Breast cancer is the most common cancer in women worldwide and the second leading cause of cancer deaths. Although early diagnosis, outcome prediction and treatment options are the ultimate objectives when assessing breast cancer patients, the methodology behind this clinical assessment varies and has gradually evolved from using standard clinical criteria into incorporating high-throughput genome-wide analysis. Early methods involved evaluating tumor size and spread as well as histological assessment (tumor grade). Later, the expression of hormone/growth receptors (ER, PR, and HER2) was added to the standard stratification of breast cancer patients. More recently, molecular approaches, which are based on the expression of a well-defined set of genes, have subdivided patients into five clinically relevant subtypes which not only predict prognosis and dictate treatment choice but also complement standard assessment. The advent of genome-wide analysis has produced the most robust classification system of breast cancers by coupling specific genetic aberrations (single nucleotide mutations and gene copy number variations) with gene expression profiles. Although these genome-wide approaches offer a promising future for breast cancer prognosis and treatment options, they are still not clinically feasible for standard population-based screening. Nonetheless, these approaches are becoming faster and more reliable in understanding the molecular architecture of breast cancer and are slowly paving the way towards personalized treatments which are tailored to individual patients. In the light of a rapidly evolving field of breast cancer genomics, this chapter highlights key standard and upcoming approaches for diagnosis, prognosis and treatment and discusses the feasibility of genome-oriented personalized treatments.
Genome-wide association (GWA) studies and tumor-specific epigenome, transcriptome and genome sequencing projects are generating an ever-growing list of susceptibility alleles, as well as putative gain- and loss-of-function gene mutations associated with cancer. These genetic changes ultimately need to be validated to determine their contribution to the initiation, progression and likelihood of treatment response for various cancers. The bottle-neck is no longer obtaining sequence data or completion of the GWA studies, but rather the ability efficiently to validate candidate genes identified by these projects. In vivo studies in animal models are the “gold standard” for validation of these candidate drivers and modifiers of cancer. Furthermore, once a gene product or molecular pathway has been validated as playing an important role in the development or progression of cancer, animal models provide the necessary preclinical data for evaluation of the efficacy and toxicity of new therapeutics targeting that gene or pathway. As such, animal models play an essential role in cancer research by facilitating the translation of genomic discoveries into preclinical studies that precede new targeted therapies for cancer. In this chapter, we will discuss vertebrate and invertebrate animal models as they apply to cancer genomics, as well as key technologies employed. In particular, we will focus on the use of murine and zebrafish human tumor xenografts and transgenic models.
From the first descriptions of cancer in Egypt around 3000 BC to our current “one week” whole-genome sequence, the history of integrating new ideas into the practice of medicine has been unrelenting, although not without its failures as well its successes. This chapter represents a brief historical summary of some of the key success stories in our understanding of cancer that has led to our current age of cancer genomics. As the Chinese proverb states, “When you drink from the well, remember who dug it”, and reflecting on this rich and varied history, we conclude the chapter with a discussion of current and future challenges to the application of our new and developing understanding of cancer genomes to patient therapy.
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