Proteomic analysis of laser-microdissected paraffin-embedded tissues: (1) Stage-related protein candidates upon non-metastatic lung adenocarcinoma

A Fast-Tracking Sample Preparation Protocol for Proteomics of Formalin-Fixed Paraffin-Embedded Tumor Tissues

Archived tumor specimens are routinely preserved by formalin fixation and paraffin embedding. Despite the conventional wisdom that proteomics might be ineffective due to the cross-linking and pre-analytical variables, these samples have utility for both discovery and targeted proteomics. Building on this capability, proteomics approaches can be used to maximize our understanding of cancer biology and clinical relevance by studying preserved tumor tissues annotated with the patients' medical histories. Proteomics of formalin-fixed paraffin-embedded (FFPE) tissues also integrates with histological evaluation and molecular pathology strategies, so that additional collection of research biopsies or resected tumor aliquots is not needed. The acquisition of data from the same tumor sample also overcomes concerns about biological variation between samples due to intratumoral heterogeneity. However, the protein extraction and proteomics sample preparation from FFPE samples can be onerous, particularly for small (i.e., limited or precious) samples. Therefore, we provide a protocol for a recently introduced kit-based EasyPep method with benchmarking against a modified version of the well-established filter-aided sample preparation strategy using laser-capture microdissected lung adenocarcinoma tissues from a genetically engineered mouse model. This model system allows control over the tumor preparation and pre-analytical variables while also supporting the development of methods for spatial proteomics to examine intratumoral heterogeneity. Data are posted in ProteomeXchange (PXD045879).

Proteomic analyses identify HK1 and ATP5A to be overexpressed in distant metastases of lung adenocarcinomas compared to matched primary tumors

Lung cancer is the leading cause of cancer-related deaths worldwide with lung adenocarcinoma (LUAD) being the most common type. Genomic studies of LUAD have advanced our understanding of its tumor biology and accelerated targeted therapy. However, the proteomic characteristics of LUAD are still insufficiently explored. The prognosis for lung cancer patients is still mostly determined by the stage of disease at the time of diagnosis. Focusing on late-stage metastatic LUAD with poor prognosis, we compared the proteomic profiles of primary tumors and matched distant metastases to identify relevant and potentially druggable differences. We performed high-performance liquid chromatography (HPLC) and electrospray ionization tandem mass spectrometry (ESI-MS/MS) on a total of 38 FFPE (formalin-fixed and paraffin-embedded) samples. Using differential expression analysis and unsupervised clustering we identified several proteins that were differentially regulated in metastases compared to matched primary tumors. Selected proteins (HK1, ATP5A, SRI and ARHGDIB) were subjected to validation by immunoblotting. Thereby, significant differential expression could be confirmed for HK1 and ATP5A, both upregulated in metastases compared to matched primary tumors. Our findings give a better understanding of tumor progression and metastatic spreads in LUAD but also demonstrate considerable inter-individual heterogeneity on the proteomic level.

Protein co-expression networks identified from HOT lesions of ER+HER2-Ki-67high luminal breast carcinomas

Patients with estrogen receptor-positive/human epidermal growth factor receptor 2-negative/Ki-67-high (ER+HER2-Ki-67high) luminal breast cancer have a worse prognosis and do not respond to hormonal treatment and chemotherapy. This study sought to identify disease-related protein networks significantly associated with this subtype, by assessing in-depth proteomes of 10 lesions of high and low Ki-67 values (HOT, five; COLD, five) microdissected from the five tumors. Weighted correlation network analysis screened by over-representative analysis identified the five modules significantly associated with the HOT lesions. Pathway enrichment analysis, together with causal network analysis, revealed pathways of ribosome-associated quality controls, heat shock response by oxidative stress and hypoxia, angiogenesis, and oxidative phosphorylation. A semi-quantitative correlation of key-protein expressions, protein co-regulation analysis, and multivariate correlation analysis suggested co-regulations via network-network interaction among the four HOT-characteristic modules. Predicted highly activated master and upstream regulators were most characteristic to ER-positive breast cancer and associated with oncogenic transformation, as well as resistance to chemotherapy and endocrine therapy. Interestingly, inhibited intervention causal networks of numerous chemical inhibitors were predicted within the top 10 lists for the WM2 and WM5 modules, suggesting involvement of potential therapeutic targets in those data-driven networks. Our findings may help develop therapeutic strategies to benefit patients.

Proteomic identification and validation of novel interactions of the putative tumor suppressor PRELP with membrane proteins including IGFI-R and p75NTR

Proline and arginine-rich end leucine-rich repeat protein (PRELP) is a member of the small leucine-rich repeat proteoglycans (SLRPs) family. Levels of PRELP mRNA are downregulated in many types of cancer, and PRELP has been reported to have suppressive effects on tumor cell growth, although the molecular mechanism has yet to be fully elucidated. Given that other SLRPs regulate signaling pathways through interactions with various membrane proteins, we reasoned that PRELP likely interacts with membrane proteins to maintain cellular homeostasis. To identify membrane proteins that interact with PRELP, we carried out coimmunoprecipitation coupled with mass spectrometry (CoIP-MS). We prepared membrane fractions from Expi293 cells transfected to overexpress FLAG-tagged PRELP or control cells and analyzed samples precipitated with anti-FLAG antibody by mass spectrometry. Comparison of membrane proteins in each sample identified several that seem to interact with PRELP; among them, we noted two growth factor receptors, insulin-like growth factor I receptor (IGFI-R) and low-affinity nerve growth factor receptor (p75NTR), interactions with which might help to explain PRELP's links to cancer. We demonstrated that PRELP directly binds to extracellular domains of these two growth factor receptors with low micromolar affinities by surface plasmon resonance analysis using recombinant proteins. Furthermore, cell-based analysis using recombinant PRELP protein showed that PRELP suppressed cell growth and affected cell morphology of A549 lung carcinoma cells, also at micromolar concentration. These results suggest that PRELP regulates cellular functions through interactions with IGFI-R and p75NTR and provide a broader set of candidate partners for further exploration.

Mutant Proteomics of Lung Adenocarcinomas Harboring Different EGFR Mutations

Epidermal growth factor receptor EGFR major driver mutations may affect downstream molecular networks and pathways, which would influence treatment outcomes of non-small cell lung cancer (NSCLC). This study aimed to unveil profiles of mutant proteins expressed in lung adenocarcinomas of 36 patients harboring representative driver EGFR mutations (Ex19del, nine; L858R, nine; no Ex19del/L858R, 18). Surprisingly, the orthogonal partial least squares discriminant analysis performed for identified mutant proteins demonstrated the profound differences in distance among the different EGFR mutation groups, suggesting that cancer cells harboring L858R or Ex19del emerge from cellular origins different from L858R/Ex19del-negative cells. Weighted gene coexpression network analysis, together with over-representative analysis, identified 18 coexpressed modules and their eigen proteins. Pathways enriched differentially for both the L858R and Ex19del mutations included carboxylic acid metabolic process, cell cycle, developmental biology, cellular responses to stress, mitotic prophase, cell proliferation, growth, epithelial to mesenchymal transition (EMT), and immune system. The IPA causal network analysis identified the highly activated networks of PARPBP, HOXA1, and APH1 under the L858R mutation, whereas those of ASGR1, APEX1, BUB1, and MAPK10 were highly activated under the Ex19del mutation. Interestingly, the downregulated causal network of osimertinib intervention showed the highest significance in overlap p-value among most causal networks predicted under the L858R mutation. We also identified the causal network of MAPK interacting serine/threonine kinase 1/2 (MNK1/2) highly activated differentially under the L858R mutation. Tumor-suppressor AMOT, a component of the Hippo pathways, was highly inhibited commonly under both L858R and Ex19del mutations. Our results could identify disease-related protein molecular networks from the landscape of single amino acid variants. Our findings may help identify potential therapeutic targets and develop therapeutic strategies to improve patient outcomes.

Disease-related cellular protein networks differentially affected under different EGFR mutations in lung adenocarcinoma

It is unclear how epidermal growth factor receptor EGFR major driver mutations (L858R or Ex19del) affect downstream molecular networks and pathways. This study aimed to provide information on the influences of these mutations. The study assessed 36 protein expression profiles of lung adenocarcinoma (Ex19del, nine; L858R, nine; no Ex19del/L858R, 18). Weighted gene co-expression network analysis together with analysis of variance-based screening identified 13 co-expressed modules and their eigen proteins. Pathway enrichment analysis for the Ex19del mutation demonstrated involvement of SUMOylation, epithelial and mesenchymal transition, ERK/mitogen-activated protein kinase signalling via phosphorylation and Hippo signalling. Additionally, analysis for the L858R mutation identified various pathways related to cancer cell survival and death. With regard to the Ex19del mutation, ROCK, RPS6KA1, ARF1, IL2RA and several ErbB pathways were upregulated, whereas AURK and GSKIP were downregulated. With regard to the L858R mutation, RB1, TSC22D3 and DOCK1 were downregulated, whereas various networks, including VEGFA, were moderately upregulated. In all mutation types, CD80/CD86 (B7), MHC, CIITA and IFGN were activated, whereas CD37 and SAFB were inhibited. Costimulatory immune-checkpoint pathways by B7/CD28 were mainly activated, whereas those by PD-1/PD-L1 were inhibited. Our findings may help identify potential therapeutic targets and develop therapeutic strategies to improve patient outcomes.

Proteomic analysis enables distinction of early- versus advanced-stage lung adenocarcinomas

Background

A gel‐free proteomic approach was utilized to perform in‐depth tissue protein profiling of lung adenocarcinoma (ADC) and normal lung tissues from early and advanced stages of the disease. The long‐term goal of this study is to generate a large‐scale, label‐free proteomics dataset from histologically well‐classified lung ADC that can be used to increase further our understanding of disease progression and aid in identifying novel biomarkers.

Methods and results

Cases of early‐stage (I‐II) and advanced‐stage (III‐IV) lung ADCs were selected and paired with normal lung tissues from 22 patients. The histologically and clinically stratified human primary lung ADCs were analyzed by liquid chromatography‐tandem mass spectrometry. From the analysis of ADC and normal specimens, 4863 protein groups were identified. To examine the protein expression profile of ADC, a peak area‐based quantitation method was used. In early‐ and advanced‐stage ADC, 365 and 366 proteins were differentially expressed, respectively, between normal and tumor tissues (adjusted P‐value < .01, fold change ≥ 4). A total of 155 proteins were dysregulated between early‐ and advanced‐stage ADCs and 18 were suggested as early‐specific stage ADC. In silico functional analysis of the upregulated proteins in both tumor groups revealed that most of the enriched pathways are involved in mRNA metabolism. Furthermore, the most overrepresented pathways in the proteins that were unique to ADC are related to mRNA metabolic processes.

Conclusions

Further analysis of these data may provide an insight into the molecular pathways involved in disease etiology and may lead to the identification of biomarker candidates and potential targets for therapy. Our study provides potential diagnostic biomarkers for lung ADC and novel stage‐specific drug targets for rational intervention.

Identification of key modules and hub genes for small-cell lung carcinoma and large-cell neuroendocrine lung carcinoma by weighted gene co-expression network analysis of clinical tissue-proteomes

Small-cell lung carcinoma (SCLC) and large-cell neuroendocrine lung carcinoma (LCNEC) are high-grade lung neuroendocrine tumors (NET). However, comparative protein expression within SCLC and LCNEC remains unclear. Here, protein expression profiles were obtained via mass spectrometry-based proteomic analysis. Weighted gene co-expression network analysis (WGCNA) identified co-expressed modules and hub genes. Of 34 identified modules, six were significant and selected for protein-protein interaction (PPI) network analysis and pathway enrichment. Within the six modules, the activation of cellular processes and complexes, such as alternative mRNA splicing, translation initiation, nucleosome remodeling and deacetylase (NuRD) complex, SWItch/Sucrose Non-Fermentable (SWI/SNF) superfamily-type complex, chromatin remodeling pathway, and mRNA metabolic processes, were significant to SCLC. Modules enriched in processes, including signal recognition particle (SRP)-dependent co-translational protein targeting to membrane, nuclear-transcribed mRNA catabolic process of nonsense-mediated decay (NMD), and cellular macromolecule catabolic process, were characteristically activated in LCNEC. Novel high-degree hub genes were identified for each module. Master and upstream regulators were predicted via causal network analysis. This study provides an understanding of the molecular differences in tumorigenesis and malignancy between SCLC and LCNEC and may help identify potential therapeutic targets.

Molecular insights into cancer drug resistance from a proteomics perspective

INTRODUCTION

Resistance to chemotherapy and development of specific and effective molecular targeted therapies are major obstacles facing current cancer treatment. Comparative proteomic approaches have been employed for the discovery of putative biomarkers associated with cancer drug resistance and have yielded a number of candidate proteins, showing great promise for both novel drug target identification and personalized medicine for the treatment of drug-resistant cancer. Areas covered: Herein, we review the recent advances and challenges in proteomics studies on cancer drug resistance with an emphasis on biomarker discovery, as well as understanding the interconnectivity of proteins in disease-related signaling pathways. In addition, we highlight the critical role that post-translational modifications (PTMs) play in the mechanisms of cancer drug resistance. Expert opinion: Revealing changes in proteome profiles and the role of PTMs in drug-resistant cancer is key to deciphering the mechanisms of treatment resistance. With the development of sensitive and specific mass spectrometry (MS)-based proteomics and related technologies, it is now possible to investigate in depth potential biomarkers and the molecular mechanisms of cancer drug resistance, assisting the development of individualized therapeutic strategies for cancer patients.

Novel biomarkers distinguishing pancreatic head Cancer from distal cholangiocarcinoma based on proteomic analysis

BACKGROUND

The differentiation between pancreatic head cancer (PHC) and distal cholangiocarcinoma (DCC) can be challenging because of their anatomical and histopathological similarity. This is an important problem, because the distinction has important implications for the treatment of these malignancies. However, there are no biomarkers for the differential diagnosis of PHC and DCC. The present study aimed to identify novel diagnostic immunohistochemical biomarkers to distinguish PHC from DCC.

METHODS

Liquid chromatography tandem mass spectrometry (LC-MS/MS) was employed to detect candidate proteins. Ten PHC and 8 DCC specimens were analyzed by LC-MS/MS. Selected proteins were evaluated, using immunohistochemical analysis, to determine whether they would be appropriate biomarkers. Finally, we generated biomarker panels to improve diagnostic accuracy. We applied these panels to clinically difficult cases (cases in which different diagnoses were made before and after operation).

RESULTS

Consequently, 1820 proteins were detected using LC-MS/MS. Fifteen differentially expressed proteins were selected as candidates based on semi-quantitative comparison. We first performed immunohistochemical staining on samples from the small cohort group (12 PHCs and 12 DCCs) using 15 candidates. KRT17, ANXA10, TMEM109, PTMS, and ATP1B1 showed favorable performances and were tested in the next large cohort group (72 PHCs and 74 DCCs). Based on immunohistochemical analysis, KRT17 performed best for the diagnosis of PHC as a single marker; additionally, PTMS exhibited good performance for the diagnosis of DCCs. Moreover, we indicated the KRT17+/ANXA10+/PTMS- staining pattern as a biomarker panel for the correct diagnosis of PHC and KRT17-/ANXA10-/PTMS+ for the diagnosis of DCC. After immunohistochemical staining for examining samples from the clinically difficult cases, these panels showed satisfactory diagnostic performance with 85.7% (6/7) accuracy.

CONCLUSIONS

We conclude that 5 proteins and 2 biomarker panels are promising for distinguishing PHC from DCC, and patients with an equivocal diagnosis would benefit from the application of these biomarkers. Confirmatory studies are needed to generalize these findings to other populations.

Decorin as a prognostic biomarker in patients with malignant peripheral nerve sheath tumors

Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive soft-tissue sarcomas. The prognosis of MPNSTs has been reported to differ among previous studies. However, there have been a number of reported prognostic biomarkers associated with MPNSTs. In the present study, a proteomics study was performed to discover the differential protein expression in patients with MPSNTs with different prognoses. The clinical data of 30 primary extremities of patients with MPNSTs, who underwent surgery at the Department of Hand Surgery, Huashan Hospital, Fudan University between January 2002 and December 2011, were acquired. A total of 16 patients succumbed to their diseases within 5 years, whereas 14 patients were disease-free for >5 years. Samples from the 9 patients who succumbed within 2 years were assigned to Group D, while samples from the 8 patients who were continuously disease-free for >5 years following diagnosis were assigned to Group L for the proteomics study. Label-free quantitative proteomics and mass spectrometry were performed to filtrate differential protein in patients with MPSNTs with different prognoses. Decorin was filtrated as a differential protein of note. The expression level of decorin was significantly lower in Group D compared with that in Group L (D/L=0.0948; P=0.0004). The result was verified by immunohistochemical staining in the 30 primary extremities of patients with MPNSTs. The 5-year survival rate of patients with positive expression of decorin was 78.57%, while the 5-year survival rate of patients negative for decorin expression was 18.75% (P=0.0014). Overall, a high level of decorin indicted a better prognosis in patients with MPNSTs. With further investigation, decorin may be a reliable prognostic biomarker for MPNSTs.

Differential Proteomic Analysis between Small Cell Lung Carcinoma (SCLC) and Pulmonary Carcinoid Tumors Reveals Molecular Signatures for Malignancy in Lung Cancer

PURPOSE

The molecular underpinnings that may prognosticate survival and increase our understanding of tumor development and progression are still poorly understood. This study aimed to define the molecular signatures for malignancy in small cell lung carcinoma (SCLC), which is known for its highly aggressive clinical features and poor prognosis.

EXPERIMENTAL DESIGN

Using clinical specimens, the authors perform a comparative proteomic analysis of high-grade SCLCs and low-grade pulmonary carcinoid tumors (PCTs), both of which are types of neuroendocrine tumors. A label-free LC-MS-based quantitative proteomic analysis is applied to tumor cells laser-microdissected from their formalin-fixed paraffin-embedded (FFPE) tissues obtained from six patients each.

RESULTS

Overall, 1991 proteins are identified from tumor cells in the FFPE tissues. Through the protein-protein interaction network analysis of 201 proteins significantly, the authors find that SCLC is functionally characterized by activation of molecular pathways for spliceosome, RNA transport, and DNA replication and cell cycle. Particularly, 11 proteins involved in tumor proliferation (MCM2, 4, 6, 7, and MSH2), metastasis (RCC2, CORO1C, CHD4, and IPO9), and cancer metabolism (PHGDH and TYMP) are identified as SCLC-specific proteins. Furthermore, their prognostic significances are demonstrated by online Kaplan-Meier survival analysis.

CONCLUSIONS AND CLINICAL RELEVANCE

These clinical tissue proteomic approach for SCLC reveals the proteins associated with aggressiveness and poor prognosis. The identified SCLC-specific proteins represent potential therapeutic targets. Moreover, MCMs and PHGDH can be poor prognostic factors for lung cancer.

A novel liver metastasis-correlated protein of pancreatic neuroendocrine neoplasm (PanNEN) discovered by proteomic analysis

The aim of this study was to identify novel liver metastasis-correlated proteins of PanNEN by proteomics to compare pancreatic tumor (PT) with paired metastatic liver tumor (LT). Of 118 surgical cases with PanNEN, 7 cases with formalin-fixed paraffin-embedded (FFPE) tissues of both PT and paired LT were evaluated by proteomics. Tumor cells were selectively collected from FFPE tissues by laser capture microdissection. A total of 3,722 proteins were detected from extracted peptides by mass spectrometry-based shotgun analysis. Selection of the candidate proteins expressed differently between PT and LT were performed by semi-quantitative comparison in silico and confirmation with immunohistochemistry. We focused on ANXA6, CNPY2, RAB11B and TUBB3, all of which had higher expressions in LT. In all surgical cases with FFPE samples, liver recurrence-free survival (RFS) was evaluated in correlation to the expression of the candidate proteins in PT by immunohistochemistry. Liver RFS was significantly poorer in CNPY2 positive patients than in negative patients (10-year liver RFS; 39.8% vs. 92.3%, p = 0.012). Also, liver RFS tended to be poorer in ANXA6 positive patients than in those who were negative (10-year liver RFS; 51.4% vs. 95.0%, p = 0.099). In the multivariate analysis, the independent predictors of liver RFS were CNPY2 positivity (HR: 6.19, 95 % CI: 1.47-42.79, p = 0.011) and tumor size ≥ 42 mm (HR: 4.63, 95 % CI: 1.03-23.23, p = 0.045). In conclusion, CNPY2 is a novel liver metastasis-correlated protein of PanNEN.

Development of drug discovery screening system by molecular interaction kinetics-mass spectrometry

RATIONALE

Drug discovery studies invariably require qualitative and quantitative analyses of target compounds at every stage of drug discovery. We have developed a system combining molecular interaction analysis and mass spectrometry (LC-MS) using the principle of nanopore optical interferometry (nPOI) called molecular interaction kinetics-mass spectrometry (MIK-MS). Since nPOI has high binding capacity, the bond-dissociated compound can be directly detected using LC-MS. In this study, we use carbonic anhydrase II (CAII) as a ligand and apply six small compounds as analytes and report the affinity analysis using MIK-MS.

METHODS

CAII was immobilized onto a COOH sensor chip using standard amine coupling. A reference surface was prepared by activating and subsequently blocking the surface under identical conditions. An amount of 50 μL of mix solution was injected over the reference channel and sample channel for CAII immobilization. The solutions eluting from the sensor chip were collected from the waste-line of the SKi Pro system every 30 s. Reconstructed elution samples were then injected into the LC-MS/MS system.

RESULTS

A mixture containing furosemide, acetazolamide, 4-sulfamoylbenzoic acid, 5-(dimethylamino)-1-naphthalene sulfonamide (DNSA), sulfanilamide and sulpiride (15 μM each) was injected into the CAII-immobilized sensor chip, and the fractions eluted from the SKi Pro system were collected and subjected to selected reaction monitoring LC-MS characterization. Specific results were obtained for acetazolamide, DNSA, furosemide and sulpiride. The results suggest that the association-dissociation curve of a mixed sample can be obtained by one-time MIK-MS analysis.

CONCLUSIONS

Six small-molecule binders of CAII were analyzed quantitatively using nPOI and MIK-MS, and the results were compared to published surface plasmon resonance (SPR) results. The nPOI and SPR results show good agreement, confirming the reliability of the analysis. Time-dependent binding results may be obtained by our MS sensorgram approach. Drugs that meet medical needs in a short period are required; this nPOI-LC-MS system is considered an important tool for rapid drug discovery.

Shotgun label-free proteomic analysis for identification of proteins in HaCaT human skin keratinocytes regulated by the administration of collagen from soft-shelled turtle

Soft‐shelled turtles (Pelodiscus sinensis) are widely distributed in some Asian countries, and we previously reported that soft‐shelled turtle tissue could be a useful material for collagen. In the present study, we performed shotgun liquid chromatography (LC)/mass spectrometry (MS)‐based global proteomic analysis of collagen‐administered human keratinocytes to examine the functional effects of collagen from soft‐shelled turtle on human skin. Using a semiquantitative method based on spectral counting, we were able to successfully identify 187 proteins with expression levels that were changed more than twofold by the administration of collagen from soft‐shelled turtle. Based on Gene Ontology analysis, the functions of these proteins closely correlated with cell–cell adhesion. In addition, epithelial–mesenchymal transition was induced by the administration of collagen from soft‐shelled turtle through the down‐regulation of E‐cadherin expression. Moreover, collagen‐administered keratinocytes significantly facilitated wound healing compared with nontreated cells in an in vitro scratch wound healing assay. These findings suggest that collagen from soft‐shelled turtle provides significant benefits for skin wound healing and may be a useful material for pharmaceuticals and medical care products. © 2017 The Authors Journal of Biomedical Materials Research Part B: Applied Biomaterials Published by Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 2403–2413, 2018.

Relative protein quantification and accessible biology in lung tumor proteomes from four LC-MS/MS discovery platforms

Discovery proteomics experiments include many options for sample preparation and MS data acquisition, which are capable of creating datasets for quantifying thousands of proteins. To define a strategy that would produce a dataset with sufficient content while optimizing required resources, we compared (1) single-sample LC-MS/MS with data-dependent acquisition to single-sample LC-MS/MS with data-independent acquisition and (2) peptide fractionation with label-free (LF) quantification to peptide fractionation with relative quantification of chemically labeled peptides (sixplex tandem mass tags (TMT)). These strategies were applied to the same set of four frozen lung squamous cell carcinomas and four adjacent tissues, and the overall outcomes of each experiment were assessed. We identified 6656 unique protein groups with LF, 5535 using TMT, 3409 proteins from single-sample analysis with data-independent acquisition, and 2219 proteins from single-sample analysis with data-dependent acquisition. Pathway analysis indicated the number of proteins per pathway was proportional to the total protein identifications from each method, suggesting limited biological bias between experiments. The results suggest the use of single-sample experiments as a rapid tissue assessment tool and digestion quality control or as a technique to maximize output from limited samples and use of TMT or LF quantification as methods for larger amounts of tumor tissue with the selection being driven mainly by instrument time limitations. Data are available via ProteomeXchange with identifiers PXD004682, PXD004683, PXD004684, and PXD005733.

Proteomic profile of the lens in a streptozotocin-induced diabetic rat model using shotgun proteomics

Streptozotocin (STZ)-induced diabetic rats (STZ rats) were used to investigate diabetic cataracts. In the current study, a shotgun liquid chromatography (LC)/mass spectrometry (MS)-based global proteomic analysis method was used to examine the mechanism of lens opacification as a result of hyperglycemia in STZ rats. The 6-week old Wistar rats were injected with STZ for 2 days (100 mg/kg/day, i.p.) and housed for 3 weeks. The plasma glucose levels were identified to be significantly higher when compared with the normal rats and insulin was not detected in the STZ rats. Furthermore, opacification of the cortical epithelium was observed in the lenses of STZ rats. A total of 235 proteins were identified in the lenses of the STZ rats and 229 in the lenses of the normal rats. A label-free semi-quantitative method, based on spectral counting, identified 52 proteins that were differentially expressed in the lenses of STZ rats compared with normal rats. In particular, superoxide dismutase, which is a critical antioxidant enzyme that detoxifies superoxide through redox cycling, was downregulated when analyzed by the semi-quantitative method. In addition, phosphorylated-p38, which is important in the signaling pathway involved in the oxidative stress response, was significantly increased in the lenses of STZ rats when compared with normal rats (P<0.05). Thus, the changes in protein expression were evaluated in the lenses of STZ rats using a shotgun LC/MS-based global proteomic analysis approach, and a decrease in antioxidant enzymes and an increase in oxidative stress were identified in the lenses of STZ rats. Further studies are required to examine the role of these proteins in the onset or progression of diabetic cataracts.

Quantitative proteomics in lung cancer

Lung cancer is the most common cause of cancer-related death worldwide, less than 7% of patients survive 10 years following diagnosis across all stages of lung cancer. Late stage of diagnosis and lack of effective and personalized medicine reflect the need for a better understanding of the mechanisms that underlie lung cancer progression. Quantitative proteomics provides the relative different protein abundance in normal and cancer patients which offers the information for molecular interactions, signaling pathways, and biomarker identification. Here we introduce both theoretical and practical applications in the use of quantitative proteomics approaches, with principles of current technologies and methodologies including gel-based, label free, stable isotope labeling as well as targeted proteomics. Predictive markers of drug resistance, candidate biomarkers for diagnosis, and prognostic markers in lung cancer have also been discovered and analyzed by quantitative proteomic analysis. Moreover, construction of protein networks enables to provide an opportunity to interpret disease pathway and improve our understanding in cancer therapeutic strategies, allowing the discovery of molecular markers and new therapeutic targets for lung cancer.

A selected reaction monitoring mass spectrometric assessment of biomarker candidates diagnosing large-cell neuroendocrine lung carcinoma by the scaling method using endogenous references

Selected reaction monitoring mass spectrometry (SRM-MS) -based semi-quantitation was performed to assess the validity of 46 selected candidate proteins for specifically diagnosing large-cell neuroendocrine lung carcinoma (LCNEC) and differentiating it from other lung cancer subtypes. The scaling method was applied in this study using specific SRM peak areas (AUCs) derived from the endogenous reference protein that normalizes all SRM AUCs obtained for the candidate proteins. In a screening verification study, we found that seven out of the 46 candidate proteins were statistically significant for the LCNEC phenotype, including 4F2hc cell surface antigen heavy chain (4F2hc/CD98) (p-ANOVA ≤ 0.0012), retinal dehydrogenase 1 (p-ANOVA ≤ 0.0029), apolipoprotein A-I (p-ANOVA ≤ 0.0004), β-enolase (p-ANOVA ≤ 0.0043), creatine kinase B-type (p-ANOVA ≤ 0.0070), and galectin-3-binding protein (p-ANOVA = 0.0080), and phosphatidylethanolamine-binding protein 1 (p-ANOVA ≤ 0.0012). In addition, we also identified candidate proteins specific to the small-cell lung carcinoma (SCLC) subtype. These candidates include brain acid soluble protein 1 (p-ANOVA < 0.0001) and γ-enolase (p-ANOVA ≤ 0.0013). This new relative quantitation-based approach utilizing the scaling method can be applied to assess hundreds of protein candidates obtained from discovery proteomic studies as a first step of the verification phase in biomarker development processes.

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.

Identification of aldolase A as a potential diagnostic biomarker for colorectal cancer based on proteomic analysis using formalin-fixed paraffin-embedded tissue

Colorectal cancer (CRC) is one of the most common cancers worldwide, and many patients are already at an advanced stage when they are diagnosed. Therefore, novel biomarkers for early detection of colorectal cancer are required. In this study, we performed a global shotgun proteomic analysis using formalin-fixed and paraffin-embedded (FFPE) CRC tissue. We identified 84 candidate proteins whose expression levels were differentially expressed in cancer and non-cancer regions. A label-free semiquantitative method based on spectral counting and gene ontology (GO) analysis led to a total of 21 candidate proteins that could potentially be detected in blood. Validation studies revealed cyclophilin A, annexin A2, and aldolase A mRNA and protein expression levels were significantly higher in cancer regions than in non-cancer regions. Moreover, an in vitro study showed that secretion of aldolase A into the culture medium was clearly suppressed in CRC cells compared to normal colon epithelium. These findings suggest that decreased aldolase A in blood may be a novel biomarker for the early detection of CRC.

Identification and Characterization of Potential Biomarkers by Quantitative Tissue Proteomics of Primary Lung Adenocarcinoma

Lung cancer is the leading cause of cancer-related death worldwide. Both diagnostic and prognostic biomarkers are urgently needed to increase patient survival. In this study, we identified/quantified 1763 proteins from paired adenocarcinoma (ADC) tissues with different extents of lymph node (LN) involvement using an iTRAQ-based quantitative proteomic analysis. Based on a bioinformatics analysis and literature search, we selected six candidates (ERO1L, PABPC4, RCC1, RPS25, NARS, and TARS) from a set of 133 proteins that presented a 1.5-fold increase in expression in ADC tumors without LN metastasis compared with adjacent normal tissues. These six proteins were further verified using immunohistochemical staining and Western blot analyses. The protein levels of these six candidates were higher in tumor tissues compared with adjacent normal tissues. The ERO1L and NARS levels were positively associated with LN metastasis. Importantly, ERO1L overexpression in patients with early-stage ADC was positively correlated with poor survival, suggesting that ERO1L overexpression in primary sites of early-stage cancer tissues indicates a high risk for cancer micrometastasis. Moreover, we found that knockdown of either ERO1L or NARS reduced the viability and migration ability of ADC cells. Our results collectively provide a potential biomarker data set for ADC diagnosis/prognosis and reveal novel roles of ERO1L and NARS in ADC progression.

Targeted proteomics of solid cancers: from quantification of known biomarkers towards reading the digital proteome maps

The concept of personalized medicine includes novel protein biomarkers that are expected to improve the early detection, diagnosis and therapy monitoring of malignant diseases. Tissues, biofluids, cell lines and xenograft models are the common sources of biomarker candidates that require verification of clinical value in independent patient cohorts. Targeted proteomics - based on selected reaction monitoring, or data extraction from data-independent acquisition based digital maps - now represents a promising mass spectrometry alternative to immunochemical methods. To date, it has been successfully used in a high number of studies answering clinical questions on solid malignancies: breast, colorectal, prostate, ovarian, endometrial, pancreatic, hepatocellular, lung, bladder and others. It plays an important role in functional proteomic experiments that include studying the role of post-translational modifications in cancer progression. This review summarizes verified biomarker candidates successfully quantified by targeted proteomics in this field and directs the readers who plan to design their own hypothesis-driven experiments to appropriate sources of methods and knowledge.

A proteomic profile of synoviocyte lesions microdissected from formalin-fixed paraffin-embedded synovial tissues of rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic inflammation of the synovial joints. Early intervention followed by early diagnosis can result in disease remission; however, both early stage diagnosis and provision of effective treatment have been impeded by the heterogeneity of RA, which details of pathological mechanism are unclear. Regardless of numerous investigations of RA by means of genomic and proteomic approaches, proteins interplaying in RA synovial tissues that contain various types of synoviocytes, are not yet sufficiently understood. Hence we have conducted an HPLC/mass spectrometry-based exploratory proteomic analysis focusing on synoviocyte lesions laser-microdissected (LMD) from formalin-fixed paraffin-embedded (FFPE) synovial tissues (RA, n = 15; OA, n = 5), where those of Osteoarthritis (OA) were used as the control.

Results

A total of 508 proteins were identified from the RA and OA groups. With the semi-quantitative comparisons, the spectral index (SpI), log2 protein ratio (R_SC) based on spectral counting, and statistical G-test, 98 proteins were found to be significant (pair-wise p < 0.05) to the RA synovial tissues. These include stromelysin-1 (MMP3), proteins S100-A8 and S100-A9, plastin-2, galectin-3, calreticulin, cathepsin Z, HLA-A, HLA-DRB1, ferritin, neutrophil defensin 1, CD14, MMP9 etc.

Conclusions

Our results confirmed the involvement of known RA biomarkers such as stromelysin-1 (MMP3) and proteins S100-A8 and S100-A9, and also that of leukocyte antigens such as HLA-DRB1. Network analyses of protein–protein interaction for those proteins significant to RA revealed a dominant participation of ribosome pathway (p = 5.91 × 10⁻⁴⁵), and, interestingly, the associations of the p53 signaling (p = 2.34 × 10⁻⁵). An involvement of proteins including CD14, S100-A8/S100-A9 seems to suggest an activation of the NF-kB/MAPK signaling pathway. Our strategy of laser-microdissected FFPE-tissue proteomic analysis in Rheumatoid Arthritis thus demonstrated its technical feasibility in profiling proteins expressed in synovial tissues, which may play important roles in the RA pathogenesis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-015-9091-8) contains supplementary material, which is available to authorized users.

A proteomic profiling of laser-microdissected lung adenocarcinoma cells of early lepidic-types

Background

In the new pathologic classification of lung adenocarcinoma proposed by IASLC/ATS/ERS in 2011, lepidic type adenocarcinomas are constituted by three subtypes; adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and lepidic predominant invasive adenocarcinoma (LPIA). Although these subtypes are speculated to show sequential progression from preinvasive lesion to invasive lung cancer, changes of protein expressions during these processes have not been fully studied yet. This study aims to glimpse a proteomic view of the early lepidic type lung adenocarcinomas.

Methods

A total of nine formalin-fixed and paraffin-embedded (FFPE) lepidic type lung adenocarcinoma tissues were selected from our archives, three tissues each in AIS, MIA and LPIA. The tumor and peripheral non-tumor cells in these FFPE tissues were collected with laser microdissection (LMD). Using liquid chromatography-tandem mass spectrometry (MS/MS), protein compositions were compared with respect to the peptide separation profiles among tumors collected from three types of tissues, AIS, MIA and LPIA. Proteins identified were semi-quantified by spectral counting-based or identification-based approach, and statistical evaluation was performed by pairwise G-tests.

Results

A total of 840 proteins were identified. Spectral counting-based semi-quantitative comparisons of all identified proteins through AIS to LPIA have revealed that the protein expression profile of LPIA was significantly differentiated from other subtypes. 70 proteins including HPX, CTTN, CDH1, EGFR, MUC1 were found as LPIA-type marker candidates, 15 protein candidates for MIA-type marker included CRABP2, LMO7, and RNPEP, and 26 protein candidates for AIS-type marker included LTA4H and SOD2. The STRING gene set enrichment resulted from the protein-protein interaction (PPI) network analysis suggested that AIS was rather associated with pathways of focal adhesion, adherens junction, tight junction, that MIA had a strong association predominantly with pathways of proteoglycans in cancer and with PI3K-Akt. In contrast, LPIA was associated broadly with numerous tumor-progression pathways including ErbB, Ras, Rap1 and HIF-1 signalings.

Conclusions

The proteomic profiles obtained in this study demonstrated the technical feasibility to elucidate protein candidates differentially expressed in FFPE tissues of LPIA. Our results may provide candidates of disease-oriented proteins which may be related to mechanisms of the early-stage progression of lung adenocarcinoma.

Electronic supplementary material

The online version of this article (doi:10.1186/s40169-015-0064-3) contains supplementary material, which is available to authorized users.

Mass spectrometry-based proteomic analysis of formalin-fixed paraffin-embedded extrahepatic cholangiocarcinoma

BACKGROUND

Extrahepatic cholangiocarcinoma is very difficult to diagnose at an early stage, and has a poor prognosis. Novel markers for diagnosis and optimal treatment selection are needed. However, there has been very limited data on the proteome profile of extrahepatic cholangiocarcinoma. This study was designed to unravel the proteome profile of this disease and to identify overexpressed proteins using mass spectrometry-based proteomic approaches.

METHODS

We analyzed a discovery set of formalin-fixed paraffin-embedded tissues of 14 extrahepatic cholangiocarcinomas using shotgun mass spectrometry, and compared proteome profiles with those of seven controls. Then, selected candidates were verified by quantitative analysis using scheduled selected reaction monitoring-based mass spectrometry. Furthermore, immunohistochemical staining used a validation set of 165 cases.

RESULTS

In total, 1,992 proteins were identified and 136 proteins were overexpressed. Verification of 58 selected proteins by quantitative analysis revealed 11 overexpressed proteins. Immunohistochemical validation for 10 proteins showed positive rates of S100P (84%), CEAM5 (75%), MUC5A (62%), OLFM4 (60%), OAT (42%), CAD17 (41%), FABPL (38%), AOFA (30%), K1C20 (25%) and CPSM (22%) in extrahepatic cholangiocarcinomas, which were rarely positive in controls.

CONCLUSIONS

We identified 10 proteins associated with extrahepatic cholangiocarcinoma using proteomic approaches. These proteins are potential targets for future diagnostic biomarkers and therapy.

Cystatin B as a potential diagnostic biomarker in ovarian clear cell carcinoma

Epithelial ovarian cancer (EOC) consists of four major subtypes: clear cell carcinoma (CCC), endometrioid adenocarcinoma (EA), mucinous adenocarcinoma (MA) and serous adenocarcinoma (SA). Relative to the other subtypes, the prognosis of CCC is poor due to a high recurrence rate and chemotherapy resistance, but CCC-specific biomarkers have yet to be identified. With the aim of identifying diagnostic and treatment biomarkers for CCC, we analyzed 96 cases of EOC (32 CCC, 13 EA, 19 MA, 32 SA) using liquid chromatography/mass spectrometry (LC/MS) followed by immunohistochemistry (IHC) and quantitative reverse transcription PCR (RT-qPCR). Semi-quantification of protein differences between subtypes showed upregulation of 150 proteins and downregulation of 30 proteins in CCC relative to the other subtypes. Based on hierarchical clustering that revealed a marked distinction in the expression levels of cystatin B (CYTB) and Annexin A4 (ANXA4) in CCC relative to the other subtypes, we focused the study on CYTB and ANXA4 expression in EOCs by IHC, RT-qPCR and western blot analyses using tissue specimens and cultured cells. As a result, compared to the other subtypes, CCC showed significantly high expression levels of CYTB and ANXA4 in the analyses. To examine the possibility of CYTB and ANXA4 as serum diagnostic biomarkers of CCC, we checked the protein levels in conditioned media and cell lysates using culture cells. Compared with the other subtypes, CCC cell lines showed a significantly higher level of expression of CYTB in both conditioned media and cell lysates, while ANXA4 showed a higher level of expression in cell lysates only. Our results demonstrate that CYTB and ANXA4 overexpression may be related to carcinogenesis and histopathological differentiation of CCC. CYTB may be a secreted protein, and may serve as a potential serum diagnostic biomarker of CCC, while ANXA4 may be useful as an intracellular marker.

A patient with medulloblastoma in its early developmental stage

Medulloblastoma is the most frequent malignant brain tumor of the posterior fossa in children and is considered an embryonal tumor. It has been suggested that medulloblastomas be categorized into 4 distinct molecular subgroups- WNT (DKK1), SHH (SFRP1), Group 3 (NPR3), or Group 4 (KCNA1)-since each subgroup is distinct and there is no overlap. The authors report on a 13-year-old boy with medulloblastoma. He presented with sudden-onset nausea and vomiting due to intratumoral hemorrhage. The medulloblastoma was thought to be in an early developmental stage because the tumor volume was extremely small. Immunohistochemical analysis showed that the tumor was mainly composed of DKK1- and NPR3-positive areas. The individual areas of the tumor stained only for DKK1 or NPR3, with no overlap-that is, DKK1 and NPR3 expression were mutually exclusive. Samples obtained by laser microdissection of individual areas and subjected to mass spectrometry confirmed that the expression patterns of proteins were different. Fluorescence in situ hybridization for chromosome 6 showed there were 2 distinct types of cells that exhibited monosomy or disomy of chromosome 6. These results demonstrated that distinct subtypes of medulloblastoma may be present within a single tumor, an observation that has not been previously reported. Our findings in this case indicate that early-stage medulloblastoma may include more than 1 distinct subtype and hint at factors involved in the origin and development of medulloblastomas.

Applications of mass spectrometry for quantitative protein analysis in formalin-fixed paraffin-embedded tissues

Proteomic analysis of tissues has advanced in recent years as instruments and methodologies have evolved. The ability to retrieve peptides from formalin-fixed paraffin-embedded tissues followed by shotgun or targeted proteomic analysis is offering new opportunities in biomedical research. In particular, access to large collections of clinically annotated samples should enable the detailed analysis of pathologically relevant tissues in a manner previously considered unfeasible. In this paper, we review the current status of proteomic analysis of formalin-fixed paraffin-embedded tissues with a particular focus on targeted approaches and the potential for this technique to be used in clinical research and clinical diagnosis. We also discuss the limitations and perspectives of the technique, particularly with regard to application in clinical diagnosis and drug discovery.

Antibody-validated proteins in inflamed islets of fulminant type 1 diabetes profiled by laser-capture microdissection followed by mass spectrometry

Background

There are no reports of proteomic analyses of inflamed islets in type 1 diabetes.

Procedures

Proteins expressed in the islets of enterovirus-associated fulminant type 1 diabetes (FT1DM) with extensive insulitis were identified by laser-capture microdissection mass spectrometry using formalin-fixed paraffin-embedded pancreatic tissues.

Results

Thirty-eight proteins were identified solely in FT1DM islets, most of which have not been previously linked to type 1 diabetes. Five protein-protein interacting clusters were identified, and the cellular localization of selected proteins was validated immunohistochemically. Migratory activity-related proteins, including plastin-2 (LCP1), moesin (MSN), lamin-B1 (LMNB1), Ras GTPase-activating-like protein (IQGAP1) and others, were identified in CD8⁺ T cells and CD68⁺ macrophages infiltrated to inflamed FT1DM islets. Proteins involved in successive signaling in innate/adaptive immunity were identified, including SAM domain and HD domain-containing protein 1 (SAMHD1), Ras GTPase-activating-like protein (IQGAP1), proteasome activator complex subunit 1 (PSME1), HLA class I histocompatibility antigen (HLA-C), and signal transducer and activator of transcription 1-alpha/beta (STAT1). Angiogenic (thymidine phosphorylase (TYMP)) and anti-angiogenic (tryptophan-tRNA ligase (WARS)) factors were identified in migrating CD8⁺ T cells and CD68⁺ macrophages. Proteins related to virus replication and cell proliferation, including probable ATP-dependent RNA helicase DEAD box helicase 5 (DDX5) and heterogeneous nuclear ribonucleoprotein H (HNRNPH1), were identified. The anti-apoptotic protein T-complex protein 1 subunit epsilon (CCT5), the anti-oxidative enzyme 6-phosphogluconate dehydrogenase (PDG), and the anti-viral and anti-apoptotic proteins serpin B6 (SERPINB6) and heat shock 70 kDa protein1-like (HSPA1L), were identified in FT1DM-affected islet cells.

Conclusion

The identified FT1DM-characterizing proteins include those involved in aggressive beta cell destruction through massive immune cell migration and proteins involved in angiogenesis and islet vasculature bleeding, cell repair, and anti-inflammatory processes. Several target proteins for future type 1 diabetes interventions were identified.

Proteomic developments in the analysis of formalin-fixed tissue

Retrospective proteomic studies, including those which aim to elucidate the molecular mechanisms driving cancer, require the assembly and characterization of substantial patient tissue cohorts. The difficulty of maintaining and accessing native tissue archives has prompted the development of methods to access archives of formalin-fixed tissue. Formalin-fixed tissue archives, complete with patient meta data, have accumulated for decades, presenting an invaluable resource for these retrospective studies. This review presents the current knowledge concerning formalin-fixed tissue, with descriptions of the mechanisms of formalin fixation, protein extraction, top-down proteomics, bottom-up proteomics, quantitative proteomics, phospho- and glycoproteomics as well as imaging mass spectrometry. Particular attention has been given to the inclusion of proteomic investigations of archived tumour tissue. This article is part of a Special Issue entitled: Medical Proteomics.

Toward improving the proteomic analysis of formalin-fixed, paraffin-embedded tissue

Archival formalin-fixed, paraffin-embedded (FFPE) tissue and their associated diagnostic records represent an invaluable source of retrospective proteomic information on diseases for which the clinical outcome and response to treatment are known. However, analysis of archival FFPE tissues by high-throughput proteomic methods has been hindered by the adverse effects of formaldehyde fixation and subsequent tissue histology. This review examines recent methodological advances for extracting proteins from FFPE tissue suitable for proteomic analysis. These methods, based largely upon heat-induced antigen retrieval techniques borrowed from immunohistochemistry, allow at least a qualitative analysis of the proteome of FFPE archival tissues. The authors also discuss recent advances in the proteomic analysis of FFPE tissue; including liquid-chromatography tandem mass spectrometry, reverse phase protein microarrays and imaging mass spectrometry.

Glycoproteomics using fluid-based specimens in the discovery of lung cancer protein biomarkers: promise and challenge

Lung cancer is the leading cancer in the United States and worldwide. In spite of the rapid progression in personalized treatments, the overall survival rate of lung cancer patients is still suboptimal. Over the past decade, tremendous efforts have been focused on the discovery of protein biomarkers to facilitate the early detection and monitoring of lung cancer progression during treatment. In addition to tumor tissues and cancer cell lines, a variety of biological material has been studied. Particularly in recent years, studies using fluid-based specimen or so-called "fluid-biopsy" specimens have progressed rapidly. Fluid specimens are relatively easier to collect than tumor tissue, and they can be repeatedly sampled during the disease progression. Glycoproteins are the major content of fluid specimens and have long been recognized to play fundamental roles in many physiological and pathological processes. In this review, we focus the discussion on recent advances of glycoproteomics, particularly in the identification of potential glyco protein biomarkers using fluid-based specimens in lung cancer. The purpose of this review is to summarize current strategies, achievements, and perspectives in the field. This insight will highlight the discovery of tumor-associated glycoprotein biomarkers in lung cancer and their potential clinical applications.

Respiratory Proteomics Today: Are Technological Advances for the Identification of Biomarker Signatures Catching up with Their Promise? A Critical Review of the Literature in the Decade 2004-2013

To improve the knowledge on a variety of severe disorders, research has moved from the analysis of individual proteins to the investigation of all proteins expressed by a tissue/organism. This global proteomic approach could prove very useful: (i) for investigating the biochemical pathways involved in disease; (ii) for generating hypotheses; or (iii) as a tool for the identification of proteins differentially expressed in response to the disease state. Proteomics has not been used yet in the field of respiratory research as extensively as in other fields, only a few reproducible and clinically applicable molecular markers, which can assist in diagnosis, having been currently identified. The continuous advances in both instrumentation and methodology, which enable sensitive and quantitative proteomic analyses in much smaller amounts of biological material than before, will hopefully promote the identification of new candidate biomarkers in this area. The aim of this report is to critically review the application over the decade 2004-2013 of very sophisticated technologies to the study of respiratory disorders. The observed changes in protein expression profiles from tissues/fluids of patients affected by pulmonary disorders opens the route for the identification of novel pathological mediators of these disorders.

Aberrant Mucin5B expression in lung adenocarcinomas detected by iTRAQ labeling quantitative proteomics and immunohistochemistry

Background

Lung cancer is the number one cause of cancer-related deaths in the United States and worldwide. The complex protein changes and/or signature of protein expression in lung cancer, particularly in non-small cell lung cancer (NSCLC) has not been well defined. Although several studies have investigated the protein profile in lung cancers, the knowledge is far from complete. Among early studies, mucin5B (MUC5B) has been suggested to play an important role in the tumor progression. MUC5B is the major gel-forming mucin in the airway. In this study, we investigated the overall protein profile and MUC5B expression in lung adenocarcinomas, the most common type of NSCLCs.

Methods

Lung adenocarcinoma tissue in formalin-fixed paraffin-embedded (FFPE) blocks was collected and microdissected. Peptides from 8 tumors and 8 tumor-matched normal lung tissue were extracted and labeled with 8-channel iTRAQ reagents. The labeled peptides were identified and quantified by LC-MS/MS using an LTQ Orbitrap Velos mass spectrometer. MUC5B expression identified by iTRAQ labeling was further validated using immunohistochemistry (IHC) on tumor tissue microarray (TMA).

Results

A total of 1288 peptides from 210 proteins were identified and quantified in tumor tissues. Twenty-two proteins showed a greater than 1.5-fold differences between tumor and tumor-matched normal lung tissues. Fifteen proteins, including MUC5B, showed significant changes in tumor tissues. The aberrant expression of MUC5B was further identified in 71.1% of lung adenocarcinomas in the TMA.

Discussions

A subset of tumor-associated proteins was differentially expressed in lung adenocarcinomas. The differential expression of MUC5B in lung adenocarcinomas suggests its role as a potential biomarker in the detection of adenocarcinomas.

Proteomic studies of formalin-fixed paraffin-embedded tissues

Formalin-fixed paraffin-embedded (FFPE) tissue specimens represent a valuable informational resource of histologically characterized specimens for proteomic studies. In this article, the authors review the advancement performed in the field of FFPE proteomics focusing on formaldehyde treatment and on strategies addressed to obtain the best recovery in the protein/peptide extraction. A variety of approaches have been used to characterize protein tissue extracts, and many efforts have been performed demonstrating the comparability between fresh/frozen and FFPE proteomes. Finally, the authors report and discuss the large numbers of works aimed at developing new strategies and sophisticated platforms in the analysis of FFPE samples to validate known potential biomarkers and to discover new ones.

Analysis of the formalin-fixed paraffin-embedded tissue proteome: pitfalls, challenges, and future prospectives

Formalin-fixed paraffin-embedded (FFPE) tissues are a real treasure for retrospective analysis considering the amount of samples present in hospital archives, combined with pathological, clinical, and outcome information available for every sample. Although unlocking the proteome of these tissues is still a challenge, new approaches are being developed. In this review, we summarize the different mass spectrometry platforms that are used in human clinical studies to unravel the FFPE proteome. The different ways of extracting crosslinked proteins and the analytical strategies are pointed out. Also, the pitfalls and challenges concerning the quality of FFPE proteomic approaches are depicted. We also evaluated the potential of these analytical methods for future clinical FFPE proteomics applications.

Proteomic analysis of formalin-fixed paraffin-embedded renal tissue samples by label-free MS: assessment of overall technical variability and the impact of block age

PURPOSE

Protein profiling of formalin-fixed paraffin-embedded (FFPE) tissues has enormous potential for the discovery and validation of disease biomarkers. The aim of this study was to systematically characterize the effect of length of time of storage of such tissue blocks in pathology archives on the quality of data produced using label-free MS.

EXPERIMENTAL DESIGN

Normal kidney and clear cell renal cell carcinoma tissues routinely collected up to 10 years prior to analysis were profiled using LC-MS/MS and the data analyzed using MaxQuant. Protein identities and quantification data were analyzed to examine differences between tissue blocks of different ages and assess the impact of technical and biological variability.

RESULTS

An average of over 2000 proteins was seen in each sample with good reproducibility in terms of proteins identified and quantification for normal kidney tissue, with no significant effect of block age. Greater biological variability was apparent in the renal cell carcinoma tissue, possibly reflecting disease heterogeneity, but again there was good correlation between technical replicates and no significant effect of block age.

CONCLUSIONS AND CLINICAL RELEVANCE

These results indicate that archival storage time does not have a detrimental effect on protein profiling of FFPE tissues, supporting the use of such tissues in biomarker discovery studies.

Impact of pre-analytical factors on the proteomic analysis of formalin-fixed paraffin-embedded tissue

Formalin-fixed paraffin-embedded (FFPE) tissue samples represent a tremendous potential resource for biomarker discovery, with large numbers of samples in hospital pathology departments and links to clinical information. However, the cross-linking of proteins and nucleic acids by formalin fixation has hampered analysis and proteomic studies have been restricted to using frozen tissue, which is more limited in availability as it needs to be collected specifically for research. This means that rare disease subtypes cannot be studied easily. Recently, improved extraction techniques have enabled analysis of FFPE tissue by a number of proteomic techniques. As with all clinical samples, pre-analytical factors are likely to impact on the results obtained, although overlooked in many studies. The aim of this review is to discuss the various pre-analytical factors, which include warm and cold ischaemic time, size of sample, fixation duration and temperature, tissue processing conditions, length of storage of archival tissue and storage conditions, and to review the studies that have considered these factors in more detail. In those areas where investigations are few or non-existent, illustrative examples of the possible importance of specific factors have been drawn from studies using frozen tissue or from immunohistochemical studies of FFPE tissue.

IL-17A receptor expression differs between subclasses of Langerhans cell histiocytosis, which might settle the IL-17A controversy

Langerhans cell histiocytosis (LCH) is a lymphoproliferative disorder consisting of abnormal Langerhans cell-like cells and other lymphoid cells. LCH presents as either a multisystem LCH (LCH-MS) or a single-system LCH (LCH-SS). Currently, neither the pathogeneses nor the factors that define these disease subclasses have been elucidated. The interleukin (IL)-17A autocrine LCH model and IL-17A-targeted therapies have been proposed and have engendered much controversy. Those authors showed high serum IL-17A levels in LCH and argued that serum IL-17A-dependent fusion activities in vitro, rather than serum IL-17A levels, correlated with LCH severity (i.e. the IL-17A paradox). In contrast, others could not confirm the IL-17A autocrine model. So began the controversy on IL-17A, which still continues. We approached the IL-17A controversy and the IL-17A paradox from a new perspective in considering the expression levels of IL-17A receptor (IL-17RA). We detected higher levels of IL-17RA protein expression in LCH-MS (n = 10) as compared to LCH-SS (n = 9) (P = 0.041) by immunofluorescence. We reconfirmed these data by re-analyzing GSE16395 mRNA data. We found that serum levels of IL-17A were higher in LCH (n = 38) as compared to controls (n = 20) (P = 0.005) with no significant difference between LCH subclasses. We propose an IL-17A endocrine model and stress that changes in IL-17RA expression levels are important for defining LCH subclasses. We hypothesize that these IL-17RA data could clarify the IL-17A controversy and the IL-17A paradox. As a potential treatment of LCH-MS, we indicate the possibility of an IL-17RA-targeted therapy.

Application of glycoproteomics for the discovery of biomarkers in lung cancer

Lung cancer is the leading cause of cancer-related deaths in the United States. Approximately 40-60% of lung cancer patients present with locally advanced or metastatic disease at the time of diagnosis. Lung cancer development and progression are a multistep process that is characterized by abnormal gene and protein expressions ultimately leading to phenotypic change. Glycoproteins have long been recognized to play fundamental roles in many physiological and pathological processes, particularly in cancer genesis and progression. In order to improve the survival rate of lung cancer patients, the discovery of early diagnostic and prognostic biomarkers is urgently needed. Herein, we reviewed the recent technological developments of glycoproteomics and published data in the field of glycoprotein biomarkers in lung cancer, and discussed their utility and limitations for the discovery of potential biomarkers in lung cancer. Although numerous papers have already acknowledged the importance of the discovery of cancer biomarkers, the systemic study of glycoproteins in lung cancer using glycoproteomic approaches is still suboptimal. Recent development in the glycoproteomics will provide new platforms for identification of potential protein biomarkers in lung cancers.