Identification of lung cancer patients by serum protein profiling using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Am J Clin Oncol. 2008;31:133-139. [PMID: 18391596 DOI: 10.1097/coc.0b013e318145b98b]

Utilizing MALDI-TOF MS and LC-MS/MS to access serum peptidome-based biomarkers in canine oral tumors

Tumors frequently found in dogs include canine oral tumors, either cancerous or noncancerous. The bloodstream is an important route for tumor metastasis, particularly for late-stage oral melanoma (LOM) and late-stage oral squamous cell carcinoma (LOSCC). The present study aimed to investigate serum peptidome-based biomarkers of dogs with early-stage oral melanoma, LOM, LOSCC, benign oral tumors, chronic periodontitis and healthy controls, using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and liquid chromatography tandem mass spectrometry. A principal component analysis plot showed distinct clusters among all groups. Four peptides were identified, including peptidyl-prolyl cis-trans isomerase FKBP4 isoform X2 (FKBP4), steroid hormone receptor ERR1 (ESRRA or ERRA), immunoglobulin superfamily member 10 (IGSF10) and ATP-binding cassette subfamily B member 5 (ABCB5). FKBP4, ESRRA and ABCB5 were found to be overexpressed in both LOM and LOSCC, whereas IGSF10 expression was markedly increased in LOSCC only. These four proteins also played a crucial role in numerous pathways of cancer metastasis and showed a strong relationship with chemotherapy drugs. In conclusion, this study showed rapid screening of canine oral tumors using serum and MALDI-TOF MS. In addition, potential serum peptidome-based biomarker candidates for LOM and LOSCC were identified.

A novel bioassay for the monitoring of carcinoembryonic antigen in human biofluid using polymeric interface and immunosensing method

Carcinoembryonic antigen (CEA) is a member of a family of cell surface glycoproteins. Recognition of CEA is needed to monitor the physiological status of the patient for treatment and also it is important to assess the severity of the disease. In this work, we reported a novel sandwich-type electrochemical immunosensor based on gold nanoparticles functionalized cysteamine-glutaraldehyde (AuNPs-CysA-GA) and it successfully designed to detection of the CEA biomarker in a human plasma sample. The AuNPs-CysA-GA provides a large surface area for the effective immobilization of CEA antibody, as well as it ascertains the bioactivity and stability of immobilized CEA antigens. Biotinylated-anti-CEA antibody (Ab1) was immobilized on the surface of glassy carbon electrode (GCE) modified AuNPs-CysA-GA. Also, secondary antibody (HRP-Ab2) was costed immobilized to complete the sandwich part of immunosensor. Field emission scanning electron microscope (FE-SEM and EDS), was employed to monitor the sensor fabrication procedure. The immunosensor was used for the detection of CEA using differential pulse voltammetry (DPVs) technique. The proposed interface led to enhancement of accessible surface area for immobilizing high amount of anti-CEA antibody, increasing electrical conductivity, boosting stability, and biocompatibility. Finally, the low limit of quantitation (LLOQ) of the proposed immunosensor was obtained as 7 ng/mL with the linear range of 0.001-5 μg/L. The proposed immunoassay was successfully applied for the monitoring of the CEA in unprocessed human plasma samples. Obtained results paved that the proposed bioassay can be used as a novel bioassay for the clinical diagnosis of cancer based on CEA monitoring.

Exploratory study on application of MALDI‑TOF‑MS to detect serum and urine peptides related to small cell lung carcinoma

Matrix‑assisted laser desorption/ionization time‑of‑flight mass spectrometry (MALDI‑TOF‑MS) was employed to analyze differential serum and urine peptides in patients with small cell lung cancer (SCLC) and healthy individuals, and SCLC diagnostic classification models were constructed. Serum and urine samples from 72 patients with SCLC, age‑ and gender‑matched with 72 healthy individuals, were divided into training and testing sets in a 3:1 ratio. Serum and urine peptides were extracted using copper ion‑chelating nanomagnetic beads, and mass spectra were obtained using MALDI‑TOF‑MS. Peptide spectra for the training set were analyzed, and the classification model was constructed using ClinProTools (CPT). The testing set was used for blinded model validation. For training‑set sera, 122 differential peptide signal peaks with a mass of 0.8‑10 kDa were observed, and 19 peptides showed significantly different expression [P<0.0005; area under curve (AUC) ≥0.80]. CPT screened 5 peptide peaks (0.8114, 0.83425, 1.86655, 4.11133 and 5.81192 kDa) to construct the classification model. The testing set was used for the blinded validation, which had 95.0% sensitivity and 90.0% specificity. For the training‑set urine, 132 differential peptide signal peaks with m/z ratios of 0.8‑10 kDa were observed, and 8 peptides had significantly different expression (P<0.0005; AUC ≥0.80). Then, 5 peaks (1.0724, 2.37692, 2.7554, 4.75475 and 4.7949 kDa) were used for classification model construction. The testing set was used for 36 blinded validation, which had 85.0% sensitivity and 80.0% specificity. Among the differential peptides, 3 had the same significant peaks at 2.3764, 0.8778 and 0.8616 kDa, identified as fibrinogen α, glucose‑6‑phosphate isomerase and cyclin‑dependent kinase‑1, respectively. The present study highlighted the differences that exist in serum and urine peptides between patients with SCLC and healthy individuals. Serum and urine peptide diagnostic classification models could be constructed using MALDI‑TOF‑MS, and showed high sensitivity and specificity.

Intelligence Algorithms for Protein Classification by Mass Spectrometry

Mass spectrometry (MS) is an important technique in protein research. Effective classification methods by MS data could contribute to early and less-invasive diagnosis and also facilitate developments in the bioinformatics field. As MS data is featured by high dimension, appropriate methods which can effectively deal with the large amount of MS data have been widely studied. In this paper, the applications of methods based on intelligence algorithms have been investigated. Firstly, classification and biomarker analysis methods using typical machine learning approaches have been discussed. Then those are followed by the Ensemble strategy algorithms. Clearly, simple and basic machine learning algorithms hardly addressed the various needs of protein MS classification. Preprocessing algorithms have been also studied, as these methods are useful for feature selection or feature extraction to improve classification performance. Protein MS data growing with data volume becomes complicated and large; improvements in classification methods in terms of classifier selection and combinations of different algorithms and preprocessing algorithms are more emphasized in further work.

Serum peptide expression and treatment responses in patients with advanced non-small-cell lung cancer

Epidermal growth factor receptor (EGFR) mutation is an important predictor for response to personalized treatments of patients with advanced non-small-cell lung cancer (NSCLC). However its usage is limited due to the difficult of obtaining tissue specimens. A novel prediction system using matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) has been reported to be a perspective tool in European countries to identify patients who are likely to benefit from EGFR tyrosine kinase inhibitor (TKI) treatment. In the present study, MALDI-TOF MS was used on pretreatment serum samples of patients with advanced non-small-cell lung cancer to discriminate the spectra between disease control and disease progression groups in one cohort of Chinese patients. The candidate features for classification were subsequently validated in a blinded fashion in another set of patients. The correlation between plasma EGFR mutation status and the intensities of representative spectra for classification was evaluated. A total of 103 patients that were treated with EGFR-TKIs were included. It was determined that 8 polypeptides peaks were significant different between the disease control and disease progression group. A total of 6 polypeptides were established in the classification algorithm. The sensitivity of the algorithm to predict treatment responses was 76.2% (16/21) and the specificity was 81.8% (18/22). The accuracy rate of the algorithm was 79.1% (34/43). A total of 3 polypeptides were significantly correlated with EGFR mutations (P=0.04, P=0.03 and P=0.04, respectively). The present study confirmed that MALDI-TOF MS analysis can be used to predict responses to EGFR-TKI treatment of the Asian population where the EGFR mutation status differs from the European population. Furthermore, the expression intensities of the three polypeptides in the classification model were associated with EGFR mutation.

Identification of cysteinylated transthyretin, a predictive biomarker of treatment response to partially hydrolyzed guar gum in type 2 diabetes rats, by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

Recent evidence has indicated that total fiber intake is inversely related to type 2 diabetes risk. The present study aimed to investigate the effects of chronic administration of partially hydrolyzed guar gum (PHGG), a water-soluble dietary fiber, on the occurrence of diabetes and its complications, fatty liver and nephropathy. We also identified predictive serum biomarkers of treatment response to PHGG by mass spectroscopy-based proteomic analysis using Otsuka Long-Evans Tokushima Fatty (OLETF) rats, a good model of human non-insulin-dependent diabetes mellitus. In this study, at 5 weeks of age, OLETF rats and control strain Long-Evans Tokushima Otsuka (LETO) rats were fed a control diet or a high-fiber diet (5% PHGG) for 57 weeks. Body weight, food intake, oral glucose tolerance test, plasma insulin levels, and urine glucose and protein levels were regularly measured. Oral glucose tolerance tests (OGTT) and storage of serum in a deep freezer were conducted at the beginning of the experiment and every 4 weeks after overnight fasting during the experiments. PHGG treatment affected neither meal patterns nor the body weight of OLETF and LETO rats. Repeated measure analysis of variance revealed significant differences in fasting plasma glucose and plasma glucose at 2 h after OGTT between control OLETF (OLETF-C) rats and OLETF rats treated with PHGG (OLETF-F). The glucose response determined by the area under the curve of OGTT was significantly greater in OLETF-C rats than that in OLETF-F rats at 25 weeks of age. HOMA-IR, an index of insulin resistance, increased at 25 weeks of age in OLETF-C rats, while this increase was significantly inhibited in OLETF-F rats. At 62 weeks of age, PHGG treatment significantly improved hepatic steatosis as well as renal mesangial matrix accumulation in OLETF rats. To identify the risk marker for diabetes mellitus by SELDI-TOF MS, we collected sera from 21-week-old individuals. Among the 12 specific peaks that were risk marker candidates for diabetes mellitus, the m/z 13,720 peak was identified as that of cysteinylated transthyretin by sequencing of four tryptic peptides using tandem mass spectrometry and peak distribution around the m/z 13,720 peak in the SELDI-TOF spectra. In conclusion, we found that chronic treatment with PHGG improved insulin resistance, delayed the onset of diabetes, and inhibited the development of diabetic complications, as well as identified cysteinylated transthyretin as a predictive biomarker of treatment response to PHGG in OLETF rats.

[Preliminary study of MALDI-TOF mass spectrometry-based screening of patients with the NSCLC serum-specific peptides]

背景与目的

早期诊断是提高肺癌生存率的关键，传统的肺癌诊断技术仍存在一定局限性。鉴于近年来以质谱为核心技术的肿瘤蛋白组学在癌症诊断方面的初步研究，本研究探索性应用基质辅助激光解析电离飞行时间质谱（matrix assisted laser desorption ionization-time of flight-mass spectrometry, MALDI-TOF-MS）分析非小细胞肺癌（non-small cell lung cancer, NSCLC）患者和健康人群的血清差异多肽，以建立NSCLC的血清分类模型。

方法

将年龄和性别匹配的133例NSCLC患者和132例健康者血清标本按照3:1的比例随机分为两组：训练组由100例NSCLC患者和100例健康者血清标本组成，用以建立分类模型；测试组由33例NSCLC患者和32例健康者血清标本组成，用以验证模型。采用铜离子鳌合纳米磁珠提取血清多肽、MALDI-TOF-MS技术检测得到质谱图。ClinProTools^TM统计软件分析训练组NSCLC患者与健康者之间的多肽图谱，从中筛选出一组差异多肽并建立分类模型，最后用测试组对模型进行盲样验证。

结果

在训练组中观察到血清质荷比（m/z）在1, 000 Da-10, 000 Da范围内有131个差异多肽信号峰，在此范围内共得到14个有统计学意义的差异多肽峰（P < 0.000, 001; AUC≥0.9），其中NSCLC患者与健康者相比，表达上调的多肽有2个，表达下调的有12个，由统计软件筛选出3个多肽峰（7, 478.59 Da、2, 271.44 Da、4, 468.38 Da）建立分类模型，然后对测试组进行验证，其盲样验证敏感性100%，特异性96.9%，准确率98.5%。

结论

本组研究显示NSCLC患者与健康人群的血清多肽存在差异，应用MALDI-TOF-MS技术可建立NSCLC的血清多肽分类模型且小规模验证具有较好的敏感性和特异性，希望大规模验证模型，并与传统诊断方法对照或结合，进而尝试建立一种新的NSCLC早期诊断模式。

Potential serum biomarkers for glioblastoma diagnostic assessed by proteomic approaches

BACKGROUND

The rapid progress of proteomics over the past years has allowed the discovery of a large number of potential biomarker candidates to improve early tumor diagnosis and therapeutic response, thus being further integrated into clinical environment. High grade gliomas represent one of the most aggressive and treatment-resistant types of human brain cancer, with approximately 9-12 months median survival rate for patients with grade IV glioma (glioblastoma). Using state-of-the-art proteomics technologies, we have investigated the proteome profile for glioblastoma patients in order to identify a novel protein biomarker panel that could discriminate glioblastoma patients from controls and increase diagnostic accuracy.

RESULTS

In this study, SELDI-ToF MS technology was used to screen potential protein patterns in glioblastoma patients serum; furthermore, LC-MS/MS technology was applied to identify the candidate biomarkers peaks. Through these proteomic approaches, three proteins S100A8, S100A9 and CXCL4 were selected as putative biomarkers and confirmed by ELISA. Next step was to validate the above mentioned molecules as biomarkers through identification of protein expression by Western blot in tumoral versus peritumoral tissue.

CONCLUSIONS

Proteomic technologies have been used to investigate the protein profile of glioblastoma patients and established several potential diagnostic biomarkers. While it is unlikely for a single biomarker to be highly effective for glioblastoma diagnostic, our data proposed an alternative and efficient approach by using a novel combination of multiple biomarkers.

Differential mitochondrial proteome analysis of human lung adenocarcinoma and normal bronchial epithelium cell lines using quantitative mass spectrometry

BACKGROUND

Lung cancer is one of the higher incidences of malignant tumors around the world. At present, tumor markers CEA, CA19-9, and CA-125 in serum are used for the diagnosis of lung cancer, however, fewer studies have shown tumor markers for early diagnosis. Therefore, using quantitative mass spectrometry, differential mitochondrial proteome analysis was performed, comparing human lung adenocarcinoma and normal bronchial epithelium cells.

METHODS

A human lung adenocarcinoma cell line A549 and a normal human bronchial epithelial cell line 16HBE were cultured in vitro. The cell mitochondria of the two cell lines were extracted and purified by differential centrifugation and percoll density gradient centrifugation. The integrity and purity of mitochondria were validated by electron microscopy and Western-blot. The proteins/peptides from lung cancer cells and normal cells were marked by the same amount of relative and absolute quantification of ectopic tags (iTRAQ). The mixed samples were analyzed and identified by two-dimensional liquid chromatography - tandem mass spectrometry (2D-LC-MS/MS). The proteome was analyzed with different bioinformatic tools.

RESULTS

One hundred and sixty-one mitochondrial proteins were identified. One hundred and fifty-three mitochondrial proteins, which were expressed differently between 16HBE cells and A549 cells, were identified. Sixty-seven proteins were high expression, while 86 proteins were lower expression. Expression of three proteins: ornithine aminotransferase (OAT), heat shock protein beta90 (HSP90), and vimentin (VIM), was increased more than twice. Our results, in combination with the literature review, suggest that HSP90 and Vimentin may be the new tumor markers of lung cancer.

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.

Potential role of proteomics in the diagnosis of lymphoma: a meta-analysis

Surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF-MS) has been approved for identifying biomarkers and diagnosing many diseases such as lymphomas. It is arguable whether the SELDI technique has its value of diagnostic accuracy for lymphomas. The purpose of our study is to determine the diagnostic accuracy of SELDI-TOF-MS for diagnosing lymphomas. The Cochrane Central Register of Controlled Trials, MEDLINE, Pub Med, EMBASE, the Chinese Biomedical Literature Database, the China Academic Journals Full-text Database, and the Chinese Scientific Journals Database were searched systematically for potential studies. Reference lists of included studies and review articles were also reviewed. All studies that reported data on patients with a confirmed diagnosis of lymphomas and that compared the measurement of SELDI-TOF-MS with pathology standard were considered for inclusion. Eleven studies were included in the systematic review. The ranges of the diagnostic value of SELDI-TOF-MS for lymphoma were as follows: sensitivity (SEN) was 0.69-0.96; specificity (SPE) was 0.70-1.00; positive likelihood ratio (PLR) was 2.99-96.09; negative likelihood ratio (NLR) was 0.04-0.35; and diagnostic odds ratio (DOR) was 18.13-1250.71, respectively. Further, we analysed serum samples as a subgroup, and the pooled endpoints were as follows: pooled SEN was 0.89 (0.85-0.91); pooled SPE was 0.91 (0.88-0.93); pooled PLR was 12.35 (5.36-28.44); pooled NLR was 0.13 (0.09-0.20); and pooled DOR was 101.04 (39.57-258.04), respectively. SELDI-TOF-MS showed high accuracy in identifying lymphoma and could be a useful screening tool for diagnosing lymphoma patients.

Better cancer biomarker discovery through better study design

BACKGROUND

High-throughput laboratory technologies coupled with sophisticated bioinformatics algorithms have tremendous potential for discovering novel biomarkers, or profiles of biomarkers, that could serve as predictors of disease risk, response to treatment or prognosis. We discuss methodological issues in wedding high-throughput approaches for biomarker discovery with the case-control study designs typically used in biomarker discovery studies, especially focusing on nested case-control designs.

METHODS

We review principles for nested case-control study design in relation to biomarker discovery studies and describe how the efficiency of biomarker discovery can be effected by study design choices. We develop a simulated prostate cancer cohort data set and a series of biomarker discovery case-control studies nested within the cohort to illustrate how study design choices can influence biomarker discovery process.

RESULT

Common elements of nested case-control design, incidence density sampling and matching of controls to cases are not typically factored correctly into biomarker discovery analyses, inducing bias in the discovery process. We illustrate how incidence density sampling and matching of controls to cases reduce the apparent specificity of truly valid biomarkers 'discovered' in a nested case-control study. We also propose and demonstrate a new case-control matching protocol, we call 'antimatching', that improves the efficiency of biomarker discovery studies.

CONCLUSIONS

For a valid, but as yet undiscovered, biomarker(s) disjunctions between correctly designed epidemiologic studies and the practice of biomarker discovery reduce the likelihood that true biomarker(s) will be discovered and increases the false-positive discovery rate.

Update on biomarkers for the detection of lung cancer

Patients at risk for lung cancer may have subclinical disease for years before presentation. The diagnosis of this disease is primarily based on symptoms, and detection often occurs after curative intervention is no longer possible. At present, no lung cancer early-detection biomarker is clinically available. This study reviews the most recent advances in early detection and molecular diagnostic biomarkers for the detection of lung cancer. This review includes an overview of the various biological specimens and matrices in which these biomarkers could be analyzed, as well as the diverse strategies and approaches for identifying new biomarkers that are currently being explored. Several novel and attractive biomarker candidates for the early detection of lung cancer exist. A remarkable shift is taking place from research based on single markers to analyzing signatures that are more complex in order to take advantage of new high-throughput technologies. However, it is still necessary to validate the most promising markers and the standardization of procedures that will lead to specific clinical applications.

Molecular biology techniques for the surgeon

New technologies are constantly being introduced into the medical and surgical fields. These technologies come in the form of newer medicines, imaging methods and prognostic tools, among others, and allow clinicians to make more rational and informed decisions on the care of their patients. Many of these technologies utilize advanced techniques which are at the forefront of many research fields and represent a transition of bench advances into the clinical realm. This review will highlight four technologies that are at the forefront in the treatment of oncology patients treated by surgeons on a daily basis. Circulating tumor cells, microarray analysis, proteomic studies and rapid sequencing technologies will be highlighted. These technologies will be reviewed and their potential use in the care of surgical patients will be discussed.

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.

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.

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.

Screening and early detection of lung cancer

Lung cancer with an estimated 342,000 deaths in 2008 (20% of total) is the most common cause of death from cancer, followed by colorectal cancer (12%), breast cancer (8%), and stomach cancer (7%) in Europe. In former smokers, the absolute lung cancer risk remains higher than in never-smokers; these data therefore call for effective secondary preventive measures for lung cancer in addition to smoking cessation programs. This review presents and discusses the most recent advances in the early detection and screening of lung cancer.An overview of randomized controlled computerized tomography-screening trials is given, and the role of bronchoscopy and new techniques is discussed. Finally, the approach of (noninvasive) biomarker testing in the blood, exhaled breath, sputum, and bronchoscopic specimen is reviewed.

Improving Detection Accuracy of Lung Cancer Serum Proteomic Profiling via Two-Stage Training Process

Background

Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for cancer biomarker research. The specificity of biomarkers detected by SELDI can be influenced by concomitant inflammation. This study aimed to increase detection accuracy using a two-stage analysis process.

Methods

Sera from 118 lung cancer patients, 72 healthy individuals, and 31 patients with inflammatory disease were randomly divided into training and testing groups by 3:2 ratio. In the training group, the traditional method of using SELDI profile analysis to directly distinguish lung cancer patients from sera was used. The two-stage analysis of distinguishing the healthy people and non-healthy patients (1^st-stage) and then differentiating cancer patients from inflammatory disease patients (2^nd-stage) to minimize the influence of inflammation was validated in the test group.

Results

In the test group, the one-stage method had 87.2% sensitivity, 37.5% specificity, and 64.4% accuracy. The two-stage method had lower sensitivity (> 70.1%) but statistically higher specificity (80%) and accuracy (74.7%). The predominantly expressed protein peak at 11480 Da was the primary splitter regardless of one- or two-stage analysis. This peak was suspected to be SAA (Serum Amyloid A) due to the similar m/z countered around this area. This hypothesis was further tested using an SAA ELISA assay.

Conclusions

Inflammatory disease can severely interfere with the detection accuracy of SELDI profiles for lung cancer. Using a two-stage training process will improve the specificity and accuracy of detecting lung cancer.

Detection of the circulating tumor cells in cancer patients

As the presence of tumor cells circulating in the blood is associated with systemic disease and shortened survival, the establishment of a method to detect circulating tumor cells (CTCs) is of critical importance for a more concise staging and follow-up of cancer patients. Recently, the most robust strategies for the determination of CTCs are the PCR-based methods and the CellSearch® system that exploits the immunofluorescent characterization and isolation of cancer cells. Herein, we analyzed the experimental strategies used for determining CTCs with respect to accuracy, sensitivity and reproducibility in cancers of the breast, colon, prostate and melanoma.

Serum peptidome profiling in patients with lung cancer

Serum peptide profiling is a promising approach for classification of cancer versus noncancer samples. In this study, we aimed to search for discriminating peptide patterns in serum samples between lung cancer patients and healthy controls. The magnetic beads-based weak cation-exchange chromatography followed by matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS) was used in this study to identify patients with lung cancer. In total, serum samples from 64 lung cancer patients (32 for training set and 32 for testing set), 64 healthy controls (32 for training set and 32 for testing set), and 10 COPD patients (for disease control) were analyzed in this study. The mass spectra data analyzed with ClinProTools software was used to distinguish between cancer patients and healthy individuals based on three different algorithm models (GA, SNN, and QC). In the training set, patients with lung cancer could be identified with the mean sensitivity of 98.9% and specificity of100%. Similar results could be obtained from testing set, showing 87% sensitivity and 84.8% specificity. Screening for serum peptide patterns using MALDI-TOF MS showed high sensitivity and specificity in identifying patients with lung cancer.

Proteome profiling reveals gender differences in the composition of human serum

Proteome analysis using human serum is a technological advancement that will enable the discovery of novel biomarkers and biomarker patterns of various human diseases. Although proteome analysis using serum has potential in disease prevention, early diagnosis and treatment of diseases, and evaluation of pharmacotherapies, this technology is still in its infancy. Thus, we sought to develop an advanced method of conducting proteome analysis on human serum. In this study, we report the development of the semi-comprehensive protein analytical technique, which involves the systematic use of iTRAQ labeling, HPLC, nano-LC and MS. We compared the composition of the serum proteome in males and females using this technique and detected gender-based differences in serum protein composition. This technology will enable the generation of databases that may ultimately lead to the discovery of specific biomarkers or biomarker patterns of various diseases.

The pursuit of oncotargets through understanding defective cell regulation

More effective anticancer agents are essential, as has too often been demonstrated by the paucity of therapeutics which preserve life. Their discovery is very difficult. Many approaches are being applied, from testing folk medicines to automated high throughput screening of large chemical libraries. Mutations in cancer cells create dysfunctional regulatory systems. This Perspective summarizes an approach to applying defective molecular control mechanisms as oncotargets on which drug discoveries against cancer can be based.

The pursuit of oncotargets through understanding defective cell regulation

More effective anticancer agents are essential, as has too often been demonstrated by the paucity of therapeutics which preserve life. Their discovery is very difficult. Many approaches are being applied, from testing folk medicines to automated high throughput screening of large chemical libraries. Mutations in cancer cells create dysfunctional regulatory systems. This Perspective summarizes an approach to applying defective molecular control mechanisms as oncotargets on which drug discoveries against cancer can be based.

Diagnostic characteristics of a serum biomarker in patients with positron emission tomography scans

BACKGROUND

Surgery for pulmonary nodules results in a benign diagnosis in 10% to 30% of cases. Computed tomography and fluorodeoxyglucose-positron emission tomography (FDG-PET) are highly sensitive but less specific. High-risk patients (age > 55 years and smoke > 15 pack-years) for lung cancer with negative FDG-PET scans, or low-risk patients (age < 55 years or smoke < 15 pack-years) with FDG-PET-avid lesions may have higher rates of benign nodules. We hypothesized that our serum biomarker improves diagnostic accuracy by providing greater specificity.

METHODS

Fifty-eight patients with pulmonary nodules (< or = 3 cm) were prospectively enrolled. We tested the accuracy of our proteomic biomarker in the serum by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Malignancy rates, contingency tables, sensitivity, and specificity analyses were calculated for the entire group and in a subset of patients at high risk for benign disease.

RESULTS

We identified 46 (79%) lung cancers and 12 (21%) benign lesions. Forty-five nodules were FDG-PET-avid. In 36 high-risk patients with FDG-PET-avid lesions, 32 (89%) had cancer. Of the remaining 22 lower-risk patients, 14 (64%) had cancer (p = 0.02). The serum biomarker sensitivity was 26.1%, specificity was 91.7%, positive predictive value was 92%, negative predictive value was 24%, and overall accuracy was 40%. The serum signature accurately predicted all eight benign nodules in this 22-patient subset.

CONCLUSIONS

The serum protein biomarker has a high specificity. This biomarker has a high positive predictive value but low negative predictive value and may improve noninvasive evaluation of lung nodules. Validation in a larger population is warranted.

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.

Detection of lung cancer using plasma protein profiling by matrix-assisted laser desorption/ionization mass spectrometry

There are no satisfactory plasma biomarkers which are available for the early detection and monitoring of lung cancer, one of the most frequent cancers worldwide. The aim of this study is to explore the application of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF MS) to plasma proteomic patterns to distinguish lung cancer patients from healthy individuals. The EDTA plasma samples have been pre-fractionated using magnetic bead kits functionalized with weak cation exchange coatings. We compiled MS protein profiles for 90 patients with squamous cell carcinomas (SCC) and compared them with profiles from 187 healthy controls. The MALDI-ToF spectra were analyzed statistically using ClinProTools bioinformatics software. Depending on the sample used, up to 441 peaks/spectrum could be detected in a mass range of 1000-20,000 Da; 33 of these proteins had statistically differential expression levels between SCC and control plasma (P < 0.001). The series of the peaks were automatically chosen as potential biomarker patterns in the training set. They allowed the discrimination of plasma samples from healthy control and samples from SCC patients (sensitivity and specificity >90%) in external validation test. These results suggest that plasma MALDI-ToF MS protein profiling can distinguish patients with SCC and also from healthy individuals with relatively high sensitivity and specificity and that MALDI- ToF MS is a potential tool for the screening of lung cancer.

SELDI-TOF biomarker signatures for cystic fibrosis, asthma and chronic obstructive pulmonary disease

OBJECTIVES

The aim of this work was to establish protein profiles in serum and nasal epithelial cells of cystic fibrosis individuals in comparison with controls, asthma and chronic obstructive pulmonary disease patients for specific biomarker signatures identification.

DESIGN AND METHODS

Protein extracts were analyzed by Surface Enhanced Laser Desorption/Ionization Time-Of-Flight Mass-Spectrometry (SELDI-TOF-MS).

RESULTS

The mass spectra revealed a set of peaks with differential expression in serum and nasal cells among the different groups studied, resulting into peak signatures representative/specific of each pathology. Logistic regressions were applied to those peaks; sensitivity, specificity, Youden's indexes and area under the curve (AUC) of the respective receiver operating characteristic (ROC) curves were compared.

DISCUSSION

Multivariate analysis demonstrated that combination of peaks has a better predictive value than the individual ones. These protein signatures may serve as diagnostic/prognostic markers for the studied diseases with common clinical features, or as follow-up assessment markers of therapeutic interventions.

Serum protein profiles in myasthenia gravis

BACKGROUND

The diagnosis of myasthenia gravis (MG) remains challenging. We performed a proteome-wide search for potential serum protein diagnostic markers for MG using surface-enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry (TOFMS).

METHODS

Proteomic spectra from 80 MG patients and 80 healthy individuals were generated by SELDI. Samples from 56 MG patients and 56 healthy individuals in the training set were analyzed to set up the decision tree. Samples from 24 MG patients and 24 healthy individuals were used for cross-validation testing.

RESULTS

The SELDI TOFMS analysis generated 101 peaks, representing differentially expressed proteins between 1000 and 20000 Da. Among them, 9 peaks were down-regulated and 30 others were up-regulated in the MG sera compared with the controls. The decision tree used the peak at M4168.94 Da and M1122.57 Da as splitters in the classification process. In the training set, 112 samples were classified as MG or control group, with a sensitivity of 100% and specificity of 89.3%; the 10-fold cross-validated analysis identified the optimal decision tree with the lowest relative cross-validated cost of 0.080. In the test set, the decision tree generated was able to identify 20 of 24 MG patients and 21 of 24 healthy individuals with a sensitivity of 83.3% and a specificity of 87.5%.

CONCLUSIONS

SELDI TOFMS is a useful tool for the detection and identification of potential serum biomarkers that can diagnose MG with high sensitivity and specificity.

Influence of sample storage duration on serum protein profiles assessed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS)

BACKGROUND

Issues have been raised concerning the robustness and validity of alleged serum markers discovered by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS). Pre-analytical variables have been shown to exert a profound effect on protein profiles, irrespective of true biological variation. However, little is known about the possible effects of sample storage duration on protein profiles. We, therefore, aimed to investigate the effects of extended storage duration on the serum protein profile.

METHODS

Archival sera from 140 breast cancer patients, stored at -30 degrees C for 1-11 years, were profiled by SELDI-TOF MS using immobilised metal affinity capture (IMAC) arrays, a condition applied in the majority of breast cancer biomarker discovery studies.

RESULTS

Fourteen peak clusters, structurally identified as C3a des-arginine anaphylatoxin and multiple fragments of albumin and fibrinogen, were found to be significantly associated with sample storage duration by five distinct patterns. These proteins have been described previously as potential cancer markers, rendering them specific to both disease and sample handling issues.

CONCLUSIONS

To prevent experimental variation being interpreted erroneously as disease associated variation, assessment of potential confounding pre-analytical parameters is a pre-requisite in biomarker discovery and validation studies. In addition, with respect to the different (non-)linear patterns observed in the current study, simply performing linear corrections to account for sample storage duration will not necessarily suffice.

New potential biomarkers in the diagnosis of esophageal squamous cell carcinoma

OBJECTIVE

To analyse the alterations of serum proteins in cases of esophageal squamous cell carcinoma (ESCC) in order to screen and validate serum marker patterns for the diagnosis of ESCC in the high-risk populations of Xinjiang, China.

METHODS

The serum proteomic patterns of 188 cases, including 139 patients with ESCC (54 Uygur, 45 Kazakh and 40 Han subjects) and 49 sex- and age-matched healthy controls, were detected using the SELDI-TOF-MS (surface-enhanced laser desorption/ionization-time of flight-mass spectrometry) technology with the CM10 ProteinChip. Differences in protein peaks between patients with ESCC and controls were analysed using the Biomarker Pattern Software, and a primary diagnosis model of ESCC was developed and validated with SVM (support vector machines). This model was further evaluated by a large-scale blind test.

RESULTS

Two hundred and eighty-three protein peaks were detected within the molecular range of 0-20 kDa, among which, 140 peaks were significantly different between ESCC cases and controls (p < 0.05). A diagnostic pattern consisting of six protein peaks (m/z 5667, 5709, 5876, 5979, 6043 and 6102) was established with a sensitivity of 97.12% and a specificity of 83.87%. The large-scale blind test generated a sensitivity of 91.43% and a specificity of 88.89%.

CONCLUSIONS

The differential protein peaks analysed by SELDI-TOF-MS may contain promising serum biomarkers for screening ESCC. The diagnostic model which combined only six protein peaks had a satisfactory discriminatory power. The model should be further evaluated in other populations of ESCC patients and tested against controls. The nature and function of the discriminating proteins have yet to be elucidated.

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.

Proteomics in thyroid tumor research

BACKGROUND

In recent years, "OMICS" technologies have paved novel ways for the broad-scale identification of molecular signatures and signaling pathways specific to tumorigenesis. Related to this are high hopes for the discovery of biomarkers facilitating diagnosis and prognosis of cancer as well as the option for pathway-targeted tumor treatment. Among the different OMICS methods, the potential of proteomics is just beginning to emerge, and according to the current literature, the proteome is to date the most feasible tool to reflect tumor biology.

OBJECTIVE

In this review we discuss the application of proteomics to the field of thyroid tumor research.

CONTEXT

First, we provide an overview of different methods for protein expression profiling and then discuss specific requirements and challenges of thyroid proteomics. Furthermore, we summarize results of published proteomics studies on human thyroid tumors and finally explore perspectives of thyroid proteomics, which, combined with mRNA expression profiling and traditional biochemical methods, is increasingly contributing to an improved understanding of thyroid tumorigenesis and may in the future open novel avenues in thyroid cancer therapy.

A genetic programming approach for Burkholderia pseudomallei diagnostic pattern discovery

MOTIVATION

Finding diagnostic patterns for fighting diseases like Burkholderia pseudomallei using biomarkers involves two key issues. First, exhausting all subsets of testable biomarkers (antigens in this context) to find a best one is computationally infeasible. Therefore, a proper optimization approach like evolutionary computation should be investigated. Second, a properly selected function of the antigens as the diagnostic pattern which is commonly unknown is a key to the diagnostic accuracy and the diagnostic effectiveness in clinical use.

RESULTS

A conversion function is proposed to convert serum tests of antigens on patients to binary values based on which Boolean functions as the diagnostic patterns are developed. A genetic programming approach is designed for optimizing the diagnostic patterns in terms of their accuracy and effectiveness. During optimization, it is aimed to maximize the coverage (the rate of positive response to antigens) in the infected patients and minimize the coverage in the non-infected patients while maintaining the fewest number of testable antigens used in the Boolean functions as possible. The final coverage in the infected patients is 96.55% using 17 of 215 (7.4%) antigens with zero coverage in the non-infected patients. Among these 17 antigens, BPSL2697 is the most frequently selected one for the diagnosis of Burkholderia Pseudomallei. The approach has been evaluated using both the cross-validation and the Jack-knife simulation methods with the prediction accuracy as 93% and 92%, respectively. A novel approach is also proposed in this study to evaluate a model with binary data using ROC analysis.

Mass spectrometry-based proteomic profiling of lung cancer

In an effort to further our understanding of lung cancer biology and to identify new candidate biomarkers to be used in the management of lung cancer, we need to probe these tissues and biological fluids with tools that address the biology of lung cancer directly at the protein level. Proteins are responsible of the function and phenotype of cells. Cancer cells express proteins that distinguish them from normal cells. Proteomics is defined as the study of the proteome, the complete set of proteins produced by a species, using the technologies of large-scale protein separation and identification. As a result, new technologies are being developed to allow the rapid and systematic analysis of thousands of proteins. The analytical advantages of mass spectrometry (MS), including sensitivity and high-throughput, promise to make it a mainstay of novel biomarker discovery to differentiate cancer from normal cells and to predict individuals likely to develop or recur with lung cancer. In this review, we summarize the progress made in clinical proteomics as it applies to the management of lung cancer. We will focus our discussion on how MS approaches may advance the areas of early detection, response to therapy, and prognostic evaluation.

Analysis of serum proteome profiles of non-Hodgkin lymphoma for biomarker identification

Serum samples from non-Hodgkin lymphoma (NHL) patients who had not undergone chemotherapy, lymphnoditis patients, and healthy adults were analyzed using surface-enhanced laser desorption-ionization time-of-flight mass spectrometry (SELDI-TOF MS) to detect the differentially expressed serum proteins. Models were developed to distinguish between the healthy adult group and the NHL group, with a sensitivity of 69% and specificity of 90%, and between the lymphnoditis group and the NHL group with a sensitivity of 74% and specificity of 84%. A protein with the m/z of M10 197.91 u was expressed at a significantly higher level in the NHL group, compared to the other groups. Furthermore, differences were also significant among different stages of NHL and among samples with different International Prognosis Index (IPI) scores or lactase dehydrogenase (LDH) levels. The three identified proteins may offer a new serological approach for early diagnosis, differential diagnosis, and pathogenic investigation of NHL. And the protein with the m/z of M10 197.91 u may be a new serological biomarker for monitoring treatment response and evaluating the prognosis of patients with NHL.