Proteomics and its applications in breast cancer

Abstract

Breast cancer is an individually unique, multi-faceted and chameleonic disease, an eternal challenge for the new era of high-integrated precision diagnostic and personalized oncomedicine. Besides traditional single-omics fields (such as genomics, epigenomics, transcriptomics and metabolomics) and multi-omics contributions (proteogenomics, proteotranscriptomics or reproductomics), several new "-omics" approaches and exciting proteomics subfields are contributing to basic and advanced understanding of these "multiple diseases termed breast cancer": phenomics/cellomics, connectomics and interactomics, secretomics, matrisomics, exosomics, angiomics, chaperomics and epichaperomics, phosphoproteomics, ubiquitinomics, metalloproteomics, terminomics, degradomics and metadegradomics, adhesomics, stressomics, microbiomics, immunomics, salivaomics, materiomics and other biomics. Throughout the extremely complex neoplastic process, a Breast Cancer Cell Continuum Concept (BCCCC) has been modeled in this review as a spatio-temporal and holistic approach, as long as the breast cancer represents a complex cascade comprising successively integrated populations of heterogeneous tumor and cancer-associated cells, that reflect the carcinoma's progression from a "driving mutation" and formation of the breast primary tumor, toward the distant secondary tumors in different tissues and organs, via circulating tumor cell populations. This BCCCC is widely sustained by a Breast Cancer Proteomic Continuum Concept (BCPCC), where each phenotype of neoplastic and tumor-associated cells is characterized by a changing and adaptive proteomic profile detected in solid and liquid minimal invasive biopsies by complex proteomics approaches. Such a profile is created, beginning with the proteomic landscape of different neoplastic cell populations and cancer-associated cells, followed by subsequent analysis of protein biomarkers involved in epithelial-mesenchymal transition and intravasation, circulating tumor cell proteomics, and, finally, by protein biomarkers that highlight the extravasation and distant metastatic invasion. Proteomics technologies are producing important data in breast cancer diagnostic, prognostic, and predictive biomarkers discovery and validation, are detecting genetic aberrations at the proteome level, describing functional and regulatory pathways and emphasizing specific protein and peptide profiles in human tissues, biological fluids, cell lines and animal models. Also, proteomics can identify different breast cancer subtypes and specific protein and proteoform expression, can assess the efficacy of cancer therapies at cellular and tissular level and can even identify new therapeutic target proteins in clinical studies.

Keywords: Proteomics; biomarkers; breast cancer cell continuum concept; breast cancer proteomic continuum concept.

Figure 1

Schematic of a general proteomic workflow. A sample can be fractionated (i.e., by electrophoresis) and then digested by trypsin (in-gel digestion), or digested in-solution by trypsin. The peptides mixture is then ionized (with or without separation by reversed phase chromatography). Peptides are then ionized and their corresponding m/z is measured in the MS mode under low collision energy, or fragmented and then measured in MS/MS mode under high collision energy. Data analysis using protemics software leads to identification of a peptide that is part of a protein, thus also identifying the protein.

Figure 2

Bottom-up and top-down proteomics. In bottom-up proteomics, the protein mixtures are digested and the peptide mixtures are analyzed by LC-MS and LC-MS/MS (shotgun approach) or separated by electrophoresis and then individual proteins are digested and analyzed by MALDI-MS in a method called peptide mass fingerprinting. In top-down proteomics, the individual proteins (or a mixture of proteins) are analyzed for molecular mass in MS mode or fragmented to provide partial fragments in MS/MS mode. Using this approach, the target protein’s mass is identified and its amino acid sequence confirmed by MS/MS fragmentation.

Figure 3

General strategy for identification of two major PTMs: phosphorylation and glycosylation.

Figure 4

Examples of methods for quantitative proteomics using labeled tags. The samples labeled case and control are mixed at the protein level prior fractionation and digestion (e.g., using classical SILAC or ICAT methods) or first labeled at the protein level with a tag (i.e. with iTRAQ or TMT) for and then mixed and further fractionated and digested. In this case, more than one labeled condition can be used (e.g., case 1, 2, 3). In the last case, chemical labeling happens at the peptide level, i.e., after the samples were fractionated and digested. Note that this method can target all peptides (global acetylation and deuterated acetylation), or specific peptides, i.e. using absolute quantitation (AQUA) peptides. Note that AQUA is an internal standard peptide custom-built to quantify a particular peptide. Note that internal standard peptides (other than AQUA peptides) can be used and applied to all proteomics methods discussed in this figure.