Development of High-Throughput Mass Spectrometry–Based Approaches for Cancer Biomarker Discovery and Implementation

The contribution and perspectives of proteomics to uncover ovarian cancer tumor markers

Despite all the advances in understanding the mechanisms involved in ovarian cancer (OC) development, many aspects still need to be unraveled and understood. Tumor markers (TMs) are of special interest in this disease. Some aspects of clinical management of OC might be improved by the use of validated TMs, such as differentiating subtypes, defining the most appropriate treatment, monitoring the course of the disease, or predicting clinical outcome. The Food and Drug Administration (FDA) has approved a few TMs for OC: CA125 (cancer antigen 125; monitoring), HE4 (Human epididymis protein; monitoring), ROMA (Risk Of Malignancy Algorithm; HE4+CA125; prediction of malignancy) and OVA1 (Vermillion's first-generation Multivariate Index Assay [MIA]; prediction of malignancy). Proteomics can help advance the research in the field of TMs for OC. A variety of biological materials are being used in proteomic analysis, among them tumor tissues, interstitial fluids, tumor fluids, ascites, plasma, and ovarian cancer cell lines. However, the discovery and validation of new TMs for OC is still very challenging. The enormous heterogeneity of histological types of samples and the individual variability of patients (lifestyle, comorbidities, drug use, and family history) are difficult to overcome in research protocols. In this work, we sought to gather relevant information regarding TMs, OC, biological samples for proteomic analysis, as well as markers and algorithms approved by the FDA for use in clinical routine.

Clinical proteomics in cancer: Where we are

Proteomics has emerged as a promising field in the post-genomic era. Notwithstanding the great advances provided by gene expression analysis in cancer, the lack of a correlation between gene expression and protein levels has highlighted the need for a proteomic focus on cancer. Although the increasing knowledge regarding cancer biology, a reliable marker to improve diagnosis, prognosis and treatment for cancer patients is not a reality at present. In this review, we address the main considerations regarding proteomics-based studies and their clinical applications on cancer research, highlighting some considerations related to strengths and limitations of proteomics-based studies and its application to clinical practice.

Detection of metabolites discriminating subtypes of thyroid cancer: Molecular profiling of FFPE samples using the GC/MS approach

One of the critical issues in thyroid cancer diagnostic is differentiation between follicular adenoma, follicular carcinoma and the follicular variant of papillary carcinoma, which in some cases is not possible based on histopathological features only. In this paper we performed molecular profiling of thyroid tissue aiming to identify metabolites characteristic for different types of thyroid cancer. FFPE tissue specimens were analysed from 5 different types of thyroid malignancies (follicular, papillary/classical variant, papillary/follicular variant, medullary and anaplastic cancers), benign follicular adenoma and normal thyroid. Extracted metabolites were identified and semi-quantified using the GC/MS approach. There were 28 metabolites identified, whose abundances were significantly different among different types of thyroid tumours, including lipids, carboxylic acids, and saccharides. We concluded, that multi-component metabolome signature could be used for classification of different subtypes of follicular thyroid lesions. Moreover, potential applicability of the GC/MS-based analysis of FFPE tissue samples in diagnostics of thyroid cancer has been proved.

Application of metabolomics in thyroid cancer research

Thyroid cancer is the most common endocrine malignancy with four major types distinguished on the basis of histopathological features: papillary, follicular, medullary, and anaplastic. Classification of thyroid cancer is the primary step in the assessment of prognosis and selection of the treatment. However, in some cases, cytological and histological patterns are inconclusive; hence, classification based on histopathology could be supported by molecular biomarkers, including markers identified with the use of high-throughput "omics" techniques. Beside genomics, transcriptomics, and proteomics, metabolomic approach emerges as the most downstream attitude reflecting phenotypic changes and alterations in pathophysiological states of biological systems. Metabolomics using mass spectrometry and magnetic resonance spectroscopy techniques allows qualitative and quantitative profiling of small molecules present in biological systems. This approach can be applied to reveal metabolic differences between different types of thyroid cancer and to identify new potential candidates for molecular biomarkers. In this review, we consider current results concerning application of metabolomics in the field of thyroid cancer research. Recent studies show that metabolomics can provide significant information about the discrimination between different types of thyroid lesions. In the near future, one could expect a further progress in thyroid cancer metabolomics leading to development of molecular markers and improvement of the tumor types classification and diagnosis.

Network views for personalized medicine

Clinical Proteomics has traveled a long way pinpointing potential biomarkers for a variety of diseases. However, the absence of clinical implementation of proteomics findings has led to a frank evaluation and reconsideration of applied practices in biomarker discovery, recruitment of technological tools for biomarker verification and generation of new guidelines for data reporting. Nevertheless, considering the need for vast clinical resources for biomarker validation, the frequent lack of clear definitions of contexts of use, in combination to the biomarker "high offer," progress toward biomarker implementation will even more require the adoption of an extensive open-minded approach: disease-focused networks are needed to ensure rapid exchange of information, initiation of appropriate studies, parallel validation of multiple biomarkers and sharing of valuable clinical resources. This viewpoint article targets to reflect on these issues and advocates the added value of multidisciplinary networks in biomarker development using bladder cancer as a paradigm.

Implementation of proteomic biomarkers: making it work

While large numbers of proteomic biomarkers have been described, they are generally not implemented in medical practice. We have investigated the reasons for this shortcoming, focusing on hurdles downstream of biomarker verification, and describe major obstacles and possible solutions to ease valid biomarker implementation. Some of the problems lie in suboptimal biomarker discovery and validation, especially lack of validated platforms with well-described performance characteristics to support biomarker qualification. These issues have been acknowledged and are being addressed, raising the hope that valid biomarkers may start accumulating in the foreseeable future. However, successful biomarker discovery and qualification alone does not suffice for successful implementation. Additional challenges include, among others, limited access to appropriate specimens and insufficient funding, the need to validate new biomarker utility in interventional trials, and large communication gaps between the parties involved in implementation. To address this problem, we propose an implementation roadmap. The implementation effort needs to involve a wide variety of stakeholders (clinicians, statisticians, health economists, and representatives of patient groups, health insurance, pharmaceutical companies, biobanks, and regulatory agencies). Knowledgeable panels with adequate representation of all these stakeholders may facilitate biomarker evaluation and guide implementation for the specific context of use. This approach may avoid unwarranted delays or failure to implement potentially useful biomarkers, and may expedite meaningful contributions of the biomarker community to healthcare.

Proteomics and biomarkers in clinical trials for drug development

Proteomics allows characterization of protein structure and function, protein-protein interactions, and peptide modifications. It has given us insight into the perturbations of signaling pathways within tumor cells and has improved the discovery of new therapeutic targets and possible indicators of response to and duration of therapy. The discovery, verification, and validation of novel biomarkers are critical in streamlining clinical development of targeted compounds, and directing rational treatments for patients whose tumors are dependent upon select signaling pathways. Studies are now underway in many diseases to examine the immune or inflammatory proteome, vascular proteome, cancer or disease proteome, and other subsets of the specific pathology microenvironment. Successful assay verification and biological validation of such biomarkers will speed development of potential agents to targetable dominant pathways and lead to selection of individuals most likely to benefit. Reconsideration of analytical and clinical trials methods for acquisition, examination, and translation of proteomics data must occur before we march further into future of drug development.

Bioinformatic-driven search for metabolic biomarkers in disease

The search and validation of novel disease biomarkers requires the complementary power of professional study planning and execution, modern profiling technologies and related bioinformatics tools for data analysis and interpretation. Biomarkers have considerable impact on the care of patients and are urgently needed for advancing diagnostics, prognostics and treatment of disease. This survey article highlights emerging bioinformatics methods for biomarker discovery in clinical metabolomics, focusing on the problem of data preprocessing and consolidation, the data-driven search, verification, prioritization and biological interpretation of putative metabolic candidate biomarkers in disease. In particular, data mining tools suitable for the application to omic data gathered from most frequently-used type of experimental designs, such as case-control or longitudinal biomarker cohort studies, are reviewed and case examples of selected discovery steps are delineated in more detail. This review demonstrates that clinical bioinformatics has evolved into an essential element of biomarker discovery, translating new innovations and successes in profiling technologies and bioinformatics to clinical application.

Laser capture microdissection applications in breast cancer proteomics

Breast cancer tissues are characterized by cellular heterogeneity, representing a mixture of, e.g., healthy epithelial ducts, invasive or in situ tumor cells, surrounding stroma, infiltrating immune cells, blood vessels, and capillaries. As a consequence, protein extracts from whole tissue lysates also represent a variety of cell types present in the tissues under examination. This, however, seriously hampers the analysis of tumor cell-specific signals, which is of interest when performing biomarker discovery-type of studies. Therefore, laser capture microdissection is a perfect tool to isolate a relatively pure population of cells of interest, such as tumor cells. In this chapter, we describe the use of the PALM MicroBeam system for laser microdissection and pressure catapulting. Protocols are provided for sectioning, staining, microdissection, sample preparation, and mass spectrometric analysis of snap frozen breast cancer tissue.

Proteomic profiling of cancer--opportunities, challenges, and context

The article by Roesch-Ely and colleagues in a recent issue of The Journal of Pathology describes the use of proteomic techniques to examine mucosal biopsies in patients with head and neck squamous cell cancer (HNSCC) and in corresponding control samples. The authors were able to determine the anatomical site of origin of the biopsies based on modelling of multiplex protein datasets, and to use the information to analyse field cancerization as a means of predicting tumour recurrence. Although the study included only a relatively small number of cases, and will require future validation in a larger patient cohort, the results point to the potential of proteomics to increase our understanding of cancer biology, and in this instance to offer clinical value.