Update on: proteome analysis in thyroid pathology – part II: overview of technical and clinical enhancement of proteomic investigation of the thyroid lesions

ABSTRACT

Introduction: An accurate diagnostic classification of thyroid lesions remains an important clinical aspect that needs to be addressed in order to avoid ‘diagnostic’ thyroidectomies. Among the several ‘omics’ techniques, proteomics is playing a pivotal role in the search for diagnostic markers. In recent years, different approaches have been used, taking advantage of the technical improvements related to mass spectrometry that have occurred.

Areas covered: The review provides an update of the recent findings in diagnostic classification, in genetic definition and in the investigation of thyroid lesions based on different proteomics approaches and on different type of specimens: cytological, surgical and biofluid samples. A brief section will discuss how these findings can be integrated with those obtained by metabolomics investigations.

Expert commentary: Among the several proteomics approaches able to deepen our knowledge of the molecular alterations of the different thyroid lesions, MALDI-MSI is strongly emerging above all. In fact, MS-imaging has also been demonstrated to be capable of distinguishing thyroid lesions, based on their different molecular signatures, using cytological specimens. The possibility to use the material obtained by the fine needle aspiration makes MALDI-MSI a highly promising technology that could be implemented into the clinical and pathological units.     

Proteomics approach to identify biomarkers for upper gastrointestinal cancer

Introduction: The prognosis for patients with upper gastrointestinal cancers remains dismal despite the development of multimodality therapies that incorporate surgery, chemotherapy, and radiotherapy. Early diagnosis and personalized treatment should lead to better prognosis. Given the advances in proteomic technologies over the past decades, proteomics promises to be the most effective technique to identify novel diagnostics and therapeutic targets.

Areas covered: For this review, keywords were searched in combination with ‘proteomics’ and ‘gastric cancer’ or ‘esophageal cancer’ in PubMed. Studies that evaluated proteomics associated with upper gastrointestinal cancer were identified through reading, with several studies quoted at second hand. We summarize the proteomics involved in upper gastrointestinal cancer and discuss potential biomarkers and therapeutic targets.

Expert commentary: In particular, the development of mass spectrometry has enabled detection of multiple proteins and peptides in more biological samples over a shorter time period and at lower cost than was previously possible. In addition, more sophisticated protein databases have allowed a wider variety of proteins in samples to be quantified. Novel biomarkers that have been identified by new proteomic technologies should be applied in a clinical setting.     

Major depressive disorder: insight into candidate cerebrospinal fluid protein biomarkers from proteomics studies

Introduction: Major Depressive Disorder (MDD) is the leading cause of global disability, and an increasing body of literature suggests different cerebrospinal fluid (CSF) proteins as biomarkers of MDD. The aim of this review is to summarize the suggested CSF biomarkers and to analyze the MDD proteomics studies of CSF and brain tissues for promising biomarker candidates.

Areas covered: The review includes the human studies found by a PubMed search using the following terms: ‘depression cerebrospinal fluid biomarker’, ‘major depression biomarker CSF’, ‘depression CSF biomarker’, ‘proteomics depression’, ‘proteomics biomarkers in depression’, ‘proteomics CSF biomarker in depression’, and ‘major depressive disorder CSF’. The literature analysis highlights promising biomarker candidates and demonstrates conflicting results on others. It reveals 42 differentially regulated proteins in MDD that were identified in more than one proteomics study. It discusses the diagnostic potential of the biomarker candidates and their association with the suggested pathologies.

Expert commentary: One ultimate goal of finding biomarkers for MDD is to improve the diagnostic accuracy to achieve better treatment outcomes; due to the heterogeneous nature of MDD, using bio-signatures could be a good strategy to differentiate MDD from other neuropsychiatric disorders. Notably, further validation studies of the suggested biomarkers are still needed.     

Proteomic approaches for cancer epigenetics research

Introduction: Epigenetic dysregulation drives or supports numerous human cancers. The chromatin landscape in cancer cells is often marked by abnormal histone post-translational modification (PTM) patterns and by aberrant assembly and recruitment of protein complexes to specific genomic loci. Mass spectrometry-based proteomic analyses can support the discovery and characterization of both phenomena.

Areas covered: We broadly divide this literature into two parts: ‘modification-centric’ analyses that link histone PTMs to cancer biology; and ‘complex-centric’ analyses that examine protein–protein interactions that occur de novo as a result of oncogenic mutations. We also discuss proteomic studies of oncohistones. We highlight relevant examples, discuss limitations, and speculate about forthcoming innovations regarding each application.

Expert commentary: ‘Modification-centric’ analyses have been used to further understanding of cancer’s histone code and to identify associated therapeutic vulnerabilities. ‘Complex-centric’ analyses have likewise revealed insights into mechanisms of oncogenesis and suggested potential therapeutic targets, particularly in MLL-associated leukemia. Proteomic experiments have also supported some of the pioneering studies of oncohistone-mediated tumorigenesis. Additional applications of proteomics that may benefit cancer epigenetics research include middle-down and top-down histone PTM analysis, chromatin reader profiling, and genomic locus-specific protein identification. In the coming years, proteomic approaches will remain powerful ways to interrogate the biology of cancer.     

Proteomics discovery of chemoresistant biomarkers for ovarian cancer therapy

Introduction: Chemoresistance is a major challenge to current ovarian cancer chemotherapy. It is important to identify biomarkers to distinguish chemosensitive and chemoresistant patients.

Areas covered: We review the medical literature, discuss MS-based technologies with respect to chemoresistant ovarian cancer and summarize the promising chemoresistant biomarkers identified. In addition, the challenges and future perspectives of biomarker discovery research are explored. With the employment of mass spectrometry-based (MS-based) proteomics, biomarker discovery of ovarian cancer has made great progress in the last decade. Many potential biomarkers were identified by MS-based proteomics technologies, some of which have been validated for further extensive studies in clinical settings.

Expert commentary: The discovery of chemoresistant biomarkers is a newly developing area and may provide a clue for predicting chemotherapeutic response and discover therapeutic targets for paving the way of personalized medicine. Multiple complementary MS-based proteomics approaches hold promise for finding novel therapeutic targets in ovarian cancer treatment.     

Metalloproteinases in disease: identification of biomarkers of tissue damage through proteomics

ABSTRACT

Introduction: Metalloproteinases play key roles in health and disease, by generating novel proteoforms with variable structure and function.

Areas covered: This review focuses on the role of endogenous [a Disintegrin and Metalloproteinase (ADAMs), ADAMs with thrombospondin motifs (ADAMTS), and matrix metalloproteinases (MMPs)] and exogenous metalloproteinases in various disease conditions, and describes the application of mass spectrometry-based proteomics to detect qualitative and quantitative changes in protein profiles in tissues and body fluids in disease. Emphasis is placed on the proteomic analysis of exudates collected from affected tissues, including methods that enrich newly generated protein fragments derived from proteolysis in cells, stroma, or extracellular matrix. The use of proteomic analysis of exudates in the study of the local tissue damage induced by metalloproteinases derived from viperid snake venoms is discussed, particularly in relation to extracellular matrix degradation and to the overall pathology of these envenomings.

Expert commentary: The information provided by these proteomics approaches is paving the way for the identification of biomarkers based on particular proteolytic signatures associated with different pathologies. Together with other methodological approaches, a comprehensive view of the mechanisms and dynamics of diseases can be achieved. Such basis of knowledge allows for the design of novel diagnostic and therapeutic approaches within the frame of ‘precision’ or ‘personalized’ medicine.     

Mining cancer biology through bioinformatic analysis of proteomic data

ABSTRACT

Introduction: Discovery proteomics for cancer research generates complex datasets of diagnostic, prognostic, and therapeutic significance in human cancer. With the advent of high-resolution mass spectrometers, able to identify thousands of proteins in complex biological samples, only the application of bioinformatics can lead to the interpretation of data which can be relevant for cancer research.

Areas covered: Here, we give an overview of the current bioinformatic tools used in cancer proteomics. Moreover, we describe their applications in cancer proteomics studies of cell lines, serum, and tissues, highlighting recent results and critically evaluating their outcomes.

Expert opinion: The use of bioinformatic tools is a fundamental step in order to manage the large amount of proteins (from hundreds to thousands) that can be identified and quantified in a cancer biological samples by proteomics. To handle this challenge and obtain useful data for translational medicine, it is important the combined use of different bioinformatic tools. Moreover, a particular attention to the global experimental design, and the integration of multidisciplinary skills are essential for best setting of tool parameters and best interpretation of bioinformatics output.     

Recent insights into human bronchial proteomics – how are we progressing and what is next?

Introduction: The human respiratory system is highly prone to diseases and complications. Many lung diseases, including lung cancer (LC), tuberculosis (TB), and chronic obstructive pulmonary disease (COPD) have been among the most common causes of death worldwide. Cystic fibrosis (CF), the most common genetic disease in Caucasians, has adverse impacts on the lungs. Bronchial proteomics plays a significant role in understanding the underlying mechanisms and pathogenicity of lung diseases and provides insights for biomarker and therapeutic target discoveries.

Areas covered: We overview the recent achievements and discoveries in human bronchial proteomics by outlining how some of the different proteomic techniques/strategies are developed and applied in LC, TB, COPD, and CF. Also, the future roles of bronchial proteomics in predictive proteomics and precision medicine are discussed.

Expert commentary: Much progress has been made in bronchial proteomics. Owing to the advances in proteomics, we now have better ability to isolate proteins from desired cellular compartments, greater protein separation methods, more powerful protein detection technologies, and more sophisticated bioinformatic techniques. These all contributed to our further understanding of lung diseases and for biomarker and therapeutic target discoveries.     

Mining the fecal proteome: from biomarkers to personalised medicine

Introduction: Fecal proteomics has gained increased prominence in recent years. It can provide insights into the diagnosis and surveillance of many bowel diseases by both identifying potential biomarkers in stool samples and helping identify disease-related pathways. Fecal proteomics has already shown its potential for the discovery and validation of biomarkers for colorectal cancer screening, and the analysis of fecal microbiota by MALDI-MS for the diagnosis of a range of bowel diseases is gaining clinical acceptance.

Areas covered: Based on a comprehensive analysis of the current literature, we introduce the range of sensitive and specific proteomics methods which comprise the current ‘Proteomics Toolbox’, explain how the integration of fecal proteomics with data processing/bioinformatics has been used for the identification of potential biomarkers for both CRC and other gut-related pathologies and analysis of the fecal microbiome, outline some of the current fecal assays in current clinical practice and introduce the concept of personalised medicine which these technologies will help inform.

Expert commentary: Integration of fecal proteomics with other proteomics and genomics strategies as well as bioinformatics is paving the way towards personalised medicine, which will bring with it improved global healthcare.     

Mass Spectrometry and Proteomics 2018: the Mass Spectrometry Society of Japan,Japanese Proteomics Society,and Asia-Oceania Human Proteome Organization

ABSTRACT

Introduction: The mass spectrometry society of Japan, Japanese proteomics society, and Asia–Oceania human proteome organization held the conference ‘Mass Spectrometry and Proteomics 2018’ in Osaka, Japan, on May 15–18, 2018. This international conference focused on cutting edge technologies and their applications in a variety of research fields such as agriculture, material science, environmental factors, and clinical applications. An overview of the conference and a summary of the major lectures are reported here.

Expert commentary: The meeting will facilitate the development of fundamental technologies and the multi-disciplinary applications of proteomics.     

Proteomic analysis of mitochondria: biological and clinical progresses in cancer

Introduction: Mitochondria play important roles in regulating multiple biological processes and signalling pathways in eukaryotic cells, and mitochondrial dysfunction may result in a wide range of serious diseases, including cancer. With improvements in the identification of mitochondrial proteins, mitochondrial proteomics has made great achievements. In particular, this approach has been widely used to compare tumour cells at different stages of malignancy. Therefore, there is an urgent need to identify and characterize the function of mitochondrial proteins in cancer progression and to determine the involved mechanisms.

Areas covered: We provide an overview of recent progress related to mitochondrial proteomics in cancer and the application of comparative mitochondrial proteomics in various biological processes, including apoptosis, necroptosis, autophagy and metastasis, as well as clinical progress in cancer. Proteomics-related reports were found using PubMed and Google Scholar databases.

Expert commentary: Understanding both post-translational modification and post-translational processing is important in the comprehensive characterization of protein function. The application of comparative mitochondrial proteomics to investigate clinical samples and cancer cells will contribute to our understanding of the molecular interplay of mitochondrial proteins in the development of cancer. This approach will mine more biomarkers for diagnosis and prognosis and improve therapeutic outcomes among cancer patients.     

Insights into apoptotic proteins in chemotherapy: quantification techniques and informing therapy choice

Introduction: Cancer is one of the leading causes of morbidity and mortality worldwide. A hallmark of cancer is evasion of apoptosis leading to tumor progression and drug resistance. Biomarker research has become a sign of the times, and proteins involved in apoptosis may be used for clinical diagnostic or prognostic purposes in cancer treatment. The recent progress in proteomic technology has triggered an emerging number of researchers to study the molecular mechanisms that regulate the apoptotic signal transduction pathways in cancer.

Areas covered: A PubMed search for ‘Proteomics’ and ‘cancer’ and ‘chemotherapy’ and ‘apoptosis’ has been conducted for literature until December 2017.

Results: The study of apoptotic protein signatures in cancer provides valuable information for more effective prognosis, response to therapy and the identification of novel drug targets. A huge number of bioinformatic tools are available to interpret raw data. For quantification, mass spectrometry is the most reliable technique.

Expert commentary: This field of research is, however, still in its infancy and more intensive research is warranted to explore the full potential of biomarkers for clinical use. Progress in this field is influenced by the detection limit of current quantification methods as well as patient and cancer inter-individual profiles.     

Mass spectrometry-based proteomic exploration of the human immune system: focus on the inflammasome,global protein secretion,and T cells

Introduction: The immune system is our defense system against microbial infections and tissue injury, and understanding how it works in detail is essential for developing drugs for different diseases. Mass spectrometry-based proteomics can provide in-depth information on the molecular mechanisms involved in immune responses.

Areas covered: Summarized are the key immunology findings obtained with MS-based proteomics in the past five years, with a focus on inflammasome activation, global protein secretion, mucosal immunology, immunopeptidome and T cells. Special focus is on extracellular vesicle-mediated protein secretion and its role in immune responses.

Expert commentary: Proteomics is an essential part of modern omics-scale immunology research. To date, MS-based proteomics has been used in immunology to study protein expression levels, their subcellular localization, secretion, post-translational modifications, and interactions in immune cells upon activation by different stimuli. These studies have made major contributions to understanding the molecular mechanisms involved in innate and adaptive immune responses. New developments in proteomics offer constantly novel possibilities for exploring the immune system. Examples of these techniques include mass cytometry and different MS-based imaging approaches which can be widely used in immunology.     

Advances and challenges in understanding the role of the lipid raft proteome in human health

ABSTRACT

Introduction: Phase separation as a biophysical principle drives the formation of liquid-ordered ‘lipid raft’ membrane microdomains in cellular membranes, including organelles. Given the critical role of cellular membranes in both compartmentalization and signaling, clarifying the roles of membrane microdomains and their mutual regulation of/by membrane proteins is important in understanding the fundamentals of biology, and has implications for health.

Areas covered: This article will consider the evidence for lateral membrane phase separation in model membranes and organellar membranes, critically evaluate the current methods for lipid raft proteomics and discuss the biomedical implications of lipid rafts.

Expert commentary: Lipid raft homeostasis is perturbed in numerous chronic conditions; hence, understanding the precise roles and regulation of the lipid raft proteome is important for health and medicine. The current technical challenges in performing lipid raft proteomics can be overcome through well-controlled experimental design and careful interpretation. Together with technical developments in mass spectrometry and microscopy, our understanding of lipid raft biology and function will improve through recognition of the similarity between organelle and plasma membrane lipid rafts and considered integration of published lipid raft proteomics data.     

Boolean modeling techniques for protein co-expression networks in systems medicine

Introduction: Application of systems biology/systems medicine approaches is promising for proteomics/biomedical research, but requires selection of an adequate modeling type.

Areas covered: This article reviews the existing Boolean network modeling approaches, which provide in comparison with alternative modeling techniques several advantages for the processing of proteomics data. Application of methods for inference, reduction and validation of protein co-expression networks that are derived from quantitative high-throughput proteomics measurements is presented. It’s also shown how Boolean models can be used to derive system-theoretic characteristics that describe both the dynamical behavior of such networks as a whole and the properties of different cell states (e.g. healthy or diseased cell states). Furthermore, application of methods derived from control theory is proposed in order to simulate the effects of therapeutic interventions on such networks, which is a promising approach for the computer-assisted discovery of biomarkers and drug targets. Finally, the clinical application of Boolean modeling analyses is discussed.

Expert commentary: Boolean modeling of proteomics data is still in its infancy. Progress in this field strongly depends on provision of a repository with public access to relevant reference models. Also required are community supported standards that facilitate input of both proteomics and patient related data (e.g. age, gender, laboratory results, etc.).     

Sequential molecular changes and dynamic oxidative stress in high-grade serous ovarian carcinogenesis

The mechanism of high-grade serous ovarian cancer (HGSC) development remains elusive. This review outlines recent advances in the understanding of sequential molecular changes associated with the development of HGSC, as well as describes oxidative stress-induced genomic instability and carcinogenesis. This article reviews the English language literature between 2005 and 2017. Clinicopathological features analysis provides a sequential progression of fallopian tubal epithelium to precursor lesions to type 2 HGSC. HGSC may develop over a long time after incessant ovulation and repeated retrograde menstruation via stepwise accumulation of genetic alterations, including PAX2, ALDH1A1, STMN1, EZH2 and CCNE1, which confer positive selection of cells with growth advantages through acquiring driver mutations such as BRCA1/2, p53 or PTEN/PIK3CA. Haemoglobin and iron-induced oxidative stress leads to the emergence of genetic alterations in fallopian tubal epithelium via increased DNA damage and impaired DNA repair. Serous tubal intraepithelial carcinoma (STIC), the likely precursor of HGSC, may be susceptible to DNA double-strand breaks, exhibit DNA replication stress and increase genomic instability. The induction of genomic instability is considered to be a driving mechanism of reactive oxygen species (ROS)-induced carcinogenesis. HGSC exemplifies the view of stepwise cancer development. We describe how genetic alterations emerge during HGSC carcinogenesis related to oxidative stress.     

Patient non-attendance at urgent referral appointments for suspected cancer and its links to cancer diagnosis and one year mortality: A cohort study of patients referred on the Two Week Wait pathway

BackgroundThe ‘Two Week Wait’ policy aims to ensure patients with suspected cancer are seen within two weeks of referral. However, patient non-attendance can result in this target being missed. This study aimed to identify predictors of non-attendance; and analyse the relationship between attendance and outcomes including cancer diagnosis and early mortality.MethodsA cohort study of 109,433 adults registered at 105 general practices, referred to a cancer centre within a large NHS hospital trust (April 2009 to December 2016) on the ‘Two Week Wait’ pathway.Results5673 (5.2%) patients did not attend. Non-attendance was largely predicted by patient factors (younger and older age, male gender, greater deprivation, suspected cancer site, earlier year of referral, greater distance to the hospital) over practice factors (greater deprivation, lower Quality and Outcomes Framework score, lower cancer conversion rate, lower cancer detection rate). 10,360 (9.6%) patients were diagnosed with cancer within six months of referral (9.8% attending patients, 5.6% non-attending patients). Among these patients, 2029 (19.6%) died within 12 months of diagnosis: early mortality risk was 31.3% in non-attenders and 19.2% in attending patients.ConclusionsNon-attendance at urgent referral appointments for suspected cancer involves a minority of patients but happens in predictable groups. Cancer diagnosis was less likely in non-attending patients but these patients had worse early mortality outcomes than attending patients. The study findings have implications for cancer services and policy.     

Exploring neuropeptide signalling through proteomics and peptidomics

Introduction: Neuropeptides are neuro-endocrine signaling molecules capable of signaling as neurotransmitters, neuromodulators or neurohormones. Studying how neuropeptide signaling is integrated in endocrine pathways and how neuropeptides regulate endogenous processes is crucial to understanding how multicellular organisms respond to environmental and internal cues.

Areas covered: This review will cover proteomics and peptidomics approaches used in researching peptide signaling systems and breakthroughs that were achieved in this field. Both differential mass spectrometry and reverse genetic approaches are commonly used to study neuropeptidergic signaling. The field of proteomics quickly developed in the past decades and expanded from gel-based approaches to include advanced liquid chromatography and mass spectrometry. We explore how proteomics is used to reveal neuropeptide maturation and identify downstream targets of neuropeptide signaling pathways. We show how peptidomics differs from standard proteomics approaches and how it is used to study both neuropeptide processing and signal pathway identification.

Expert commentary: Thanks to recent advancements in isolation techniques and increased sensitivity of equipment, proteomics and peptidomics studies of neuropeptide signaling are contributing increasingly to elucidating functional implications of endocrine signaling. Further technical progress should allow for full peptidomic profiling of single neurons, eventually providing us with a complete comprehension of endocrine signaling.     

Characterizing functional consequences of DNA copy number alterations in breast and ovarian tumors by spaceMap

We propose a novel conditional graphical model—spaceMap—to construct gene regulatory networks from multiple types of high dimensional omic profiles. A motivating application is to characterize the perturbation of DNA copy number alterations(CNAs) on downstream protein levels in tumors. Through a penalized multivariate regression framework, spaceMap jointly models high dimensional protein levels as responses and high dimensional CNAs as predictors. In this setup, spaceMap infers an undirected network among proteins together with a directed network encoding how CNAs perturb the protein network. spaceMap can be applied to learn other types of regulatory relationships from high dimensional molecular profiles, especially those exhibiting hub structures. Simulation studies show spaceMap has greater power in detecting regulatory relationships over competing methods. Additionally, spaceMap includes a network analysis toolkit for biological interpretation of inferred networks. We applies spaceMap to the CNAs, gene expression and proteomics data sets from CPTAC-TCGA breast(n=77) and ovarian(n=174) cancer studies. Each cancer exhibits disruption of ‘ion transmembrane transport' and‘regulation from RNA polymerase Ⅱ promoter' by CNA events unique to each cancer. Moreover, using protein levels as a response yields a more functionally-enriched network than using RNA expressions in both cancer types. The network results also help to pinpoint crucial cancer genes and provide insights on the functional consequences of important CNA in breast and ovarian cancers. The R package spaceMap—including vignettes and documentation—is hosted on https://topherconley.github.io/spacemap.