Deep proteome profiling of sera from never-smoked lung cancer patients

Absolute quantification of DcR3 and GDF15 from human serum by LC-ESI MS

Biomarkers are widely used in clinical diagnosis, prognosis and therapy monitoring. Here, we developed a protocol for the efficient and selective enrichment of small and low concentrated biomarkers from human serum, involving a 95% effective depletion of high-abundant serum proteins by partial denaturation and enrichment of low-abundant biomarkers by size exclusion chromatography. The recovery of low-abundance biomarkers was above 97%. Using this protocol, we quantified the tumour markers DcR3 and growth/differentiation factor (GDF)15 from 100 μl human serum by isotope dilution mass spectrometry, using ¹⁵N metabolically labelled and concatamerized fingerprint peptides for the both proteins. Analysis of three different fingerprint peptides for each protein by liquid chromatography electrospray ionization mass spectrometry resulted in comparable concentrations in three healthy human serum samples (DcR3: 27.23 ± 2.49 fmol/ml; GDF15: 98.11 ± 0.49 fmol/ml). In contrast, serum levels were significantly elevated in tumour patients for DcR3 (116.94 ± 57.37 fmol/ml) and GDF15 (164.44 ± 79.31 fmol/ml). Obtained data were in good agreement with ELISA and qPCR measurements, as well as with literature data. In summary, our protocol allows the reliable quantification of biomarkers, shows a higher resolution at low biomarker concentrations than antibody-based strategies, and offers the possibility of multiplexing. Our proof-of-principle studies in patient sera encourage the future analysis of the prognostic value of DcR3 and GDF15 for colon cancer patients in larger patient cohorts.

Antibody-based proteomics and biomarker research - current status and limitations

Antibody-based proteomics play a very important role in biomarker discovery and validation, facilitating the high-throughput evaluation of candidate markers. Most proteomics-driven discovery is nowadays based on the use of MS. MS has many advantages, including its suitability for hypothesis-free biomarker discovery, since information on protein content of a sample is not required prior to analysis. However, MS presents one main caveat which is the limited sensitivity in complex samples, especially for body fluids, where protein expression covers a huge dynamic range. Antibody-based technologies remain the main solution to address this challenge since they reach higher sensitivity. In this article, we review the benefits and limitations of antibody-based proteomics in preclinical and clinical biomarker research for discovery and validation in body fluids and tissue. The combination of antibodies and MS, utilizing the best of both worlds, opens new avenues in biomarker research.

Application of clinical proteomics in acute respiratory distress syndrome

Acute Respiratory Distress Syndrome (ARDS) is a devastating cause of hypoxic respiratory failure, which continues to have high mortality. It is expected that a comprehensive systems- level approach will identify global and complex changes that contribute to the development of ARDS and subsequent repair of the damaged lung. In the last decade, powerful genome-wide analytical and informatics tools have been developed, that have provided valuable insights into the mechanisms of complex diseases such as ARDS. These tools include the rapid and precise measure of gene expression at the proteomic level. This article reviews the contemporary proteomics platforms that are available for comprehensive studies in ARDS. The challenges of various biofluids that could be investigated and some of the studies performed are also discussed.

Integrating genomics and proteomics-oriented biomarkers to comprehend lung cancer

BACKGROUND

Lung cancer is the leading cause of cancer deaths worldwide. Recent years have brought tremendous progress in the development of genomic and proteomic platforms to study lung cancer progression and biomarker identification.

OBJECTIVE

To evaluate and integrate potential innovations of 'omics' (e.g., genomics and proteomics) technologies in dissecting biomarkers for lung cancer.

METHODS

Omics technologies permit simultaneous monitoring of many hundreds or thousands of macro and small molecules, as well as functional monitoring of multiple pivotal cellular pathways. Discussion follows to explore the principal challenges in the development of cancer biomarkers integrating genomics with proteomics data sets with their functional counterparts in conjunction with clinical data.

RESULTS/CONCLUSION

Sets of genes and gene interactions affecting different subsets of cancers can be determined using genomics in lung cancer. Proteomic studies have generated numerous functional data sets of potential diagnostic, prognostic and therapeutic significance in lung cancer. It is likely that omics will take a central place in the understanding, diagnosis, monitoring and treatment of lung cancer. Here the potential benefits and pitfalls of these methodologies are reviewed for the faster discovery of therapeutically valuable biomarkers for lung cancer.

Mass spectrometry-based proteomics: the road to lung cancer biomarker discovery

Lung cancer is the leading cause of cancer death in men and women in Western nations, and is among the deadliest cancers with a 5-year survival rate of 15%. The high mortality caused by lung cancer is attributable to a late-stage diagnosis and the lack of effective treatments. So, it is crucial to identify new biomarkers that could function not only to detect lung cancer at an early stage but also to shed light on the molecular mechanisms that underlie cancer development and serve as the basis for the development of novel therapeutic strategies. Considering that DNA-based biomarkers for lung cancer showed inadequate sensitivity, specificity, and reproducibility, proteomics could represent a better tool for the identification of useful biomarkers and therapeutic targets for this cancer type. Among the proteomics technologies, the most powerful tool is mass spectrometry. In this review, we describe studies that use mass spectrometry-based proteomics technologies to analyze tumor proteins and peptides, which might represent new diagnostic, prognostic, and predictive markers for lung cancer. We focus in particular on those findings that hold promise to impact significantly on the clinical management of this disease.

Detection of serum protein biomarkers by surface enhanced laser desorption/ionization in patients with adenocarcinoma of the lung

AIM

Early diagnosis of lung cancer is important for successful treatment and improving the outcome of patients. We explored novel tools for screening serum biomarkers to distinguish adenocarcinoma of the lung from healthy controls by serum protein profiles.

METHODS

Serum samples were taken from 31 patients with adenocarcinoma of the lung and 31 healthy controls, matched for age, sex and smoking status. Serum samples were applied to strong anion 2(SAX-2) protein chips to generate mass spectra by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Protein peak identification and clustering were performed using Biomarker Wizard, compared by MATLAB 7.5 and a classification tree was constructed using R weka software. The validity of the classification tree was then challenged with a blind test.

RESULTS

The software identified 102 peaks and m/z 14022.9 and m/z 3735.99 was used to construct a classification tree. The classification tree effectively separated adenocarcinoma of lung patients from healthy controls, achieving a validity of 100%. The blind test challenged the model with a sensitivity of 100% and a specificity of 100%.

CONCLUSION

The results suggested that the SELDI-TOF-MS technique can correctly distinguish adenocarcinoma of lung patients from healthy controls and showed great potential for development as a screening test for the detection of lung cancer.

A classification method based on principal components of SELDI spectra to diagnose of lung adenocarcinoma

Purpose

Lung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on principal components of surface-enhanced laser desorption/ionization (SELDI) spectral data. This method was applied to SELDI spectral data from 71 lung adenocarcinoma patients and 24 healthy individuals. Unlike other peak-selection-based methods, this method takes each spectrum as a unity. The aim of this paper was to demonstrate that this unity-based classification method is more robust and powerful as a method of diagnosis than peak-selection-based methods.

Results

The results showed that this classification method, which is based on principal components, has outstanding performance with respect to distinguishing lung adenocarcinoma patients from normal individuals. Through leaving-one-out, 19-fold, 5-fold and 2-fold cross-validation studies, we found that this classification method based on principal components completely outperforms peak-selection-based methods, such as decision tree, classification and regression tree, support vector machine, and linear discriminant analysis.

Conclusions and Clinical Relevance

The classification method based on principal components of SELDI spectral data is a robust and powerful means of diagnosing lung adenocarcinoma. We assert that the high efficiency of this classification method renders it feasible for large-scale clinical use.

"The quest for biomarkers": are we on the right technical track?

The discovery phase of biomarkers of diagnostic or therapeutic interest started a decade ago with the very rapid development of proteomic investigations. In spite of the development of innovative technologies and multiple approaches, the "harvest" is still modest. Various reasons justified the encountered difficulties and most of them have been circumvented by specific sample treatments or dedicated analytical approaches. Nevertheless, the situation of very modest biomarker discovery level did not change much. This review intends to specifically analyze the main approaches used for biomarker discovery phase and evaluate related advantages and disadvantages. Thus, preliminary sample treatments such as fractionation, depletion and reduction of dynamic concentration range will critically be discussed and then the main differential expression investigation methods analyzed. Combinations of technologies are also discussed along with possible proposals to federate associations of complementary technologies for better chances of success.

Enriched sera protein profiling for detection of non-small cell lung cancer biomarkers

Background

Non Small Cell Lung Cancer (NSCLC) is the major cause of cancer related-death. Many patients receive diagnosis at advanced stage leading to a poor prognosis. At present, no satisfactory screening tests are available in clinical practice and the discovery and validation of new biomarkers is mandatory. Surface Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-ToF-MS) is a recent high-throughput technique used to detect new tumour markers. In this study we performed SELDI-ToF-MS analysis on serum samples treated with the ProteoMiner™ kit, a combinatorial library of hexapeptide ligands coupled to beads, to reduce the wide dynamic range of protein concentration in the sample. Serum from 44 NSCLC patients and 19 healthy controls were analyzed with IMAC30-Cu and H50 ProteinChip Arrays.

Results

Comparing SELDI-ToF-MS protein profiles of NSCLC patients and healthy controls, 28 protein peaks were found significantly different (p < 0.05), and were used as predictors to build decision classification trees. This statistical analysis selected 10 protein peaks in the low-mass range (2-24 kDa) and 6 in the high-mass range (40-80 kDa). The classification models for the low-mass range had a sensitivity and specificity of 70.45% (31/44) and 68.42% (13/19) for IMAC30-Cu, and 72.73% (32/44) and 73.68% (14/19) for H50 ProteinChip Arrays.

Conclusions

These preliminary results suggest that SELDI-ToF-MS protein profiling of serum samples pretreated with ProteoMiner™ can improve the discovery of protein peaks differentially expressed between NSCLC patients and healthy subjects, useful to build classification algorithms with high sensitivity and specificity. However, identification of the significantly different protein peaks needs further study in order to provide a better understanding of the biological nature of these potential biomarkers and their role in the underlying disease process.

Combined use of a solid-phase hexapeptide ligand library with liquid chromatography and two-dimensional difference gel electrophoresis for intact plasma proteomics

The intact plasma proteome is of great interest in biomarker studies because intact proteins reflect posttranslational protein processing such as phosphorylation that may correspond to disease status. We examined the utility of a solid-phase hexapeptide ligand library in combination with conventional plasma proteomics modalities for comprehensive profiling of intact plasma proteins. Plasma proteins were sequentially fractionated using depletion columns for albumin and immunoglobulin, and separated using an anion-exchange column. Proteins in each fraction were treated with a solid-phase hexapeptide ligand library and compared to those without treatment. Two-dimensional difference gel electrophoresis demonstrated an increased number of protein spots in the treated samples. Mass spectrometric studies of these protein spots with unique intensity in the treated samples resulted in the identification of high- and medium-abundance proteins. Our results demonstrated the possible utility of a solid-phase hexapeptide ligand library to reveal greater number of intact plasma proteins. The characteristics of proteins with unique affinity to the library remain to be clarified by more extensive mass spectrometric protein identification, and optimized protocols should be established for large-scale plasma biomarker studies.

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

New pre-analytical approach for the deep proteome analysis of sera from pancreatitis and pancreas cancer patients

The presence of high-abundance proteins and the wide dynamic range of protein-distribution complicates the proteome analysis of crude serum. The aim was to establish a new preanalytical protocol for analysis of the deep serum-proteome for biomarker discovery.

METHODS

We investigated the stability and functionality of ProteoMiner™ and tested the new protocol by SELDI-TOF-MS profiling with serum samples obtained from patients with different pancreatic diseases.

RESULTS

We developed a high-throughput protocol and proved the convenience of ProteoMiner™ in the 96-well format to provide insights into the deep serum proteome and facilitate the detection of novel serum biomarkers. Serum samples spiked with defined amounts of insulin, processed with ProteoMiner™ and analyzed by SELDI-TOF-MS revealed that the concentration of the spiked insulin was not altered by ProteoMiner™ treatment.

CONCLUSION

ProteoMiner™ technology is robust preanalytical step and can be used in a high-throughput format for analysis of low-abundant proteins in serum.

The proteome buccaneers: how to unearth your treasure chest via combinatorial peptide ligand libraries

The latest advances in combinatorial peptide ligand libraries, with their unique performance in discovering low-abundance species in proteomes, are reviewed here. Explanations of mechanism, potential applications, capture of proteomes at different pH values to enhance the total catch and quantitative elutions, such as boiling in the presence of 5% sodium dodecyl sulfate and 3% dithiothreitol are included. The reproducibility of protein capture among different experiments with the same batch of beads or with different batches is also reported to be very high, with coefficient of variations in the order of 10-20%. Miniaturized operations, consisting of capture with as little as 20 or even 5 microl of peptide beads are reported, thus demonstrating that the described technology could be exploited for routine biomarker discovery in a biomedical environment. Finally, it is shown that the signal of captured proteins is linear over approximately three orders of magnitude, ranging from nM to microM, thus ensuring that differential quantitative proteomics for biomarker discovery can be fully implemented, providing species do not saturate their ligands.

Lung cancer proteomics, clinical and technological considerations

The overall survival of lung cancer patients is disappointingly low. This is due to several factors, including the lack of an effective screening strategy to detect tumors at a potentially curable early stage, a marked resistance of lung cancer cells to drug treatment and a still superficial knowledge about the multifactorial cellular networks that are activated or suppressed during cancer progression. Furthermore, the armamentarium of clinicians and researchers in the field does not yet include reliable biomarkers to predict tumor response to treatment and foresee the natural history of the disease. In the present situation, a potential breakthrough is presented by proteomics technologies with the potential to discover relevant biomarkers which can be accurately quantified in multiplexed assays. Proteomics field can also contribute greatly in the understanding of mechanisms in tumor progression and treatment response. In this review we will describe the work that is being done in the field of lung cancer proteomics, focusing on clinically relevant questions that need to be addressed and on the possible applications of novel technologies.

Deep plasma proteomic analysis of patients with left ventricular remodeling after a first myocardial infarction

PURPOSE

Depletion of major blood proteins is one of the most promising approaches to accessing low abundance biomarkers for proteomics studies. The use of combinatorial peptide ligand library (CPLL) for accessing these low abundance proteins in plasma from patients with a myocardial infarction (MI) was tested to identify candidate protein biomarkers of left ventricular remodeling (LVR).

EXPERIMENTAL DESIGN

Serial blood samples of MI patients followed for one year (at inclusion, 1 month, 3 months, and 1 year) were treated with CPLL and analyzed by SELDI-TOF-MS.

RESULT

The use of CPLL increased resolution, with loss of most abundant plasma proteins, reproducibly and improved the intensity of low-abundance proteins. Longitudinal information allowed us to reduce by 55% the final number of peaks identified as significantly modulated throughout the 1-year follow-up after MI. Interestingly, 19 of the 26 peaks finally selected were detected only in samples treated from CPLL. The 2777 m/z peak, found in less elevated level in high remodeling patients, was identified as being DAHKSEVAHR FKDLGEENFKALVL, the N-terminal peptide (24-48 aa) generated from albumin by pepsin cleavage.

CONCLUSIONS AND CLINICAL RELEVANCE

This finding shows the potential of CPLL in accessing low-abundance proteins to select and identify candidate biomarkers in patients with LVR.

Confounding Effects of Benign Lung Diseases on Non-Small Cell Lung Cancer Serum Biomarker Discovery

Introduction

Lung cancer is the leading cause of cancer-related death worldwide. The discovery of new biomarkers could aid early diagnosis and monitoring of recurrence following tumor resection.

Methods

We have prospectively collected serum from 97 lung cancer patients undergoing surgery with curative intent and compared their serum proteomes with those of 100 noncancer controls (59 disease-free and 41 with a range of nonmalignant lung conditions). We initially analyzed serum from 67 lung cancer patients and 73 noncancer control subjects by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry using immobilized metal affinity capture ProteinChip arrays and subsequently validated our findings with an independent analysis of 30 lung cancer patients and 27 noncancer subjects.

Results

The data from both experiments show many significant differences between the serum proteomes of lung cancer patients and nondiseased control subjects, and a number of these polypeptides have been identified. However, the profiles of patients with benign lung diseases resembled those of lung cancer patients such that very few significant differences were found when these cohorts were compared.

Conclusions

This report provides clear evidence of the need to account for the confounding effects of benign diseases when designing lung cancer serum biomarker discovery projects.

Restoration of tumour suppressor hsa-miR-145 inhibits cancer cell growth in lung adenocarcinoma patients with epidermal growth factor receptor mutation

BACKGROUND

In Hong Kong, about 30% of non-small cell lung cancer patients have never smoked tobacco. Among women, 83% are never-smokers and their histological type is invariably adenocarcinoma with 70% incidence of epidermal growth factor receptor (EGFR) mutation. The present study focuses on the microRNA (miRNA) expression profiles of this important subset of lung cancer.

METHODS

Paired samples collected from the lung cancer tissue and adjacent normal lung parenchyma of 10 non-smoking patients with lung adenocarcinoma were profiled by miRNA microarray. Results were validated by quantitative reverse transcription polymerase chain reaction. Transfected cell viability assays were applied to determine the effects of candidate miRNAs on lung cancer cells.

RESULTS

Comparing paired lung cancer tissue with adjacent normal lung parenchyma, hsa-miR-126*, hsa-miR-145, hsa-miR-21, hsa-miR-182, hsa-miR-183 and hsa-miR-210 were found to be the most differentially expressed miRNAs. Most interestingly, an obvious inhibition of cell growth was observed in the EGFR mutant lung adenocarcinoma after transfection of hsa-pre-miR-145.

CONCLUSIONS

Our study is the first report to connect miR-182 to lung cancer. Our results also show that restoration of tumour suppressor hsa-miR-145 inhibits cancer cell growth in EGFR mutant lung adenocarcinoma. Further study on these specific differentially expressed miRNAs may provide important information on peculiar tumourigenetic pathways and may identify useful biomarkers.

Serum proteomics using mass spectrometry

The identification and eventual application of tumor markers in cancer screening, early detection, diagnosis, and prognosis is a continuing focus of significant translational cancer research. While many new candidate markers have been discovered and at least partly characterized, very few have found widespread clinical application limited presently to the use of CA-125 in ovarian cancer, CEA, primarily in colon cancer, and PSA in prostate cancer screening and patient monitoring. The rapidly emerging field of cancer genomics and proteomics, and their clinical translation as "molecular diagnosis" and "molecular medicine" are already beginning to transform the field, and the accelerating growth of information and technology in this research area will undoubtedly transform the field of tumor markers and their application in the near future leading to improved molecular tools for cancer diagnosis, prognosis, and treatment and ultimately, to the emergence of novel and more effective cancer therapies, including improved approaches for immunotherapy and cancer prevention strategies. Toward this goal, herein are described detailed methods and workflows for mass spectrometry-based biomarker discovery in serum/plasma utilizing two complementary approaches - matrix-assisted laser desorption ionization time of flight (MALDI-TOF) and nanoflow reversed-phase liquid chromatography (RPLC)-tandem mass spectrometry (MS/MS). These discovery workflows incorporate both abundant protein depletion and sample fractionation upstream of analytical mass spectrometry to optimize the identification and quantitation of lower abundant species.

Use of ProteinChip technology for identifying biomarkers of parasitic diseases: the example of porcine cysticercosis (Taenia solium)

Taenia solium cysticercosis is a significant public health problem in endemic countries. The current serodiagnostic techniques are not able to differentiate between infections with viable cysts and infections with degenerated cysts. The objectives of this study were to identify specific novel biomarkers of these different disease stages in the serum of experimentally infected pigs using ProteinChip technology (Bio-Rad) and to validate these biomarkers by analyzing serum samples from naturally infected pigs. In the experimental sample set 30 discriminating biomarkers (p<0.05) were found, 13 specific for the viable phenotype, 9 specific for the degenerated phenotype and 8 specific for the infected phenotype (either viable or degenerated cysts). Only 3 of these biomarkers were also significant in the field samples; however, the peak profiles were not consistent among the two sample sets. Five biomarkers discovered in the sera from experimentally infected pigs were identified as clusterin, lecithin-cholesterol acyltransferase, vitronectin, haptoglobin and apolipoprotein A-I.

Evaluation of the combination of bead technology with SELDI-TOF-MS and 2-D DIGE for detection of plasma proteins

Today biomarker discovery is one of the most active aspects of proteomic investigations. However, the wide dynamic range of plasma proteins makes the analysis very challenging because high abundance proteins tend to mask those of lower abundance. Using a large bead-based library of combinatorial peptide ligands (Equalizer beads or ProteoMiner), the dynamic range of the protein concentration is compressed, the high abundance proteins present in the sample are reduced and the low abundance proteins are enriched, while retaining representatives of all proteins within the sample. In the present study, the combination of beads with surface enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and two-dimensional differential gel electrophoresis (2-D DIGE) technology were evaluated considering efficiency, reproducibility, sensitivity, and compatibility. The bead technology is easily compatible with both SELDI-TOF-MS and 2-D DIGE and the samples can be analyzed directly without any processing of the sample. The use of the beads prior SELDI-TOF-MS and 2-D DIGE enabled detection of many new protein spots/peaks and increased resolution and improved intensity of low abundance proteins in a reproducible fashion compared with the depletion technique. Several proteins have been identified by the combination of beads, 2-D DIGE and MS for example different kinds of complement factors and cytoskeletal proteins. Our data suggest that integration of the bead technology with our current proteomic technologies will enhance the possibility to deliver new peptide/protein biomarker candidates in our projects.