cell nuclei morphology, proliferation, and the extent to which the tumor cells form tubular structures.55 Disease stage (TNM classification) is determined

20 according to tumor size (T), spread to adjacent lymph nodes (N), and distant metastases (M).56 Histopathologic classification relies on multicellular structures and proportion of infiltrating cells. The most common histological type is ‘Invasive carcinoma of no special type’ (IC-NST), previously called ‘Invasive ductal carcinoma’, and the second most common is ‘Invasive lobular carcinoma’ (ILC). Additionally, expression of four marker proteins, ER, PgR (progesterone receptor), HER2 (human epidermal growth factor receptor), and ki67 is often assessed in clinics. ER/PgR or HER2 positivity is a direct indicator for endocrine or HER2-targeted therapies, respectively, whereas ki67 expression indicates high proliferation and suggests that the patient may benefit from adjuvant chemotherapy.57 The diversity in external breast cancer features has evoked an attempt to classify tumors by their inherent cellular biology, leading to development of the intrinsic tumor subtypes and corresponding gene expression signatures.15, 58-60 The current consensus signature consist of 50 genes, whose expression levels divide breast tumors into luminal A, luminal B, basal-like, and HER2+ enriched subtypes.15, 61 The proportions of different subtypes have varied in the published literature, depending on cohort composition. However, in unselected patient series, luminal A is the most common subtype, assigned typically to about half of all cases. Luminal B is more frequent than basal-like and HER2+ enriched subtypes, which are equally common.62 The classification has gained popularity because of the added value it gives to prognostic estimation and treatment choices. In brief, the good prognosis luminal A tumors could be spared from chemotherapy, whereas for the other three subgroups chemotherapy would be justified.63, 64 If gene expression data are unavailable, the intrinsic breast cancer subtypes can also be estimated using surrogate histopathological markers; ER expression makes the major division between luminal and basal branches, and HER2 expression divides the ER-positive luminal and ER-negative basal branches further. PgR, ki67, and grade can be used for subdividing the luminal branch (Table 3).63-65 However, it is not uncommon that the surrogate marker-based prediction deviates from the gene expression-based classification. Especially, the differentiation of luminal A from luminal B tumors or HER2+ enriched from basal-like tumors remains challenging.66

Table 3. Surrogate intrinsic subtypes defined on the basis of immunohistochemical measurement of marker proteins as suggested in St Gallen 2013.63 Triple negative / Luminal A-like Luminal B-like HER2 positive Basal like ER positive positive negative negative PR positive Either PR-negative, negative negative Ki-67 low Ki-67 high, or any any HER2 negative HER2-positive positive negative

Alongside tumor subtypes, gene expression signatures have been developed either for classifying breast cancer patients into good and poor prognosis groups,67-72 for predicting benefit from a certain treatment specimen,73, 74 or for estimating specific tumor characteristics.75, 76 Even though there is little overlap in the individual genes included in these signatures, they have not been shown to differ in their ability to predict patient survival.77, 78 A critical meta-analysis showed that the common feature for these signatures is their ability to detect tumor proliferation, which has a direct association with patient survival.79 In fact, gene expression in general is confounded by cell

21 proliferation status and the breast cancer-associated signatures have not been able to outperform random signatures in predicting patient survival.80 Furthermore, different classification methods give opposite predictions on an individual patient level, and the traditional approach based on ER, HER2, grade, and TNM is still widely used for prognostic estimation.81, 82 Recently, IntClust classification has challenged the intrinsic subtypes as the best method for molecular taxonomy of breast cancer. IntClust is based on identification of recurrent chromosomal aberrations driving the tumorigenesis and affecting gene expression in cis.83 The method was developed using copy number and gene expression data in parallel, but later a surrogate signature using only gene expression was developed.84 The IntClust classification has 10 categories. Intrinsic basal tumors cluster almost exclusively to a single IntClust category, HER2+ enriched cancers to another category, but luminal tumors are dispersed across multiple IntClust categories. In the original study, IntClust outperformed the intrinsic subtyping especially in identifying a subgroup of chemo-insensitive ER-positive/luminal A breast cancers with poor prognosis, as well as in defining a signature indicative of a high number of infiltrating T-cells associated with good prognosis irrespective of the intrinsic subtype.57, 83-85 However, perhaps the most important contribution of the IntClust classification to breast cancer taxonomy is the shift in focus from primarily prognostic estimation to identification of the molecular events leading to breast cancer and characterization of specific druggable aberrations.85 2.2.4 Origin of breast cancer Development of the intrinsic molecular subtypes encouraged scientists to hypothesize that luminal tumors originate from luminal layer cells and basal tumors from myoepithelial cells.86 However, multiple ensuing studies have disputed this and indicated that luminal and basal breast cancers share a common origin, the luminal progenitor cells. Only later steps in tumor progression determine the resulting tumor phenotype.87-89 BRCA1-deficient cancer has served as an archetypic model for basal breast cancer, and the luminal origin of basal tumors was first discovered in a BRCA1 knock-down experiment. Loss of BRCA1 function in organoid and mouse models altered gene expression in luminal progenitor cells and diverted them from their predestined differentiation program.29, 90-93 Furthermore, it has been shown that even though the basal branch tumors typically do not express estrogen receptor, estrogen exposure plays an important role in the initiation of basal tumors.94 Interestingly, transformation of the basal progenitors leads to development of a metaplastic carcinoma, a rare breast cancer subtype, characterized by low expression of the Claudin genes.87, 95 Another model for breast cancer etiology has been proposed by mathematical modeling of age- related breast cancer incidence. The key observations were that the age distribution of breast cancer risk had a bimodal shape and that the risk of ER-negative breast cancer decreased with age. According to the suggested model, the two etiologic breast cancer subtypes would include ER- negative early-onset breast cancer and ER-positive breast cancer with a linearly increasing cumulative lifetime risk. The division was suggested to be caused by differences in the steps of tumor progression arising from biological differences between pre- and postmenopausal mammary glands.96

22 2.3 Breast cancer treatment The primary treatment for breast cancer is surgical removal of the tumor mass with sufficient margin or excision of the entire mammary gland. Radiation is commonly recommended to lower the risk of local recurrence. Furthermore, the treatment regimen generally includes a combination of hormonal, cytostatic, and biological adjuvant therapies to reduce the risk of death due to metastatic disease. Neoadjuvant therapy, i.e. therapy preceding the surgery, can be used for increasing the operability of an inflammatory or locally advanced breast cancer, or for reducing the tumor size for better success of a breast-conserving operation.97 2.3.1 Adjuvant endocrine therapy Estrogen receptor expression in tumor cells is an indication of benefit from adjuvant endocrine therapy.97 The rationale behind endocrine therapy is that the proliferation of tumor cells depends on uninterrupted supply of ovarian hormones.98, 99 Tamoxifen, the first antiestrogen drug used in an adjuvant setting, is still routinely used in treatment of premenopausal women with ER-positive breast cancer.97, 99, 100 Tamoxifen competes with endogenous estrogens in binding to estrogen receptor. The tamoxifen-receptor complex is able to dimerize and bind to estrogen-responsive elements in DNA, but does not induce transcription of estrogen target genes in breast tissue.98 Tamoxifen therapy in premenopausal women may be combined with drugs suppressing ovarian function, e.g. luteinizing hormone releasing hormone (LHRH) agonists, which overstimulate LHRH receptors in the pituitary gland, thus reducing LHRH levels and estrogen production in the ovaries.97, 99 In postmenopausal women, the primary estrogen source is androgen metabolism in peripheral tissues. Aromatase inhibitors (AIs) bind either covalently or reversibly to aromatase enzyme, blocking androgen conversion, and outperform antiestrogens in efficiency in treating post- menopausal women with ER-positive breast cancer.97, 99, 100 The most common mechanism for acquisition of resistance to endocrine therapy involves activation of the PIK3CB-AKT-MTOR pathway (phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta; AKT serine/threonine kinase 1; mechanistic target of rapamycin kinase).101 A specific MTOR inhibitor, Everolimus, has recently been approved for treatment of advanced ER-positive breast cancer.97, 102 Furthermore, a specific PIK3CB inhibitor has recently been reported to be effective in clinical trials, but it has also been associated with severe adverse effects, preventing its wider use.103 There is a continuously ongoing research effort to further improve endocrine regimens. The most recent advances include cyclin dependent kinase (CDK4/6) inhibitors and histone deacetylase inhibitors as well as refinement of AI therapy by addition of adjuvant bisphosphonates to protect against fractures and bone metastasis.100, 101, 104-108 2.3.2 Other targeted biological therapies Biological cancer therapy is based on agents that specifically target the drivers of tumorigenesis. Since the driver events often arise as a consequence of somatic mutations or by re-activation of embryonic pathways, they should not be present in the healthy adult system,14 and the systemic adverse effects of the treatment should be minimal. On the other hand, the treatment benefit is restricted to the subgroup of patients whose tumors carry these specific aberrations.101

23 Besides the estrogen receptor, the most important target in breast cancer therapy is the HER2 receptor, which is overexpressed or amplified in about 15% of breast cancers. Trastuzumab, a monoclonal antibody against HER2, was first introduced in a clinical trial in 1998. After the release of impressive and consistent results of two large trials combining trastuzumab with adjuvant chemotherapy in 2005, trastuzumab was widely adopted for adjuvant treatment of HER2- positive breast cancer.109 Subsequently, other anti-HER2 agents and regimen modifications have become a focus of intensive research.110 Dual inhibition of the HER2 pathway with a combination of drugs targeting different components of the pathway has been the most promising approach.111 Poly(ADP-ribose) polymerase (PARP) inhibitors represent the most promising emerging therapy for breast cancer. PARP silencing is lethal for cells devoid of BRCA1 or BRCA2 function. Thus, the PARP inhibitors are an ideal therapy with minimal side-effects for carriers of germline BRCA1 or BRCA2 mutations.112 Furthermore, since most of the moderate-risk breast cancer susceptibility genes, including ATM, PALB2, FANCM, and CHEK2, are involved in the same pathway controlling DNA repair via homologous recombination, PARP inhibitors may have potential also in treatment of breast cancer patients with germline mutations in any of these other risk genes.113 The potential of other biological therapies for breast cancer, including antiangiogenic agents and inhibitors of epidermal growth factor receptor (EGFR), has been studied intensively. To date, however, the success has been limited.101 2.3.3 Adjuvant chemotherapy Chemotherapy refers to a wide range of cytotoxic agents targeting actively proliferating cells on a systemic level. The rationale is that since active proliferation is what distinguishes cancer from normal tissue the agents would have selective toxicity for cancer cells. However, typical side- effects include immunosuppression and hair loss due to killing of actively dividing precursor cells.114-116 Ovarian suppression or premature menopause resulting from death of germ cell precursors may contribute to chemotherapy efficacy, but is an unwanted side-effect for younger women.114, 117 Cytotoxic compounds have been used in combinations to treat breast cancer since the 1970s.118 CMF (cyclophosphamide; methotrexate; 5-fluorouracil) was introduced in a clinical trial in 1973115, 119, 120 and is still included in the recommended adjuvant regimens.97 CMF combines one alkylating agent (cyclophosphamide) and two antimebolites (methotrexate and 5-fluorouracil/ capecitabine) administered at regular intervals separated by periods of recovery.115 Cyclophosphamide and its metabolites (4-hydroxycyclo-phosphamide and aldophosphamide) pass through the circulation in chemically inactive forms. Cytotoxic phosphoramide mustard is generated from aldophosphamide only in target cells with a low concentration of aldehyde dehydrogenase (ALDH), which is able to metabolize aldo-phosphamide into inactive carboxyphosphamide. Many normal tissues have sufficiently high expression of ALDH to protect them from the toxic side-effects.121 Phosphoramide mustard causes DNA cross-strand links at guanine nucleotides, leading to DNA damage and cell death. Additionally, cyclophosphamide has antiangiogenic and immunostimulatory effects, which may contribute to its efficacy as a chemotherapeutic agent.122 Methotrexate and 5-fluorouracil specifically block two enzymes required for thymine metabolism, dihydrofolate reductase and thymidylate synthase, respectively, halting DNA synthesis and leading to DNA degradation and cell death.123, 124

24 Anthracyclines (epirubicin, doxorubicin/adriamycin) stabilize topoisomerase II alpha (TOP2A) complex bound on cleaved DNA. This results in a mitotic catastrophe when the cell cycle proceeds from G2- (gap 2) to M-phase (mitosis).125 TOP2A is expressed from late S-phase (synthesis) to M-phase and regulates DNA topology during DNA replication.126 TOP2A copy number aberrations, HER2 gene amplification, high proliferation rate, and ki67 expression have been suggested as individual markers for benefit from anthracycline therapy.127-133 On the other hand, NQO1 (NAD(P)H:quinone oxidoreductase) germline variant rs1800566 has been suggested as a counter indication for anthracycline use.134 However, none of these markers have currently been included in the treatment guidelines.97, 116 Anthracyclines have proven superior to the older- generation CMF in prolonging overall and relapse-free survival.115, 135 The treatment efficacy has, however, come at the cost of an increasing amount of adverse side-effects. Anthracyclines cause neutropenia in up to 25% of patients, secondary leukemia in about 1% of patients, and increased short- and long-term risk of heart failure, depending on the cumulative dose.135, 136 Taxanes (paclitaxel and docetaxel) represent a newer generation of chemotherapeutic agents. Their administration alternately with anthracyclines improves patient prognosis relative to anthracycline-based therapies alone.136 Taxanes promote rapid assembly of overly stable microtubules, stalling cells in the M-phase and leading to apoptosis or immune-related cell lysis.137, 138 Adverse effects include fatigue, neutropenia, peripheral neuropathy, and decreased cognitive performance as a result of axon demyelination and direct neural cell toxicity.139-141 Other microtubule-targeting drugs with similar toxicity profiles include eribulin and vinca alkaloids, which are primarily used in treatment of metastatic breast cancer.97, 142-144 Chemotherapy is recommended as adjuvant treatment for patients at intermediate or high risk of recurrence or progression.97 Few biological markers indicating sensitivity or resistance to any specific agent have been identified thus far.132, 145, 146 The choice of an optimal treatment combi- nation for an individual patient is made following general guidelines, and adverse effects are monitored and the agent or dose is changed if necessary.97 As the most efficient agents tend to be increasingly toxic, further research is required to identify the patient groups for whom the benefit will outweigh the harm, and to develop better-tolerated means for drug administration. 57, 114, 116 2.4 Genetic predisposition to breast cancer 2.4.1 Breast cancer heritability Genetic factors account for slightly less than one-third of breast cancer incidence; twin studies have estimated the heritability to be about 31%.6 Having a first-degree relative with breast cancer is associated with a twofold increase in breast cancer risk, and the risk has been suggested to increase along with the number of affected relatives, so that the cumulative lifetime risk for a woman with three affected first-degree relatives would reach 30-40%.147, 148 The familial aggregation of breast cancer is attributable to shared environmental factors as well as genetic variants acting multiplicatively to increase the risk, with common low-penetrance variants modifying the penetrance of higher-risk mutations. 6, 149-152 Typically for a polygenic disorder, the currently known genetic risk variants are distributed on a diagonal, reaching from rare high-risk mutations to moderate-risk mutations and further to common low-penetrance variants, covering an area in which the balance between effect size and variant frequency is sufficient to make them biologically or clinically interesting (Figure 4).153-156

25 The breast cancer-predisposing variants have been discovered mainly using three different methods, each best-suited to discovery of certain classes of variants on the risk-frequency axis (Figure 4, Table 4). Historically, the golden age of linkage studies preceded the burst of resequencing of candidate genes, which has given way to the currently dominating genome-wide studies.4, 11

Figure 4. Breast cancer-predisposing genes and variants. High- and moderate-risk genes are named in the figure, the rest being low-penetrance variants with a relative risk below 1.5.

The most important single risk factors are truncating mutations of BRCA1 or BRCA2 genes, explaining about 16% of breast cancer genetic background (Figure 5). These together with mutations in other high- or moderate-risk genes add up to about 20%.7 Common predisposing variants with low effect sizes account for an additional 18%. Interestingly, a recent genome-wide association study identifying multiple novel predisposing variants indicated that an equally large fraction of breast cancer predisposition could be explained by as yet unidentified variants, which were included on or imputable from the genotyping array, but which alone did not exceed the genome-wide significance threshold (‘Unknown common variants’ in Figure 5). 153 Still, a large proportion of breast cancer genetic background remains unexplained. The missing heritability has been suggested to be attributable to rare or private variants, structural variants, imprinting, or epistasis resulting from genetic interactions.157, 158

26 Table 4. Methods used for discovery of genetic variants predisposing to breast cancer.

Method Study subjects Usability Discoveries Advantages Limitations Co-segregation of Low number of marker variants and Pedigree data – study subjects Well-suited to study disease defines the multiple closely required Unsuitable for Mendelian-like High-risk protein Linkage study region of interest related genotyped variants with lower traits with high truncating mutations Brings new individuals from effect sizes Sequencing is used penetrance information on to detect coding each family biological processes mutations causing the trait

Functional approach Functional in target selection Limited to information defines predetermined Resequencing the genes of interest Suitable for cohorts Moderate- Can detect pathways of candidate Case-control data enriched with penetrance coding mutations causing Sequencing is used genes familial index cases mutations familial breast No novel biological to detect coding cancer in situations information mutations where linkage fails Comparison of allele frequencies in No family data Suitable for cases and in Large case-control Low-penetrance required Genome-wide detection of controls defines risk datasets, usually variants, typically Very large number association common Brings new variants combined from from non-coding of samples required analysis predisposing information on multiple studies regulatory regions Fine-mapping is variants biological processes used to identify the causing the trait causal variants

27 2.4.2 High-risk genes Loss-of-function mutations in BRCA1 or BRCA2 cause hereditary breast and ovarian cancer syndrome. It is an autosomally dominantly inherited syndrome with high penetrance. Female BRCA1 mutation carriers have about a 72% lifetime risk of breast cancer and a 44% risk of ovarian cancer. For female carriers of BRCA2 mutations, the risks are somewhat lower: 69% for breast cancer and 17% for ovarian cancer.156 Other cancers of the syndrome spectrum include melanoma, pancreatic cancer, prostate cancer, and male breast cancer. However, lifetime risks of these other cancers are considerably lower.159

Figure 5. Relation of environmental and genetic factors predisposing to breast cancer.

BRCA1 and BRCA2 mutations follow Knudson’s hypothesis for inherited cancer susceptibility; when one impaired allele is inherited, complete loss of the tumor suppressor’s function on a cellular level is more probable than if both alleles were inherited intact. Therefore, the disease onset occurs earlier for mutation carriers than for non-carriers, and the risk of multiple primary cancers is elevated. Furthermore, mutations are manifested in familial clustering of cancer cases.12, 13, 156, 159

Over 70% of breast cancers of BRCA1 or BRCA2 mutation carriers are diagnosed as high grade. However, there are clear phenotypic differences between breast cancers of carriers of BRCA1 and BRCA2 mutations. About 80% of BRCA1 carrier cancers are ER-negative and about 70% triple- negative. For BRCA2 carrier cancers, the numbers are reverse; almost 80% are ER-positive. Furthermore, BRCA1 mutations are associated with a medullary histopathological type.160 Mutations in other high-risk susceptibility genes (Figure 4) do not cause only breast cancer, but also hereditary cancer syndromes with characteristic cancer spectra: TP53 mutations cause Li- Fraumeni syndrome characterized by sarcomas, breast cancer, brain tumors, leukemias, adrenocortical tumors, and multiple primary cancers;161 PTEN (phosphatase and tensin homolog) mutations cause Cowden syndrome with multiple hamartomas in different tissues as well as breast, thyroid, and endometrial cancers;162 STK11 (serine/threonine kinase 11) mutations cause Peutz- Jeghers syndrome with hamartomatous gastrointestinal polyps and cancer, mucocutaneous pigmentation, and pancreatic, breast, ovarian, and gallbladder cancers;163, 164 CDH1 (cadherin 1) mutations cause Hereditary Diffuse Gastric and Lobular Breast Cancer Syndrome;165 and NF1 (neurofibromin 1) mutations cause neurofibromatosis and confer a moderately elevated risk of

28 breast cancer.4 However, the contribution of mutations in these genes to breast cancer incidence overall is limited (Figure 5).7 2.4.3 Moderate-risk genes Moderate-penetrance genes have been identified by resequencing candidate genes, which have typically included direct binding partners of BRCA1, BRCA2, or TP53. By definition, mutations in moderate penetrance genes are associated with only two- to threefold increase in risk and do not segregate consistently with breast cancer within pedigrees.7 In most cases, their role in breast cancer predisposition has been validated by discovery of founder mutations enriched in certain populations. The evidence has been based both on mutation clustering in breast cancer families and on elevated risk of mutation carriers in unselected cohorts.1, 7, 155, 166, 167 For example, CHEK2 (checkpoint kinase 2) c.1100delC was established as a susceptibility mutation simultaneously in Dutch and Finnish populations, and NBN (nibrin) c.657del5 in the Polish population, all with relatively high, about 1% carrier frequencies.1, 166, 168 Recently, a recurrent FANCM (Fanconi anemia complementation group M) c.5101Cௗ!ௗT mutation was discovered in the Finnish population, and subsequently, another FANCM variant was indicated as a risk mutation for triple- negative breast cancer.155, 169 Also PALB2 (partner and localizer of BRCA2) c.1592delT was found in Finnish breast cancer families using the candidate gene approach.167 Due to the rarity of truncating PALB2 mutations on a population level, it has been difficult to determine their exact effect size. Estimates made in unselected series have been less than fourfold.154, 167, 170 However, a collaborative international study with the largest number of PALB2 mutation carriers so far and exhaustive family data estimated the lifetime risk of PALB2 carriers to reach 35% and the relative risk to be over ninefold, suggesting that PALB2 could be considered a high-risk gene instead.171 ATM (ATM serine/threonine kinase) truncating, splice junction, and certain rare missense mutations have been validated as breast cancer risk factors in collaborative studies,154, 172, 173 whereas for many other candidate genes the reports have been inconclusive and their contribution to breast cancer remains to be determined.4, 174 2.4.4 The Breast cancer pathway The majority of the high- and moderate-risk genes as well as candidate genes with some evidence of breast cancer predisposition are involved in a single cellular pathway, namely the BRCA/Fanconi anemia pathway involved in repair of DNA double-strand breaks via homologous recombination.175 Fanconi anemia is a rare recessively inherited disease characterized by bone marrow failure, developmental defects, and a predisposition to acute myeloid leukemia and a range of soft tissue sarcomas. It is caused by biallelic mutations in genes encoding central components of the BRCA/Fanconi anemia pathway. Currently, about twenty genes have been linked to Fanconi anemia, including several breast cancer risk and candidate genes (Figure 6).176 The Fanconi anemia protein complexes – core, anchor, and ID2 – recognize DNA lesions, especially the locations of stalled replication forks, and recruit DNA repair proteins, including BRCA1, BRCA2, and RAD51 (RAD51 recombinase).176 On the other hand, the MRN complex (MRE11-RAD50-NBN; MRE11 homolog, double strand break repair nuclease; RAD50 double strand break repair protein; nibrin) senses double-strand breaks induced by intrinsic reactive oxygen species or extrinsic genotoxic agents resulting in ATM activation by auto-phosphorylation. This leads to two parallel processes: local signal spread along chromatin via binding of additional

29 MRN complexes and diffuse signal amplification via kinase cascade leading to cell cycle delay or apoptosis.177, 178 BRCA1 is a key node in making the choice between different mechanisms for DNA repair and recruiting the effector proteins. BARD1 (BRCA1 associated RING domain 1) is an obligate N-terminal BRCA1 binding partner, but the C-terminal BRCT repeats of BRCA1 can be occupied by one of three alternative proteins forming bridges to different functional or regulatory units. The BRCA1-PALB-BRCA2 bridge is obligatory for recruiting RAD51 to sites of DNA double-strand breaks in order to repair the lesions via homologous recombination (Figure 6).179, 180

Figure 6. BRCA/Fanconi anemia –pathway.176, 178-184 FA: Fanconi anemia; HR: homologous recombination.

30 In summary, the high- and moderate-penetrance breast cancer risk and candidate genes are involved in making the decision of whether to fix DNA double-strand breaks with a fast but error- prone non-homologous end joining or with the time-consuming but accurate homologous recombination. Apart from this, there is the decision of whether to delay the cell cycle, giving time for repair, whether to enter into senescence, or whether to drive the cell into apoptosis. In these processes, the balance between multiple pathways makes the ultimate choice. It is noteworthy that these pathways never rest; intrinsic factors alone have been estimated to cause up to 200 000 DNA lesions per day.177, 179 2.4.5 Common predisposing variants The common risk variants have been discovered in genome-wide association studies (GWAS). Since GWAS genotyping arrays have been designed to be non-redundant and to cover most of the total genomic variation, the best hits have been assumed to be only markers of risk loci or tag- SNPs (single-nucleotide polymorphisms), not causative variants per se.9, 153, 185 Causal inference requires fine-mapping the region of interest for finding the strongest signal or the relations of multiple independent signals and testing those in functional in silico and in vitro models.186 The common risk variants are enriched on regulatory sites active in breast cancer cell lines and their predicted target genes are enriched in pathways essential for breast tissue development, but also in cancer-related pathways.153 The relative risk associated with any single common variant is so low that it lacks all clinical applicability. However, when multiple variants’ effects are combined into a polygenic risk score it could be used in patient risk stratification, especially in combination with other measurable risk factors, such as mammographic density or family history-based prediction models.10, 187, 188 2.5 CHEK2 2.5.1 CHEK2 protein function Checkpoint kinase 2 (CHEK2) is a serine/threonine kinase involved in regulation of cell cycle delay in response to DNA double-strand breaks.189 It is expressed in a wide range of actively proliferating, quiescent, and terminally differentiated cells in different tissues and contributes to regulation of both G1/S and G2/M checkpoints as well as to timely and proper assembly of the mitotic spindle.28, 190-192 The CHEK2 protein consists of three distinct domains: the N-terminal SQ/TQ cluster domain (SCD; residues 19-69; S:serine, Q:glutamine, T:threonine), central fork head-associated domain (FHA; residues 112-175), and C-terminal kinase domain (KD; residues 220-486).193 In the basal inactive state, CHEK2 monomers localize to the nucleus.194-196 Phosphorylation of threonine 68 (Thr68) on the SCD-domain leads to CHEK2 homodimerization via reciprocal phosphoThr68- FHA, FHA-FHA, and FHA-KD interactions, which enable intermolecular phosphorylation of multiple amino acids, including Thr383 and Thr387 of the activation loop as well as Ser516 of the kinase domain.193, 197, 198 The activation process ends with dissociation of the two CHEK2 monomers.193, 198 In summary, SCD is primarily a regulatory domain, FHA participates in target binding via protein-protein interactions, whereas KD is the functional domain. CHEK2 has a messenger role in response to DNA double-strand breaks. It is phosphorylated by ATM at sites of DNA damage, thereafter rapidly spreading the signal throughout the nucleus

31 owing to its diffuse mobility.194 At G1, CHEK2 phosphorylates CDC25A (Cell division cycle 25 A) phosphatase, preventing activation of CDC25A targets CDK1 and CDK2, entailing a rapid but short-term delay in entering the S-phase of cell cycle.190, 191 Similarly, at G2 CHEK2 induces only a transient arrest, allowing time for double-strand break repair.192 In parallel to regulating cell cycle progression, CHEK2 stimulates double-strand break repair via homologous recombination by phosphorylating BRCA1.189 CHEK2 may also contribute to TP53 stabilization. However, additional cues are required for a full, long-term, TP53-dependent G1/S or G2/M arrest, and CHEK2 possibly has only a redundant role in the regulation of TP53 activity.191-193 During normal mitosis CHEK2 Thr68 is phosphorylated by PRKDC (protein kinase, DNA- activated, catalytic polypeptide).197 Activated CHEK2 localizes to centrosomes and by phosphorylating BRCA1 contributes to regulation of mitotic spindle assembly.28, 195, 196 Compro- mised CHEK2 or BRCA1 function has been shown to cause abnormal spindle morphology and irregular chromosomal alignment, leading to increased chromosomal instability and aneuploidy, but not to evoke spindle assembly checkpoint or to decrease cell viability.28 CHEK2-dependent phosphorylation of BRCA1 protects it from inhibitory effects of AURKA (aurora kinase A), thereby preventing acceleration of microtubule plus-end assembly, which has been suggested to be one potential cause for chromosome missegregation.199, 200 Altogether, the cellular phenotype associated with CHEK2 deficiency is mild: the error correction for kinetochore attachment functions normally, chromosomes are aligned with modest delay, chromatids segregate with minor loss of fidelity and cells proceed through mitosis as usual. However, the slight weaknesses in maintaining chromosomal stability may be enough to explain the increased cancer risk associated with CHEK2 loss-of-function mutations. 28, 199 Consistently with the two roles of CHEK2 in response to DNA double-strand breaks and in mitosis, the CHEK2-depleted mice are characterized by radioresistance, increased chromosomal instability, and increased rate of spontaneous tumors.201-203 Noteworthy is that the tumorigenesis rate is increased especially in female mice, raising the possibility that female hormones contribute to CHEK2-related cancer progression.203 2.5.2 CHEK2 mutations CHEK2 was first considered as a candidate gene for Li-Fraumeni and Li-Fraumeni-like syndrome due to its role as an upstream activator of TP53.204 The initial screen in Li-Fraumeni families revealed three mutations, of which two founder mutations (c.1100delC and p.(I157T)) were later connected to breast cancer predisposition.1, 2, 9, 166, 204 However, the third mutation (c.1422delT) was found to be a false discovery – a variant located on a pseudogene with sequence homology with CHEK2. Furthermore, subsequent screens of Li-Fraumeni families indicated that CHEK2 was unlikely to be associated with Li-Fraumeni or Li-Fraumeni-like syndromes.205, 206 C.1100delC is the most widely studied CHEK2 mutation owing to its relatively high carrier frequency in Finland (1.4%) and in the Netherlands (1.1%).1, 166 A single-nucleotide (C) deletion at codon 366 induces premature stop at codon 381, truncating the kinase domain.204, 207 Other recurrent truncating CHEK2 mutations include c.IVS2+1G>A and del5395, which are founder mutations in Slavic populations and have the highest, about 0.4%, carrier frequency in Poland.208 The former is a splice variant, resulting in a four-base insertion in mRNA and premature stop codon in exon three, truncating the FHA-domain.209 The latter deletion abolishes exons nine and

32 ten, leading to a premature stop at codon 381, similarly as with c.1100delC.210 C.1100delC has been shown to evoke nonsense-mediated mRNA decay, leading to rapid degradation of the mutated transcript in living cells.211 Furthermore, both c.1100delC and c.IVS2+1G>A are associated with drastically reduced cellular levels of the CHEK2 protein.209 The overall European allele frequency of p.(I157T) is below 0.1%, but it is enriched in certain populations, like Finland with 5.3% and Poland with 4.8% carrier frequencies.2, 208, 212 The mutated protein is expressed in normal cells and breast tumors at the same level as the wild-type protein.2, 209 Furthermore, the intact kinase domain is able to phosphorylate downstream target proteins.207 However, CHEK2 conformation modeling has indicated that isoleucine-157 is located at a crucial point on the hydrophobic surface between the dimerizing CHEK2 proteins. Its mutation to threonine severely disturbs the reciprocal interactions between the two CHEK2 monomers and reduces the rate of autophosphorylation required for CHEK2 activation.198, 213 2.5.3 CHEK2 and breast cancer risk Protein truncating CHEK2 mutations are associated with a two- to threefold increase in breast cancer risk. Estimates vary in different studies such that cohorts enriched with familial patients give slightly higher estimates than unselected cohorts. A meta-analysis of 42 studies suggested ORs of 2.7 [2.1-3.4] for c.1100delC for unselected cases and 4.8 [3.3-7.2] for cases with positive family history.214 A recent large-scale study aiming at accurate risk prediction reported the c.1100delC-associated OR for invasive breast cancer to be 2.26 [1.90-2.69].3 A polish study including also the two other truncating founder mutations, c.IVS2+1G>A and del5395, suggested OR 3.3 [2.3-4.7] for any truncating mutation for sporadic cases and 5.0 [3.3-7.6] for familial cases.215 Despite the variation in the estimates of the relative risk, the population-level cumulative lifetime risk for carriers of truncating CHEK2 mutations has been set to about 20% and over 30% for women with a positive family history of breast cancer.3, 215 C.1100delC is associated also with increased risk of bilateral breast cancer, but the effect size complies with a model where the two cancers arise independently and the association could be explained by the baseline risk associated with c.1100delC.1, 216-219 Breast cancer risk associated with p.(I157T) is considerably lower than the risk associated with the truncating mutations. Therefore, instead of being considered as a moderate-penetrance mutation, p.(I157T) is more comparable to the common low-penetrance variants. In the discovery study, p.(I157T) OR was 1.43 [1.06-1.95] in an unselected cohort, and no association with familial breast cancer was found.2 A meta-analysis of 18 studies suggested a slightly higher risk estimate with OR 1.58 [1.42-1.75].5 Recently, resequencing CHEK2 in large cohorts has enabled a discovery of rare coding variants. Calvez-Kelm et al. reported a discovery of six novel and unique truncating variants in breast cancer patients and 34 missense variants, which they categorized according to evolutionary conservation. A crude risk analysis suggested that the tolerable variants would be associated with a risk comparable to p.(I157T) and deleterious variants with similar risk effects as the protein- truncating founder mutations.220 Decker et al. reported 14 rare truncating variants in a cohort of early-onset breast cancer and suggested a combined OR 3.11 [2.15-4.69] for all truncating variants.221 Converging results were obtained also from a genotyping of six CHEK2 variants in a

33 large cohort of invasive breast cancer cases: OR point estimate was higher than 2.0 for deleterious variants and lower than 1.5 for variants predicted to be benign.154 2.5.4 CHEK2 in breast tumors CHEK2 protein expression is reduced or absent in c.1100delC carrier breast tumors, corroborating the causal role of the mutation in tumorigenesis.1, 216 The low expression is likely to be caused by the instability of the mutated transcript accompanied by haplo-insufficiency.211 Loss of the intact allele may be a contributory factor in some cancer cases, but loss of heterozygosity cannot be considered a general mechanism associated with c.1100delC-related tumorigenesis.205, 222 On the contrary, CHEK2 protein expression appears to be normal in breast tumors of p.(I157T) carriers.2 However, the mutation may reduce the overall level of CHEK2 activity since the mutated protein can bind intact CHEK2 monomers and compete with the wild-type alleles in the formation of homodimers in the CHEK2 activation process.2, 198, 223 Thus, presumably, the consequence of both c.1100delC and p.(I157T) would be reduced CHEK2 kinase activity, and the mutations would differ only in the extent of CHEK2 silencing. It is noteworthy that CHEK2 expression has been reported to be reduced in 21% of unselected non-carrier breast tumors, suggesting that the role of CHEK2 in breast cancer tumorigenesis extends beyond the carriers of germline mutations.1 Over 90% of CHEK2 mutation carrier breast tumors are ER-positive, compared with about 70% of non-carriers, and the strong association is shared by the truncating and missense mutations. C.1100delC has been suggested to be associated with poor patient survival,218 and also with pathologic markers of poor prognosis such as higher grade and larger tumor size.216 However, all studies have not replicated these findings.217, 218, 224, 225 Interestingly, lobular histological type is enriched among p.(I157T) breast tumors, but not in tumors of carriers of truncating CHEK2 mutations, implying that there may be subtle differences in breast cancer tumorigenesis associated with these mutations.224

34 3 Aims of the Study This study was designed to gain a deeper understanding of breast cancer pathogenesis associated with germline CHEK2 mutations, to examine tumor phenotype and survival of mutation carrier patients, and to explore possibilities for enhanced risk stratification for CHEK2 mutation carriers and for women from non-BRCA1/2 families. The work was divided into four sub-studies whith explicit aims as follows: I To identify recurrent copy number aberrations in breast cancers of c.1100delC carriers and to use gene expression analysis to identify cellular level pathways driving the c.1100delC-associated breast cancer. II To examine survival of p.(I157T) carriers as well as tumor phenotype associated with germline p.(I157T) mutation in a comprehensive setting including pathology analysis of a large international dataset of the Breast Cancer Association Consortium and gene expression analysis of 180 breast cancers. III To investigate the contribution of common genomic variation to the breast cancer risk of c.1100delC mutation carriers as well as to assess the applicability of a polygenic risk score in clinical risk stratification of the mutation carriers. IV To examine the risk associated with the polygenic risk score in Finnish breast cancer families in order to assist in outlining the principles for using the polygenic risk score in genetic counseling.

35 4 Materials and Methods 4.1 Study subjects and data sources 4.1.1 Breast tumors (I, II) For genomic profiling of CHEK2 c.1100delC mutation carrier breast cancers (I), archival tumor samples from 121 breast cancer patients, including 30 c.1100delC carriers, were examined. For 37 (17) patients, only formalin-fixed paraffin-embedded (FFPE) samples, and for 79 (11) patients only fresh frozen tissue samples were available (number of 1100delC carriers in parentheses). Additionally, both FFPE and fresh frozen tissue samples were available for five (two) patients. Both FFPE and fresh frozen tumors were used as a source for genomic DNA samples for the array comparative genomic hybridization (aCGH) using a custom-made genomic array of BAC (bacterial artificial chromosome) clones (SCIBLU genomics, Lund, Sweden),226 whereas total RNA samples for gene expression (GEX) analysis using a custom-made genomic oligonucleotide array (SCIBLU genomics, Lund, Sweden)226 were extracted only from the fresh frozen tumors. DNA was successfully extracted from all FFPE and 59 (9) fresh frozen samples, and RNA from 78 (13) fresh frozen samples. A separate gene expression dataset of 183 fresh frozen tumor samples from female breast cancer patients hybridized on Illumina HumanHT-12 v3 Expression BeadChips (Illumina Inc, San Diego, CA, USA) was used for clinical validation of the CHEK2 c.1100delC-associated gene expression signature (I) as well as in the analysis of p.(I157T)-associated gene expression (II). Details on sample preparation and data preprocessing have been published elsewhere.227 This data included six carriers of c.1100delC and ten carriers of p.(I157T). The overlap between this Illumina dataset and the above-described older dataset was 49 tumor samples, which included two samples from p.(I157T) carriers and five samples from c.1100delC carriers. The clinical relevance of the CHEK2 c.1100delC-associated gene expression signature was validated using three publicly available gene expression datasets, cohorts of 315 and 249 breast cancers from Uppsala75, 228 (GEO – Gene Expression Omnibus: GSE3494; GSE4922), and a cohort of 159 breast cancers from Stockholm229 (GEO: GSE1456). 4.1.2 Study subjects from the Breast Cancer Association Consortium (II, III) The Breast Cancer Association Consortium (BCAC) is an international forum for breast cancer research. Currently, it consists of 108 studies that participate in collaborative projects, providing genotype data on breast cancer patients and controls with the same ethnicity. Due to the multi- ethnic nature of the consortium, in analyses concentrating on founder variants, such as the CHEK2 mutations, it is meaningful to include only those studies that provide adequate numbers of variant carriers. Furthermore, since the BCAC studies have originally been established to serve different scientific purposes, the patient follow-up or clinical data availability differ between studies, affecting the selection of eligible BCAC studies in the collaborative projects. In the analyses of p.(I157T)-associated patient survival and tumor characteristics (II), we included female breast cancer patients from 15 BCAC studies. The study selection based on the number of informative p.(I157T) carriers (•9) yielded a dataset of 26 801 study subjects, including 590 p.(I157T) carriers and 271 c.1100delC carriers.

36 In the analyses of synergistic risk effects of CHEK2 c.1100delC and common low-penetrance variants (III), we included data from 32 BCAC studies. The total of 39 139 female invasive breast cancer patients and 40 063 healthy population controls with European ethnic background included 624 cases and 224 controls carrying the c.1100delC variant. However, complete data of all 77 common variants and CHEK2 c.1100delC were available only for 17 640 cases and 15 984 controls, including 285 and 84 c.1100delC carrier cases and controls, respectively. All available data were utilized in pairwise interaction analyses between CHEK2 c.1100delC and the common variants, and the complete data were used in analyses of the polygenic risk score combining the risk effects of all common variants. The study subjects for Studies II and III were genotyped centrally as a part of the Collaborative Oncological Gene-Environment Study9 (COGS) or by individual BCAC studies following the BCAC genotyping standards as described previously.185, 230 4.1.3 Study subjects of the Helsinki breast cancer study (IV) Predictive potential of a polygenic risk score was investigated in two Finnish datasets, a case- control dataset and a breast cancer family dataset. The case-control dataset of 1 681 breast cancer cases and 1 272 population controls consisted of three series of unselected breast cancer patients and additional familial index cases described in detail elsewhere.1, 134, 216, 231, 232 The breast cancer family dataset consisted of 52 systematically collected breast cancer families,232 including 493 genotyped family members (183 affected women, 246 healthy women, and 64 men) and registry data for a further 3 992 relatives. All study subjects were genotyped for 75 common breast cancer risk variants using the array designed for the COGS consortium studies described above. Three moderate-penetrance mutations (CHEK2:c.1100delC, PALB2:c.1592delT, and FANCM: c.5101C>T) were genotyped locally as described previously.155, 170, 218 4.2 Methods 4.2.1 Microarray data processing and analyses (I, II) Array comparative genomic hybridization and gene expression data were background-corrected, log-transformed, and then processed and analyzed according to the flowchart in Figure 7 using standard methods suitable for each technology. Analysis results included mutation-associated regions of copy number aberrations, differentially expressed genes, potential tumor driver events, and cellular pathways characteristic for the mutation carrier breast cancers. In the two-color aCGH and GEX arrays, used in Study I (Figure 7), normal human male genomic DNA and Universal Human RNA were used as reference samples, respectively, because patient reference samples from adjacent healthy tissue were not available. The use of a universal reference sample may increase noise in the data and, especially in the aCGH analysis, blur the effect of personal variation in germline copy-number. However, the BAC array resolution was approximately a signal per 100 000 base pairs. Furthermore, the data preprocessing aimed at a drastic dimensionality reduction, so that the analysis of c.1100delC-associated regions was able to capture differences at the level of chromosome bands, where differences in germline copy- number variation are not relevant. In the GEX analysis, the reference sample was used for data normalization, and as the denominator it was reduced at the analysis stage.

37 The normalization methods applied to different datasets were chosen to best suit the technology used for hybridization and to make the samples comparable to each other. The popLowess method used for the aCGH data (Figure 7) is based on the assumption that in tumor data there should be three main clusters of copy number, i.e. loss, normal, and gain, and that these could be defined by analysis of the distribution of the intensity ratios.233 Thus, the normalization with popLowess transforms continuous data to categorical data. The following steps – segmentation and region calling – were done to smooth the data so that the consecutive probes covering large genomic regions would have the same call. Of note, we used soft calls, i.e. probabilities of a specific call (loss, normal, or gain) in copy number calling. Therefore, the preprocessed data were continuous data with values between [-1, 1], instead of categorical calls (-1, 0, or 1). The non-parametric Wilcoxon rank-sum test was chosen for identifying the c.1100delC-associated CNAs (Figure 7) because we were unwilling to make assumptions about the distribution of the soft calls associated with the defined genomic regions. We suspected that in many regions relevant for tumor progression, the soft call distribution would have a skewed or bimodal shape, rendering e.g. Student’s t-test a suboptimal choice. The intensity ratios of the two-color gene expression array used in Study I were normalized within arrays with print-tip loess to get rid of uneven sample concentration across the array (Figure 7). In Study II, the within-array normalization of the gene expression data were done as a part of the service by SCIBLU genomics. In both studies, the data were normalized between arrays with quantile method. The quantile normalization is based on the assumption that the log-transformed gene expression intensity values of any multi-cell sample should be normally distributed. Forcing the samples to have identical distributions makes the between-sample comparison of individual mRNAs reliable. Differences in gene expression between c.1100delC carrier and non-carrier tumors were analyzed with moderated t-test with Bayesian probability estimation. The nominal p-values were used to prioritize gene selection to functional annotation. Multiple testing correction was not used at this stage, because the differences between c.1100delC carriers and non-carriers were likely to be modest on a single gene level. However, the functional analyses with DAVID annotation tool and Gene Set Enrichment Analysis (GSEA) were corrected for multiple testing to identify the significant differences on a pathway level. The clinical relevance of the CHEK2 c.1100delC-associated 182-gene signature was assessed in four independent gene expression datasets by clustering the samples according to the expression of the signature genes and comparing the breast cancer-specific and distant relapse-free survival between the two main clusters using Kaplan-Meier curves234 and log-rank test.

38 Raw intensity data aCGH (I) GEX (custom, I) GEX (illumina, II) Two-color Two-color One-color BAC clone array: oligonucleotide array: beadchip array: intensity ratios intensity ratios intensity values

Normalization: Normalization (within arrays): popLowess, Print-tip loess, Base R library limma

Segmentation: Normalization (between arrays): Circular binary segmentation, Quantile normalization, R library DNAcopy R library limma

Copy number calling: Differential expression analysis: Soft calls, Moderated t-test with Bayesian R library CGHcall probability estimation, R library limma

Data dimensionality reduction: regions of constant calls, R library CGHregions Functional enrichment analysis of the differentially expressed genes: DAVID functional annotation tool Testing for mutation-specific copy number aberrations: Wilcoxon rank-sum test Gene set enrichment analysis: GSEA Identification of potential tumor java application driver genes: Overlap between c.1100delC associated regions and differentially expressed genes

Gained and lost Candidate Enriched Differentially regions driver genes pathways expressed genes

Figure 7. Microarray data processing and analysis flowchart (Processing or analysis step: method, environment).233, 235- 244

4.2.2 Permutation analysis (I: unpublished data) The non-randomness of enrichment of the olfactory pathway among the genes with higher expression in c.1100delC carrier tumors was assessed by randomizing the mutation carrier status and running the analysis of differential expression with the same parameters as in the original work for 500 times. In short, analyses were performed using Bioconductor package limma, adjusting for ER-status and other potentially confounding covariates as described in I: Materials

39 and methods. P-value threshold for differential expression was set to 0.05 similarly as in extracting the 862 c.1100delC-associated genes for DAVID functional enrichment analysis. The number and proportion of olfactory receptor genes on each of the 500 generated gene lists were calculated and compared with the number and proportion of olfactory genes on the c.1100delC-associated gene list. 4.2.3 Survival analyses (II) Patient survival in a group of interest, e.g. carriers of a specific mutation, was compared with patient survival in a reference group using Cox proportional hazards model.245 Study subjects were considered to become at risk at the time of their first invasive breast cancer diagnosis. The data were left truncated to account for late enrollment and right censored at the event of interest or at the end of follow-up, whichever occurred first. The events of interest in the parallel analyses included death by any cause, breast cancer-associated death, distant metastasis, locoregional relapse, and second breast cancer. 4.2.4 Tumor pathology analyses (II) Tumor pathology analyses of the BCAC study subjects were based on the data that had been collected by individual studies, as described earlier.246 Associations between the CHEK2 mutations and pathologic characteristics were tested by study-stratified Cochran-Mantel-Haenszel test, as implemented in R library vcdExtra.247 Mutation-associated differences in age at diagnosis were tested with meta-analysis of age distribution using R library meta.248 The breast tumors of the BCAC study subjects were categorized into molecular subtypes using histopathological markers following the St Gallen 2013 criteria.136 The subtypes of the 183 breast tumors in the gene expression dataset were defined according to the PAM50 signature as implemented in the R library genefu.15, 249 4.2.5 The Polygenic risk score (III, IV) Polygenic risk score (PRS) summarizes the risk effects associated with single low-penetrance variants. In Studies III and IV, the PRS was calculated as a sum of per variant log odds ratios, weighed by the number of risk alleles carried.10 The raw PRS was normally distributed in the population and individual PRS values were standardized by subtracting the population mean and dividing by the population standard deviation. The PRS used in Study III was based on 74 common variants, and the PRS used in Study IV was based on 75 variants. The difference was due to CHEK2:p.(I157T) (rs17879961), which was not included in the analyses of Study III, because the number of study subjects carrying c.1100delC and p.(I157T) was very low. 4.2.6 Risk association analyses (III, IV) The association between genetic risk factors and breast cancer was tested primarily with logistic regression. Pairwise interaction between variants was assessed by including in the model an interaction factor coded as a product of the two. Nested models were compared with likelihood- ratio test. 4.2.7 Feature selection (III: unpublished data) To build a sparse model of common breast cancer susceptibility variants modifying the risk of CHEK2 c.1100delC carriers, we used stepwise logistic regression as implemented in StataSE 10 (StataCorp, College Station, TX, USA) on 77 common variants (linked variants of the same region were both included) with the following p-value thresholds: entry into model 0.05; removal from

40 the model 0.1. Feature selection was performed also with R libraries glmnet250, 251 and Boruta252, which enabled significance estimation by cross-validation or by introduction of random covariates, respectively. 4.2.8 In silico functional analysis (III: unpublished data) We retrieved tagging variants (r2>0.8) for the six putative CHEK2 c.1100delC risk-modifying variants using SNAP proxy search (version 2.2)253 on European haplotype data and utilized Genevar,254-256 RegulomeDB,257 and HaploReg v2258, 259 database tools for identifying the target genes of these variants. For annotation of the 71 remaining risk-modifying variants, we performed a HaploReg analysis and collected data from previously published investigations. To investigate more thoroughly the pathway enrichments of the six putative modifiers and the other 71 breast cancer susceptibility variants, we performed a literature search where we looked for functional connections between the potential culprit genes and WNT or FGF signaling as well as DNA repair pathways or CHEK2 itself. Furthermore, we used QIAGEN’s Ingenuity® Pathway Analysis (IPA®, QIAGEN, Redwood City, CA, USA, www.qiagen.com/ingenuity) for a more systematic approach for functional enrichment analysis. Pathway enrichment was tested by comparing the loci of the six putative modifiers against 63 loci covered by the 77 common variants with Fisher’s exact test. Here, all variants connected to the same culprit gene were considered to belong to the same locus. 4.3 Ethics statement This study was carried out with permission of the Helsinki University Central Hospital Ethics Committee (Dnro207/E9/07) and with written informed consent from all patients. Each individual BCAC study followed national guidelines for participant inclusion and for informed consent procedures and was approved by the appropriate local institutional review committee.

41 5 Results 5.1 c.1100delC and p.(I157T) carrier tumors (I, II) Genomic characterization of c.1100delC and p.(I157T) carrier tumors was included in Studies I and II, respectively. The latter study also covered pathological comparison of tumors from the carriers of the two mutations. The results are summarized below. 5.1.1 c.1100delC-associated copy number aberrations (I) Array Comparative Genomic Hybridization of 26 CHEK2 c.1100delC carrier and 76 non-carrier tumors identified seven genomic locations whose copy-number aberrations (CNAs) were more common in mutation carrier tumors than in non-carrier tumors (I: Figure 1). These included a wide deletion of 1p13.3-31.3 and amplification of 12q13.11-3. Narrow focal copy number aberrations included deletions at 8p21.1-2, 8p23.1-2, and 17p12-13.1 as well as amplifications at 16p13.3, and 19p13.3. The genomic locus of the CHEK2 gene was diploid in the majority (16) of c.1100delC carrier tumors, deleted in six, and amplified in four. 5.1.2 c.1100delC-associated differences in gene expression (I and unpublished data) Differential gene expression analysis of 12 CHEK2 c.1100delC carrier and 61 non-carrier tumors with a 0.01 p-value threshold revealed a 188-gene c.1100delC-associated gene signature (I: Additional file 6). There was little overlap between this signature and previously published breast cancer signatures. However, when breast cancer patients from two independent datasets were split into two categories based on expression of the 188 genes of the c.1100delC signature, there was a significant difference between these categories in patient survival (I: Figure 2). We applied a looser, 0.05 p-value threshold to retrieve the top-ranking differentially expressed genes (I: Additional file 7) for functional enrichment analysis using David functional annotation tool and Gene Ontology database. The top-ranking 522-gene list with higher expression in c.1100delC carrier tumors was enriched for genes involved in olfactory signaling, transcription regulation, adherens junction, and WNT signaling pathway. Furthermore, the number of RAS protein family genes was 6-7 times higher than expected and 16q22.1 appeared as a genomic hot spot for elevated expression (I: Additional file 8). The 340 genes with lower expression in c.1100delC tumors included multiple genes involved in RNA processing and translation, mitochondria, cytoskeleton and centrosome organization as well as in response to DNA damage (I: Additional file 10). According to permutation analysis (unpublished data), the enrichment of olfactory pathway among the genes with higher expression in c.1100delC carrier tumors was unlikely to be a random association. When the mutation carrier status was shuffled for the samples on the array and a similar analysis of differential gene expression run for 500 times, the probability of finding equally strong or stronger enrichment of olfactory receptor genes was lower than 0.006. GSEA is a method for recognizing subtle, but consistent patterns in differential gene expression analysis results. Instead of focusing only on the top-ranking differentially expressed genes, it takes as an input the complete genomic list of genes ranked from elevated to suppressed expression. Here (unpublished data), the ranking of the input gene list was done according to a product of log2(fold change) and –log10(p-value). The results from the GSEA analysis concurred with the

42 DAVID analysis; genes of olfactory signaling had consistently higher expression in c.1100delC carrier than non-carrier tumors (Table 5).  Another significant enrichment among the genes with elevated expression was the 16q22 genes. Other gene sets were not significant after p-value correction, but the top-ranking gene sets with elevated expression in c.1100delC included genes involved in cell junction and KRAS signaling (Table 5). Top-ranking gene sets with suppressed expression in c.1100delC carriers included MYC and E2F target genes and genes involved in G2/M checkpoint and cellular respiration. Table 5 includes enriched annotations with FWER (family-wise error rate) below 0.5. Of note, the top- ranking hallmark gene sets beyond the threshold included DNA repair, apoptosis, and TP53 pathway. 5.1.3 Combined analysis of aCGH and GEX data (I and unpublished data) Combined copy number and gene expression analysis identified seven candidate tumor drivers (I: Table 2): TMED5 (transmembrane p24 trafficking protein 5), ALG14 (ALG14, UDP-N- acetylglucosaminyltransferase subunit), and LRRC8D (leucine rich repeat containing 8 VRAC subunit D) from 1p21-22 and CALCOCO1 (calcium binding and coiled-coil domain 1), OR6C3 (olfactory receptor family 6 subfamily C member 3), CSAD (cysteine sulfinic acid decarboxylase), and KRT5 (keratin 5) from 12q13 (Figure 8). The top-ranking differentially expressed gene CLCA1 (chloride channel accessory 1) was also located on a c.1100delC-associated region (1p22.3). However, the higher expression in c.1100delC carrier tumors (fold change: 1.82, p-value: 6.9E-6) was inconsistent with the c.1100delC-associated copy number (deletion, I: Table 2).

Figure 8. Expression values of potential tumor driver genes from 1p21-22 and 12q13 based on combined analysis of the aCGH and GEX data (nc: non-carrier).

43 Table 5. Results from the Gene Set Enrichment Analysis (GSEA) of 12 CHEK2 c.1100delC carrier and 61 non-carrier tumors with FWER (family-wise error rate) cut-off of 0.5 (unpublished data).

Normalized Corrected Gene Genes in Rank at Enrichment enrichment p-value for p-value set size data max score score enrichment (FWER) Gene set description

Hallmark gene sets representing well-defined biological states or processes. HALLMARK_APICAL_JUNCTION 200 151 1050 0.64 1.65 0.00 0.14 Genes encoding components of apical junction complex

Genes encoding proteins involved in oxidative HALLMARK_OXIDATIVE_PHOSPHORYLATION 200 103 1805 -0.69 -1.84 0.03 0.334 phosphorylation

HALLMARK_MYC_TARGETS_V1 200 92 1662 -0.65 -1.72 0.01 0.34 A subgroup of genes regulated by MYC - version 1 (v1)

Genes encoding cell cycle related targets of E2F HALLMARK_E2F_TARGETS 200 129 1996 -0.6 -1.62 0.03 0.36 transcription factors

HALLMARK_MYC_TARGETS_V2 58 34 2036 -0.72 -1.61 0.02 0.36 A subgroup of genes regulated by MYC - version 2 (v2)

Genes encoding proteins involved in metabolism of fatty HALLMARK_FATTY_ACID_METABOLISM 158 106 1505 -0.57 -1.52 0.03 0.43 acids Genes encoding components of blood coagulation HALLMARK_COAGULATION 138 106 461 -0.57 -1.52 0.02 0.43 system; also up-regulated in platelets Genes involved in the G2/M checkpoint, as in HALLMARK_G2M_CHECKPOINT 200 133 1220 -0.53 -1.5 0.02 0.45 progression through the cell division cycle

Oncogenic signatures Genes up-regulated in epithelial lung cancer cell lines KRAS.AMP.LUNG_UP.V1_UP 144 94 1058 0.66 1.63 0.01 0.41 over-expressing KRAS [ID:3845] gene

Curated gene sets of canonical pathways KEGG_OLFACTORY_TRANSDUCTION 389 113 976 0.81 2.07 0.00 0.000 Olfactory transduction REACTOME_OLFACTORY_SIGNALING_PATHWAY 328 85 976 0.82 1.98 0.00 0.001 Genes involved in Olfactory Signaling Pathway KEGG_CYSTEINE_AND_METHIONINE_METABOLISM 34 21 4 0.93 1.85 0.03 0.09 Cysteine and methionine metabolism REACTOME_CELL_JUNCTION_ORGANIZATION 78 58 1139 0.78 1.8 0.07 0.28 Genes involved in Cell junction organization

Gene ontology NITROGEN_COMPOUND_METABOLIC_PROCESS 155 106 133 -0.86 -2.29 0.00 0.11 GO:0006807 AMINO_ACID_AND_DERIVATIVE_METABOLIC_PROCESS 101 65 133 -0.91 -2.28 0.00 0.134 GO:0006519 AMINE_METABOLIC_PROCESS 141 99 133 -0.87 -2.27 0.00 0.21 GO:0009308 ACTIVE_TRANSMEMBRANE_TRANSPORTER_ACTIVITY 122 82 198 -0.87 -2.25 0.00 0.27 GO:0022804

Positional gene sets CHR16Q22 168 95 974 0.76 1.86 0.00 0.02 Genes in cytogenetic band chr16q22 CHR2Q12 61 31 294 -0.94 -2.12 0.00 0.35 Genes in cytogenetic band chr2q12 DAVID functional enrichment analysis of the c.1100delC-associated CNAs (unpublished data) highlighted clusters of homologous genes. The region with the highest signal on chromosome 1, 1p21.3-22.2 encompassing 81 contiguous BAC clones and over 10 Mb (I: Table 1), covered a cluster of genes encoding all human guanylate binding proteins (GBP1-7). The c.1100delC- associated region on 12q13 was enriched for type II keratin (n: 27) and olfactory (n: 17) genes, 8p23.1-2 covered 14 defensin genes, 16p13.3 four hemoglobin alpha and eight serine protease genes, and 17p12-13.1 a cluster of six myosin genes. 5.1.4 p.(I157T)-associated gene expression (II) The expression levels of 21 genes were associated with p.(I157T) carriership in an analysis of data from 183 breast tumors (10 p.(I157T) carriers). All of these genes had higher expression in p.(I157T) carrier tumors (II: Table 4). One-third of them were collagen genes, forming the most prominent functional group. Gene set enrichment analysis suggested that CDH1 and RB1 would be important regulators of the overall gene expression differences between p.(I157T) carrier and non-carrier tumors (II: Table S6). Furthermore, gene signatures associated with cell adhesion, interaction with stroma, and epithelial-to-mesenchymal transition were enriched at the top of differentially expressed genes in p.(I157T) carrier tumors. 5.1.5 Clinico-pathological characteristics (II) Analysis of tumor clinico-pathological characteristics in a BCAC dataset of 26801 breast cancer patients showed that the p.(I157T) and c.1100delC carrier tumors shared some features that set them apart from non-carrier tumors, but there were also significant differences between tumors of p.(I157T) and c.1100delC carriers (II: Table 1). CHEK2 mutation carrier tumors were significantly more often ER- or PR-positive than non-carrier tumors. P.(I157T) was associated with low-grade and lobular tumors, whereas c.1100delC carrier tumors did not differ from non-carrier tumors in this regard. 5.2 p.(I157T) or c.1100delC carrier survival (II) Survival of carriers of p.(I157T) and c.1100delC mutations was investigated in collaboration with the BCAC for Study II. P.(I157T) was not associated with increased risk of early death, breast cancer-specific death, or disease recurrence; there was no significant difference in p.(I157T) carrier and non-carrier survival (II: Table 3). However, between carriers of p.(I157T) and c.1100delC, a significant difference was seen in the risk of early death and a marginally significant difference in the risk of breast cancer-associated death such that c.1100delC was associated with poorer prognosis. 5.3 Genetic modifiers of c.1100delC-associated breast cancer risk (III) 5.3.1 Synergistic risk effect of common variants for c.1100delC carriers (III) The combined risk effect of 74 common low penetrance variants summarized in a polygenic risk score was very similar for CHEK2 c.1100delC carriers and non-carriers (OR 1.59 [1.21 - 2.09] and 1.58 [1.55 - 1.62] per unit standard deviation, respectively. III: Table 1). We estimated that 20% of c.1100delC carriers with the highest PRS values would have over 32% lifetime risk of breast cancer, and for the lowest 20% the risk would be comparable to the average population risk. Thus, the PRS could be used for personal risk stratification of c.1100delC carriers.

45 In pairwise interaction analyses between c.1100delC and 77 previously reported common risk variants, we did not find evidence for deviation from the multiplicative (log-additive) risk model for breast cancer (III: Table S4). 5.3.2 The sparse model (III: unpublished data) We further performed an exploratory analysis for biological characterization of the CHEK2 c.1100delC-associated risk variants using forward stepwise logistic regression and pathway enrichment analyses. In the stepwise analysis, six variants (rs11249433, rs11780156, rs2981582, rs11075995, rs2363956, and rs4808801) appeared as independent and nominally significant (p”0.05) risk factors for the c.1100delC carriers (Table 6). The three first-mentioned also had nominally significant interaction with c.1100delC in the pairwise analyses. In the following, we refer to these six variants as the candidate CHEK2 c.1100delC modifiers. The six candidate modifier variants were not verified by the more stringent feature selection methods, which used randomization to estimate the model significance: only rs11780156 appeared as a relevant risk factor for c.1100delC carriers in the Boruta analysis,252 whereas none of the variants was considered significant in the glmnet analysis.250, 251

Table 6. Regression model of the candidate modifier variants explaining breast cancer risk in CHEK2 c.1100delC carriers. (unpublished data)

OR [95% CI] p-value rs11249433 0.68 [0.47 - 1.00] 0.050 rs11780156 2.16 [1.17 - 4.00] 0.014 rs2981582 1.52 [1.01 - 2.30] 0.045 rs11075995 1.85 [1.12 - 3.06] 0.017 rs2363956 1.60 [1.07 - 2.38] 0.021 rs4808801 0.63 [0.42 - 0.93] 0.020

5.3.3 In silico functional characterization (III: unpublished data) The genetic loci tagged by the 77 common risk variants as well as the subgroup of the six candidate modifiers were enriched for predicted enhancer elements in human mammary epithelial cells (HMEC) (p= 3.0E-6 and p=0.004 for enrichment for all 77 variants and the six candidates, respectively, when tested against the whole genome by HaploReg258). Three of the six candidate modifiers were located on and two others tagged HMEC enhancers, in comparison with 14/21 of the remaining 71 variants (p=0.15 for difference) (Appendix: Supplementary Table 1, Supplementary Table 2: first column). Assuming that the six candidate modifier variants have a regulatory role, their target genes could highlight pathways that have an essential role in CHEK2 c.1100delC-related breast cancer pathogenesis. Previously, rs2981582 has been linked to expression of FGFR2 (fibroblast growth factor receptor 2).260-262 eQTL analysis indicated ATE1 (arginyltransferase 1) to be another potential target gene for rs2981582 (Appendix: Supplementary Table 1). rs2363956, an ANKLE1 (ankyrin repeat and LEM domain containing 1) missense variant, was associated with expression of ANKLE1 and three other nearby genes (Appendix: Supplementary Table 1). Rs4808801 was strongly associated with the expression of ELL (elongation factor for RNA polymerase II) in

46 different cell types (Appendix: Supplementary Table 1). MYC (MYC proto-oncogene, bHLH transcription factor) and its downstream target PVT1 (Pvt1 oncogene) have been suggested as probable target genes of rs11780156, NOTCH2 as the target gene of rs11249433, and FTO (FTO, alpha-ketoglutarate dependent dioxygenase) as the target gene of rs11075995.9, 263, 264 Altogether, the target genes of five of these variants (rs11075995, rs11249433, rs11780156, rs2981582, rs4808801) were linked to the interconnected WNT and FGF (fibroblast growth factor) signaling routes,265-273 which have been implicated in CHEK2 c.1100delC-associated breast cancer tumorigenesis by us (I) and others.274 Furthermore, the locus tagged by rs2363956 possibly is under the regulation of WNT signaling because rs2363956 is in linkage disequilibrium with another variant (r2=1.0) on a MYC transcription factor binding site (Appendix: Supplementary Table 1). In contrast, only 28 of the 63 genomic loci of the 77 common variants had a similar connection to WNT/FGF signaling (p=0.011 for enrichment, Appendix: Supplementary Table 2). Interestingly, none of the six candidate modifiers were associated with DNA repair pathway genes (Appendix: Supplementary Table 2). 5.4 Risk modifiers in breast cancer families (IV) Our analyses of the polygenic risk score in 52 breast cancer families and a case-control dataset including index cases from breast cancer families indicated that the PRS is positively associated with family history of breast cancer and that it could add to pedigree-based individual risk prediction. The PRS was associated with slightly higher relative risk of breast cancer in women with a positive family history (IV: Table 1, OR 1.81 [1.59-2.06] when comparing cases with positive family history with population controls) than in women without this history (OR 1.41 [1.30-1.54] when comparing sporadic cases with population controls). Furthermore, the PRS was on average higher for unaffected women of breast cancer families than for population controls (OR 1.29 [1.12-1.48]). Within the breast cancer families, the OR associated with the PRS was 1.55 [1.26-1.91] when the model was adjusted for the magnitude of family history as measured by the BOADICEA risk estimator. In these 52 families, especially the higher PRS values were strongly associated with increased breast cancer risk, whereas the association between lower PRS values and protection from breast cancer was not as evident.

47 6 Discussion 6.1 CHEK2-associated breast cancer (I, II, III) One ambitious goal of this thesis was to build a hypothesis of the events leading to breast cancer for CHEK2 mutation carriers. Clues were searched from genomic profiling of the mutation carrier tumors as well as from functional characterization of the genetic modifiers of CHEK2-associated breast cancer risk. Since CHEK2 mutation carrier breast cancers are not very different from the non-carrier breast cancers, a CHEK2-associated model was not expected to depart much from any general model for breast cancer. Even so, we rationalized that when the tumor-initiating event is shared, there could also be similarities in later steps of the tumorigenesis and these could be seen as enrichment of genomic features in the mutation carrier breast cancers. As breast cancer is a heterogeneous disease and its etiology is still imperfectly known, a shared origin served as an interesting starting point for a study on breast cancer tumorigenesis. 6.1.1 Germline CHEK2 mutations are associated with ER-positive breast cancer (II) CHEK2 c.1100delC and p.(I157T) carrier breast cancers are primarily ER-positive. Our analyses on the BCAC dataset (II) confirmed this association, which has earlier been reported by multiple studies.3, 216-218, 224, 275-277 The strong association suggests that endocrine exposure would be a major driver of breast cancer tumorigenesis for CHEK2 mutation carriers, an inference with little specificity, as breast cancer in general and even ER-negative breast cancer are driven by sex hormones.52, 94 However, the question of which factors make almost all CHEK2 carrier breast cancers ER-positive has seldom been addressed in the literature. Common downstream targets of CHEK2 and ER have been suggested to explain the association, but the mechanism has not been elucidated further.203 An interesting point of comparison is BRCA1, which has been studied more widely than CHEK2 in cell-line, organoid, and mouse models;29, 90-92 CHEK2 and BRCA1 are functionally linked in two cellular processes, DNA double-strand break repair and centrosome organization.28, 189, 200 Truncating mutations of both genes are associated with earlier age of onset and premenopausal breast cancer,3, 278-280 which has been suggested to be enriched for ER-negative tumors.96 BRCA1 mutations cause mainly ER-negative breast cancer, while CHEK2 cause mutations mainly ER- positive breast cancer. Loss of BRCA1 function has been inferred to have a direct causal role in the oncogenic transformation of luminal epithelial cells, with concomitant loss of ER expression.29 The phenotypic switch is transmitted via BRCA1-dependent gene expression, and this functional role apparently is not shared with CHEK2.90 If we generalized that breast cancer originated from luminal progenitor cells expressing ER and that abrogation of the BRCA1/CHEK2 functional node was required for breast cancer to develop, at the same time remembering that cancer is a stepwise-progressive disease driven by randomly arising events and that the final phenotype arises from an evolutionary process characterized by survival of the fittest, the association between CHEK2 mutations and ER-positive breast cancer could be seen as a result of lack of selection for somatic BRCA1 silencing. In other words, if the oncogenic transformation of luminal progenitors involved the loss of BRCA1 function, the consequences would be chromosomal instability with the loss of ER expression and eventually development of ER-negative breast cancer; if the luminal progenitor transformation took place via loss of CHEK2 function, there would not be any further need for loss of BRCA1, the consequences

48 being chromosomal instability without loss of ER expression and eventually ER-positive breast cancer. Naturally, BRCA1 could be lost as a random event, and accordingly, a small proportion of CHEK2 carrier breast cancers are ER-negative. Furthermore, there could be mechanisms other than BRCA1 silencing, leading to a switch in gene expression and the loss of ER expression. Organoid model of CHEK2-deficient breast cancer in comparison with a BRCA1-driven model could serve as an interesting research setup to test this hypothesis. 6.1.2 Genomic profiling elucidates the steps of CHEK2-related tumorigenesis (I, II) In order to study CHEK2-associated tumorigenesis in depth, we performed parallel aCGH and GEX analyses on c.1100delC carrier tumors (I) as well as a separate GEX analysis on p.(I157T) carrier tumors (II). In silico functional analyses revealed unexpected cellular pathways and any overarching principles shared by the two mutations could not be found. Altogether, the analyses produced building blocks for multiple parallel hypotheses, the biggest challenge being how to separate true driver events from their possibly oncogenic consequences and further from mere passengers. Only in vitro and in vivo experimental models could make the final distinction, but in building a testable hypothesis, one must rely on recurrent and converging evidence from in silico work, together with experimental knowledge on molecular-level relations of biological pathways. Another challenge was the striking differences between tumors from carriers of different CHEK2 mutations. Factually, c.1100delC and p.(I157T) carrier tumors were not analyzed together in the same dataset, but instead both were compared with non-carriers in separate datasets. However, on both occasions the number of non-carriers far exceeded the number of mutation carriers, and the numbers were sufficient to represent breast cancers in general. Had the driver events in these c.1100delC and p.(I157T) carrier tumors been related to the CHEK2 function, there should have been similarities in the genomic features associated with the two mutations. Here, however, the enriched features of c.1100delC and p.(I157T) carrier tumors had nothing in common. Furthermore, regarding CDH1 expression and related pathways, the c.1100delC carrier tumors appeared totally different from p.(I157T) carrier tumors. In the aCGH analysis (I), we identified two large (loss of 1p13.3-31.3 and gain of 12q13.11-3) and five focal copy number aberrations, which were more common in c.1100delC carrier tumors than in non-carrier tumors. Additionally, the GEX analysis (I) indicated that 16q22.1 amplification could be a common event in c.1100delC carrier breast cancers since the region covered a focal enrichment of genes with higher expression in c.1100delC carrier than non-carrier tumors. In the GEX analysis, the significant biological enrichments in c.1100delC tumors included elevated expression of the olfactory pathway and lowered expression of mitochondrial genes (I: Additional file 8, Additional file 10). Furthermore, genes of an oncogenic KRAS signature (Table 5 (unpublished data)) and WNT pathway genes (I: Additional file 8, Additional file 9) had higher expression, whereas genes involved in cell cycle regulation and genes responding to DNA damage had lower expression (I: Additional file 10) in c.1100delC tumors than in non-carrier tumors. On the other hand, p.(I157T)-associated differential expression was dominated by genes involved in cell-cell and cell-matrix contacts. Collagen genes had significantly elevated expression in p.(I157T) tumors and epithelial-to-mesenchymal transition and focal adhesion were enriched annotations at elevated expression, whereas E cadherin (CDH1) and its target genes had lower expression in p.(I157T) tumors than in non-carrier tumors (II: Table S5, Table S6). Intriguingly, in c.1100delC tumors CDH1 ranked among the top 20 genes with elevated expression, the genes

49 of the CDH1 locus 16q22.1 formed a focal point of elevated expression, and genes encoding the apical junction proteins were enriched at elevated expression (Table 5 (unpublished data); I: Additional file 8). In the following, the enriched pathways and biological processes are first discussed separately and then drawn together for a comprehensive hypothesis of CHEK2-associated breast cancer. 6.1.3 1p22 loss might complement CHEK2 deficiency in breast cancer progression (I) The Cancer Genome Atlas (TCGA), a cross-cancer genomic study aiming to build a molecular taxonomy for different types of cancers, suggested that breast cancer arises primarily from chromosomal instability leading to copy number aberrations and that the oncogenic drivers of breast cancer could best be identified by studying recurrent copy number patterns.281 In concert with this, an IntClust classification method based on characteristic CNA patterns associated with gene expression in cis was suggested for subtyping breast cancers.83 The c.1100delC-associated CNAs were not included in the characteristic signatures of any IntClust subgroups. Instead, a wide loss of 1p was present at low but detectable frequency in at least four IntClust subgroups dominated by luminal tumors (clusters 1, 2, 6, and 9), and in one of these (cluster 1) there was also evidence on a low-frequency 12q amplification.83 Loss of 1p21.3-22.2 appears to be the best candidate for tumor driver event in c.1100delC- associated breast cancer. The region had the strongest association with c.1100delC in our analyses, and the association between c.1100delC and loss of 1p has been recently replicated in an independent study.282 Massink et al. did not report detailed statistics so it was not possible to compare the signal distribution along the 1p chromosome arm. Neither we nor they were able to link the 1p loss with expression of any gene in cis. In our analyses, the copy number and gene expression data of the c.1100delC carriers came essentially from different tumors due to the small number of samples from which good-quality specimens of both DNA and RNA were retrievable. Therefore, we had to rely on converging evidence instead of direct inference in functional annotation of the driver events. The highest signal came from a region covering a cluster of GBP genes. GBP function was the only significantly enriched annotation on 1p21.3-22.2, as all seven GBP genes and one pseudogene of the human genome are located on this region. GBPs are large GTPases whose transcription is induced by pro-inflammatory interferons, especially IFN-Ȗ, and by interleukin IL-1ȕ or tumor necrosing factor TNF-Į in phagocytic cells, but also in a range of other cell types, including epithelial cells. The best-characterized function of GBPs is cell-autonomous defense against cytosolic pathogens, including bacteria, protozoans, and viruses.283-285 The GBP proteins form homo- and heterotetramers upon GTP hydrolysis and the oligomerization accounts for specificity of pathogen recognition. In cancer, high GBP1 or GBP2 expression is indicative of infiltrating T helper type I cells and associated with increased patient survival.285-287 Furthermore, GBP1 functions as a tumor suppressor that mediates anti-proliferative, anti-angiogenic, anti- migratory, and pro-apoptic effects induced by IFN-Ȗ in colorectal cancer cell lines.288, 289 The anti- migratory effects of GBP1 are exerted via direct interactions with ȕ-actin and the anti-proliferative effects via ȕ-catenin/TCF-dependent transcription.285, 289 Furthermore, GBP1 knock-down in macrophages has been reported to cause mitochondrial dysfunction: decreased oxygen

50 consumption and ATP production, lowered expression of mitochondrial genes, and impaired mitophagy.290 As a summary, loss of GBP gene cluster could be a novel candidate for a key driver of c.1100delC- associated tumorigenesis. The hypothesis is supported by validated evidence on the association between c.1100delC and loss of the GBP locus. The GBP genes have a proven and versatile role as tumor suppressors in different carcinomas, including breast cancer.287 Additionally, the genes with lower expression in c.1100delC tumors were enriched with mitochondrial genes, which could be a consequence of GBP silencing. We did not see any significant differences in the gene expression of the GBP genes between c.1100delC carrier and non-carrier tumors. However, because the GBP genes are not constitutively expressed, but induced in response to interferons and cytokines, it may be that we failed to detect the differential expression in this dataset, but were able to capture the longer-lasting downstream effects in lowered expression of mitochondrial genes, possibly indicating mitochondrial dysfunction (Table 5 (unpublished data), I: Additional file 8). 6.1.4 Elevated expression of olfactory receptors in c.1100delC carrier tumors (I) Another strong candidate for a driver of c.1100delC-associated breast cancer was the olfactory pathway. The peculiar association raised doubts about the array specificity. Previously, a sequence analysis of tumor/normal tissue pairs indicated olfactory receptor (OR) genes as hot spots for somatic mutations in cancer. However, this was shown to be an artefact stemming from a low expression rate of the olfactory receptor genes in epithelial cells and their uniformly late replication in relation to the cell cycle.291 However, there was no evidence questioning the association between c.1100delC and elevated expression of the OR genes in our data. When the mutation carrier status was randomly sampled, finding an equally strong enrichment of ORs in genes with either elevated or lowered expression was highly unlikely. In addition, the 34 OR genes with higher expression in c.1100delC carriers came from 17 distinct OR clusters located on nine different chromosomes, and thus, coincidental hyperactivity of a single promoter or amplification of a particular locus did not appear to be a plausible explanation. Of note, 12q13.11-3, whose amplification was more common in c.1100delC tumors than in non-carrier tumors, covered also an OR gene cluster including three of the 34 c.1100delC-associated OR genes. Furthermore, GSEA analysis indicated that not only the 34 OR genes had higher expression, but the expression of OR genes in general was higher in c.1100delC carrier than non-carrier tumors. Finally, probe cross-reactivity had been ruled out by blasting the sequences and including only perfectly specific probes. Olfactory receptors are the largest gene family in humans, including about 400 protein coding genes and at least as many pseudogenes.292 ORs were first characterized in olfactory sensory neurons, where their generally high expression level follows a one-cell-one-receptor model contributing to specificity in odorant detection.293 Later, low-level ectopic expression of multiple ORs has been detected in all studied tissues, but their functional roles are only beginning to unravel.294, 295 Specific ORs have been assigned roles in sperm chemotaxis, cytokinesis regulation, myocyte migration, cell adhesion, and proliferation of prostate cancer cells.295 Ectopic expression of certain ORs has been reported in different types of cancer where the ligand-dependent OR activation has led to reduced proliferation and migration, making the respective ORs potential targets for therapeutic intervention.296-301 However, Sanz et al. reported that exposing

51 PSGR/OR51E2-expressing prostate cancer cells to the PSGR ligand promoted cell invasion on collagen gels and increased the number of metastases in a mouse model, rendering the earlier findings based on 2D culture questionable, and suggesting that if the apparent lower proliferation in vitro is translated to enhanced invasiveness in vivo, the ORs are poor targets for cancer therapy.296, 302 Quite recently, Weber et al. described a set of OR genes whose expression was higher in breast cancer (number of samples: 45) or in breast cancer cell lines (21) than in healthy breast tissue (7). They suggested that OR2B6 could be used as a marker for breast cancer because its expression was present in a great majority of breast cancers and cell lines, but absent in all healthy tissue samples.303 In addition to OR2B6, two other olfactory receptor genes, OR10AD1 and OR13H1, which Weber et al. suggested as tumors markers in breast cancer, were included in the 34 OR genes with higher expression in c.1100delC carrier tumors than in non-carrier tumors, confirming the relevance of at least part of the 34-gene olfactory signature in breast cancer.

Upon ligand binding, OR conformational change activates GNAL (G protein subunit alpha L), an olfactory-specific G-protein, and ACIII (adenylate cyclase III), leading to increased cellular cAMP levels and Ca2+ impulse through cyclic nucleotide-gated channels and in turn inducing calcium-activated chloride channels.295 Also Ras and MAPK pathways are involved in sensory function of olfactory receptors,304, 305 and it is plausible that the same downstream pathways operate in OR-mediated signaling also in non-chemosensory tissues.295, 303, 306 Ras-type guanine nucleotide exchange factors (RasGEF) and KRAS oncogenic signature were enriched among the genes with elevated expression in c.1100delC tumors, possibly contributing to c.1100delC-related tumor progression in interaction with the elevated OR activity. 6.1.5 WNT pathway deregulation – typical for c.1100delC breast cancers (I,III)? DAVID analysis suggested that elevated expression of WNT pathway genes is associated with c.1100delC (I: Additional file 8), with the exception of LRP1 (LDL receptor related protein 1, a regulator of WNT-ligand receptor FZD1 (frizzled class receptor 1), Figure 9)307 and LEF1 (lymphoid enhancer binding factor 1, a key transcription factor of the WNT pathway, Figure 9),308 which had lower expression in c.1100delC carrier tumors (I: Additional file 9). On the other hand, GSEA indicated that MYC target genes in general have lower expression in c.1100delC carriers than in non-carriers (Table 5 (unpublished data)). Of note, there were two distinct probes for MYC, specific for two alternative protein coding transcripts on the gene expression array. One of the probes, mapping to 5’ UTR (untranslated region) of a transcript variant encoding a protein of 257 residues, was among the top 60 probes with elevated expression in c.1100delC tumors, whereas the expression level of the other probe, mapping to exon three of the main splice variant, did not differ between c.1100delC tumors and non-carrier tumors. Altogether, it seemed that the canonical WNT pathway through beta-catenin, TCF/LEF1, and MYC (Figure 9) had lower activity in c.1100delC tumors than in non-carrier tumors, and that the pathway genes with higher expression in c.1100delC were in regulatory and inhibitory roles. Instead, the non-canonical branch of the WNT pathway, mediated via calcium/calmodulin-dependent protein kinases and NFAT- dependent transcription (Figure 9), appeared as a potential candidate driving the c.1100delC- associated tumorigenesis (I: Additional file 9).308

52 Figure 9. WNT pathway (KEGG: hsa04310). Reproduced with the permission of Kanehisa Laboratories.

53 The WNT pathway (Figure 9) regulates both embryonic and pubertal development of the mammary gland influencing cell proliferation, fate, migration, and differentiation (Table 1). The canonical WNT pathway has a pronounced oncogenic role in breast carcinomas; WNT ligands have elevated expression in the majority of breast cancer cell lines, ȕ-catenin overexpression or mutations have been detected in about 50% of breast cancers, and MYC amplification has been reported in 30-50% of high-grade breast cancers.309, 310 It is noteworthy that MYC amplification and ȕ-catenin-independent expression are particularly common in triple-negative/basal breast cancer, whereas in ER-positive breast cancer MYC activity is sustained in an estrogen-dependent manner, and estrogen-independent MYC expression is associated with resistance to endocrine treatment.310, 311 While the canonical WNT pathway contributes to neoplastic transformation, increased activity of the non-canonical WNT pathway via NFAT and JNK endows breast cancer cells with invasive and migratory properties.309, 312 Therefore, assuming that the balance of the WNT pathway in c.1100delC carrier tumors had shifted towards the non-canonical branch, there should have been also other indications of invasiveness on the gene expression level. This, however, was not the case. Instead, CDH1 and genes of the apical junction had higher expression in c.1100delC tumors than in non-carrier tumors. In summary, it is difficult to draw conclusions about the role of the WNT pathway based on the gene expression data (I). The signals are contradictory and in addition to true driver events may reflect also compensatory mechanisms required to secure the tumor cell integrity. WNT pathway and MYC-related signaling are essential drivers of breast carcinogenesis,309, 310 but how they complement CHEK2 deficiency remains a topic for further investigations. Another perspective on c.1100delC-associated breast cancer development was provided by the literature-based functional characterization of the common variants modifying breast cancer risk of c.1100delC carriers (III: unpublished data). The six nominally significant and independent risk variants (Table 6 (unpublished data)) were associated with genes that could be linked to the WNT pathway (Appendix: Supplementary Table 1, Supplementary Table 2). Loss of the candidate culprit gene of rs11075995, FTO, has been reported to cause downregulation of the canonical WNT pathway and upregulation of the non-canonical Ca2+ signaling branch (Figure 9).265 The risk allele of rs11249433 is associated with elevated expression of NOTCH2, which is a direct downstream target of the canonical WNT pathway and TCF/LEF1.263, 266 rs11780156 is located on a gene desert downstream of MYC and its regulator PVT1. The locus of rs2981582 has been shown to regulate FGFR2 expression in breast fibroblasts,262 and FGFR2 regulates WNT pathway target gene expression via direct interaction with beta-catenin.269, 270 rs4808801 is associated with expression of ELL (Appendix: Supplementary Table 1), which regulates the WNT pathway in interaction with DVL, upstream of beta-catenin.313 Lastly, rs2363956 tags another variant located on a MYC binding site. Taken together, these associations do not themselves provide any mechanistic information, and the direction of the effects caused by these common variants on the culprit genes potentially contributing to a single pathway can at best only be speculated. It is noteworthy that the associations between the candidate variants and c.1100delC were only nominally significant without correction for multiple hypotheses. Furthermore, no significant interaction suggesting deviation from the multiplicative model including all 74 variants was found (III: Table S4). However, the enrichment of associations with the WNT pathway among the top-ranking candidate

54 risk modifier variants could indicate that WNT pathway deregulation is an early event and possibly a rate-limiting step in the development of breast cancer in c.1100delC carriers. 6.1.6 Hypothetical model for c.1100delC-associated breast cancer progression (I, III) To summarize the model on c.1100delC-associated carcinogenesis with reference to the Cancer Hallmarks (Figure 2),14 C.1100delC itself accounts for both ‘Genome instability and mutation’ and ‘Persisting cell death’, possibly mostly via decreased CHEK2 protein expression.1, 28, 189, 202, 209, 216 According to the genomic analysis of c.1100delC carrier tumors, the loss of GBP genes on 1p21.3-22.2 could be a novel driver candidate of c.1100delC-associated breast cancer, contributing possibly to three cancer hallmarks and one enabling characteristic, namely ‘Avoiding immune destruction’, ‘Inducing angiogenesis’, ‘Deregulating cellular energetics’, and ‘Tumor- promoting inflammation’. 283-290 Furthermore, increased activity of olfactory receptor signaling and the non-canonical WNT pathway are presented here as potential candidates promoting the fifth hallmark of ‘Activating invasion and metastasis’.302, 309, 312 In terms of the remaining hallmarks related to proliferation and ‘Enabling replicative immortality’, c.1100delC carrier tumors do not seem to markedly differ from non-carrier tumors. Estrogen is the most prominent growth factor in mammary tissue and deregulated signaling via estrogen receptor downstream pathways probably is the most important event contributing to ‘Sustaining proliferative signaling’ and ‘Evading the growth suppressors’ in both c.1100delC and non-carrier breast cancers.53, 94 6.1.7 Is p.(I157T) ‘the first hit’ for germline mutation carriers (II)? The results from differential gene expression analysis of p.(I157T) carrier tumors could be interpreted to be related to the invasive growth pattern typical for lobular breast cancer, where the normal contacts between epithelial cells sustaining the tissue integrity has been lost. CDH1 expression was lower in p.(I157T) carrier tumors than in non-carrier tumors (II: Figure 3). Similarly, expression of a gene sets previously reported to respond to CDH1 knock-down (II: Table S6, ONDER_CDH1_TARGETS_2_UP, ONDER_CDH1_TARGETS_2_DN) was consistent in p.(I157T) carrier tumors, confirming that the CDH1 activity in these tumors was reduced. Other gene sets enriched at high or low expression in p.(I157T) vs. non-carrier tumors suggested that the p.(I157T) tumors would have gone through epithelial-to-mesenchymal transition. However, this could have been a reflection of the discohesive growth pattern caused by the loss of CDH1 activity because if p.(I157T) was associated with more aggressive and invasive breast cancer, it should have been seen also in associations with higher grade and worse patient survival, which was not the case here (II: Table 1, Table 3). Lastly, the top genes with higher expression in p.(I157T) included a significantly elevated number of collagen genes, suggesting a higher degree of stromal contamination in the p.(I157T) tumors than in non-carrier tumors, which is another feature associated with lobular breast cancer and, on the other hand, speaks against increased invasiveness of the p.(I157T) tumors.314, 315 In conclusion, p.(I157T) is associated with diagnosis of lobular breast cancer, and molecular features characteristic of lobular breast cancer are present also in many p.(I157T) carrier tumors with other histologic diagnoses (II). Lobular breast cancer, the most common ‘special’ histological breast cancer subtype, is characterized by small, round, unattached cells invading the stroma alone or in single files. Loss of CDH1 function has been recognized as an early event in lobular breast cancer and suggested as a characteristic feature aiding in diagnosis of borderline cases.315-317 Lobular breast cancer is largely driven by the lifelong cumulative exposure to female sex hormones. The reproductive risk

55 factors have a stronger association with lobular breast cancer than with ductal cancer. For example, changes in the use of post-menopausal hormone replacement therapy induced respective fluctuations in the incidence of lobular breast cancer at the same time as the incidence of ductal breast cancer remained essentially constant. Furthermore, lobular breast cancer patients have on average three years higher age at diagnosis than patients diagnosed with ductal cancer, but larger tumor size and more advanced stage. 318 This is possibly caused by the fact that lobular tumors are hard to detect by palpation or mammography,315, 318 giving the tumors more time to develop before detection. Alternatively, the difference in diagnosis age could be explained if the tumor initiating and driving events took place at an older age, whereby the nature of the events would be influenced by the intrinsic biology of the mammary gland at involution favoring the lobular phenotype. It is tempting to speculate that the differences between c.1100delC and p.(I157T) are mainly related to the age at which the tumor driver events take place. P.(I157T) carriers are diagnosed at an older age (57.9 years) than c.1100delC carriers (54.3 years; II: Table I), and possibly certain types of driver events are more likely to take place in premenopausal than postmenopausal mammary glands. Furthermore, because the effect of p.(I157T) on CHEK2 function is milder, it is possible that the accumulation of somatic mutations is slower in p.(I157T) carrier cells and the total loss of CHEK2 function is likely to take place later than in c.1100delC carrier cells. However, the data included in this work do not support this hypothesis. First, the association between p.(I157T) and lobular breast cancer or molecular-level lobular features cannot be explained by age because the p.(I157T) carriers did not differ from non-carriers either in the BCAC dataset or in the gene expression dataset by age at diagnosis. On the other hand, the c.1100delC carriers in Study I were on average older than the non-carriers. Furthermore, the six risk variants with nominally significant associations with c.1100delC-associated breast cancer were not associated with earlier diagnosis age or premenopausal breast cancer.9 The evidence presented in this work suggests different models for c.1100delC- and p.(I157T)- associated breast cancer and different roles for CHEK2 in the development of breast cancer for c.1100delC and p.(I157T) carriers. C.1100delC is likely to be a true initiating factor in the mutation carrier breast cancers. Compromised CHEK2 activity as a result of lowered expression causes increased risk of somatic mutations and occurrence of further neoplastic events. Whether the preneoplastic CHEK2-deficient cells ever develop into a full-blown tumor is possibly primarily decided by interactions between the preneoplastic cells and the innate and adaptive immune system. By contrast, the loss of CHEK2 might not be an early or even late event in p.(I157T) carrier tumors, the cancer being driven by other factors in most cases. As a matter of fact, a normal level of CHEK2 protein expression was previously detected in four out of five examined invasive breast cancers from p.(I157T) carriers.2 Loss of CDH1 function as a result of large-scale deletion or point mutation has been suggested as an early event in lobular breast cancer.315-317 Interestingly, loss of 22q12.1 covering the CHEK2 gene has been reported by four studies to be a recurrent event in lobular breast cancer, suggesting that loss of CHEK2 could bring a growth advantage to lobular neoplasia.319-322 This could explain the unexpectedly high number of p.(I157T) carriers among patients diagnosed with lobular cancer, but raises the question, why is lobular cancer rare among carriers of c.1100delC and other truncating mutations?224 One possible answer could be the order of events: if CHEK2 function were lost first, consequent loss of CDH1 could be disadvantageous in clonal evolution and selected against in agreement with our

56 observation that CDH1 and genes involved in apical junction had higher expression in c.1100delC carrier than in non-carrier tumors. Whereas if CDH1 is lost first, there could be an increased advantage in slightly compromised fidelity in control of cell cycle checkpoints and spindle assembly as a result of p.(I157T) or loss of CHEK2, so that these would be targeted by positive selection.

6.2 Survival of breast cancer patients carrying germline CHEK2 mutations (II) Our analyses on the BCAC large international dataset indicated that the overall or breast cancer- specific survival of p.(I157T) carriers did not differ from survival of non-carriers. However, the risk of locoregional relapse and risk of second breast cancer were marginally increased especially in multivariate models adjusted for conventional clinico-pathological prognostic factors (II: Table 3). Compared with c.1100delC carriers, p.(I157T) carriers had better prognosis irrespective of the analysis endpoint, in agreement with a previous study analyzing the survival of c.1100delC carriers and non-carriers in essentially the same dataset.218 6.2.1 Increased mortality associated with c.1100delC The analysis endpoints used in Study II are not necessarily connected (II: Table 3), although all refer to adverse events occurring after and presumably as a consequence of breast cancer. Death of any cause is an imperfect estimator of breast cancer-associated mortality since primary breast cancer is rarely a life-threatening disease. Mortality is increased only after metastasis to vital organs. However, in many BCAC studies, death of any cause is the only unbiased estimate available. Breast cancer-associated death or occurrence of distant metastasis would be the best measures of disease severity, but these records were comprehensively provided only by a subgroup of BCAC studies; thus, the number of patients included in these analyses was substantially lower than in the overall analysis, hindering the search for significant associations. There are at least three important mechanisms mediating survival associations of germline mutations, the most important being intrinsic tumor aggressiveness. Subtyping, grading, and staging all aim to measure the aggressiveness in terms of proliferation and invasiveness.37, 61, 83, 97 If a mutation predisposes to a particularly aggressive type of cancer, the mutation would also be associated with poor patient prognosis, as in the case of BRCA1, PALB2 and FANCM, whose risk mutations increase the risk of triple-negative breast cancer.169, 170, 323 Apparent survival association could also be an outcome of either good or poor response to adjuvant therapy. For example, breast cancer patients carrying germline BRCA1 mutations have been reported to have good response to treatment with PARP inhibitors, but poor response to taxane therapy.324-326 In order to avoid bias caused by the availability of different adjuvant regimens at the time of cohort recruitment, the treatment choice should be included in retrospective studies of BRCA1-associated patient survival. A third important factor influencing patient survival via tumor invasiveness and metastatic potential is the control of local and systemic microenvironments, where the immune response plays a crucial role.32, 33, 327 The survival analyses suggesting that c.1100delC would be associated with increased mortality of breast cancer patients were adjusted for phenotypic features measuring tumor aggressivity.218 Furthermore, the proportion of poor prognosis subtypes (Luminal B, Basal, and Her2) was lower in c.1100delC carriers than in non-carriers (II: Table 1). Therefore it seems unlikely that an

57 especially aggressive tumor phenotype would be cause of reduced survival of breast cancer patients carrying c.1100delC. The effect associated with treatment choice was not addressed in our study. However, this should be taken into careful consideration when proceeding with analyses on CHEK2-associated breast cancer patient survival in future studies now that the evidence is accumulating for sensitivity and resistance associated with BRCA1. 324-326, 328 Carriers of predisposing germline CHEK2 mutations would be expected to have a similar response to treatment because of the related functions of CHEK2 and BRCA1 in DNA double-strand break repair and in regulation of spindle assembly.28, 189, 200 A verified association between CHEK2 and treatment outcome would have implications for a wider spectrum of breast cancer patients than just the mutation carriers since CHEK2 has been reported to be lost in about 20% of unselected breast cancers.216 It is noteworthy that the relatively high frequency of loss of CHEK2 expression in non-carrier tumors could be a confounding factor in treatment outcome analyses, diluting possible effects associated with the germline mutations. In an ideal situation, the analyses would be adjusted with an immunohistochemical measure of CHEK2 expression in patients’ tumors. Previously, CHEK2 mutation carrier tumors have been reported to have an unfavorable response to anthracycline-based neoadjuvant chemotherapy in a small study including three patients.329 On the other hand, a recent study found no difference in survival of c.1100delC carriers treated with anthracycline-based and non-anthracycline-based chemotherapy.330 In this study, however, the impact of taxanes was not taken into account. All in all, retrospective studies on treatment outcome should factor in the treatment choice and carry out parallel functional experiments to clarify the mechanisms causing enhanced or reduced patient survival. No reports on the frequency of infiltrating lymphocytes or the tumor immunogenicity related to germline CHEK2 mutations have been published to date. The findings presented in this thesis (I) suggest that the loss of the GBP gene cluster on 1p21.3-22.2 would complement CHEK2 deficiency associated with germline c.1100delC in development of breast cancer. The loss could possibly regulate the tumor microenvironment, affecting tumor-promoting inflammation and immune surveillance, and thus, contributing to reduced survival of c.1100delC carriers. 6.2.2 CHEK2 mutations and increased risk of local recurrence or new primary tumors Compared with distant metastasis, the causal relation of locoregional relapse or second breast cancer to breast cancer mortality is less clear. These two analysis endpoints could have been intertwined in our analyses, because ‘second breast cancer’ included ipsilateral cases, some of which may have been only local recurrences. Furthermore, some of the events classified as ‘locoregional relapse’ could have actually been new primaries since genomic analyses confirming or excluding clonality had not been performed. Both c.1110delC and p.(I157T) were associated with increased risk of second breast cancer. The hazard ratios were consistent with the primary risks associated with these mutations (II: Table 3),3-5 and it is unlikely that the mutations would cause any surplus risk of second breast cancer.219 The marginally significant finding that carriers of any of the two CHEK2 mutations had increased hazard of locoregional relapse was in agreement with a previous report studying c.1100delC in patients treated with breast-conserving surgery and radiotherapy.331 The adverse effect of ionizing radiation on CHEK2 mutation carriers had been suggested also earlier,332 and poor treatment outcome could at least partly explain the observed

58 increase in risk of local relapse. However, this topic would need to be addressed in future studies with more complete records on treatment history. 6.2.3 p.(I157T), lobular carcinoma, and patient survival warrant further research Lobular breast cancer has been associated with reduced long-term survival,333, 334 and loss of CDH1 expression has been suggested as an independent adverse prognostic factor in breast cancer.335-338 With this background, it was surprising that p.(I157T) was not associated with reduced survival, even though it was associated with lobular breast cancer in the BCAC dataset and lowered CDH1 activity in the gene expression analysis. However, in the referenced studies, the poor long-term prognosis of lobular cancer patients was visible only after ten years following the diagnosis, and our analyses may have failed to capture this effect due to an overly short follow- up or a high proportion of prevalent cases among subjects at risk at the later time-points. On the other hand, in the studies of non-lobular tumors, CDH1 downregulation possibly represented a marker for epithelial-to-mesenchymal transition,316 whereas in Study II low CDH1 activity was more likely to be an indication of infiltrating lobular-type growth pattern, as discussed above. 6.3 Common genetic variants in breast cancer risk prediction (III, IV) 6.3.1 PRS could be used in risk stratification of c.1100delC carriers (III) Higher values of a polygenic risk score (PRS) based on 74 common predisposing variants were associated with increased risk of breast cancer for CHEK2 c.1100delC carriers. The effect size was comparable to previous more stable estimates made in a much larger group of breast cancer cases.10 When PRS was used to stratify c.1100delC carriers into categories of high and low lifetime risk, 20% of carriers at highest risk were estimated to have upwards of 30% lifetime risk. Correspondingly, for 20% of carriers at lowest risk the lifetime risk would be comparable to the population average, about 10%. Further extrapolation of the model suggested that for 10% of carriers at the high end of the PRS distribution the lifetime risk would exceed 40%, which has been considered as the threshold of the high-risk category in Finland. In Finland, the relevance of c.1100delC in genetic counseling is attributable to its high carrier frequency, 1.4%.1 Ten percent of carriers in the high-risk group means 0.14% of all women and about 40 women of each annual birth cohort.339 As a comparison, BRCA1 mutations have about 0.2% carrier frequency in many Western populations and BRCA2 mutations slightly higher, about 0.4-0.5% frequency.340 Thus, using CHEK2 mutation analysis as a part of risk estimation for women with a positive family history would be well founded, especially in combination with the PRS. 6.3.2 No epistatic interaction exists between c.1100delC and the common variants (III) In pairwise interaction analyses of c.1100delC and common predisposing variants, we did not detect any deviation from the assumed multiplicative model (III: Table S4) suggesting that the risk effect associated with the common variants would be roughly the same for c.1100delC carriers and non-carriers. The common variants included in Study III were estimated to explain about 14% of the disease heritability and the most recent novel loci an additional 4%, which together with the 20% associated with mutations in high- and moderate-risk genes summed up to about 38%. 9, 153 The ‘missing heritability’ has been suggested to be partly attributable to an epistatic interaction between loci.157, 158, 341 According to a hypothetical ‘limiting pathway model’, genetic variation affecting any single pathway would increase the risk in a linear fashion, whereas parallel variation

59 in another pathway would confer a rapid increase in risk.158 Despite a systematic search, no significant epistatic effect associated with breast cancer predisposition has been discovered thus far.342, 343 Furthermore, fine mapping and functional studies of the established risk loci have indicated that the common variants per se contribute to breast cancer predisposition by regulating the activity of nearby genes.261-263, 344-346 However, even though epistasis appears to be rare in breast cancer genetics, this does not mean that there is none. Studies on c.1100delC- and p.(I157T) carrier tumor genomes presented in this work could assist in building models for tumorigenesis associated with these mutations. Furthermore, the discovered driver regions and genes could serve as candidates in further analyses of genetic risk modifiers. 6.3.3 PRS explains part of the increased familial risk (IV) Our results confirm the hypothesis that some part of familial clustering of breast cancer can be explained by aggregation of common variants with low individual effect sizes.8 The PRS values of both healthy and affected members of breast cancer families were elevated relative to the general population level (IV: Table 1). Furthermore, the magnitude of risk (OR 1.55 [1.26–1.91] per unit standard deviation) associated with the PRS within breast cancer families, when comparing healthy and affected individuals, was very similar to estimates made between unselected cases and population controls (ORs 1.47 [1.38-1.62] in IV and 1.55 [1.52- 1.58] in Mavaddat et al.8). Earlier, the risk associated with a PRS of 22 common variants was studied in Australian breast cancer families. They reported OR 1.88 [0.99-3.25] when comparing the highest quartile against the lowest quartile.347 The 22 variants known then, in 2011, were estimated to explain about 8% of excess familial risk of breast cancer,348, 349 whereas with the 75 variants the proportion of explained heritability rose to 14%.9 Addition of novel predisposing variants enhanced the discriminatory potential of PRS since we reported a comparable risk effect (OR 1.88 [0.93-3.78]) for women at the 80-90 percentile of PRS distribution compared with the average PRS. Now that an additional 65 loci have been confirmed in Oncoarray analyses to be significantly associated with breast cancer predisposition, their incorporation into PRS is expected to improve it further.153 However, although all predisposing variants even those below the significance threshold were included, a large proportion of breast cancer heritability remains unexplained and the PRS incomplete. In Study IV, BOADICEA score measuring the family history of breast cancer was weakly correlated to PRS in breast cancer cases, but not in healthy women from the same families. Similarly, the effect of family history in predicting breast cancer was previously reported to be attenuated when PRS was included in the same model.10 Basically, PRS explains part of the familial risk. However, when considering individual consultands, the PRS gives information beyond the family history. For example, sisters of the same family have the same familial risk, but each has a unique genome and therefore a different PRS, which could be used in analyzing which sisters have inherited the risk variants segregating in that particular family. In addition to individual-level variation in PRS, there is also variation between families. Even though the PRS on average is elevated in breast cancer families, some families have lower values than other families, suggesting that the number of currently known common risk variants is lower in some families than in others.

60 In the 52 families, the number of individuals with low PRS values was small, but in this group the PRS did not appear to have much predictive potential. Most of the variants included in the 75- variant PRS have stronger associations with ER-positive breast cancer than with ER-negative breast cancer.10 Had ER-negative breast cancer been more common in women with low PRS values, the poor predictive potential of the PRS at the low end would have been explained. However, this was not the case. Instead, it seemed likely that in the families with low PRS, the effect of unidentified risk variants exceeded the effect of identified variants, and because the unidentified variants do not segregate together with the known variants, the PRS based on known variants lost its discriminatory power. All in all, our analyses suggest that the high end of PRS could be used in risk stratification of women from breast cancer families, preferably using a model with both the family history and the PRS incorporated into it. However, caution should be exercised when interpreting the low PRS values in a familial context, as long as the PRS is far from complete.

61 7 Summary and Conclusions The work presented in this thesis validated the usability of a polygenic risk score for c.1100delC mutation carriers and Finnish women with a positive family history of breast cancer. Out of consultands with positive family history not fulfilling the Lund criteria350 or consultands carrying c.1100delC with about 20% lifetime risk, the PRS could be used in identifying women at high (30-40%) lifetime risk for intensive follow-up programs. Therefore, as soon as proper and standardized methods combining the PRS with pedigree information are developed, the PRS could be taken into clinical practice. It is noteworthy that for c.1100delC carriers the lower PRS values indicate reduced absolute risk such that for about 20% of the carriers the risk is comparable to that in the general population. However, a similar association was not seen within breast cancer families. Instead, low PRS values were relatively rare within the families and lacked predictive potential. Therefore, as long as a notable proportion of breast cancer heritability remains unexplained, care should be taken in interpreting the low PRS values so that women at elevated risk due to their family history of breast cancer are not deprived of regular surveillance and counseling. The analyses of c.1100delC- and p.(I157T)-associated tumor phenotype suggested that the two mutations could be associated with different tumor etiologies. This was unexpected since both mutations cause reduced CHEK2 kinase activity, c.1100delC via abrogation of the mutated allele accompanied by generally lowered expression, and p.(I157T) via reduced autophosphorylation required for kinase domain activation. Thus, the difference between the effects of the two mutations was assumed to be more quantitative than qualitative in nature. We were able to nominate putative driver events for breast cancer of c.1100delC carriers, but these were not replicated in p.(I157T) carrier tumors. Furthermore, CDH1 expression was elevated in c.1100delC carrier tumors, but reduced in p.(I157T) carrier tumors relative to non-carriers, in agreement with the association between p.(I157T) and lobular cancer, which is not shared with the truncating CHEK2 mutations. The two mutations could have different roles in promoting cancer development, c.1100delC typically being a true causal mutation initiating tumorigenesis and p.(I157T) being more often only an accelerating or risk-modifying factor for a cancer with CHEK2-independent origin. The genomic analysis of c.1100delC carrier tumors highlighted two protein families that could complement CHEK2 deficiency in the development of breast cancer. Recurrent loss of 1p21.3- 22.2 suggested that silencing of the GBP gene/protein family could confer a growth advantage for breast cancer driven by loss of CHEK2 activity, possibly via promotion of cancer-driving inflammation or via escape from immune surveillance. Increased expression of olfactory receptors was another feature significantly associated with germline c.1100delC. Further study of these novel hypotheses would require a CHEK2 knock-down organoid or mouse model. Examination of the epithelial cell phenotype after targeted silencing of GBP genes or stimulation of olfactory receptor genes, with or without concurrent exposure to estrogen, could ascertain the role of these two gene families in c.1100delC-associated breast cancer. The study of c.1100delC carrier tumors could also identify future directions in the search for germline risk modifiers in the putative driver regions.

62 The causes of c.1100delC-associated poor survival are yet to be explained, and the effect of treatment choice cannot be excluded. On the other hand, if GBP silencing was complementary to c.1100delC in breast cancer development, as hypothesized in this work, it could explain the reduced survival as a result of compromised immune surveillance. Unlike c.1100delC, p.(I157T) was not associated with increased breast cancer mortality. However, as discussed above, c.1100delC tumors are phenotypically different from p.(I157T) carrier tumors and in view of this background the difference in survival association is not unexpected. Unsolved issues include the marginal association of both CHEK2 mutations with increased risk of locoregional relapse. Further studies are also warranted for investigating the response to DNA-damaging chemotherapy in CHEK2-deficient cancer to determine whether the CHEK2 mutations could be used in treatment stratification. Optimally, these studies would need to take into consideration the somatic loss of CHEK2 function in non-carrier tumors as well as potential differences in the effects of the germline mutations.

63 8 Acknowledgments This study was carried out at the Department of Obstetrics and Gynecology, Helsinki University Hospital, during 2009-2018. I thank the foundations that have financially supported this work: the Finnish Cultural Foundation, the Ida Montin Foundation, the Cancer Society of Finland, the Orion Research Foundation, the Sigrid Juselius Foundation, the Helsinki Hospital Research Fund, and the Academy of Finland. I wish to express my sincere gratitude to all people who have supported my work, especially: The former and present head of the Department of Obstetrics and Gynecology, Professor Jorma Paavonen and Professor Juha Tapanainen, respectively, for excellent research facilities and a fruitful working environment, and Professor Tapanainen also for accepting the post of custos at the defense of my dissertation. My supervisor, Adjunct Professor Heli Nevanlinna, for the opportunity to work in her group and for believing in me. I greatly appreciate the challenges in various research projects that she entrusted to me, her advice and encouragement, and the chance to work on international collaborative projects. Her optimism and enthusiasm for research have been a wonderful example for my own career as a scientist. My supervisor, Associate Professor Dario Greco, for his advice and support of my work. With his vast experience in ‘omics’, he was always able to help find a way around the cul-de-sacs and to advise on how to proceed with analyses and where to retrieve more information, without giving any easy answers. While he was working in our group, his passion for science motivated us all on a daily basis, and his teaching made complicated analysis processes simple and understandable. Adjunct Professor Carl Blomqvist for intensive collaboration on the projects included in this work. His clinical expertise and extensive knowledge of published literature about breast cancer helped to focus and motivate my work. I am grateful for the time he generously gave for my projects in our regular meetings, where his clinical point of view was always valuable. The members of my thesis committee, Adjunct Professor Minna Pöyhönen and Adjunct Professor Outi Monni, for their support and encouragement; the official reviewers of my thesis, Professor Matti Nykter and Adjunct Professor Minna Tanner, for insightful comments that made this thesis more accurate and profound; and my author-editor Carol Ann Pelli for careful language revision. I warmly thank my co-authors and collaborators: All of the great scientists of the Breast Cancer Association Consortium with whom I have had the privilege to work. I owe my deepest gratitude to Professor Douglas Easton for his advice and guidance as well as for hospitability during my visit to the Strangeways Research Laboratory, and Dr. Marjanka Schmidt for her friendly advice on so many aspects of this work, starting with detailed instructions on data handling and analyses and finishing with fine-tuning of publications. Furthermore, I would like to express my gratitude especially to Dr. Nasim Mavaddat, Professor Thilo Dörk and Professor Anna Jakubowska for sharing their expertise and for collaborating on the projects included in this thesis.

64 Professor Åke Borg, Dr. Markus Ringnér, Dr. Johan Vallon-Christersson, and Dr. Göran Jönsson for their collaboration and the introduction to microarray data analysis and the BASE database as well as for their hospitability during my visits to Lund. Professor Kristiina Aittomäki and Adjunct Professor Päivi Heikkilä for their clinical expertise and interest in my research. Research nurses Irja Erkkilä, Outi Malkavaara, and Hanna Jäntti for their diligence in patient data management and their patience with my recurrent data requests. My present and former coworkers, especially Dr. Tuomas Heikkinen, my first instructor in the lab, whose dedication to the task and good spirit made the very first working days bright, and who continued to be a trusted senior fellow in the lab for many years; Liisa Pelttari and Johanna Kiiski, my fellow scientists with whom I have pushed through the doctoral education – I greatly value their peer support and good humor during the ups and downs of the process; Dr. Sofia Khan for her friendship, encouragement and devotion to science; All former and present personnel of the Nevanlinna lab – Anitta Tamminen, Jenny Forsström, Dr. Kati Kämpjärvi, Dr. Outi Kilpivaara, Dr. Johanna Tommiska, Dr. Kirsimari Aaltonen, Dr. Reetta Vainionpää, Marja-Liisa Nuotio, Gynel Arifsdhan, Rainer Fagerholm, Dr. Hanni Kärkkäinen, Dr. Netta Mäkinen, Maral Jamshidi, Ali Oghabian, Salla Ranta, Anna Nurmi, Dr. Maija Suvanto, Erja Nynäs and Himanshu Chheda – for their companionship and support, for sharing good and bad times, for the intelligent and not- always-so-intelligent-but-amusing discussion on science, politics, and everyday life. Good coworkers have always been one of the best parts of my work. I am grateful to my parents Helinä and Hannu for their love, for all material and practical support, and for always letting me pursue my dreams; my siblings Turo, Sara and Mira for love and friendship; and most of all Paavo, my beloved husband and best friend, for his love and companionship, for his support and patience during these years, for his keen interest in my work, and for his readiness to challenge and discuss everything from methods to results and conclusions. Finally, I thank the breast cancer patients and their family members for participating in the study. This work would not have been possible without their contribution.

Helsinki, September 2018

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94 Appendix

Supplementary Table 1. (III: unpublished data) Supplementary Table 2. (III: unpublished data) Supplementary Table 1. In silico functional characterization of the six candidate modifier loci of CHEK2:c.1100delC associated breast cancer risk. The table includes the functional summary* of the risk variant (bold) as well as those tagging variants (r2>0.8) that have been predicted to have a functional consequence (RegulomeDB score<5).

Variant Distance r2 r2 r2 RegulomeDB Closest Variant eQTL Cell lines Cell lines Cell lines Bound proteins Transcription factor binding (bp) (1000gen) (HM3) (HM2.2) score gene consequence with active with active with open motifs promoter enhancer chromatin 5: minimal rs11249433 0 binding EMBP1 intronic K562 HSMM 4 cell lines Pit-1, Mef2, Pax-2, Pou1f1 evidence 5: minimal HMEC and BCL, Pax-4, Sin3Ak-20, rs11780156 0 binding MIR1208 intergenic 4 other cell Nhek, Htr8 Zfp281 evidence lines HMEC and Foxp1, GATA, HDAC2, Irf, 2b: likely to rs72722756 8531 1 MIR1208 intergenic 6 other cell 19 cell lines EP-300, FOS Nanog, Pax-5, RXRA, STAT, affect binding lines p300

2b: likely to HMEC and HCF1, SPI-B, Irf4, Ets, HNF4, rs12542202 10086 0.882 0.936 1 MIR1208 intergenic GM12878 5 cell lines RFX3, EP300, NFKB1, FOS affect binding 5 other cell Hsf, STAT lines 5: minimal ATE1 rs2981582 0 binding FGFR2 intronic NHEK 8 cell lines NF-kappaB, ZEB1 (skin) evidence HMEC and 2b: likely to NHEK, rs2981578 12006 0.844 FGFR2 intronic HepG2 13 other FOXA1, E2F1 Oct-1, Foxa, Pou2f2, Pou3f2 affect binding HMEC, H1 cell lines

HMEC and rs11075995 0 7: no data FTO intronic 3 other cell TP53 lines 4: minimal Melano, rs9923295 6631 0.957 binding FTO intronic HMEC NT2-D1, NANOG Egr-1, Irf, VDR evidence H7es 1f: likely to ELL affect binding (monocytes, HMEC and HMEC and rs4808801 0 and linked to ELL intronic LCL); 4 other cell 27 other Nrf-1 expression of ARRDC2 lines cell lines a target gene (skin) ELL CTCF, EBF1, PAX5C20, CTCFL, CTCF, E2A, HEN1, HMEC and 2a: likely to (LCL) RAD21, SMC3, YY1, ZNF143, Lmo2-complex, Myf, RXRA, rs8103622 1693 1 1 ELL intronic 118 other affect binding GABP, CTCFL, MAX, HSF1, Rad21, SMC3, ZBTB7A, cell lines GABPA, PAX5 MyoD, E12, E47 Variant Distance r2 r2 r2 RegulomeDB Closest Variant eQTL Cell lines Cell lines Cell lines Bound proteins Transcription factor binding (bp) (1000gen) (HM3) (HM2.2) score gene consequence with active with active with open motifs promoter enhancer chromatin HMEC and HDAC2, Irf, Sox, TATA, 2b: likely to ELL rs2385089 20707 1 0.981 1 SSBP4 intergenic HepG2 4 other cell 25 cell lines CTCF Zfp105, p300, Srf, SRY, affect binding (LCL) lines Tcfap2e, Sox4, Sox11 1f: likely to SSBP4; HMEC and rs10442 25800 1 0.981 1 affect binding SSBP4 intronic (LCL) GM12878 H1, Huvec 33 other EGR1 Foxo, Hoxa9, Pbx-1, Pou3f2 and linked to ELL; cell lines HMEC and IKZF1, CREBBP, CTCFL, CCNT2, ERalpha-a, Egr-1, 2b: likely to K562 rs34746918 31397 1 SSBP4 intronic 4 cell lines 46 other POLR2A, CTCF, HMGN3, INSM1, PU.1, RXRA, SP1, affect binding (leukemia) cell lines RFX3, POL2 ZNF219, KROX 1f: likely to ELL HMEC and GM12878, rs7258465 37499 1 0.981 1 affect binding SSBP4 intronic (monocytes, 17 other ETS1, CREBBP, ELF1 Huvec and linked to LCL); cell lines 1f: likely to ELL rs7252848 41210 1 0.943 affect binding ELL intronic 4 cell lines (monocytes) and linked to 1d: likely to ELL rs4808136 47726 1 0.943 0.964 affect binding ELL intronic (monocytes, HepG2 47 cell lines HNF4A, RAD21, USF1, RXRA CTCF and linked to LCL) ELL HMEC and E2F, GATA, Pbx-1, Pbx3, 2b: likely to H1, K562, rs10408290 28440 0.965 1 SSBP4 intronic (LCL); 24 other ZNF263 UF1H3BETA, ZBTB33, Tcf7l2, affect binding GM12878 ARRDC2 cell lines Pbx ANKLE1, MRPL34 5: minimal (LCL), rs2363956 0 binding ANKLE1 missense K562 8 cell lines NF-I, Pbx3, Smad4 GTPBP3, evidence OCEL1 (adipose) POLR2A, TFAP2C, TFAP2A, MAX, HMGN3, E2F6, ZNF263, ETS1, CCNT2, PAX5, AP-2, AP-4, Ascl2, BHLHE40, HMEC and HMEC and TAF1, CTCF, ZEB1, ELF1, 2b: likely to E2A, HEN1, LBP-1, Myf, rs8108174 594 1 ANKLE1 missense 8 other cell 100 other NRF1, TBP, GABPA, GATA1, affect binding NRSF, Rad21, Sin3Ak-20, lines cell lines YY1, ATF3, SMC3, EBF1, TCF12, p300 RAD21, RFX5, MYC, POL2, PAX5C20, POL24H8, GABP, AP2ALPHA, AP2GAMMA 1000gen = 1000 genomes; HM = HapMap; rel = release; eQTL = expressed quantitative trait locus Distance to the index variant is given for all variants in base pairs (bp). Tagging variants were searched from 1000 genomes (1000gen)351 and from HapMap releases 3 (HM3)352 and 2.2 (HM2.2)353. * Functional data are a summary of SNAP253, RegulomeDB257, HaploReg258 and GeneVar254 database searches. Supplementary Table 2. Functional summary of the 77 common low penetrance breast cancer risk variants based on published literature and Ingenuity® Pathway Analysis (IPA®).

IPA® term* (IPA® enrichment p-value)† PubMed (search key-words bolded) Repair of Cell Cell cycle Proliferation Differentiation Locus / Potential DNA death progression of cells of cells variant culprit genes (1,4E-04) (4,1E-04) (2,1E-06) (2,8E-05) (2,4E-06) WNT signaling FGF signaling DNA repair Link to CHEK2 References Interaction Interaction rs1045485 Downstream of CASP8 CASP8 CASP8 CASP8 between between 230, 354-358 rs17468277 CHEK2 pathways pathways rs999737 Homologous 359, 360 RAD51L1 rs10483813E recombination Downstream target of beta- Downstream of rs10069690 TERT TERT TERT TERT TERT TERT catenin, FGF signaling modulator of 264, 361-364 beta-catenin CDKN2A: CDKN2B, CDKN2A, CDKN2A, CDKN2A, downstream rs1011970 CDKN2A CDKN2A CDKN2A CDKN2B CDKN2B CDKN2B target of beta- 264, 365 catenin 9 rs10472076 RAB3C, PDE4D PDE4D PDE4D PDE4D

Interaction with Downstream of TP53 target 264, 361, 366-372 rs10759243 KLF4 KLF4 KLF4 KLF4 KLF4 beta-catenin FGF signaling selectivity Upstream of beta- Downstream of 264, 373, 374 rs10771399E PTHLH PTHLH PTHLH PTHLH PTHLH catenin and LEF1 FGF signaling

rs10941679

Double-strand break repair via rs10995190 ZNF365 ZNF365 homologous 264, 375 recombination Regulation of canonical and rs11075995E FTO 264, 265, 376 non-canonical WNT pathways Interaction rs11199914E FGFR2 FGFR2 FGFR2 FGFR2 FGFR2 between Core FGF pathway 264, 269, 270, 377 pathways Downstream 9, 264, 378 rs11242675 FOXQ1 FOXQ1 target of WNT h IPA® term* (IPA® enrichment p-value)† PubMed (search key-words bolded) Repair of Cell Cell cycle Proliferation Differentiation Locus / Potential DNA death progression of cells of cells variant culprit genes (1,4E-04) (4,1E-04) (2,1E-06) (2,8E-05) (2,4E-06) WNT signaling FGF signaling DNA repair Link to CHEK2 References NOTCH2: NOTCH2: interaction rs11249433 NOTCH2, EMBP1 NOTCH2 NOTCH2 NOTCH2 NOTCH2 downstream of between 263, 264, 266-268 beta-catenin pathways DCLRE1B: PTPN22, interstrand cross- DCLRE1B, PHTF1, rs11552449PE DCLRE1B PTPN22 link repair; BCL2L15, RSBN1, telomere 9, 264, 379-381 AP4B1 protection Double-strand break repair Double-strand rs11571833 BRCA2 BRCA2 BRCA2 BRCA2 BRCA2 through break repair homologous pathway 9, 264, 382, 383 recombination MYC: canonical MYC: coupling of pathway MYC: interaction DNA replication MYC, MIR1208, CHEK2: direct MYC rs11780156E MYC MYC MYC MYC PVT1: MYC between and DNA repair PVT1 target downstream pathways (target genes of 9, 264, 272, 384-387 target MYC) 9, 264 rs11814448E DNAJC1

WNT effector 264, 388 rs11820646E BARX2 complex 264 rs12422552E ATF7IP

Interaction Upstream rs12493607 TGFBR2 TGFBR2 TGFBR2 between regulation of FGF 9, 264, 389-391 pathways signaling Upstream of rs12662670 ESR1 ESR1 ESR1 ESR1 ESR1 WNT/beta-catenin 348, 392 pathway Upstream OSR1: rs12710696E OSR1, MIR4757 regulation of WNT 264, 376, 393, 394 downstream signaling IPA® term* (IPA® enrichment p-value)† PubMed (search key-words bolded) Repair of Cell Cell cycle Proliferation Differentiation Locus / Potential DNA death progression of cells of cells variant culprit genes (1,4E-04) (4,1E-04) (2,1E-06) (2,8E-05) (2,4E-06) WNT signaling FGF signaling DNA repair Link to CHEK2 References TBX3: downstream of beta-catenin TBX3: paracrine rs1292011 TBX3, MED13 TBX3 TBX3 TBX3 TBX3 MED13: upstream signaling regulation of WNT 264, 395-398 signaling 264 rs132390 EMID1

MYC: coupling of MYC: interaction DNA replication MYC: canonical CHEK2: direct MYC rs13281615E MYC, POU5F1B MYC MYC MYC MYC between and DNA repair pathway target pathways (target genes of 185, 264, 272, 385-387 MYC) 9, 264 rs13329835 CDYL2

264 rs13387042 TNP1 TNP1

264 rs1353747E RAB3C, PDE4D PDE4D PDE4D PDE4D

9, 264 rs1432679 EBF1 EBF1

9, 264 rs1436904 CHST9, AQP4

9, 264 rs1550623P RPS2P18,CDCA7 CDCA7 rs16857609E DIRC3

USP44: counteracts double-strand rs17356907E NTN4, USP44 NTN4 NTN4 break repair repair through RNF8 and 9, 264, 399 RNF168 264 rs17529111 FAM46A IPA® term* (IPA® enrichment p-value)† PubMed (search key-words bolded) Repair of Cell Cell cycle Proliferation Differentiation Locus / Potential DNA death progression of cells of cells variant culprit genes (1,4E-04) (4,1E-04) (2,1E-06) (2,8E-05) (2,4E-06) WNT signaling FGF signaling DNA repair Link to CHEK2 References Regulation of canonical and rs17817449E FTO non-canonical 9, 264, 265 WNT pathways Downstream of Downstream of rs2016394PE DLX2 beta-catenin, MYC 264, 400-402 FGF8 binding site 9, 264 rs204247PE RANBP9 RANBP9

ESR1: Upstream of ESR1, CCDC170, rs2046210PE ESR1 ESR1 ESR1 ESR1 WNT/beta-catenin 264, 392, 403 C6orf97 pathway Downstream of 9, 264, 404, 405 rs2236007P PAX9,SLC25A21 PAX9 FGF signaling ANKLE1, ABHD8, 264 rs2363956PE PDE4C FBXO18: ANKRD16, GDI2, rs2380205E homologous 264, 406, 407 FBXO18 recombination Downstream of Homologous 9, 264, 360 rs2588809E RAD51L1 FGF signaling recombination Downstream target of beta- Downstream of rs2736108 TERT TERT TERT TERT TERT TERT catenin, FGF signaling modulator of 9, 361-364 beta-catenin 264 rs2823093 NRIP1 NRIP1

9, 264 rs2943559 HNF4G

Interaction rs2981579E FGFR2 FGFR2 FGFR2 FGFR2 FGFR2 between Core FGF pathway 9, 269, 270, 377 pathways Interaction rs2981582E FGFR2 FGFR2 FGFR2 FGFR2 FGFR2 between Core FGF pathway 185, 264, 269, 270, 377 pathways IPA® term* (IPA® enrichment p-value)† PubMed (search key-words bolded) Repair of Cell Cell cycle Proliferation Differentiation Locus / Potential DNA death progression of cells of cells variant culprit genes (1,4E-04) (4,1E-04) (2,1E-06) (2,8E-05) (2,4E-06) WNT signaling FGF signaling DNA repair Link to CHEK2 References Upstream of 348, 392 rs3757318 ESR1 ESR1 ESR1 ESR1 ESR1 WNT/beta-catenin pathway KCNN4, ZNF283, 9, 264 E rs3760982 SMG9, LYPD5, KCNN4 264 rs3803662 TOX3

264 rs3817198 TNNT3, LSP1 LSP1

FOSL1: downstream of MUS81: Halliday CFL1, OVOL1, beta-catenin and junction release, SNX32, MUS81, lef1 FOSL1: CFL1, homologous MUS81: direct rs3903072E AP5B1, FOSL1, MUS81 MUS81 FOSL1 CFL1: planar cell downstream of MUS81 recombination, CHEK2 target C11orf68, BANF1, polarity pathway FGF8 and FGF18 interstrand cross- CTSW, EFEMP2 (non-canonical WNT pathway) link repair 9, 264, 408-413 OVOL1: MYC MDM4, MDM4: TP53 MDM4: direct 264, 376, 414-416 rs4245739PE MDM4, PIK3C2B MDM4 MYC binding site PIK3C2B regulator CHEK2 target ELL: upstream ELL: transcription ELL, ISYNA1, ELL: oncoprotein rs4808801E ELL ELL regulation of WNT restart after DNA 9, 264, 273, 313, 417 SSBP4, UBA52 upstream of FGF2 signaling repair LOC84931, 9, 264 rs4849887 INHBB 264 rs4973768 NEK10

264 rs527616E LOC728606

Downstream rs554219E CCND1 CCND1 CCND1 CCND1 CCND1 CCND1 target of beta- 418-423 catenin 9, 264 rs6001930E MKL1, SGSM3 MKL1

Downstream Homology- TP53 signaling rs614367 CCND1 CCND1 CCND1 CCND1 CCND1 CCND1 target of beta- directed DNA 264, 419-423 pathway catenin repair IPA® term* (IPA® enrichment p-value)† PubMed (search key-words bolded) Repair of Cell Cell cycle Proliferation Differentiation Locus / Potential DNA death progression of cells of cells variant culprit genes (1,4E-04) (4,1E-04) (2,1E-06) (2,8E-05) (2,4E-06) WNT signaling FGF signaling DNA repair Link to CHEK2 References 9, 264 rs616488 PEX14

264 rs6472903 HNF4G

COX11: metabolic STXBP4, HLF, 264, 424, 425 rs6504950E COX11 protection against COX11 oxidative stress Interaction rs6678914E LGR6 between 264, 376, 426, 427 pathways 9, 264 rs6762644E ITPR1, EGOT ITPR1 ITPR1

9, 264 rs6828523 ADAM29

Co-activator of 264, 428 rs704010E ZMIZ1 ZMIZ1 TP53 MLLT10: DNAJC1,MLLT10, rs7072776 TCF4/beta-catenin 9, 264, 429, 430 C10orf114 coeffector ARHGEF5, 9, 264 rs720475 MYC binding site NOBOX Downstream Homology- TP53 signaling rs75915166 CCND1 CCND1 CCND1 CCND1 CCND1 CCND1 target of beta- directed DNA 418-423 pathway catenin repair Interaction Canonical WNT rs7904519E TCF7L2 TCF7L2 TCF7L2 TCF7L2 TCF7L2 between 9, 264, 431, 432 pathway pathways C19orf62: Double- strand break rs8170PE C19orf62 C19orf62 NR2F6 repair through homologous 9, 433 recombination Interaction with Downstream of TP53 target 264, 361, 366-372 rs865686 KLF4 KLF4 KLF4 KLF4 KLF4 beta-catenin FGF signaling selectivity MAP3K1, MIER3, MAP3K1: MAP3K1: 264, 434-436 rs889312 SETD9, MAP3K1 MAP3K1 interaction interaction MGC33648 b b IPA® term* (IPA® enrichment p-value)† PubMed (search key-words bolded) Repair of Cell Cell cycle Proliferation Differentiation Locus / Potential DNA death progression of cells of cells variant culprit genes (1,4E-04) (4,1E-04) (2,1E-06) (2,8E-05) (2,4E-06) WNT signaling FGF signaling DNA repair Link to CHEK2 References DVL suppression, rs941764 CCDC88C upstream of beta- 9, 264, 437 catenin RPL17P33, DUSP4: FGF 9, 264, 438 rs9693444E DUSP4 DUSP4 MYC binding site C8orf75, DUSP4 signaling inhibitor 9, 264 rs9790517P TET2 TET2

P Variant tags a region of promoter histone marks in human mammary epithelial cells according to HaploReg results. EVariant tags a region of enhancer histone marks in human mammary epithelial cells according to HaploReg results. * Biological functions with which the 77 common breast cancer risk variants are associate. From the analyses, a general term covering the highest number of variants was selected to this table from each group of multiple related functions (e.g. “cell death” instead of “cell death of ovarian cancer cell lines”). † P-value for enrichment of the biological function in the 77 common breast cancer risk variants.