Mass spectrometry-based proteomics: from cancer biology to protein biomarkers, drug targets, and clinical applications

Proteogenomic Approaches for Diseasome Studies

During the last three decades, technological advancements in high-throughput next-generation sequencing have resulted in an increased understanding of proteomic and genomic data, aptly termed proteogenomics. Efforts in developing such approaches have not only been limited but also focused on protein identification and subcellular localization. These approaches, however, have also been explored for their broad understanding of how genomics/transcriptomics data have yielded measures, for example, gene expression regulation/signal cascading and diseasome studies. In this review, we discuss methods and tools developed through sequence-centric integrative modeling of proteogenomic approaches.

Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases

Thyroid diseases, including functional and neoplastic diseases, bring a huge burden to people's health. Therefore, a timely and accurate diagnosis is necessary. Mass spectrometry (MS) based multi-omics has become an effective strategy to reveal the complex biological mechanisms of thyroid diseases. The exponential growth of biomedical data has promoted the applications of machine learning (ML) techniques to address new challenges in biology and clinical research. In this review, we presented the detailed review of applications of ML for MS-based multi-omics in thyroid disease. It is primarily divided into two sections. In the first section, MS-based multi-omics, primarily proteomics and metabolomics, and their applications in clinical diseases are briefly discussed. In the second section, several commonly used unsupervised learning and supervised algorithms, such as principal component analysis, hierarchical clustering, random forest, and support vector machines are addressed, and the integration of ML techniques with MS-based multi-omics data and its application in thyroid disease diagnosis is explored.

Newer Biomarkers in Gallbladder Carcinoma: A Scoping Review

Biomarkers have the potential to play a crucial role in managing gallbladder cancer post-surgery. They can identify patients more likely to experience a recurrence, allowing oncologists to tailor a more intensive surveillance plan and consider additional therapies. Some biomarkers can even predict how well a patient will respond to specific chemotherapy or targeted treatments. By monitoring these biomarkers, clinicians can track how effective the ongoing treatment is and detect any signs of early recurrence. Various biomarkers, like tumor markers, genetic markers, and genomic and epigenetic markers, are being investigated. The goal is to find the most reliable and accurate biomarkers to enhance patient care and outcomes. Integrating biomarker data into treatment plans can help personalize therapy and make better informed decisions. By identifying which patients are likely to benefit from specific treatments, biomarkers have the potential to improve long-term survival rates significantly. This scoping review discusses newer biomarkers in gallbladder carcinoma; some of them are in clinical use, while most of them are used in research settings. This provides a broad insight to practicing clinicians about the present biomarkers and the futuristic biomarkers.

Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology

The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.

Candidate biomarkers for treatment benefit from sunitinib in patients with advanced renal cell carcinoma using mass spectrometry-based (phospho)proteomics

The tyrosine kinase inhibitor sunitinib is an effective first-line treatment for patients with advanced renal cell carcinoma (RCC). Hypothesizing that a functional read-out by mass spectrometry-based (phospho, p-)proteomics will identify predictive biomarkers for treatment outcome of sunitinib, tumor tissues of 26 RCC patients were analyzed. Eight patients had primary resistant (RES) and 18 sensitive (SENS) RCC. A 78 phosphosite signature (p < 0.05, fold-change > 2) was identified; 22 p-sites were upregulated in RES (unique in RES: BCAR3, NOP58, EIF4A2, GDI1) and 56 in SENS (35 unique). EIF4A1/EIF4A2 were differentially expressed in RES at the (p-)proteome and, in an independent cohort, transcriptome level. Inferred kinase activity of MAPK3 (p = 0.026) and EGFR (p = 0.045) as determined by INKA was higher in SENS. Posttranslational modifications signature enrichment analysis showed that different p-site-centric signatures were enriched (p < 0.05), of which FGF1 and prolactin pathways in RES and, in SENS, vanadate and thrombin treatment pathways, were most significant. In conclusion, the RCC (phospho)proteome revealed differential p-sites and kinase activities associated with sunitinib resistance and sensitivity. Independent validation is warranted to develop an assay for upfront identification of patients who are intrinsically resistant to sunitinib.

Plasma exosomal protein PLG and SERPINA1 in colorectal cancer diagnosis and coagulation abnormalities

PURPOSE

Early diagnosis of colorectal cancer (CRC) is critical to patient prognosis; however, there is lack of non-invasive biomarkers that are extremely sensitive and specific for early screening and diagnosis. Exosomes are a novel tool applied to the diagnosis and treatment of cancer. Changes in plasma exosomal proteins have a certain relationship with the development of various diseases including tumors. Here, we aimed to find exosomal biomarkers for early diagnosis of CRC.

METHODS

Exosomes obtained by ultracentrifugation from CRC patients and healthy donors were characterized by transmission electron microscopy (TEM), qNano and western blotting. Proteomic and functional enrichment analyses confirmed differences in the specific expression of exosomal proteins in plasma between CRC patients and healthy donors. Western blotting with enzyme-linked immunosorbent assay (ELISA) was used to verify the difference proteins. Statistical methods were used to analyze the relationship between protein levels and CRC.

RESULTS

The expression levels of serpin peptidase inhibitor clade A member 1 (SERPINA1) and fibrinogen (PLG) in CRC patients were significantly higher than those in healthy groups. Receptor operating characteristic (ROC) curves analysis was superior to CEA and CA19-9 for the diagnosis of colorectal cancer and early-stage colorectal cancer. The two were related to TNM staging and coagulation, and the difference was statistically significant.

CONCLUSION

The results of this study have potential value in advancing the clinical diagnosis of colorectal cancer.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

Phosphoproteomics of patient-derived xenografts identifies targets and markers associated with sensitivity and resistance to EGFR blockade in colorectal cancer

Epidermal growth factor receptor (EGFR) is a well-exploited therapeutic target in metastatic colorectal cancer (mCRC). Unfortunately, not all patients benefit from current EGFR inhibitors. Mass spectrometry-based proteomics and phosphoproteomics were performed on 30 genomically and pharmacologically characterized mCRC patient-derived xenografts (PDXs) to investigate the molecular basis of response to EGFR blockade and identify alternative drug targets to overcome resistance. Both the tyrosine and global phosphoproteome as well as the proteome harbored distinctive response signatures. We found that increased pathway activity related to mitogen-activated protein kinase (MAPK) inhibition and abundant tyrosine phosphorylation of cell junction proteins, such as CXADR and CLDN1/3, in sensitive tumors, whereas epithelial-mesenchymal transition and increased MAPK and AKT signaling were more prevalent in resistant tumors. Furthermore, the ranking of kinase activities in single samples confirmed the driver activity of ERBB2, EGFR, and MET in cetuximab-resistant tumors. This analysis also revealed high kinase activity of several members of the Src and ephrin kinase family in 2 CRC PDX models with genomically unexplained resistance. Inhibition of these hyperactive kinases, alone or in combination with cetuximab, resulted in growth inhibition of ex vivo PDX-derived organoids and in vivo PDXs. Together, these findings highlight the potential value of phosphoproteomics to improve our understanding of anti-EGFR treatment and response prediction in mCRC and bring to the forefront alternative drug targets in cetuximab-resistant tumors.

An Inflection Point in Cancer Protein Biomarkers: What Was and What's Next

Biomarkers remain the highest value proposition in cancer medicine today - especially protein biomarkers. Yet despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last two decades have seen an explosion of multi-omics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single cell analysis, artificial intelligence (machine and deep learning) for data analysis and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking towards viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to re-define biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.

Introduction to Mass Spectrometry for Bimolecular Analysis in a Clinical Laboratory

Mass spectrometry is a technique that identifies analytes based on mass-to-charge (m/z) ratio and structural fragments. Although this technique has been used in research and specialized clinical laboratories for decades, only in recent years has mass spectrometry become popular in routine clinical laboratories. Mass spectrometry, especially when coupled with gas chromatography or liquid chromatography, provides very specific and often sensitive analysis of many analytes. Other advantages of mass spectrometry include simultaneous analysis of multiple analytes (>100) and generally limited requirement for specialized reagents. Commonly measured analytes by mass spectrometry include metabolites, drugs, hormones, and proteins.

Tumor Drug Concentration and Phosphoproteomic Profiles After Two Weeks of Treatment With Sunitinib in Patients with Newly Diagnosed Glioblastoma

PURPOSE

Tyrosine kinase inhibitors (TKI) have poor efficacy in patients with glioblastoma (GBM). Here, we studied whether this is predominantly due to restricted blood-brain barrier penetration or more to biological characteristics of GBM.

PATIENTS AND METHODS

Tumor drug concentrations of the TKI sunitinib after 2 weeks of preoperative treatment was determined in 5 patients with GBM and compared with its in vitro inhibitory concentration (IC50) in GBM cell lines. In addition, phosphotyrosine (pTyr)-directed mass spectrometry (MS)-based proteomics was performed to evaluate sunitinib-treated versus control GBM tumors.

RESULTS

The median tumor sunitinib concentration of 1.9 μmol/L (range 1.0-3.4) was 10-fold higher than in concurrent plasma, but three times lower than sunitinib IC50s in GBM cell lines (median 5.4 μmol/L, 3.0-8.5; P = 0.01). pTyr-phosphoproteomic profiles of tumor samples from 4 sunitinib-treated versus 7 control patients revealed 108 significantly up- and 23 downregulated (P < 0.05) phosphopeptides for sunitinib treatment, resulting in an EGFR-centered signaling network. Outlier analysis of kinase activities as a potential strategy to identify drug targets in individual tumors identified nine kinases, including MAPK10 and INSR/IGF1R.

CONCLUSIONS

Achieved tumor sunitinib concentrations in patients with GBM are higher than in plasma, but lower than reported for other tumor types and insufficient to significantly inhibit tumor cell growth in vitro. Therefore, alternative TKI dosing to increase intratumoral sunitinib concentrations might improve clinical benefit for patients with GBM. In parallel, a complex profile of kinase activity in GBM was found, supporting the potential of (phospho)proteomic analysis for the identification of targets for (combination) treatment.

THE ROLE OF PHOSPHORYLATION IN ATRIAL FIBRILLATION: A FOCUS ON MASS SPECTROMETRY APPROACHES

Atrial fibrillation (AF) is the most common arrhythmia worldwide. It is associated with significant increases in morbidity in the form of stroke and heart failure, and a doubling in all-cause mortality. The pathophysiology of AF is incompletely understood, and this has contributed to a lack of effective treatments and disease-modifying therapies. An important cellular process that may explain how risk factors give rise to AF includes post-translational modification (PTM) of proteins. As the most commonly occurring PTM, protein phosphorylation is especially relevant. Although many methods exist for studying protein phosphorylation, a common and highly resolute technique is mass spectrometry (MS). This review will discuss recent evidence surrounding the role of protein phosphorylation in the pathogenesis of AF. MS-based technology to study phosphorylation and uses of MS in other areas of medicine such as oncology will also be presented. Based on these data, future goals and experiments will be outlined that utilize MS technology to better understand the role of phosphorylation in AF and elucidate its role in AF pathophysiology. This may ultimately allow for the development of more effective AF therapies.

Computational advances of tumor marker selection and sample classification in cancer proteomics

Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.

Kinase Inhibitor Treatment of Patients with Advanced Cancer Results in High Tumor Drug Concentrations and in Specific Alterations of the Tumor Phosphoproteome

Identification of predictive biomarkers for targeted therapies requires information on drug exposure at the target site as well as its effect on the signaling context of a tumor. To obtain more insight in the clinical mechanism of action of protein kinase inhibitors (PKIs), we studied tumor drug concentrations of protein kinase inhibitors (PKIs) and their effect on the tyrosine-(pTyr)-phosphoproteome in patients with advanced cancer. Tumor biopsies were obtained from 31 patients with advanced cancer before and after 2 weeks of treatment with sorafenib (SOR), erlotinib (ERL), dasatinib (DAS), vemurafenib (VEM), sunitinib (SUN) or everolimus (EVE). Tumor concentrations were determined by LC-MS/MS. pTyr-phosphoproteomics was performed by pTyr-immunoprecipitation followed by LC-MS/MS. Median tumor concentrations were 2-10 µM for SOR, ERL, DAS, SUN, EVE and >1 mM for VEM. These were 2-178 × higher than median plasma concentrations. Unsupervised hierarchical clustering of pTyr-phosphopeptide intensities revealed patient-specific clustering of pre- and on-treatment profiles. Drug-specific alterations of peptide phosphorylation was demonstrated by marginal overlap of robustly up- and downregulated phosphopeptides. These findings demonstrate that tumor drug concentrations are higher than anticipated and result in drug specific alterations of the phosphoproteome. Further development of phosphoproteomics-based personalized medicine is warranted.

An Overview on G Protein-coupled Receptor-induced Signal Transduction in Acute Myeloid Leukemia

BACKGROUND

Acute Myeloid Leukemia (AML) is a genetically heterogeneous disease characterized by uncontrolled proliferation of precursor myeloid-lineage cells in the bone marrow. AML is also characterized by patients with poor long-term survival outcomes due to relapse. Many efforts have been made to understand the biological heterogeneity of AML and the challenges to develop new therapies are therefore enormous. G Protein-coupled Receptors (GPCRs) are a large attractive drug-targeted family of transmembrane proteins, and aberrant GPCR expression and GPCR-mediated signaling have been implicated in leukemogenesis of AML. This review aims to identify the molecular players of GPCR signaling, focusing on the hematopoietic system, which are involved in AML to help developing novel drug targets and therapeutic strategies.

METHODS

We undertook an exhaustive and structured search of bibliographic databases for research focusing on GPCR, GPCR signaling and expression in AML.

RESULTS AND CONCLUSION

Many scientific reports were found with compelling evidence for the involvement of aberrant GPCR expression and perturbed GPCR-mediated signaling in the development of AML. The comprehensive analysis of GPCR in AML provides potential clinical biomarkers for prognostication, disease monitoring and therapeutic guidance. It will also help to provide marker panels for monitoring in AML. We conclude that GPCR-mediated signaling is contributing to leukemogenesis of AML, and postulate that mass spectrometrybased protein profiling of primary AML cells will accelerate the discovery of potential GPCR related biomarkers for AML.

Multi-omic approaches to improve outcome for T-cell acute lymphoblastic leukemia patients

In the last decade, tremendous progress in curative treatment has been made for T-ALL patients using high-intensive, risk-adapted multi-agent chemotherapy. Further treatment intensification to improve the cure rate is not feasible as it will increase the number of toxic deaths. Hence, about 20% of pediatric patients relapse and often die due to acquired therapy resistance. Personalized medicine is of utmost importance to further increase cure rates and is achieved by targeting specific initiation, maintenance or resistance mechanisms of the disease. Genomic sequencing has revealed mutations that characterize genetic subtypes of many cancers including T-ALL. However, leukemia may have various activated pathways that are not accompanied by the presence of mutations. Therefore, screening for mutations alone is not sufficient to identify all molecular targets and leukemic dependencies for therapeutic inhibition. We review the extent of the driving type A and the secondary type B genomic mutations in pediatric T-ALL that may be targeted by specific inhibitors. Additionally, we review the need for additional screening methods on the transcriptional and protein levels. An integrated 'multi-omic' screening will identify potential targets and biomarkers to establish significant progress in future individualized treatment of T-ALL patients.

INKA, an integrative data analysis pipeline for phosphoproteomic inference of active kinases

Identifying hyperactive kinases in cancer is crucial for individualized treatment with specific inhibitors. Kinase activity can be discerned from global protein phosphorylation profiles obtained with mass spectrometry-based phosphoproteomics. A major challenge is to relate such profiles to specific hyperactive kinases fueling growth/progression of individual tumors. Hitherto, the focus has been on phosphorylation of either kinases or their substrates. Here, we combined label-free kinase-centric and substrate-centric information in an Integrative Inferred Kinase Activity (INKA) analysis. This multipronged, stringent analysis enables ranking of kinase activity and visualization of kinase-substrate networks in a single biological sample. To demonstrate utility, we analyzed (i) cancer cell lines with known oncogenes, (ii) cell lines in a differential setting (wild-type versus mutant, +/- drug), (iii) pre- and on-treatment tumor needle biopsies, (iv) cancer cell panel with available drug sensitivity data, and (v) patient-derived tumor xenografts with INKA-guided drug selection and testing. These analyses show superior performance of INKA over its components and substrate-based single-sample tool KARP, and underscore target potential of high-ranking kinases, encouraging further exploration of INKA's functional and clinical value.

Mass Spectrometry Analyses of Multicellular Tumor Spheroids

Multicellular tumor spheroids (MCTS) are a powerful biological in vitro model, which closely mimics the 3D structure of primary avascularized tumors. Mass spectrometry (MS) has established itself as a powerful analytical tool, not only to better understand and describe the complex structure of MCTS, but also to monitor their response to cancer therapeutics. The first part of this review focuses on traditional mass spectrometry approaches with an emphasis on elucidating the molecular characteristics of these structures. Then the mass spectrometry imaging (MSI) approaches used to obtain spatially defined information from MCTS is described. Finally the analysis of primary spheroids, such as those present in ovarian cancer, and the great potential that mass spectrometry analysis of these structures has for improved understanding of cancer progression and for personalized in vitro therapeutic testing is discussed.

An Out-of-Core GPU based dimensionality reduction algorithm for Big Mass Spectrometry Data and its application in bottom-up Proteomics

Modern high resolution Mass Spectrometry instruments can generate millions of spectra in a single systems biology experiment. Each spectrum consists of thousands of peaks but only a small number of peaks actively contribute to deduction of peptides. Therefore, pre-processing of MS data to detect noisy and non-useful peaks are an active area of research. Most of the sequential noise reducing algorithms are impractical to use as a pre-processing step due to high time-complexity. In this paper, we present a GPU based dimensionality-reduction algorithm, called G-MSR, for MS2 spectra. Our proposed algorithm uses novel data structures which optimize the memory and computational operations inside GPU. These novel data structures include Binary Spectra and Quantized Indexed Spectra (QIS). The former helps in communicating essential information between CPU and GPU using minimum amount of data while latter enables us to store and process complex 3-D data structure into a 1-D array structure while maintaining the integrity of MS data. Our proposed algorithm also takes into account the limited memory of GPUs and switches between in-core and out-of-core modes based upon the size of input data. G-MSR achieves a peak speed-up of 386x over its sequential counterpart and is shown to process over a million spectra in just 32 seconds. The code for this algorithm is available as a GPL open-source at GitHub at the following link: https://github.com/pcdslab/G-MSR.

Comparative Analysis for Glycopatterns and Complex-Type N-Glycans of Glycoprotein in Sera from Chronic Hepatitis B- and C-Infected Patients

Background: Chronic infection with HBV (CHB) or HCV (CHC) is the most common chronic viral hepatitis that can lead to cirrhosis and hepatocellular carcinoma in humans, their infections have distinct pathogenic processes, however, little is known about the difference of glycoprotein glycopatterns in serum between hepatitis B virus (HBV)- and hepatitis C virus (HCV)-infected patients.

Methods: A method combining the lectin microarrays, letin-mediated affinity capture glycoproteins, and MALDI-TOF/TOF-MS was employed to analyze serum protein glycopatterns and identify the glycan structures from patients with CHB (n = 54) or CHC(n = 47), and healthy volunteers (HV, n = 35). Lectin blotting was further utilized to validate and assess the expression levels of their serum glycopatterns. Finally, the differences of the glycoprotein glycopatterns were systematically compared between CHB and CHC patients.

Conclusions: As a result, there were 11 lectins (e.g., HHL, GSL-II, and EEL) exhibited significantly increased expression levels, and three lectins (LCA, VVA, and ACA) exhibited significantly decreased expression levels of serum protein glycopatterns only in the CHB patients. However, DBA exhibited significantly decreased expression levels, and two lectins (WGA and SNA) exhibited significantly increased expression levels of serum glycopatterns only in the CHC patients. Furthermore, LEL and MAL-I showed a coincidentally increasing trend in both CHC and CHB patients compared with the HV. The individual analysis demonstrated that eight lectins (MPL, GSL-I, PTL-II, UEA-I, WGA, LEL, VVA, and MAL-I) exhibited a high degree of consistency with the pooled serum samples of HV, CHB, and CHC patients. Besides, a complex-type N-glycans binder PHA-E+L exhibited significantly decreased NFIs in the CHB compared with HV and CHC subjects (p < 0.01). The MALDI-TOF/TOF-MS results of N-linked glycans from the serum glycoproteins isolated by PHA-E+L-magnetic particle conjugates showed that there was an overlap of 23 N-glycan peaks (e.g., m/z 1419.743, 1663.734, and 1743.581) between CHB, and CHC patients, 5 glycan peaks (e.g., m/z 1850.878, 1866.661, and 2037.750) were presented in virus-infected hepatitis patients compared with HV, 3 glycan peaks (1460.659, 2069.740, and 2174.772) were observed only in CHC patients. Our data provide useful information to find new biomarkers for distinguishing CHB and CHC patients based on the precision alteration of their serum glycopatterns.

The impact of growth hormone on proteomic profiles: a review of mouse and adult human studies

Growth hormone (GH) is a protein that is known to stimulate postnatal growth, counter regulate insulin's action and induce expression of insulin-like growth factor-1. GH exerts anabolic or catabolic effects depending upon on the targeted tissue. For instance, GH increases skeletal muscle and decreases adipose tissue mass. Our laboratory has spent the past two decades studying these effects, including the effects of GH excess and depletion, on the proteome of several mouse and human tissues. This review first discusses proteomic techniques that are commonly used for these types of studies. We then examine the proteomic differences found in mice with excess circulating GH (bGH mice) or mice with disruption of the GH receptor gene (GHR^-/-). We also describe the effects of increased and decreased GH action on the proteome of adult patients with either acromegaly, GH deficiency or patients after short-term GH treatment. Finally, we explain how these proteomic studies resulted in the discovery of potential biomarkers for GH action, particularly those related with the effects of GH on aging, glucose metabolism and body composition.

Feasibility of urinary extracellular vesicle proteome profiling using a robust and simple, clinically applicable isolation method

Extracellular vesicles (EVs) secreted by prostate cancer (PCa) cells contain specific biomarkers and can be isolated from urine. Collection of urine is not invasive, and therefore urinary EVs represent a liquid biopsy for diagnostic and prognostic testing for PCa. In this study, we optimised urinary EV isolation using a method based on heat shock proteins and compared it to gold-standard ultracentrifugation. The urinary EV isolation protocol using the Vn96-peptide is easier, time convenient (≈1.5 h) and no special equipment is needed, in contrast to ultracentrifugation protocol (>3.5 h), making this protocol clinically feasible. We compared the isolated vesicles of both ultracentrifugation and Vn96-peptide by proteome profiling using mass spectrometry-based proteomics (n = 4 per method). We reached a depth of >3000 proteins, with 2400 proteins that were commonly detected in urinary EVs from different donors. We show a large overlap (>85%) between proteins identified in EVs isolated by ultracentrifugation and Vn96-peptide. Addition of the detergent NP40 to Vn96-peptide EV isolations reduced levels of background proteins and highly increased the levels of the EV-markers TSG101 and PDCD6IP, indicative of an increased EV yield. Thus, the Vn96-peptide-based EV isolation procedure is clinically feasibly and allows large-scale protein profiling of urinary EV biomarkers.

Proteomic analysis of bronchoalveolar lavage fluid (BALF) from lung cancer patients using label-free mass spectrometry

BACKGROUND

Lung cancer is the leading cause of cancer-related mortality in both men and women throughout the world. The need to detect lung cancer at an early, potentially curable stage, is essential and may reduce mortality by 20%. The aim of this study was to identify distinct proteomic profiles in bronchoalveolar fluid (BALF) and plasma that are able to discriminate individuals with benign disease from those with non-small cell lung cancer (NSCLC).

METHODS

Using label-free mass spectrometry analysis of BALF during discovery-phase analysis, a significant number of proteins were found to have different abundance levels when comparing control to adenocarcinoma (AD) or squamous cell lung carcinoma (SqCC). Validation of candidate biomarkers identified in BALF was performed in a larger cohort of plasma samples by detection with enzyme-linked immunoassay.

RESULTS

Four proteins (Cystatin-C, TIMP-1, Lipocalin-2 and HSP70/HSPA1A) were selected as a representative group from discovery phase mass spectrometry BALF analysis. Plasma levels of TIMP-1, Lipocalin-2 and Cystatin-C were found to be significantly elevated in AD and SqCC compared to control.

CONCLUSION

The results presented in this study indicate that BALF is an important proximal biofluid for the discovery and identification of candidate lung cancer biomarkers.

GENERAL SIGNIFICANCE

There is good correlation between the trend of protein abundance levels in BALF and that of plasma which validates this approach to develop a blood biomarker to aid lung cancer diagnosis, particularly in the era of lung cancer screening. The protein signatures identified also provide insight into the molecular mechanisms associated with lung malignancy.

Glycans and glycoproteins as specific biomarkers for cancer

Protein glycosylation and other post-translational modifications are involved in potentially all aspects of human growth and development. Defective glycosylation has adverse effects on human physiological conditions and accompanies many chronic and infectious diseases. Altered glycosylation can occur at the onset and/or during tumor progression. Identifying these changes at early disease stages may aid in making decisions regarding treatments, as early intervention can greatly enhance survival. This review highlights some of the efforts being made to identify N- and O-glycosylation profile shifts in cancer using mass spectrometry. The analysis of single or panels of potential glycoprotein cancer markers are covered. Other emerging technologies such as global glycan release and site-specific glycosylation analysis and quantitation are also discussed. Graphical Abstract Steps involved in the biomarker discovery.

Integrative subcellular proteomic analysis allows accurate prediction of human disease-causing genes

Proteomic profiling on subcellular fractions provides invaluable information regarding both protein abundance and subcellular localization. When integrated with other data sets, it can greatly enhance our ability to predict gene function genome-wide. In this study, we performed a comprehensive proteomic analysis on the light-sensing compartment of photoreceptors called the outer segment (OS). By comparing with the protein profile obtained from the retina tissue depleted of OS, an enrichment score for each protein is calculated to quantify protein subcellular localization, and 84% accuracy is achieved compared with experimental data. By integrating the protein OS enrichment score, the protein abundance, and the retina transcriptome, the probability of a gene playing an essential function in photoreceptor cells is derived with high specificity and sensitivity. As a result, a list of genes that will likely result in human retinal disease when mutated was identified and validated by previous literature and/or animal model studies. Therefore, this new methodology demonstrates the synergy of combining subcellular fractionation proteomics with other omics data sets and is generally applicable to other tissues and diseases.

Mass Spectrometry in Clinical Laboratory: Applications in Biomolecular Analysis

Mass spectrometry (MS) is a technique that can identify analytes on the basis of mass-to-charge (m/z) ratio. Although this technique has been used in research and specialized clinical laboratories for decades, however, in recent years, MS has been increasingly used in routine clinical laboratories. MS, especially when coupled to gas chromatography or liquid chromatography, provides very specific and often sensitive analysis of many analytes. Other advantages of MS include simultaneous analysis of multiple analytes (>100) and generally without need for specialized reagents. Commonly measured analytes by MS include drugs, hormones, and proteins.

Mass Spectrometry in Clinical Laboratory: Applications in Therapeutic Drug Monitoring and Toxicology

Mass spectrometry (MS) has been used in research and specialized clinical laboratories for decades as a very powerful technology to identify and quantify compounds. In recent years, application of MS in routine clinical laboratories has increased significantly. This is mainly due to the ability of MS to provide very specific identification, high sensitivity, and simultaneous analysis of multiple analytes (>100). The coupling of tandem mass spectrometry with gas chromatography (GC) or liquid chromatography (LC) has enabled the rapid expansion of this technology. While applications of MS are used in many clinical areas, therapeutic drug monitoring, drugs of abuse, and clinical toxicology are still the primary focuses of the field. It is not uncommon to see mass spectrometry being used in routine clinical practices for those applications.

Lessons from Toxicology: Developing a 21st-Century Paradigm for Medical Research

Biomedical developments in the 21st century provide an unprecedented opportunity to gain a dynamic systems-level and human-specific understanding of the causes and pathophysiologies of disease. This understanding is a vital need, in view of continuing failures in health research, drug discovery, and clinical translation. The full potential of advanced approaches may not be achieved within a 20th-century conceptual framework dominated by animal models. Novel technologies are being integrated into environmental health research and are also applicable to disease research, but these advances need a new medical research and drug discovery paradigm to gain maximal benefits. We suggest a new conceptual framework that repurposes the 21st-century transition underway in toxicology. Human disease should be conceived as resulting from integrated extrinsic and intrinsic causes, with research focused on modern human-specific models to understand disease pathways at multiple biological levels that are analogous to adverse outcome pathways in toxicology. Systems biology tools should be used to integrate and interpret data about disease causation and pathophysiology. Such an approach promises progress in overcoming the current roadblocks to understanding human disease and successful drug discovery and translation. A discourse should begin now to identify and consider the many challenges and questions that need to be solved.

Quantitative mass spectrometric profiling of cancer-cell proteomes derived from liquid and solid tumors

In-depth analyses of cancer cell proteomes are needed to elucidate oncogenic pathomechanisms, as well as to identify potential drug targets and diagnostic biomarkers. However, methods for quantitative proteomic characterization of patient-derived tumors and in particular their cellular subpopulations are largely lacking. Here we describe an experimental set-up that allows quantitative analysis of proteomes of cancer cell subpopulations derived from either liquid or solid tumors. This is achieved by combining cellular enrichment strategies with quantitative Super-SILAC-based mass spectrometry followed by bioinformatic data analysis. To enrich specific cellular subsets, liquid tumors are first immunophenotyped by flow cytometry followed by FACS-sorting; for solid tumors, laser-capture microdissection is used to purify specific cellular subpopulations. In a second step, proteins are extracted from the purified cells and subsequently combined with a tumor-specific, SILAC-labeled spike-in standard that enables protein quantification. The resulting protein mixture is subjected to either gel electrophoresis or Filter Aided Sample Preparation (FASP) followed by tryptic digestion. Finally, tryptic peptides are analyzed using a hybrid quadrupole-orbitrap mass spectrometer, and the data obtained are processed with bioinformatic software suites including MaxQuant. By means of the workflow presented here, up to 8,000 proteins can be identified and quantified in patient-derived samples, and the resulting protein expression profiles can be compared among patients to identify diagnostic proteomic signatures or potential drug targets.

Pre-symptomatic diagnosis and treatment of filovirus diseases

Filoviruses are virulent human pathogens which cause severe illness with high case fatality rates and for which there are no available FDA-approved vaccines or therapeutics. Diagnostic tools including antibody- and molecular-based assays, mass spectrometry, and next-generation sequencing are continually under development. Assays using the polymerase chain reaction (PCR) have become the mainstay for the detection of filoviruses in outbreak settings. In many cases, real-time reverse transcriptase-PCR allows for the detection of filoviruses to be carried out with minimal manipulation and equipment and can provide results in less than 2 h. In cases of novel, highly diverse filoviruses, random-primed pyrosequencing approaches have proved useful. Ideally, diagnostic tests would allow for diagnosis of filovirus infection as early as possible after infection, either before symptoms begin, in the event of a known exposure or epidemiologic outbreak, or post-symptomatically. If tests could provide an early definitive diagnosis, then this information may be used to inform the choice of possible therapeutics. Several exciting new candidate therapeutics have been described recently; molecules that have therapeutic activity when administered to animal models of infection several days post-exposure, once signs of disease have begun. The latest data for candidate nucleoside analogs, small interfering RNA (siRNA) molecules, phosphorodiamidate (PMO) molecules, as well as antibody and blood-product therapeutics and therapeutic vaccines are discussed. For filovirus researchers and government agencies interested in making treatments available for a nation's defense as well as its general public, having the right diagnostic tools to identify filovirus infections, as well as a panel of available therapeutics for treatment when needed, is a high priority. Additional research in both areas is required for ultimate success, but significant progress is being made to reach these goals.

Coupling enrichment methods with proteomics for understanding and treating disease

Owing to recent advances in proteomics analytical methods and bioinformatics capabilities there is a growing trend toward using these capabilities for the development of drugs to treat human disease, including target and drug evaluation, understanding mechanisms of drug action, and biomarker discovery. Currently, the genetic sequences of many major organisms are available, which have helped greatly in characterizing proteomes in model animal systems and humans. Through proteomics, global profiles of different disease states can be characterized (e.g. changes in types and relative levels as well as changes in PTMs such as glycosylation or phosphorylation). Although intracellular proteomics can provide a broad overview of physiology of cells and tissues, it has been difficult to quantify the low abundance proteins which can be important for understanding the diseased states and treatment progression. For this reason, there is increasing interest in coupling comparative proteomics methods with subcellular fractionation and enrichment techniques for membranes, nucleus, phosphoproteome, glycoproteome as well as low abundance serum proteins. In this review, we will provide examples of where the utilization of different proteomics-coupled enrichment techniques has aided target and biomarker discovery, understanding the drug targeting mechanism, and mAb discovery. Taken together, these improvements will help to provide a better understanding of the pathophysiology of various diseases including cancer, autoimmunity, inflammation, cardiovascular disease, and neurological conditions, and in the design and development of better medicines for treating these afflictions.