Recent mass spectrometry-based proteomics for biomarker discovery in lung cancer,COPD, and asthma

Introduction: Lung cancer and related diseases have been one of the most common causes of deaths worldwide. Genomic-based biomarkers may hardly reflect the underlying dynamic molecular mechanism of functional protein interactions, which is the center of a disease. Recent developments in mass spectrometry (MS) have made it possible to analyze disease-relevant proteins expressed in clinical specimens by proteomic challenges.

Areas covered: To understand the molecular mechanisms of lung cancer and its subtypes, chronic obstructive pulmonary disease (COPD), asthma and others, great efforts have been taken to identify numerous relevant proteins by MS-based clinical proteomic approaches. Since lung cancer is a multifactorial disease that is biologically associated with asthma and COPD among various lung diseases, this study focused on proteomic studies on biomarker discovery using various clinical specimens for lung cancer, COPD, and asthma.

Expert commentary: MS-based exploratory proteomics utilizing clinical specimens, which can incorporate both experimental and bioinformatic analysis of protein-protein interaction and also can adopt proteogenomic approaches, makes it possible to reveal molecular networks that are relevant to a disease subgroup and that could differentiate between drug responders and non-responders, good and poor prognoses, drug resistance, and so on.     

A mouse model repository for cancer biomarker discovery

Early detection of cancer using biomarkers obtained from blood or other easily accessible tissues would have a significant impact on reducing cancer mortality. However, identifying new blood-based biomarkers has been hindered by the dynamic complexity of the human plasma proteome, confounded by genetic and environmental variability, and the scarcity of high quality controlled samples. In this report, we discuss a new paradigm for biomarker discovery through the use of mouse models. Inbred mouse models of cancer recapitulate many critical features of human cancer, while eliminating sources of environmental and genetic variability. The ability to collect samples from highly matched cases and controls under identical conditions further reduces variability which is critical for successful biomarker discovery. We describe the establishment of a repository containing tumor, plasma, urine, and other tissues from 10 different mouse models of human cancer, including two breast, two lung, two prostate, two gastrointestinal, one ovarian, and one skin tumor model. We present the overall design of this resource and its potential use by the research community for biomarker discovery.     

False discovery rates and related statistical concepts in mass spectrometry-based proteomics

Development of statistical methods for assessing the significance of peptide assignments to tandem mass spectra obtained using database searching remains an important problem. In the past several years, several different approaches have emerged, including the concept of expectation values, target-decoy strategy, and the probability mixture modeling approach of PeptideProphet. In this work, we provide a background on statistical significance analysis in the field of mass spectrometry-based proteomics, and present our perspective on the current and future developments in this area.     

Evaluation of fluorescence- and mass spectrometry-based CYP inhibition assays for use in drug discovery

The potential for metabolism-related drug-drug interactions by new chemical entities is assessed by monitoring the impact of these compounds on cytochrome P450 (CYP) activity using well-characterized CYP substrates. The conventional gold standard approach for in vitro evaluation of CYP inhibitory potential uses pooled human liver microsomes (HLM) in conjunction with prototypical drug substrates, often quantified by LC-MS/MS. However, fluorescent CYP inhibition assays, which use recombinantly expressed CYPs and fluorogenic probe substrates, have been employed in early drug discovery to provide low-cost, high-throughput assessment of new chemical entities. Despite its greatly enhanced throughput, this approach has been met with mixed success in predicting the data obtained with the conventional gold standard approach (HLM+LC-MS). The authors find that the predictivity of fluorogenic assays for the major CYP isoforms 3A4 and 2D6 may depend on the quality of the test compounds. Although the structurally more optimized marketed drugs yielded acceptable correlations between the fluorogenic and HLM+LC-MS/MS assays for CYPs 3A4, 2D6, and 2C9 (r2 = 0.5-0.7; p < 0.005), preoptimization, early discovery compounds yielded poorer correlations (r2 < or = 0.2) for 2 of these major isoforms, CYPs 3A4 and 2D6. Potential reasons for the observed differences are discussed.     

Secretome proteomics for discovery of cancer biomarkers

“Secretome” is referred to as the rich, complex set of molecules secreted from living cells. In a less strict definition frequently followed in “secretome” studies, the term also includes molecules shed from the surface of living cells. Proteins of secretome (will be referred to as secreted) play a key role in cell signaling, communication and migration. The need for developing more effective cancer biomarkers and therapeutic modalities has led to the study of cancer cell secretome as a means to identify and characterize diagnostic and prognostic markers and potential drug and therapeutic targets. Significant technological advances in the field of proteomics during the last two decades have greatly facilitated research towards this direction. Nevertheless, secretome analysis still faces some difficulties mainly related to sample collection and preparation. The goal of this article is to provide an overview of the main findings from the analysis of cancer cell secretome. Specifically, we focus on the presentation of main methodological approaches that have been developed for the study of secreted proteins and the results thereof from the analysis of secretome in different types of malignancies; special emphasis is given on correlation of findings with protein expression in body fluids.     

Application of mass spectrometry to the discovery of biomarkers for detection of prostate cancer

There has been an impressive emergence of mass spectrometry based technologies applied toward the study of proteins. Equally notable is the rapid adaptation of these technologies to biomedical approaches in the realm of clinical proteomics. Concerted efforts toward the elucidation of the proteomes of organ sites or specific disease state are proliferating and from these efforts come the promise of better diagnostics/prognostics and therapeutic intervention. Prostate cancer has been a focus of many such studies with the promise of improved care to patients via biomarkers derived from these proteomic approaches. The newer technologies provide higher analytical capabilities, employ automated liquid handling systems, fractionation techniques and bioinformatics tools for greater sensitivity and resolving power, more robust and higher throughput sample processing, and greater confidence in analytical results. In this prospects, we summarize the proteomic technologies applied to date in prostate cancer, along with their respective advantages and disadvantages. The development of newer proteomic strategies for use in future applications is also discussed.     

Coherent pipeline for biomarker discovery using mass spectrometry and bioinformatics

Background  

Robust biomarkers are needed to improve microbial identification and diagnostics. Proteomics methods based on mass spectrometry can be used for the discovery of novel biomarkers through their high sensitivity and specificity. However, there has been a lack of a coherent pipeline connecting biomarker discovery with established approaches for evaluation and validation. We propose such a pipeline that uses in silico methods for refined biomarker discovery and confirmation.     

Evaluating the potential of a novel oral lesion exudate collection method coupled with mass spectrometry-based proteomics for oral cancer biomarker discovery

Introduction  

Early diagnosis of Oral Squamous Cell Carcinoma (OSCC) increases the survival rate of oral cancer. For early diagnosis, molecular biomarkers contained in samples collected non-invasively and directly from at-risk oral premalignant lesions (OPMLs) would be ideal.     

Lectin chromatography/mass spectrometry discovery workflow identifies putative biomarkers of aggressive breast cancers

We used a lectin chromatography/MS-based approach to screen conditioned medium from a panel of luminal (less aggressive) and triple negative (more aggressive) breast cancer cell lines (n=5/subtype). The samples were fractionated using the lectins Aleuria aurantia (AAL) and Sambucus nigra agglutinin (SNA), which recognize fucose and sialic acid, respectively. The bound fractions were enzymatically N-deglycosylated and analyzed by LC-MS/MS. In total, we identified 533 glycoproteins, ～90% of which were components of the cell surface or extracellular matrix. We observed 1011 glycosites, 100 of which were solely detected in ≥3 triple negative lines. Statistical analyses suggested that a number of these glycosites were triple negative-specific and thus potential biomarkers for this tumor subtype. An analysis of RNaseq data revealed that approximately half of the mRNAs encoding the protein scaffolds that carried potential biomarker glycosites were up-regulated in triple negative vs luminal cell lines, and that a number of genes encoding fucosyl- or sialyltransferases were differentially expressed between the two subtypes, suggesting that alterations in glycosylation may also drive candidate identification. Notably, the glycoproteins from which these putative biomarker candidates were derived are involved in cancer-related processes. Thus, they may represent novel therapeutic targets for this aggressive tumor subtype.     

基于质谱的蛋白质生物标志物发现中的特征选择与机器学习方法研究进展

随着质谱技术的进步以及生物信息学与统计学算法的发展,以疾病研究为主要目的之一的人类蛋白质组计划正快速推进。蛋白质生物标志物在疾病早期诊断和临床治疗等方面有着非常重要的意义,其发现策略和方法的研究已成为一个重要的热点领域。特征选择与机器学习对于解决蛋白质组数据"高维度"及"稀疏性"问题有较好的效果,因而逐渐被广泛地应用于发现蛋白质生物标志物的研究中。文中主要阐述蛋白质生物标志物的发现策略以及其中特征选择与机器学习方法的原理、应用实例和适用范围,并讨论深度学习方法在本领域的应用前景及局限性,以期为相关研究提供参考。     

Integrated mass spectrometry-based analysis of plasma glycoproteins and their glycan modifications

We present a protocol for the identification of glycosylated proteins in plasma followed by elucidation of their individual glycan compositions. The study of glycoproteins by mass spectrometry is usually based on cleavage of glycans followed by separate analysis of glycans and deglycosylated proteins, which limits the ability to derive glycan compositions for individual glycoproteins. The methodology described here consists of 2D HPLC fractionation of intact proteins and liquid chromatography-multistage tandem mass spectrometry (LC-MS/MS(n)) analysis of digested protein fractions. Protein samples are separated by 1D anion-exchange chromatography (AEX) with an eight-step salt elution. Protein fractions from each of the eight AEX elution steps are transferred onto the 2D reversed-phase column to further separate proteins. A digital ion trap mass spectrometer with a wide mass range is then used for LC-MS/MS(n) analysis of intact glycopeptides from the 2D HPLC fractions. Both peptide and oligosaccharide compositions are revealed by analysis of the ion fragmentation patterns of glycopeptides with an intact glycopeptide analysis pipeline.     

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.     

Low abundance protein enrichment for discovery of candidate plasma protein biomarkers for early detection of breast cancer

Molecular biomarkers of early stage breast cancer may improve the sensitivity and specificity of diagnosis. Plasma biomarkers have additional value in that they can be monitored with minimal invasiveness. Plasma biomarker discovery by genome-wide proteomic methods is impeded by the wide dynamic range of protein abundance and the heterogeneity of protein expression in healthy and disease populations which requires the analysis of a large number of samples. We addressed these issues through the development of a novel protocol that couples a combinatorial peptide ligand library protein enrichment strategy with isobaric label-based 2D LC-MS/MS for the identification of candidate biomarkers in high throughput. Plasma was collected from patients with stage I breast cancer or benign breast lesions. Low abundance proteins were enriched using a bead-based combinatorial library of hexapeptides. This resulted in the identification of 397 proteins, 22% of which are novel plasma proteins. Twenty-three differentially expressed plasma proteins were identified, demonstrating the effectiveness of the described protocol and defining a set of candidate biomarkers to be validated in independent samples. This work can be used as the basis for the design of properly powered investigations of plasma protein expression for biomarker discovery in larger cohorts of patients with complex disease.     

Mass spectrometry-based analysis of glycoproteins and its clinical applications in cancer biomarker discovery

Glycosylation is one of the most important posttranslational modifications of proteins and plays essential roles in various biological processes. Aberration in the glycan moieties of glycoproteins is associated with many diseases. It is especially critical to develop the rapid and sensitive methods for analysis of aberrant glycoproteins associated with diseases. Mass spectrometry (MS) has become a powerful tool for glycoprotein analysis. Especially, tandem mass spectrometry can provide highly informative fragments for structural identification of glycoproteins. This review provides an overview of the development of MS technologies and their applications in identification of abnormal glycoproteins and glycans in human serum to screen cancer biomarkers in recent years.     

Detection and confirmation of serum lipid biomarkers for preeclampsia using direct infusion mass spectrometry

Despite substantial research, the early diagnosis of preeclampsia remains elusive. Lipids are now recognized to be involved in regulation and pathophysiology of some disease. Shotgun lipidomic studies were undertaken to determine whether serum lipid biomarkers exist that predict preeclampsia later in the same in pregnancy. A discovery study was performed using sera collected at 12–14 weeks pregnancy from 27 controls with uncomplicated pregnancies and 29 cases that later developed preeclampsia. Lipids were extracted and analyzed by direct infusion into a TOF mass spectrometer. MS signals, demonstrating apparent differences were selected, their abundances determined, and statistical differences tested. Statistically significant lipid markers were reevaluated in a second confirmatory study having 43 controls and 37 preeclampsia cases. Multi-marker combinations were developed using those lipid biomarkers confirmed in the second study. The initial study detected 45 potential preeclampsia markers. Of these, 23 markers continued to be statistically significant in the second confirmatory set. Most of these markers, representing several lipid classes, were chemically characterized, typically providing lipid class and potential molecular components using MS². Several multi-marker panels with areas under the curve >0.85 and high predictive values were developed. Developed panels of serum lipidomic biomarkers appear to be able to identify most women at risk for preeclampsia in a given pregnancy at 12–14 weeks gestation.     

Mass spectrometry-based study of the plasma proteome in a mouse intestinal tumor model

Early detection of cancer can greatly improve prognosis. Identification of proteins or peptides in the circulation, at different stages of cancer, would greatly enhance treatment decisions. Mass spectrometry (MS) is emerging as a powerful tool to identify proteins from complex mixtures such as plasma that may help identify novel sets of markers that may be associated with the presence of tumors. To examine this feature we have used a genetically modified mouse model, Apc(Min), which develops intestinal tumors with 100% penetrance. Utilizing liquid chromatography-tandem mass spectrometry (LC-MS/MS), we identified total plasma proteome (TPP) and plasma glycoproteome (PGP) profiles in tumor-bearing mice. Principal component analysis (PCA) and agglomerative hierarchial clustering analysis revealed that these protein profiles can be used to distinguish between tumor-bearing Apc(Min) and wild-type control mice. Leave-one-out cross-validation analysis established that global TPP and global PGP profiles can be used to correctly predict tumor-bearing animals in 17/19 (89%) and 19/19 (100%) of cases, respectively. Furthermore, leave-one-out cross-validation analysis confirmed that the significant differentially expressed proteins from both the TPP and the PGP were able to correctly predict tumor-bearing animals in 19/19 (100%) of cases. A subset of these proteins was independently validated by antibody microarrays using detection by two color rolling circle amplification (TC-RCA). Analysis of the significant differentially expressed proteins indicated that some might derive from the stroma or the host response. These studies suggest that mass spectrometry-based approaches to examine the plasma proteome may prove to be a valuable method for determining the presence of intestinal tumors.