‘Omics Approaches to Explore the Breast Cancer Landscape

Abstract

Breast cancer incidence is increasing worldwide with more than 600,000 deaths reported in 2018 alone. In current practice treatment options for breast cancer patients consists of surgery, chemotherapy, radiotherapy or targeting of classical markers of breast cancer subtype: estrogen receptor (ER) and HER2. However, these treatments fail to prevent recurrence and metastasis. Improved understanding of breast cancer and metastasis biology will help uncover novel biomarkers and therapeutic opportunities to improve patient stratification and treatment. We will first provide an overview of current methods and models used to study breast cancer biology, focusing on 2D and 3D cell culture, including organoids, and on in vivo models such as the MMTV mouse model and patient-derived xenografts (PDX). Next, genomic, transcriptomic, and proteomic approaches and their integration will be considered in the context of breast cancer susceptibility, breast cancer drivers, and therapeutic response and resistance to treatment. Finally, we will discuss how 'Omics datasets in combination with traditional breast cancer models are useful for generating insights into breast cancer biology, for suggesting individual treatments in precision oncology, and for creating data repositories to undergo further meta-analysis. System biology has the potential to catalyze the next great leap forward in treatment options for breast cancer patients.

The ZNF750–RAC1 axis as potential prognostic factor for breast cancer

“…In the last decade, OMICS technologies have contributed to our understanding of the pathogenesis of cancer [27][28][29][30] leading to the development of precision oncology 31,32 , where selection of the patients for treatment is fundamental for better therapy outcome 33 . Therefore, identification of novel biomarkers for guiding treatment selection is a key requirement 14,[34][35][36] .…”

A Critical Review of Bottom-Up Proteomics: The Good, the Bad, and the Future of This Field

“…Mass spectrometry (MS)-based proteomics has led to the possibility of characterizing and quantifying the protein profile of biological specimens, as well as the possibility to discover their complex interactions involved in various specific pathologies. For example, various proteomic approaches combined with genomic analysis have been used in cancer research for obtaining more information about the molecular basis of tumor genesis and the development of more effective anticancer therapies [17][18][19].…”

“…This type of quantitation works well enough for pilot and preliminary studies, but findings should be confirmed later using a more precise, reliable method. Labeling methods for quantification are also abundant, including methods such as isotope-coded affinity tag (ICAT), stable isotope labeling by amino acids in cell culture (SILAC), 15 N/ 14 N metabolic labeling, 18 O/ 16 O enzymatic labeling, isotope coded protein labeling (ICPL), tandem mass tags (TMT), and isobaric tags for relative and absolute quantification (iTRAQ) [173]. The absolute quantification (AQUA) method allows for the precise determination of protein expression and even post-translational modification levels by mimicking the exact peptide of interest, with the exception of stable isotope enrichment [174].…”

The ongoing trends of patient‐derived xenograft models in oncology

“…However, a study from the Mayo Clinic showed that only a small number of patients with actionable genetic mutations could benefit from genotype‐directed therapy [17]. Recently, the integration of PDX models to multi‐omics technologies, such as genomics, transcriptomics, proteomics, and metabolomics, has contributed abundantly to the understanding of cancer biology and the discovery of novel targets or biomarkers [18]. It is reasonable to assume that the combination of PDX 2.0 models with omics analysis could be a robust method to indicate individual therapeutic regimens.…”