Exosomes belong to the group of extracellular vesicles (EVs) that derive from various cell populations and mediate intercellular communication in health and disease. Like hormones or cytokines, exosomes released by cells can play a potent role in the communication between the cell of origin and distant cells in the body to maintain homeostatic or pathological processes, including tumorigenesis. The nucleic acids, and lipid and protein cargo present in the exosomes are involved in a myriad of carcinogenic processes, including cell proliferation, tumor angiogenesis, immunomodulation, and metastasis formation. The ability of exosomal proteins to mediate direct functions by interaction with other cells qualifies them as tumor-specific biomarkers and targeted therapeutic approaches. However, the heterogeneity of plasma-derived exosomes consistent of (a) exosomes derived from all kinds of body cells, including cancer cells and (b) contamination of exosome preparation with other extracellular vesicles, such as apoptotic bodies, makes it challenging to obtain solid proteomics data for downstream clinical application. In this manuscript, we review these challenges beginning with the choice of different isolation methods, through the evaluation of obtained exosomes and limitations in the process of proteome analysis of cancer-derived exosomes to identify novel protein targets with functional impact in the context of translational oncology.

• cancer
• extracellular vesicles
• heterogeneity of exosomes
• proteomics of exosomes
• tumor progression

Protein biomarker discovery and validation in current omics era are vital for healthcare professionals to improve diagnosis, detect cancers at an early stage, identify the likelihood of cancer recurrence, stratify stages with differential survival outcomes, and monitor therapeutic responses. The success of such biomarkers would have a huge impact on how we improve the diagnosis and treatment of patients and alleviate the financial burden of healthcare systems. In the past, the genomics community (mostly through large-scale, deep genomic sequencing technologies) has been steadily improving our understanding of the molecular basis of disease, with a number of biomarker panels already authorized by the U.S. Food and Drug Administration (FDA) for clinical use (e.g., MammaPrint, two recently cleared devices using next-generation sequencing platforms to detect DNA changes in the cystic fibrosis transmembrane conductance regulator (CFTR) gene). Clinical proteomics, on the other hand, albeit its ability to delineate the functional units of a cell, more likely driving the phenotypic differences of a disease (i.e., proteins and protein–protein interaction networks and signaling pathways underlying the disease), “staggers” to make a significant impact with only an average ∼1.5 protein biomarkers per year approved by the FDA over the past 15–20 years. This statistic itself raises the concern that major roadblocks have been impeding an efficient transition of protein marker candidates in biomarker development despite major technological advances in proteomics in recent years.

• MRM−MS
• immunoassays
• multiplex proteomics assays
• protein biomarker
• quantitative targeted proteomics

• Mathematical and computational modeling
• Omics integration
• Post-translational modification
• Protein identification and quantitation
• Protein networks
• Proteogenomics
• Signaling pathways
• Systems biology
• Targeted proteomics

• Antibodies
• Female
• Gene Expression Profiling
• Humans
• Male
• Neoplasms/diagnosis
• Neoplasms/genetics
• Protein Array Analysis/methods
• Protein Array Analysis/trends
• Proteomics/methods
• Proteomics/trends

Since the advent of the new proteomics era more than a decade ago, large-scale studies of protein profiling have been used to identify distinctive molecular signatures in a wide array of biological systems, spanning areas of basic biological research, clinical diagnostics, and biomarker discovery directed toward therapeutic applications. Recent advances in protein separation and identification techniques have significantly improved proteomic approaches, leading to enhancement of the depth and breadth of proteome coverage.

Proteomic signatures, specific for multiple diseases, including cancer and pre-invasive lesions, are emerging. This article combines, in a simple manner, relevant proteomic and OMICS clues used in the discovery and development of diagnostic and prognostic biomarkers that are applicable to all clinical fields, thus helping to improve applications of clinical proteomic strategies for translational medicine research.

"It's not what we do, it's the way that we do it". Never has this maxim been truer in proteomics than now. Mass Spectrometry-based proteomics/phosphoproteomics tools are critical to understand the structure and dynamics (spatial and temporal) of signalling that engages and migrates through the entire proteome. Approaches such as affinity purification followed by Mass Spectrometry (MS) have been used to elucidate relevant biological questions disease vs. health. Thousands of proteins interact via physical and chemical association. Moreover, certain proteins can covalently modify other proteins post-translationally. These post-translational modifications (PTMs) ultimately give rise to the emergent functions of cells in sequence, space and time.

Understanding the functions of phosphorylated proteins thus requires one to study proteomes as linked-systems rather than collections of individual protein molecules. Indeed, the interacting proteome or protein-network knowledge has recently received much attention, as network-systems (signalling pathways) are effective snapshots in time, of the proteome as a whole. MS approaches are clearly essential, in spite of the difficulties of some low abundance proteins for future clinical advances.

Clinical proteomics-MS has come a long way in the past decade in terms of technology/platform development, protein chemistry, and together with bioinformatics and other OMICS tools to identify molecular signatures of diseases based on protein pathways and signalling cascades. Hence, there is great promise for disease diagnosis, prognosis, and prediction of therapeutic outcome on an individualized basis. However, and as a general rule, without correct study design, strategy and implementation of robust analytical methodologies, the efforts, efficiency and expectations to make biomarkers (especially phosphorylated kinases) a useful reality in the near future, can easily be hampered.

• quantitative proteomics
• biomarker
• multiplex protein assays
• mrm-ms
• immunoassays

• Biomarkers/analysis
• Clinical Laboratory Techniques/standards
• Genomics
• Humans
• Mass Spectrometry/methods
• Mass Spectrometry/standards
• Neoplasms/diagnosis
• Neoplasms/genetics
• Proteomics
• Quality Control
• United States
• United States Food and Drug Administration

• biomarker
• multiple reaction monitoring mass spectrometry
• proteomics
• verification

• Biomarkers/analysis
• Humans
• Mass Spectrometry/methods
• Mass Spectrometry/trends
• National Cancer Institute (U.S.)
• Proteomics/methods
• Proteomics/standards
• Proteomics/trends
• Reproducibility of Results
• United States
• United States Food and Drug Administration

Colorectal cancer is the third most common cancer and is highly fatal. During the last several years, research has been primarily based on the study of expression profiles using microarray technology. But now, investigators are putting into practice proteomic analyses of cancer tissues and cells to identify new diagnostic or therapeutic biomarkers for this cancer. Because the proteome reflects the state of a cell, tissue or organism more accurately, much is expected from proteomics to yield better tumor markers for disease diagnosis and therapy monitoring. This review summarizes the most relevant applications of proteomics the biomarker discovery for colorectal cancer.

• Proteomics
• Colorectal cancer
• Biomarker

• Algorithms
• Biomarkers/analysis
• Biomarkers, Tumor/analysis
• Calibration
• Data Interpretation, Statistical
• Drug Design
• Electrophoresis, Gel, Two-Dimensional/methods
• Endpoint Determination
• Humans
• Pattern Recognition, Automated/statistics & numerical data
• Protein Array Analysis/methods
• Proteins/analysis
• Reference Values
• Sensitivity and Specificity
• Software/standards
• Software Validation