Genomics may be described as the comprehensive analysis of genes and their DNA structure and function or as the scientific discipline looking for information about the entire genome. Before the existence of omics technologies, DNA was sequenced gene by gene, but improving informatics technology and integrating them to the biology have allowed the sequencing of the entire genomes of many types of organisms and the classification and storage of DNA sequences in genomic databases. Variations of the genes or DNA sequences can be created through gene amplification, gene deletion, or gene rearrangement [8].

Copy number aberrations (CNA) or copy number variations (CNV) are the names that encompass the alterations of the organization and amount of DNA within a cell. Genetic alterations are generated by deletions, duplications, inversions, or translocations of chromosomes; moreover these modifications are capable of being inherited. CNA or CNV can disturb the normal transcriptional activity of a cell, increasing or decreasing it, changing the normal expression of genes [9].

Gene amplification is one of the mechanisms in which a gene can be overexpressed and cause the activation of oncogenes; therefore it is related to disease progression and poor prognosis. It is created by an increase in the copy number of a loci or restricted area of a chromosome arm. Amplification occurs recurrently on some chromosomal locations, indicating the common activation of some oncogenes during tumor development [10]. The HER2 is the most studied breast cancer oncogene; it is located on chromosome 17q21.1 and encodes a transmembrane protein that is similar to the epidermal growth factor the HER1. A normal cell presents two copies of the HER2 gene and about 50,000 copies of the protein, whereas by gene amplification in a breast cancer cell, there can be more than one copy of the HER2 gene and more than one million copies of the protein [11].

High-throughput technologies are needed to discover, monitor, and quantify these DNA modifications. The most widely used technique involves the microarrays that simultaneously monitor the expression levels of thousands of genes inside different samples. This technique has advanced rapidly in recent years. It involves a surface fixed with either a tissue or a panel of different DNA or RNA molecules, proteins, or antibodies that have the possibility of linking up with a corresponding DNA/RNA/protein/antibody from a sample. Genomic microarrays, also called array comparative genomic hybridization (array-CGH), are used to study and quantify chromosomal abnormalities, microdeletions and microduplications, and copy number aberrations (CAN), at a wide level in an entire genome. Array-CGH has a refined process and resolution, which enables evaluation of the genome of any cell, while chromosomal-CGH allows the identification of gene regions where there is DNA gain or DNA loss [1, 12].

Chromosomal-CGH studies have identified that the loss of 16q is one of the most consistent DNA aberrations found in infiltrating lobular carcinoma (ILC). Etzell et al. studied genomic alterations in lobular carcinoma in situ (LCIS). Using comparative genomic hybridization, they found loss of chromosome 16q was in 88 % of cases [13]. Mastracci et al. studied the genetic profile of atypical lobular hyperplasia (ALH) and lobular carcinoma in situ (LCIS), using microarray comparative genomic hybridization (CGH). They found a common alteration in both ALH and LCIS—the loss of 16q21-q23.1 [14]. This common genetic alteration highlights the possibility of a relationship between ILC and LCIS as they may be different forms of the same disease, and it seems that lobular carcinoma in situ (LCIS) is a precursor to infiltrating lobular carcinoma and therefore a marker of risk for breast cancer. This serves as an example of the application of genomic studies in order to discover breast cancer initiation, progression, and metastasis [13, 14].

Breast cancer gene expression profiling permits the collection of all the information about a whole set of genes in a tumor cell, including variations, gene expression, and the way those genes interact with each other and with the environment. The associations of specific genes with a common characteristic of expression or phenotypes are called gene profiles or gene signatures. Gene expression profiling has been used to create a complete new classification of breast carcinomas. The different kinds of clinical breast cancer have been correlated with diversity in gene expression profiles, which usually are studied using DNA microarray techniques [1]. Perou et al. using DNA microarrays in samples of breast cancer and normal breast tissue studied the presence of sets of genes that they called intrinsic genes, because these genes were found repeatedly inside the same patient biopsies but were not found repeatedly in all samples from other patients. A new group of genes associated with different breast cancer types were discovered, showing four distinct molecular subgroups: ER+/luminal-like, basal-like, HER2 enriched, and normal breast-like [15]. This new classification is being studied and recognized as distinct diseases with different treatment options [16, 17]. Table 1.1 summarizes the main findings of several studies that confirm the existence of the intrinsic subtypes, the technology used, and the novel finding that each study provided to this new breast cancer classification.

The molecular subtypes in breast cancer have shown different tumor characteristics, tumor aggressiveness, and different response to certain types of chemotherapy. They all have the ability to progress to metastases, but with differences regarding their leaning toward relapse into different organs. Smid et al. used microarrays and analyzed them with statistical significance analysis of microarrays (SAM). Studying the preference for organ-specific metastases of each of the intrinsic subtypes, they found that the rate and location of relapses are correlated to different molecular subtypes and metastases conserve genetic similarities with their primaries [24].

Following the intrinsic gene research of today, a new classification of breast cancer exists using omics technologies. New trials show the possibility of more subtypes, but this pathway is barely started, and currently it is not possible to exactly compare the new subtypes with the classical ones, and it is necessary for more studies to set up many new targeted therapies for these new molecular subtypes [11, 15].

Applying this new genetic information on the cluster of genes related to breast cancer, and using the new subtypes or intrinsic types of breast cancer, there is now a connection between the lab and the patient with the creation of several molecular assay tests that use genomics to calculate risk assessment and prognosis, creating a personalized diagnosis, prognosis, and therapy. This genomic test calculates certain clinical outcomes, such as patient risk of relapse if avoiding chemotherapy or if treated only with hormonal therapy and patients having more global risk of relapse. Although these tests searched different genes, when they are applied within the same patient data, they show similar results on prognosis of breast cancer. The MammaPrint and Oncotype DX are the tests most used and studied [1, 3].

The MammaPrint analyzes 70 genes and shows which patients have a low-risk molecular profile and therefore can avoid chemotherapy. It also identifies those women with good clinical factors but who carry poor prognosis genes [1, 3, 4]. Van de Vijver used inkjet-synthesized oligonucleotide microarrays to study the 70-gene expression profile that is associated with prognosis in patients with breast cancer and validate the classification system to predict the likelihood of distant metastases within 5 years [25].

Oncotype DX uses the intrinsic subtype model with 21 genes and is used in estrogen and progesterone receptor-positive and lymph node-negative breast cancer, to identify patients who can avoid adjuvant chemotherapy [1, 3]. Paik et al., using reverse transcription polymerase chain reaction (RT-PCR), studied 21 selected genes and found the correlation with the likelihood of distant recurrence in patients with node-negative disease and treated with tamoxifen, validating then a recurrence score [26, 27].

Although these tests are beginning to be used in daily medical practice, there is still no consensus about their reliability or guidelines for use, because there is little information about the impact that these tests may have on clinical outcomes in the long term. Where there seems to be agreement is that the information provided by these tests must be taken into account as additional information to that obtained by conventional methods [16, 17].

Regarding the possibility of properly selecting the patient who should use chemotherapy and the one who should avoid it, as personalized medicine should be able to do, it seems necessary to relate a set of genes and its regulation systems with a clinical outcome [4, 11]. In order to accomplish more scientific evidence, prospective clinical trials—MINDACT for MammaPrint and TAILORx for Oncotype Dx— are trying to improve their clinical application [28–30].

One of the main drawbacks of applying past genomics research on a daily clinical basis is the lack of validation because these trials were based on small samples. That is why the latest research is focused on using more biopsy samples and more genetic analysis than the first trials, creating a genomics landscape of breast cancer [6]. Using larger samples in order to integrate breast cancer genome and transcriptome, and search for molecular drivers and gene expression, seems to be more reliable than obtaining information through the analysis of smaller samples [6, 31]. The creation of biobanks allows the storage of multiple samples of different types of cancer, from different patients, and treatments using different techniques of conservation. The existence of a useful biobank offers multiple and extensive databases using many categories, such as family samples, pre- and posttreatment samples, and population characteristics, to classify and identify the samples. To improve the biobank utilization, ethical issues must be addressed regarding the use of the samples for different studies [32].

One example of storage of new public oncogenome data is the Cancer Genome Atlas (TCGA), which is “a comprehensive and coordinated effort to accelerate the understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing.” This atlas is created with the efforts of the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI) that are applying omics technologies to profile DNA, RNA, protein, and others that can be related to this disease. This data is being stored within several samples of cancer, creating a new list of genes, mutations, and molecules.

Now there are tons of genetic data related to breast cancer, but there is lack in the final connection with the actual patient [6, 11]. Curtis et al. used almost 2,000 samples of breast cancer to analyze their genome and transcriptome within copy number variants, single nucleotide polymorphism, and acquired somatic copy number aberrations, and they found new subgroups to create a novel molecular stratification of the breast cancer population [33]. The Cancer Genome Atlas network 2012 used the analysis of large amounts of data through the TGGA platform studying the DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing, and reverse-phase protein arrays from breast cancer, showing the existence of four main breast cancer classes but associating them with several gene mutations and subgroups of proteins expression. They also found many molecular commonalities between basal-like breast tumors and high-grade serous ovarian tumors [34].

Epigenetic modifications are the alterations found in DNA not affecting its primary structure, such as DNA methylation, histone modifications, and chromatin or nucleosomal remodeling; these alterations are heritable and reversible. Therefore epigenomics is the study of the complete set of those epigenetic modifications [7, 35].

DNA methylation, histone modifications, and chromatin or nucleosomal remodeling mutually interact with each other to regulate gene expression. The modifications obtained through them sometimes end up with aberrant transcription disrupting the normal regulation of a cell. The epigenetics processes modulation of chromatin structure to form a loosely packed DNA that is transcriptionally active, called euchromatin, or a tightly packed DNA that is transcriptionally inactive, the heterochromatin. Therefore, these processes can either turn on or turn off the gene expression.

Cancer seems to be driven by these epigenetic alterations, which cause aberrant cell regulation that result in a change in expression patterns of genes implicated in cellular proliferation, survival, and differentiation. Therefore, in breast cancer cells, DNA methylation, histone modifications, and chromatin or nucleosomal remodeling can lead to initiation, promotion, and maintenance of carcinogenesis and treatment failure. Understanding epigenetic alterations that initiate and maintain gene silencing and oncogene activation in cancer cells is necessary in order to find clinical applications of epigenomics [36–38].