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. 2013 Dec;73(12):1228–1235. doi: 10.1055/s-0033-1360178

Breast Cancer Risk – From Genetics to Molecular Understanding of Pathogenesis

Mammakarzinomrisiko – Genetik und molekulare Mechanismen der Pathogenese

P A Fasching 1,✉, A B Ekici 2, D L Wachter 3, A Hein 1, C M Bayer 1, L Häberle 1, C R Loehberg 1, M Schneider 1, S M Jud 1, K Heusinger 1, M Rübner 1, C Rauh 1, M R Bani 1, M P Lux 1, R Schulz-Wendtland 4, A Hartmann 3, M W Beckmann 1
PMCID: PMC3964347  PMID: 24771903

Abstract

Several advancements over the last decade have triggered the developments in the field of breast cancer risk research. One of them is the availability of the human genome sequence along with cheap genotyping possibilities. Another is the globalization of research, which has led to the growth of research collaboration into large international consortia that facilitate the pooling of clinical and genotype data of hundreds of thousands of patients and healthy control individuals. This review concerns with the recent developments in breast cancer risk research and focuses on the discovery of new genetic breast cancer risk factors and their meaning in the context of established non-genetic risk factors. Finally the clinical application is highly dependent on the accuracy of breast cancer risk prediction models, not only for all breast cancer patients, but also for molecular subtypes, preferably for those which are associated with an unfavorable prognosis. Recently risk prediction incorporates all possible risk factors, which include epidemiological risk factors, mammographic density and genetic risk factors.

Key words: breast cancer risk, genetic risk factors, prediction models, gene–environment interaction, BRCA

Introduction

Advancements of breast cancer treatment and prevention have emerged over the last decade. The developments in both fields imply that both breast cancer treatment and breast cancer risk assessment have to develop towards each other.

Concerning breast cancer treatment and prognostic assessment the implementation and understanding of molecular subgroups has changed the approach how to treat breast cancer fundamentally 1, 2, 3. It has been learnt that molecular subgroups of breast cancer represent either genetic or otherwise unique molecular patterns that determine the prognosis and seem to be the consequence of a specific pathogenesis 4.

With regard to breast cancer risk research the formation of large scale international networks with tens of thousands of breast cancer patients and controls with available genetic and non-genetic information sheds some light on the direction into which breast cancer risk research will develop in the next years. Efforts aim at both, large scale genotyping and integrating genetic and non-genetic risk factors into risk assessment. The identification of molecular pathways of pathogenesis linked to specific risk factors might be the next steps needed to take breast cancer risk assessment into clinical practice.

Genetic Risk Factors

Since the discovery of the breast cancer genes 1 and 2 (BRCA1 and BRCA2) in 1994 and 1995 5, 6 many genetic variants have been described to contribute to breast cancer risk. After years of candidate gene research with its problems concerning false positive reporting 7 large consortia have enabled research at a genome-wide level, discovering and validating genetic variants that are associated with breast cancer risk. These genetic, inheritable variants are the reason for familial breast cancer risk. While higher penetrant loci make up for about 20 % of familial breast cancer risk, about 28 % are explained by newly discovered low penetrant loci, of which 14 % are explained by about 70 low penetrant loci 8.

Recently more than 45 new common genetic breast cancer susceptibility loci were identified 8. The following section will describe the discovery of those low penetrant loci, as this effort demonstrated the kind of collaborative efforts that lead to such success. In order to facilitate large scale genotyping the multicenter Collaborative Oncological Gene-environment Study (COGS) was founded aiming at the discovery of genetic factors of three hormone-related cancer types (breast, ovarian and prostate cancer), represented by several consortia, BCAC (Breast Cancer Association Consortium), CIMBA (The Consortium of Investigators of Modifiers of BRCA1/2), OCAC (Ovarian Cancer Association Consortium) and PRACTICAL (Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome). For further information see www.cogseu.org. These consortia built a custom genotyping array with more than 200 000 SNPs (iCOGS Chip), which was used for germline DNA genotyping of more than 200 000 individuals.

SNPs that were tested for breast cancer risk as part of COGS were selected from genome-wide association studies, performed in more than 10 000 breast cancer cases and more than 12 500 controls. Almost 30 000 SNPs were re-genotyped in more than 45 000 breast cancer cases and almost 42 000 healthy controls. This study identified more than 45 new breast cancer risk genetic variants 8, 9, 10 additionally to already published SNPs. An overview of validated breast cancer risk SNPs over the last decade is given in Table 1. Besides these validated breast cancer SNPs, that explain about 14 % of familial breast cancer risk, it is suggested that about 1000 additional loci are involved in breast cancer susceptibility 8.

Table 1 Validated single nucleotide polymorphisms (SNPs) for sporadic breast cancer (Chr: Chromosome; SNP: Single Nucleotid Polymorphism; OR: Odds Ratio; CI: Confidence Interval).

Chr Gene name or Chr Region SNP MAF OR (95 % CI) Reference
1 1p11.2 rs11249433 0.40 1.09 (1.07, 1.11) 8, 51
1 1p13.2 rs11552449 0.17 1.07 (1.04, 1.10) 8
1 LGR6 rs6678914 0.41 1.00 (0.98, 1.02) 10
1 MDM4 rs4245739 0.26 1.02 (1.00, 1.04) 10
1 PEX14 rs616488 0.33 0.94 (0.92, 0.96) 8
2 2p24.1 rs12710696 0.36 1.04 (1.01, 1.06) 10
2 2q14.2 rs4849887 0.098 0.91 (0.88, 0.94) 8
2 2q31.1 rs2016394 0.48 0.95 (0.93, 0.97) 8
2 2q35 rs13387042 0.47 0.88 (0.86, 0.90) 8, 49, 52
2 2q35 rs16857609 0.26 1.08 (1.06, 1.10) 8
2 CASP8 rs1045485 0.13 0.97 (0.94, 1.00) 8, 53
2 CDCA7 rs1550623 0.16 0.94 (0.92, 0.97) 8
3 3p26.2 rs6762644 0.40 1.07 (1.04, 1.09) 8
3 SLC4A7 rs4973768 0.47 1.10 (1.08, 1.12) 8, 54
3 TGFBR2 rs12493607 0.35 1.06 (1.03, 1.08) 8
4 ADAM29 rs6828523 0.13 0.90 (0.87, 0.92) 8
4 TET2 rs9790517 0.23 1.05 (1.03, 1.08) 8
5 5p12 rs10941679 0.25 1.13 (1.10, 1.15) 8, 55
5 EBF1 rs1432679 0.43 1.07 (1.05, 1.09) 8
5 MAP3K1 rs889312 0.28 1.12 (1.10, 1.15) 8, 56
5 PDE4D rs1353747 0.095 0.92 (0.89, 0.95) 8
5 RAB3C rs10472076 0.38 1.05 (1.03, 1.07) 8
5 TERT rs10069690 0.26 1.06 (1.04, 1.09) 8, 15
5 TERT rs2736108 0.29 0.94 (0.92, 0.95) 9
6 6q14.1 rs17529111 0.22 1.05 (1.03, 1.08) 8
6 ESR1 rs2046210 0.34 1.08 (1.06, 1.10) 8, 57
6 ESR1 rs3757318 0.07 1.16 (1.12, 1.21) 8, 58
6 FOXQ1 rs11242675 0.39 0.94 (0.92, 0.96) 8
6 RANBP1 rs204247 0.43 1.05 (1.03, 1.07) 8
7 7q35 rs720475 0.25 0.94 (0.92, 0.96) 8
8 8p21.1 rs9693444 0.32 1.07 (1.05, 1.09) 8
8 8q21.11 rs6472903 0.18 0.91 (0.89, 0.93) 8
8 8q24 rs13281615 0.41 1.09 (1.07, 1.12) 8, 56
8 8q24.21 rs11780156 0.16 1.07 (1.04, 1.10) 8
8 HNF4 G rs2943559 0.07 1.13 (1.09, 1.17) 8
9 9q31 rs865686 0.38 0.89 (0.88, 0.91) 8, 59
9 9q31.2 rs10759243 0.39 1.06 (1.03, 1.08) 8
9 CDKN2A/B rs1011970 0.17 1.06 (1.03, 1.08) 8, 58
10 10q26.12 rs11199914 0.32 0.95 (0.93, 0.96) 8
10 ANKRD16 rs2380205 0.44 0.98 (0.96, 1.00) 8, 58
10 DNAJC1 rs7072776 0.29 1.07 (1.05, 1.09) 8
10 DNAJC1 rs11814448 0.02 1.26 (1.18, 1.35) 8
10 FGFR2 rs2981579 0.40 1.27 (1.24, 1.29) 8, 58
10 FGFR2 rs2981582 0.40 1.27 (1.24, 1.29) 8, 56
10 TCF7L2 rs7904519 0.46 1.06 (1.04, 1.08) 8
10 ZMIZ1 rs704010 0.38 1.08 (1.06, 1.10) 8, 58
10 ZNF365 rs10995190 0.16 0.86 (0.84, 0.88) 8, 58
11 11q13.1 rs3903072 0.47 0.95 (0.93, 0.96) 8
11 11q24.3 rs11820646 0.41 0.95 (0.93, 0.97) 8
11 CCDN1 rs614367 0.15 1.21 (1.18, 1.24) 8, 58
11 CCND1 rs554219 0.12 1.33 (1.28, 1.37 60
11 CCND1 rs75915166 0.06 1.38 (1.32, 1.44) 60
11 LSP1 rs3817198 0.31 1.07 (1.05, 1.09) 8, 56
12 12p13.1 rs12422552 0.26 1.05 (1.03, 1.07) 8
12 12q24 rs1292011 0.42 0.92 (0.90, 0.94) 8, 61
12 NTN4 rs17356907 0.30 0.91 (0.89, 0.93) 8
12 PTHLH rs10771399 0.12 0.86 (0.83, 0.88) 8, 61
13 BRCA2 rs11571833 0.008 1.26 (1.14, 1.39) 8
14 CCDC88C rs941764 0.34 1.06 (1.04, 1.09) 8
14 PAX9 rs2236007 0.21 0.93 (0.91, 0.95) 8
14 RAD51L1 rs999737 0.23 0.92 (0.90, 0.94) 8, 51
14 RAD51L1 rs2588809 0.16 1.08 (1.05, 1.11) 8
16 CDYL2 rs13329835 0.22 1.08 (1.05, 1.10) 8
16 FTO rs11075995 0.24 1.04 (1.02, 1.06) 10
16 FTO rs17817449 0.40 0.93 (0.91, 0.95) 8
16 TOX3 rs3803662 0.26 1.24 (1.21, 1.27) 8, 56
17 COX11 rs6504950 0.28 0.94 (0.92, 0.96) 8, 54
18 18q11.2 rs527616 0.38 0.95 (0.93, 0.97) 8
18 CHST9 rs1436904 0.40 0.96 (0.94, 0.98) 8
19 19q13.31 rs3760982 0.46 1.06 (1.04, 1.08) 8
19 MERIT40 rs8170 0.19 1.04 (1.01, 1.06) 8, 46
19 MERIT40 rs2363956 0.50 1.01 (0.98–1.04) 16
19 SSBP4 rs4808801 0.35 0.93 (0.91, 0.95) 8
21 NRIP1 rs2823093 0.27 0.92 (0.90, 0.94) 8, 61
22 22q12.2 rs132390 0.036 1.12 (1.07, 1.18) 8
22 MKL1 rs6001930 0.11 1.12 (1.09, 1.16) 8
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SNPs and Disease Risk Modification in BCRA Mutation Carriers

The same collaborative group was used to discover and validate genetic risk modifiers for individuals with a BRCA mutation 11. The CIMBA within COGS used a genome-wide association study in more than 2700 BRCA1 mutation carriers and selected about 32 000 SNPs to be genotyped with the iCOGS chip. A total of 11 705 BRCA1 mutation carriers were genotyped with the iCOGS chip. This analysis gave indication that 17 SNPs were of interest and were genotyped in further about 2500 BRCA1 mutation carriers 11. After analysis for breast and ovarian cancer risk in this specific population, a novel breast cancer risk modifier locus at 1q32 for BRCA1 mutation carriers was described (rs2290854, HR = 1.14, 95 % CI: 1.09, 1.20). Two further SNPs were associated with ovarian cancer risk, one locus at 17q21.31 (rs17631303, HR = 1.27, 95 % CI: 1.17, 1.38) and one at 4q32.3 (rs4691139, HR = 1.20, 95 % CI: 1.17–1.38). Previously described risk modifiers and this new locus are summarized in Table 2.

Table 2 Single nucleotide polymorphisms (SNPs) as modifiers of lifetime risk in BRCA mutation carriers (adapted from 11, 50, 62).

BRCA1 mutation carriers BRCA2 mutation carriers
SNP Gene/region HR (95 % CI) p value HR (95 % CI) p value Reference
CI: confidence intervals; HR: hazard ratio; SNP: single nucleotide polymorphism
rs1801320 RAD51 1.59 (0.96 to 2.63) 0.07 3.18 (1.39 to 7.27) < 0.001 63
rs1045485 CASP8 0.85 (0.76 to 0.97) 0.01 1.06 (0.88 to 1.27) 0.60 64
rs2981522 FGFR2 1.02 (0.95 to 1.09) 0.60 1.32 (0.20 to 1.45) < 10−7 65
rs3803662 TOX3 1.11 (1.03 to 1.19) < 0.01 1.15 (1.03 to 1.27) < 0.01 65
rs889312 MAPK3K1 0.99 (0.93 to 1.06) 0.90 1.12 (1.02 to 1.24) 0.02 65
rs3817198 LSP1 1.05 (0.99 to 1.11) 0.90 1.16 (1.07 to 1.25) < 0.001 66
rs13387042 2q35 1.14 (1.04 to 1.25) < 0.01 1.18 (1.04 to 1.33) < 0.01 66
rs13281615 8q24 1.00 (0.94 to 1.05) 0.90 1.06 (0.98 to 1.14) 0.20 66
rs8170 MERIT40 1.26 (1.17 to 1.35) < 10−8 0.90 (0.77 to 1.05) 0.20 46
rs2363956 MERIT40 0.84 (0.80 to 0.89) < 10−8 1.12 (0.99 to 1.27) 0.07 46
rs2046210 6q25.1 1.17 (1.11 to 1.23) < 10−8 1.06 (0.99 to 1.14) 0.09 67
rs9397435 6q25.1 1.28 (1.18 to 1.40) < 10−7 1.14 (1.01 to1.28) 0.03 67
rs11249433 1p11.2 0.97 (0.92 to 1.02) 0.2 1.09 (1.02 to 1.17) 0.015 67
rs2290854 1q32 1.14 (1.09 to 1.20) < 10−7 11
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When modeling breast cancer lifetime risk for BRCA1 mutation carriers it becomes clear that additional loci can help to estimate different lifetime risks substantially more accurate. In this model BRCA1 mutation carriers have an average lifetime risk of about 65 % (Fig. 1 from 11). When incorporating genetic variants from 10 additional genes into the lifetime risk calculation of BRCA1 mutation carriers 9, 11 it can be seen that at the 5 % and 95 % percentiles of the population, lifetime risks of about 80 % and 50 % can be calculated. At the rare maximum and minimum level risks, even almost 100 % and about 30 % lifetime risk are calculated, however these constellations are very rare.

Fig. 1.

Fig. 1

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 Predicted breast and ovarian cancer absolute risks for BRCA1 mutation carriers at the 5th, 10th, 90th, and 95th percentiles of the combined SNP profile distributions. The minimum, maximum and average risks are also shown. Predicted cancer risks are based on the associations of known breast cancer susceptibility loci (identified through GWAS) with cancer risk for BRCA1 mutation carriers and loci identified through the present study. Breast cancer risks based on the associations with: 1q32, 10q25.3, 19p13, 6q25.1, 12p11, TOX3, 2q35, LSP1, RAD51L1 TERT (figure and figure legend from 11).

Pathways for triple negative breast cancers

It has been known already for some time, that BRCA mutations occur more frequently in patients with triple negative breast cancer tumors. This implies that the missing function of BRCA during the pathogenesis of breast cancer ultimatively leads to a triple negative breast cancer. In unselected cases the frequency of BRCA mutations among triple negative breast cancer patients has been reported to range between 4 and 21 % (Table 3), however all populations were rather small and the study with the highest reported BRCA mutation prevalence included some familial cases. Other rare and medium to high penetrant mutations are discussed to be associated with triple negative breast cancers such as RAD51, TP53, BRAF, KRAS and others 12, 13.

Table 3 BRCA somatic mutation frequencies in unselected triple negative breast cancer patients.

Country Population Genotype method Number of triple negatives Number of BRCA2 mutations Number of all BRCA mutations Number of BRCA mutations (%) Reference
China unselected PCR-DHPLC 79 3 (3.8) 6 (7.6) 3 (3.8) 68
USA unselected Myriad 199 13 (6.6) 8 (4.0) 21 (10.6) 69
USA unselected Myriad 77 11 (14.3) 3 (3.9) 14 (18.2) 70
Netherlands unselected and familial Sanger, MLPA 199 36 (18.1) 6 (3) 42 (21.1) 71
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Besides these high penetrant genes there are several common variants that have been associated with a triple negative pathogenesis of breast cancer 14. There have been reports about genetic variants in the following genes or regions to be associated with triple negative breast cancer risk: TERT, MDM4, 19p13.1 10, 15, 16. Additionally some loci were specific for estrogen receptor negative tumors and not estrogen receptor positive tumors. These loci are FTO, LGR6, RALY and 2p24.1 10, 17.

Non-Genetic Risk Factors and Molecular Mechanism of Pathogenesis

Only few non-genetic risk factors can be linked to a specific mechanism of action, such as DNA damage by radiation or some chemicals. For most non-genetic risk factors the molecular mechanism of action is unclear. However during the last years some studies have been published that shed some light on the pathogenesis of non-genetic risk factors. These studies explore whether breast cancer risk factors are specifically modifying the risk for intrinsic subtypes of breast cancer. Others studies investigate the interaction between molecular mechanisms of pathogenesis and risk factors. Finally interactions between commonly established risk factors and genetic risk factors could help as well to acquire knowledge about the molecular pathogenesis of non-genetic risk factors.

Pregnancies and breastfeeding

Pregnancies and breastfeeding are thought to have two effects on a womanʼs breast cancer risk. During and shortly after pregnancy, women have an increased risk of breast cancer, but later in life the breast cancer risk is lower in comparison with women who have never given birth to a child 18. Most studies use a design that examines women at a later stage of their life cycle and provides data on the risk-reducing effect. Women with no live deliveries have a lifetime risk of about 6.3 % up to the age of 70 19. The risk decreases with each pregnancy. The relative risk of breast cancer decreases by 4.3 % (95 % CI, 2.9 to 5.8) for every 12 months of breastfeeding, in addition to a decrease of 7.0 % (95 % CI, 5.0 to 9.0) for each birth 19.

There is some evidence that nulliparity and increasing age at first birth are associated with an increased risk for estrogen receptor positive breast cancer as well as progesterone receptor positive breast cancer, but not for triple negative breast cancer 20. Similar associations are seen in case-case analyses. Patients with ER negative tumors are more likely to be nulliparous or having a high age at first birth 20.

The molecular mechanisms that link non-genetic risk factors to the pathogenesis of breast cancer are mainly unknown, however some first insights have been found in studies that look at the interaction between non-genetic and genetic risk factors with regard to breast cancer risk. A study within the COGS examined the interaction between 10 established environmental risk factors and 23 SNPs 21. This study could show that the effect of rs3817198 in LSP1 (Lymphocyte-specific protein 1) is greater in women with more births 21. LSP1 is expressed in white blood cells and has been described to regulate neutrophil motility and adhesion to extracellular matrix proteins 22.

However it has to be pointed out that these kind of interaction analyses require really large sample sizes and a well-performed quality control of genotype and clinical data, as earlier studies with a smaller sample size (26 000 cases and 32 000 controls) were not able to show this LSP1-association after adjustment for multiple testing, although the nominal p-value for this association was 0.002 23.

Mammographic density

Mammographic density (MD) is one of the most important risk factors for breast cancer. Women with a high MD have an up to fivefold increase in the risk for breast cancer 24, 25, 26. Because of its importance, knowledge about the molecular mechanisms, and how mammographic density is linked to breast cancer pathogenesis, could be of special use for breast cancer prevention.

Some studies investigated whether mammographic density is a risk factor for a certain type of cancer. Early studies have shown that, for example, tumor histology was associated with breast cancer risk 27. In a recent case-control study mammographic density seemed to be a stronger risk factor for cancer with a higher grading and estrogen receptor negative breast cancer. These results were confirmed in a case-case analysis associating a higher mammographic density with a lower percentage of estrogen receptor positive tumors 28. The same study implied that the association with progesterone receptor positivity was inverse to that with the estrogen receptor. The authors concluded that mammographic density could specifically increase breast cancer risk through the progesterone receptor pathway, possibly involving RANK and RANKL 28.

The same case-case study did not see an association between mammographic density and tumor proliferation as assessed by Ki-67, however the association of commonly known breast cancer risk factors with mammographic density was different in patients with a high and a low proliferative tumor 29, suggesting that there might be a more complex relation between several risk factors and breast cancer pathogenesis.

Recently it could be shown on the protein level that the surface protein CD36 might be the mediator, which is responsible for high mammographic density in both healthy breast tissue and breast tumors 30. It was shown that CD36 induces the transformation of stromal cells to adipocytes and that it suppressed the expression of extracellular matrix proteins. This was seen in both healthy breast tissue and tumor tissue 30. This work is one of the first to shed light on the molecular background of breast density regulation in healthy and malignant tissue.

Finally there have been genetic risk factors that could be associated with several risk factors for breast cancer and breast cancer risk itself. One of these examples is the SNP rs3817198 in the gene LSP1. The rare allele was associated with both, a higher breast cancer risk and a higher mammographic density 31. The same was seen for rs10483813 in RAD51L1 31. The SNP in LSP1 seems to be of special interest for breast cancer as it does not only alter breast cancer risk, is interacting with parity and mammographic density as breast cancer risk factors, but has been described as a prognostic factor in hormone receptor breast cancer patients as well 32.

These findings are some of the first examples that can help to understand through which molecular pathways breast density is linked to the pathogenesis of breast cancer. It will need more comprehensive analyses to identify further molecules and pathways that help to understand those connections.

Breast Cancer Assessment in Practice

The information available about breast cancer risk has now become truly comprehensive. It has been applied in practice in the large breast cancer prevention trials, selecting for women with an increased risk for breast cancer, but the use of breast cancer risk assessment tools in clinical practice appears to be limited with regard to all aspects of prevention, intensified early detection, prophylactic medication, and prophylactic surgery.

Several tools have been developed for assessing breast cancer risk; some of the most frequently used are summarized in Table 4 in relation to their use of risk factor information 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43. Some have been in use for decades already, such the Gail model 41. More recently developed risk models aim at the combination of several risk factors, specifically mammographic density and genetic risk factors. The Tice model was one of the first to include mammographic density 44. Together with genetic risk factors these models will possibly improve with regard to clinical utility. A case-control study has already combined breast density, breast cancer risk SNPs and clinical risk factors for the prediction of breast cancer risk 43.

Table 4 Breast cancer risk assessment tools (adapted from 50).

Risk factor NCI model Claus model BRCAPro Tyrer et al. BOADICEA Tice et al. Darabi et al.
Reference 40, 41 42 33, 34, 35 38 36, 37, 39 44 43
BMI: body mass index; BOADICEA: Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm; HRT: hormone replacement therapy; NCI: National Cancer Institute
Age + + + + + + +
Age at menarche + + +
Age at menopause +
BMI + +
Age at first birth + + +
History of breast biopsies + + + +
History of premalignant lesions + + +
HRT +
Family history of breast cancer + + + + + +
Family history of ovarian cancer + + +
Family history of other cancers +
Contralateral breast cancer + + +
Male breast cancer +
BRCA mutation (+) +
Low penetrant genetic variants +
Ethnicity + + +
Mammographic density + +
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Models for breast cancer risk prediction do not at present distinguish between distinct molecular subtypes, although subtype-specific risk factors have already been identified 20, 45, 46, 47, 48, 49. However, predicting breast cancer and assessing specific risks can only make sense if they address women who have a high likelihood of developing a cancer with an unfavorable prognosis 50. As early detection and cancer treatment also have an impact on survival, studies would ideally have to be designed in order to predict which women are likely to have an aggressive tumor that can be detected early 50.

Conclusions

Large international consortia and large scale genotyping studies can help to systematically work on the molecular background of breast cancer etiology and ultimately shed light on how these risk factors are connected to the pathogenesis on the molecular level. Over the last few years, very first insights have been described how non-genetic risk factors are linked to breast cancer risk on the molecular level. The scarceness of data that link breast cancer risk factors to molecular pathways of the pathogenesis makes clear that many more efforts have to be made in the future to elucidate these important causal links. However the plenty of new risk factors on the genetic side promise the discovery of new connections in the network of breast cancer risk factors.

Footnotes

Conflict of Interest The authors declare that they have no conflict of interest.

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