Advances in mass spectrometry-based cancer research and analysis: from cancer proteomics to clinical diagnostics

Introduction: The last 20 years have seen significant improvements in the analytical capabilities of biological mass spectrometry. Studies using advanced mass spectrometry (MS) have resulted in new insights into cell biology and the aetiology of diseases as well as its use in clinical applications. Areas Covered: This review will discuss recent developments in MS-based technologies and their cancer-related applications with a focus on proteomics. It will also discuss the issues around translating the research findings to the clinic and provide an outline of where the field is moving. Expert Opinion: Proteomics has been problematic to adapt for the clinical setting. However, MS-based techniques continue to demonstrate potential in novel clinical uses beyond classical cancer proteomics

Developing an Integrative Glycobiology Workflow for the Identification of Disease Markers for Pancreatic Cancer

A deeper understanding of dysregulated glycosylation in pancreatic cancer can provide insights into disease mechanisms and the identification of novel disease markers. Recent improvements in mass spectrometry techniques have been instrumental in profiling biologically relevant tissue sections in order to identify disease marker candidates, but have either not yet been adopted for studying glycosylation or applied directly to pancreatic cancer. In the dissertation herein, new methods have been developed and adapted to the study of aberrant glycosylation in pancreatic cancer, with the ultimate goal of identifying novel disease marker candidates. For the first time, we describe a mass spectrometry imaging approach to study the localization of N-glycans. This technique demonstrated a histology-derived localization of N-glycans across tissue sections, with identifications displaying remarkable consistency with documented studies. Furthermore, the technique provides superior structural information compared to preexisting methodologies. In the analysis of diseased specimen, changes in glycosylation can be linked to aberrations in glycosyltransferase expression. When applied to pancreatic cancer in a high-throughput and high-dimensional analysis, panels of glycans displayed an improved ability to differentiate tumor from non-tumor tissues compared to current disease markers. Furthermore, the data suggest that glycosylation can identify premalignant lesions, as well as differentiate between malignant and benign conditions. These observations overcome significant limitations that hinder the efficacy of current disease markers. In an effort to link aberrant glycosylation to the modified protein, a subset of glycosylated proteins were enriched and analyzed by mass spectrometry to identify proteins that are integral to disease progression and can be probed for the early detection of pancreatic cancer. Known disease markers were among the glycoproteins identified, validating the utility of the enrichment and detection strategy outlined. This approach also differentiated the role of N- and O-glycosylation in antigen expression. Finally, we outline an integrated workflow that takes advantage of the unique capabilities of high resolution mass spectrometers. This workflow can capitalize on prior glycomic and proteomic experiments to provide a comprehensive analysis of dysregulated protein glycosylation in pancreatic cancer

MALDI Imaging Mass Spectrometry (MALDI-IMS)—Application of Spatial Proteomics for Ovarian Cancer Classification and Diagnosis

MALDI imaging mass spectrometry (MALDI-IMS) allows acquisition of mass data for metabolites, lipids, peptides and proteins directly from tissue sections. IMS is typically performed either as a multiple spot profiling experiment to generate tissue specific mass profiles, or a high resolution imaging experiment where relative spatial abundance for potentially hundreds of analytes across virtually any tissue section can be measured. Crucially, imaging can be achieved without prior knowledge of tissue composition and without the use of antibodies. In effect MALDI-IMS allows generation of molecular data which complement and expand upon the information provided by histology including immuno-histochemistry, making its application valuable to both cancer biomarker research and diagnostics. The current state of MALDI-IMS, key biological applications to ovarian cancer research and practical considerations for analysis of peptides and proteins on ovarian tissue are presented in this review

MALDI Mass Spectrometry Imaging for the Discovery of Prostate Carcinoma Biomarkers

The elucidation of new biological markers of prostate cancer (PCa) should aid in the detection, and prognosis of this disease. Diagnostic decision making by pathologists in prostate cancer is highly dependent on tissue morphology. The ability to localize disease-specific molecular changes in tissue would help improve this critical pathology decision making process. Direct profiling of proteins in tissue sections using MALDI imaging mass spectrometry (MALDI-IMS) has the power to link molecular detail to morphological and pathological changes, enhancing the ability to identify candidates for new specific biomarkers. However, critical questions remain regarding the integration of this technique with clinical decision making. To address these questions, and to investigate the potential of MALDI-IMS for the diagnosis of prostate cancer, we have used this approach to analyze prostate tissue for the determination of the cellular origins of different protein signals to improve cancer detection and to identify specific protein markers of PCa. We found that specific protein/peptide expression changes correlated with the presence or absence of prostate cancer as well as the presence of micro-metastatic disease. Additionally, the over-expression of a single peptide (m/z = 4355) was able to accurately define primary cancer tissue from adjacent normal tissue. Tandem mass spectrometry analysis identified this peptide as a fragment of MEKK2, a member of the MAP kinase signaling pathway. Validation of MEKK2 overexpression in moderately differentiated PCa and prostate cancer cell lines was performed using immunohistochemistry and Western Blot analysis. Classification algorithms using specific ions differentially expressed in PCa tissue and a ROC cut-off value for the normalized intensity of the MEKK2 fragment at m/z 4355 were used to classify a blinded validation set. Finally, the optimization of sample processing in a new fixative which preserves macromolecules has led to improved through-put of samples making MALDI-IMS more compatible with current histological applications, facilitating its implementation in a clinical setting. This study highlights the potential of MALDI-IMS to define the molecular events involved in prostate tumorigenesis and demonstrates the applicability of this approach to clinical diagnostics as an aid to pathological decision making in prostate cancer

Ovarian cancer molecular pathology.

Peer reviewe

EPMA position paper in cancer:current overview and future perspectives

At present, a radical shift in cancer treatment is occurring in terms of predictive, preventive, and personalized medicine (PPPM). Individual patients will participate in more aspects of their healthcare. During the development of PPPM, many rapid, specific, and sensitive new methods for earlier detection of cancer will result in more efficient management of the patient and hence a better quality of life. Coordination of the various activities among different healthcare professionals in primary, secondary, and tertiary care requires well-defined competencies, implementation of training and educational programs, sharing of data, and harmonized guidelines. In this position paper, the current knowledge to understand cancer predisposition and risk factors, the cellular biology of cancer, predictive markers and treatment outcome, the improvement in technologies in screening and diagnosis, and provision of better drug development solutions are discussed in the context of a better implementation of personalized medicine. Recognition of the major risk factors for cancer initiation is the key for preventive strategies (EPMA J. 4(1):6, 2013). Of interest, cancer predisposing syndromes in particular the monogenic subtypes that lead to cancer progression are well defined and one should focus on implementation strategies to identify individuals at risk to allow preventive measures and early screening/diagnosis. Implementation of such measures is disturbed by improper use of the data, with breach of data protection as one of the risks to be heavily controlled. Population screening requires in depth cost-benefit analysis to justify healthcare costs, and the parameters screened should provide information that allow an actionable and deliverable solution, for better healthcare provision

Biomarkers of inflammatory arthritis and proteomics

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Ovarian cancer: can proteomics give new insights for therapy and diagnosis?

The study of the ovarian proteomic profile represents a new frontier in ovarian cancer research, since this approach is able to enlighten the wide variety of post-translational events (such as glycosylation and phosphorylation). Due to the possibility of analyzing thousands of proteins, which could be simultaneously altered, comparative proteomics represent a promising model of possible biomarker discovery for ovarian cancer detection and monitoring. Moreover, defining signaling pathways in ovarian cancer cells through proteomic analysis offers the opportunity to design novel drugs and to optimize the use of molecularly targeted agents against crucial and biologically active pathways. Proteomic techniques provide more information about different histological types of ovarian cancer, cell growth and progression, genes related to tumor microenvironment and specific molecular targets predictive of response to chemotherapy than sequencing or microarrays. Estimates of specificity with proteomics are less consistent, but suggest a new role for combinations of biomarkers in early ovarian cancer diagnosis, such as the OVA1 test. Finally, the definition of the proteomic profiles in ovarian cancer would be accurate and effective in identifying which pathways are differentially altered, defining the most effective therapeutic regimen and eventually improving health outcomes