Abstract
Breast cancer is the most diagnosed cancer in women, and the second cause of cancer-related deaths among women worldwide. It is expected that more than 240,000 new cases and 40,450 deaths related to the disease will occur in 2016. It is well known that inherited genetic variants are drivers for breast cancer development. There are many mechanisms through which germline genetic variation affects prognosis, such as BRCA1 and BRCA2 genes, which account for approximately 20% of the increased hereditary risks. Therefore, it is evident that the genetic pathways that underlie cancer development are complex in which networks of multiple alleles confer disease susceptibility and risks. Global analyses through genome-wide association studies (GWAS) have revealed several loci across the genome are associated with the breast cancer. This chapter compiles all breast GWAS released since 2007, year of the first article published in this area, and discuss the future directions of this field. Currently, hundreds of genetic markers are linked to breast cancer, and understanding the underlying mechanisms of these variants might lead to the discover of biomarkers and targets for therapy in patients.
Keywords
- breast cancer
- genome‐wide association studies (GWAS)
- susceptibility
- Loci
- SNPs
1. Introduction
One of the main goals of human genetics is to understand genetic pathways underlying traits. It has been highly successful the gene mapping of disorders with a Mendelian pattern of inheritance using the tendency of genes and other genetic markers to be inherited together. It is well known that genetic variants underlying these single‐gene Mendelian disorders are rare in the population and tend to be highly penetrant, which means that a high percentage of carriers of the genotype will manifest the phenotype. On the other hand, mapping of non‐Mendelian (or complex) traits, cases in which variants in multiple genes contribute to the phenotype, was only possible after sequencing and study of the human genome. Inherited variants underlying complex diseases, opposing the Mendelian disorders, have modest penetrance but higher frequency in the population [1–4] (Figure 1). Thus, efforts have been made to identify genes and pathways that control human traits, and, in the future, predict illness and establish more appropriated methods of treatment.
A reflection of the urgency to unveil this research field is notable when looking through breast cancer numbers. Worldwide, the scenario is dramatic, with more than one million new cases of breast cancer diagnosed yearly (cancer genome atlas network 2012), and the fifth cause of death from cancer overall. In developing countries, breast cancer is the second cause of death from cancer and accounts for 15.4% of overall cancer‐related deaths in women [5]. Moreover, it corresponds to the most common cancer‐related death in women in the less developed regions (14.3%). In the United States, breast cancer is the second cause of cancer‐related deaths among women, and it is estimated that one of eight American women will develop invasive breast cancer over the course of her lifetime. Accordingly, in the year of 2016, only in the United States, more than 240,000 new cases of the disease and 40,450 related deaths are expected [6].
Breast cancer comprises multiple diseases harbouring different genetic alterations; each subtype responds differently to treatments, and this feature leads to distinct clinical outcomes [7, 8]. Based on tumour histological biomarkers, breast cancer can be separated into three basic clinical types, such as HR positive (estrogen receptor and progesterone receptor), HER2+ (human epidermal growth factor receptor 2 positive), and triple‐negative breast cancer, which are an essential part of the diagnostic workup of all breast cancer patients [9]. Approximately, 85% of all breast cancers are HR positive, about 20% are HER2+ and nearly 15% are triple‐negative.
It is well understood that breast cancer is a complex and heterogeneous disease with a multi‐factorial etiology involving genetic, dietary, hormonal and reproductive factors. Among these, genetic is of particular importance. Epidemiological studies estimate that women with history of breast cancer in a first‐degree relative show nearly twofold higher risk to develop breast cancer than women without a family history, indicating that the genetic factors are important determinants of disease risk [10]. At least 10–15% of all breast cancer cases may be due to the inheritance of a single gene mutation or multiple genetic variants [10, 11]. In the 1990s, two major susceptibility genes for breast cancer, breast cancer 1 (BRCA1) and breast cancer 2 (BRCA2), were the first ones to be identified on the long arm of chromosome 17 and the short arm of chromosome 13, respectively [12–14]. These genes are responsible for 20–30% of hereditary breast cancer cases worldwide. BRCA1 and BRCA2 are important on the maintenance of genome stability by playing a critical role in the regulation of different cellular processes, such as transcription, cell cycle, DNA repair, cell proliferation and differentiation, in response to DNA damage [15]. Indeed, woman carrying such pathogenic variants have an increased risk of 60–80% of breast cancer [16, 17]. Moreover, inherited BRCA1/2 gene mutations are associated with a 39–80% lifetime risk of female breast cancer [18–21]. It is also well established that BRCA1/2 carriers with breast cancer have a strong lifetime risk of developing contralateral breast cancer range from 10 to 40% and are 2–6 times higher than the risk for non‐carriers [22–27].
The identification of mutations in BRCA, considered as a critical factor for the development of breast cancer in some women, has boosted the interest of scientists to discover more mutations that drive tumour development. In this context, advances in DNA sequencing technologies empowered massive parallel sequencing, and, as a consequence, it has led to a fantastic discovery and assignment of other hereditary pre‐disposition genes to high (TP53, PALB2, PTEN), moderated (CHEK2, ATM, NF1, NBN) and elevated, but imprecise, breast cancer risk (CDH1, STK11) [28–34]. Altogether, high and moderate penetrance breast cancer susceptibility mutations in these genes account for just over 30% of familial breast cancer cases, because linkage studies are not amenable to the identification of common alleles with small effects.
However, the major advance over the several years has led by genome‐wide association studies (GWAS). This approach is based on genome‐wide genotyping for thousands to millions of single‐nucleotide polymorphisms (SNPs) in a large number of individuals and contrast between the groups with and without a specific phenotype. Therefore, this approach has successfully identified thousands of
2. Genome‐wide association studies
In the past, studying polymorphisms were limited by the technologies that only permitted analysis of one or a few
GWAS have emerged as a powerful new approach that has the capacity of analysing the whole human genome in order to identify common variations in the population possibly associated with genetic factors of a specific disease. In other words, the intent of GWAS is to predict who is at the risk and develop new strategies for prevention and treatments of genetic diseases [39]. One of the initial successes of GWAS was the identification of the
The GWAS technology is based on genotyping platforms (chip‐based microarray technology) that can evaluate hundreds to thousands of SNPs simultaneously. The two primary platforms that have been used for most GWAS were developed by Illumina (San Diego, CA) and Affymetrix (Santa Clara, CA). These two competing technologies use different approaches to detect SNP variation. Accordingly, the Affymetrix platform prints short DNA sequences on a chip that recognizes a specific SNP allele. Alleles (i.e. nucleotides) are detected by a differential DNA hybridization between the samples. Illumina, on the other hand, uses a bead‐based technology with slightly longer DNA sequences to detect alleles. The Illumina technology is more expensive, but provides better specificity. Hence, it is possible to conduct association studies using sets of SNPs that tag most known common variants in the genome, and therefore, scan for the associations without prior knowledge of function or position [39, 43].
GWAS arrays have identified SNPs that are associated with many complex diseases or traits [44]; although they do not contain all mapped SNPs, rather they contain only index SNPs that represent SNPs in the same linkage disequilibrium (LD) block. The SNPs identified by GWAS are significantly correlated with a disease (or case) and are called as risk‐associated SNPs, and the genomic regions containing the SNPs are called as risk
To move from the index SNP to a more refined list of putative causal SNPs located within the identified region, another approach called fine‐mapping has also been used. Fine‐mapping studies employ dense genotyping arrays that contain all common SNPs within the previously identified risk
3. GWAS in breast cancer
Over the past years, the results from GWAS have been published for breast cancer reporting well‐validated novel associations. In total, these scans have identified approximately 100 common genetic susceptibility
The first GWAS for breast cancer was published in 2007 and identified novel susceptibility
In the following years, nine articles using GWAS to identify genetic factors linked with breast cancer were published [61–69]. These works have not only increased the number of new markers associated with the illness, but also validated the genetic factors that were previously identified. Furthermore, the cancer genetic markers of susceptibility (CGEMS) group detected the association of FGFR2 in a second genome scan, genotyping 528,173 SNPs in 1145 cases of invasive breast cancer among postmenopausal white women and 1142 controls they detected a set of four SNPs in intron 2 of FGFR2 [62]. All the variants are related with FGFR2 expression in normal breast tissue, and interesting two of them are likely related to biological mechanism for interrupting active transcription factor‐binding sites [70]. The deCODE group later on, using approximately 1000 unselected breast cancer cases and illumina 317k panel, found two additional
In 2010, a group conducted a new GWAS in which 582,886 SNPs were genotyped in 3659 cases with a family history of the disease and 4897 controls. They identified five new susceptibility
Three studies were published in 2011 revealing new
Long et al. [74] aimed to discover novel genetic susceptibility
In order to obtain a more comprehensive knowledge on the genetic factors controlling breast cancer development, the project collaborative oncological gene‐environment study (COGS) was created through collaboration among four consortia [56]. The project consisted of a meta‐analysis of nine GWAS, involving 10,052 breast cancer cases and 12,575 controls of European ancestry. 29,807 SNPs were selected for further genotyping. The selected SNPs were genotyped in 41 studies in BCAC, using 45,290 cases and 41,880 controls in European ancestry population. Another important point of the study was the custom Illumina iSelect genotyping array (iCOGS) utilized that comprises more than 200,000 SNPs. The combined efforts identified SNPs at 41 new breast cancer susceptibility
GWAS have also been proven to be a powerful strategy to identify genetic factors associated with adverse reactions caused by drugs. The first GWAS for chemotherapy‐induced alopecia was conducted in Japanese breast cancer patients, and identified SNPs significantly associated with drug‐induced grade 2 alopecia. For instance, the rs3820706 (calcium channel voltage‐dependent subunit beta) on 2q23 and its nearby SNP rs16830728 could be associated with significant molecular alterations in genes such as ion channel‐related genes and genes related to the β‐catenin signalling pathway [81].
The lack of concordance among some studies for breast cancer led a group to study 41 common non‐synonymous SNP (nsSNP) for which evidence of association with breast cancer risk had been previously reported. This work combined 38 studies of white European women (46,450 cases and 42,600 controls), and showed strong association for one previously reported, 7q21; one novel susceptibility
One particular article published in 2014 called attention for running a meta‐analysis of GWAS of three mammographic density phenotype: dense area, non‐dense and percent density in up to 7916 women in stage 1 and 10,379 women in the second stage. The results showed
In 2015, there were more than 90 established breast cancer risk
In 2016, three GWAS were three GWAS were published describing novel genetic susceptibility
4. Conclusion
GWAS have been successful in identifying many genetic variants that are significantly associated with human diseases. However, a gap has emerged between the ability to detect these associations and the ability to meaningfully interpret their biological significance [90]. Currently, the challenges facing GWAS include the translation of associated
Abbreviations
GWAS | Genome‐wide association studies |
BRCA 1 | Breast cancer 1 gene |
BRCA 2 | Breast cancer 2 gene |
SNP | Single‐nucleotide polymorphisms |
HER 2+ | Human epidermal growth factor receptor 2 positive |
HR | Progesterone receptor |
LOD | Logarithm of odds |
LD | Linkage disequilibrium |
TP53 | Tumour protein p53 |
PALB2 | Partner and localizer of BRCA2 |
PTEN | Phosphatase and tensin homolog |
CHEK2 | Checkpoint kinase 2 |
ATM | Serine/threonine kinase |
NF1 | Nuclear factor 1 |
NBN | Nibrin |
CDH1 | Cadherin‐1 |
STK11 | Serine/threonine kinase 11 |
FGFR2 | Fibroblast growth factor receptor 2 |
TNRC9 | Trinucleotide‐repeat‐containing 9 |
LSP1 | Lymphocyte‐specific protein 1 |
IGF2 | Insulin‐like growth factor 2 |
CGEMS | Cancer genetic markers of susceptibility |
RNF146 | RING finger protein 146 |
RAD51L1 | DNA repair protein RAD51 homolog 2 |
NOTCH2 | Neurogenic |
FCGR1B | Cluster of differentiation 64 |
BCAC | Breast Cancer Association Consortium |
SLC4A7 | Solute carrier family 4, sodium bicarbonate cotransporter, member 7 |
NEK10 | NIMA‐related kinase 10 |
COX11 | Cytochrome C oxidase copper chaperone |
ER | Estrogen receptor |
KLF4 | Kruppel‐like factor 4 |
RAD23B | UV excision repair protein RAD23 homolog B |
ACTL7A | Actin‐like protein 7A |
ESR1 | Estrogen receptor 1 |
ERB4 | Epidermal growth factor receptor |
PTHLH | Parathyroid hormone‐related protein |
NRIP1 | Nuclear receptor‐interacting protein 1 |
COGS | Collaborative oncological gene‐environment study |
LGR6 | Leucine‐rich repeat‐containing G‐protein coupled receptor 6 |
AREG | Amphiregulin |
ZNF365 | Zinc finger protein 365 |
IGF1 | Insulin‐like growth factor 1 |
TMEM184B | Transmembrane protein 184B |
SGSM3 | Small G protein signaling modulator 3 |
KLF5 | Krueppel‐like factor 5 |
WDR43 | WD repeat domain 43 |
PPIL3 | Peptidyl‐prolyl cis‐trans isomerase‐like 3 |
WT1 | Wilms tumour protein |
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