Proteomic analysis in cancer research: potential application in clinical use

Challenges in the Isolation and Proteomic Analysis of Cancer Exosomes-Implications for Translational Research

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.

Analytical validation considerations of multiplex mass-spectrometry-based proteomic platforms for measuring protein biomarkers

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.

Proteogenomic convergence for understanding cancer pathways and networks

During the past several decades, the understanding of cancer at the molecular level has been primarily focused on mechanisms on how signaling molecules transform homeostatically balanced cells into malignant ones within an individual pathway. However, it is becoming more apparent that pathways are dynamic and crosstalk at different control points of the signaling cascades, making the traditional linear signaling models inadequate to interpret complex biological systems. Recent technological advances in high throughput, deep sequencing for the human genomes and proteomic technologies to comprehensively characterize the human proteomes in conjunction with multiplexed targeted proteomic assays to measure panels of proteins involved in biologically relevant pathways have made significant progress in understanding cancer at the molecular level. It is undeniable that proteomic profiling of differentially expressed proteins under many perturbation conditions, or between normal and "diseased" states is important to capture a first glance at the overall proteomic landscape, which has been a main focus of proteomics research during the past 15-20 years. However, the research community is gradually shifting its heavy focus from that initial discovery step to protein target verification using multiplexed quantitative proteomic assays, capable of measuring changes in proteins and their interacting partners, isoforms, and post-translational modifications (PTMs) in response to stimuli in the context of signaling pathways and protein networks. With a critical link to genotypes (i.e., high throughput genomics and transcriptomics data), new and complementary information can be gleaned from multi-dimensional omics data to (1) assess the effect of genomic and transcriptomic aberrations on such complex molecular machinery in the context of cell signaling architectures associated with pathological diseases such as cancer (i.e., from genotype to proteotype to phenotype); and (2) target pathway- and network-driven changes and map the fluctuations of these functional units (proteins) responsible for cellular activities in response to perturbation in a spatiotemporal fashion to better understand cancer biology as a whole system.

High-throughput proteomics using antibody microarrays: an update

Antibody-based microarrays are a rapidly emerging technology that has advanced from the first proof-of-concept studies to demanding serum protein profiling applications during recent years, displaying great promise within disease proteomics. Miniaturized micro- and nanoarrays can be fabricated with an almost infinite number of antibodies carrying the desired specificities. While consuming only minute amounts of reagents, multiplexed and ultrasensitive assays can be performed targeting high- as well as low-abundance analytes in complex nonfractionated proteomes. The microarray images generated can then be converted into protein expression profiles or protein atlases, revealing a detailed composition of the sample. The technology will provide unique opportunities for fields such as disease diagnostics, biomarker discovery, patient stratification, predicting disease recurrence and drug target discovery. This review describes an update of high-throughput proteomics, using antibody-based microarrays, focusing on key technological advances and novel applications that have emerged over the last 3 years.

Ligand-directed profiling: applications to target drug discovery in cancer

BACKGROUND

Generation of targeted therapy remains a major challenge in medicine. The development of drugs that can discriminate between tumor cells and non-malignant cells would improve efficacy and reduce general side effects. Phage display allows identification of specific supramolecular complexes that can target therapeutic compounds or imaging agents, both in vitro and in vivo. The use of phage display to identify molecules expressed on the surface of human cancer cells without bias, as well as to provide initial steps toward identification of a ligand/receptor-based map of the human microvasculature, has broad implications for drug discovery in general, especially for cancer therapy.

OBJECTIVE/METHOD

In this review, we discuss the use of phage display technology as a ligand-directed targeting strategy and its applications to drug discovery.

CONCLUSION

Compared to other existing drug discovery platforms, phage display technology has the advantage to provide valuable clues pointing to target proteins in an unbiased biological context. The result from various display library screenings indicates that in many cases the selected peptide motifs mimic biological ligands. Analysis of peptide motifs targeting a receptor provides a basis for rational drug design of targeted peptidomimetics.

Mass spectrometry-based targeted quantitative proteomics: achieving sensitive and reproducible detection of proteins

Traditional shotgun proteomics used to detect a mixture of hundreds to thousands of proteins through mass spectrometric analysis, has been the standard approach in research to profile protein content in a biological sample which could lead to the discovery of new (and all) protein candidates with diagnostic, prognostic, and therapeutic values. In practice, this approach requires significant resources and time, and does not necessarily represent the goal of the researcher who would rather study a subset of such discovered proteins (including their variations or posttranslational modifications) under different biological conditions. In this context, targeted proteomics is playing an increasingly important role in the accurate measurement of protein targets in biological samples in the hope of elucidating the molecular mechanism of cellular function via the understanding of intricate protein networks and pathways. One such (targeted) approach, selected reaction monitoring (or multiple reaction monitoring) mass spectrometry (MRM-MS), offers the capability of measuring multiple proteins with higher sensitivity and throughput than shotgun proteomics. Developing and validating MRM-MS-based assays, however, is an extensive and iterative process, requiring a coordinated and collaborative effort by the scientific community through the sharing of publicly accessible data and datasets, bioinformatic tools, standard operating procedures, and well characterized reagents.

Clinical proteomics and OMICS clues useful in translational medicine research

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.

Relevant phosphoproteomic and mass spectrometry: approaches useful in clinical research

Background

"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.

Findings

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.

Conclusion

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.

The path to clinical proteomics research: integration of proteomics, genomics, clinical laboratory and regulatory science

Better biomarkers are urgently needed to cancer detection, diagnosis, and prognosis. While the genomics community is making significant advances in understanding the molecular basis of disease, proteomics will delineate the functional units of a cell, proteins and their intricate interaction network and signaling pathways for the underlying disease. Great progress has been made to characterize thousands of proteins qualitatively and quantitatively in complex biological systems by utilizing multi-dimensional sample fractionation strategies, mass spectrometry and protein microarrays. Comparative/quantitative analysis of high-quality clinical biospecimen (e.g., tissue and biofluids) of human cancer proteome landscape has the potential to reveal protein/peptide biomarkers responsible for this disease by means of their altered levels of expression, post-translational modifications as well as different forms of protein variants. Despite technological advances in proteomics, major hurdles still exist in every step of the biomarker development pipeline. The National Cancer Institute's Clinical Proteomic Technologies for Cancer initiative (NCI-CPTC) has taken a critical step to close the gap between biomarker discovery and qualification by introducing a pre-clinical "verification" stage in the pipeline, partnering with clinical laboratory organizations to develop and implement common standards, and developing regulatory science documents with the US Food and Drug Administration to educate the proteomics community on analytical evaluation requirements for multiplex assays in order to ensure the safety and effectiveness of these tests for their intended use.

The journey to regulation of protein-based multiplex quantitative assays

BACKGROUND

Clinical proteomics presents great promise in biology and medicine because of its potential for improving our understanding of diseases at the molecular level and for detecting disease-related biomarkers for diagnosis, prognosis, and prediction of therapeutic responses. To realize its full potential to improve clinical outcome for patients, proteomic studies have to be well designed, from biosample cohorts to data and statistical analyses. One key component in the biomarker development pipeline is the understanding of the regulatory science that evaluates diagnostic assay performance through rigorous analytical and clinical review criteria.

CONTENT

The National Cancer Institute's Clinical Proteomic Technologies for Cancer (CPTC) initiative has proposed an intermediate preclinical "verification" step to close the gap between protein-based biomarker discovery and clinical qualification. In collaboration with the US Food and Drug Administration (FDA), the CPTC network investigators recently published 2 mock submission review documents, first-of-their-kind educational materials that may help the scientific community interested in developing products for the clinic in understanding the likely analytical evaluation requirements for multiplex protein technology-based diagnostic tests.

CONCLUSIONS

Building on this momentum, the CPTC continues with this report its collaboration with the FDA, as well as its interactions with the AACC and the Centers for Medicare and Medicaid Services, to further the understanding of regulatory requirements for approving multiplex proteomic platform-based tests and analytically validating multiple analytes.

Restructuring proteomics through verification

Proteomics technologies have revolutionized cell biology and biochemistry by providing powerful new tools to characterize complex proteomes, multiprotein complexes and post-translational modifications. Although proteomics technologies could address important problems in clinical and translational cancer research, attempts to use proteomics approaches to discover cancer biomarkers in biofluids and tissues have been largely unsuccessful and have given rise to considerable skepticism. The National Cancer Institute has taken a leading role in facilitating the translation of proteomics from research to clinical application, through its Clinical Proteomic Technologies for Cancer. This article highlights the building of a more reliable and efficient protein biomarker development pipeline that incorporates three steps: discovery, verification and qualification. In addition, we discuss the merits of multiple reaction monitoring mass spectrometry, a multiplex targeted proteomics platform, which has emerged as a potentially promising, high-throughput protein biomarker measurements technology for preclinical 'verification'.

Reconstructing the pipeline by introducing multiplexed multiple reaction monitoring mass spectrometry for cancer biomarker verification: an NCI-CPTC initiative perspective

Proteomics holds great promise in personalized medicine for cancer in the post-genomic era. In the past decade, clinical proteomics has significantly evolved in terms of technology development, optimization and standardization, as well as in advanced bioinformatics data integration and analysis. Great strides have been made for characterizing a large number of proteins qualitatively and quantitatively in a proteome, including the use of sample fractionation, protein microarrays and MS. It is believed that differential proteomic analysis of high-quality clinical biospecimen (tissue and biofluids) can potentially reveal protein/peptide biomarkers responsible for cancer by means of their altered levels of expression and/or PTMs. Multiple reaction monitoring, a multiplexed platform using stable isotope dilution-MS with sensitivity and reproducibility approaching that of traditional ELISAs commonly used in the clinical setting, has emerged as a potentially promising technique for next-generation high-throughput protein biomarker measurements for diagnostics and therapeutics.

Use of genome-wide high-throughput technologies in biomarker development

In recent years, the usage of high-throughput technologies in the fields of genomics, transcriptomics, proteomics and metabolomics for biomarker discovery has expanded enormously. Biomarkers can be applied for many purposes, including diagnosis, prognosis, staging and selecting appropriate patient therapy. In addition, biomarkers can provide information on disease mechanism or progression. Biomarker development for clinical application encompasses phases for their discovery and characterization, assay development and, finally, implementation using automated platforms employed in clinical laboratories. However, translation from bench to bedside outside a research-oriented environment has proven to be more difficult. This is reflected by only few new biomarkers being integrated into clinical application in the last years. This article reviews currently used high-throughput technologies for the identification of biomarkers, as well as present approaches to increase the percentage of biomarkers that pass the barriers for clinical application.

Moving forward in colorectal cancer research, what proteomics has to tell

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.

Application of fluorescence difference gel electrophoresis technology in searching for protein biomarkers in chick myopia

The lens-induced myopia (LIM) in response to concave lens (negative lens) is a well established animal model for studying myopia development. However, the exact visual and neurochemical signaling mechanisms involving myopic eye growth are yet to be elucidated. The feasibility of applying a novel two-dimensional fluorescence difference gel electrophoresis technique for global protein profilings and a search for differential protein expressions in LIM were explored in the present study. Two-dimensional polyacrylamide gel electrophoresis was performed employing a "minimal Lysine labeling" approach and a reverse CyeDye experimental protocol using retinal tissue from chicks. The retinal protein profiles between myopic and control eyes were found to be very similar. More than a thousand protein spots could be detected on a 2D gel. Sixteen and ten protein spots were found to be up-regulated and down-regulated respectively in the myopic eyes according to our preset criteria with the inclusion of an internal pool standard. About 65% of those filtered spots could be successfully identified by peptide mass fingerprinting by matrix-assisted laser desorption ionization-time-of-flight mass spectrometry . Most of the differentially expressed proteins were found to be related to cytoskeletal or oxidative functions. According to the prediction of subcellular locations, most of them (about 84%) were classified as cytoplasmic proteins. The cellular functions for those differentially expressed proteins were reported and their possible involvements in the compensated eye growth were discussed. We have optimized a workable protocol for the study of the differential retinal protein expressions in the LIM using 2D-DIGE approach which was shown to have a number of advantages over the traditional 2D electrophoresis technique.

What does it need to be a biomarker? Relationships between resolution, differential quantification and statistical validation of protein surrogate biomarkers

The separation of proteins with the aim of discovering surrogate biomarkers defining differences between various stages of biological materials is the core occupation of every project in Proteomics. There are numerous recent publications suggesting a wide array of separation technologies, ranging from 2-DE, MS-linked LC, CE or chip-based surface-enhanced laser desorption ionization claiming to be useful for this purpose, and addressing the urgent clinical, diagnostic or toxicological needs for such surrogates. However, many potential biomarkers emerging from proteomic studies did not survive validation in, for example, large-scale clinical studies or simply independent experiments, and at the same time being tested in settings with case numbers bigger than perhaps a few hundreds. The major problems of protein biomarkers are associated with the huge dynamic range of possible concentrations and the ever-increasing number of molecular species due to post-translational modifications. In particular, the chemical diversity of the latter imposes a necessity of improved resolution of separation technologies, because otherwise the crucial quantitative information is lost in pools of poorly resolved peptides. Here, we present and analyze some examples of successful developments of protein biomarkers, and show the prerequisites and necessary considerations while moving protein candidates from purely descriptive phenomena to a stage of validated surrogate biomarkers. This includes a detailed discussion of requirements regarding resolution of initial separation techniques, linear dynamic range and statistics of differential quantification, but also the subsequent clinical validation, testing the biomarker in clinical settings and using large numbers of patient samples.