Discovery of distinct protein profiles specific for lung tumors and pre-malignant lung lesions by SELDI mass spectrometry

Deep Learning Techniques to Diagnose Lung Cancer

Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. This paper presents the recent development of deep learning-based imaging techniques for early lung cancer detection.

Understanding the Characteristics of Mass Spectrometry Data through the use of Simulation

Background

Mass spectrometry is actively being used to discover disease-related proteomic patterns in complex mixtures of proteins derived from tissue samples or from easily obtained biological fluids. The potential importance of these clinical applications has made the development of better methods for processing and analyzing the data an active area of research. It is, however, difficult to determine which methods are better without knowing the true biochemical composition of the samples used in the experiments.

Methods

We developed a mathematical model based on the physics of a simple MALDI-TOF mass spectrometer with time-lag focusing. Using this model, we implemented a statistical simulation of mass spectra. We used the simulation to explore some of the basic operating characteristics of MALDI or SELDI instruments.

Results

The simulation reproduced several characteristics of actual instruments. We found that the relative mass error is affected by the time discretization of the detector (about 0.01%) and the spread of initial velocities (about 0.1%). The accuracy of calibration based on external standards decays rapidly outside the range spanned by the calibrants. Natural isotope distributions play a major role in broadening peaks associated with individual proteins. The area of a peak is a more accurate measure of its size than the height.

Conclusions

The model described here is capable of simulating realistic mass spectra. The simulation should become a useful tool for generating spectra where the true inputs are known, allowing researchers to evaluate the performance of new methods for processing and analyzing mass spectra.

Availability

http://bioinformatics.mdanderson.org/cromwell.html

The Need for Review and Understanding of SELDI/MALDI Mass Spectroscopy Data Prior to Analysis

Multiple studies have reported that surface enhanced laser desorption/ionization time of flight mass spectroscopy (SELDI-TOF-MS) is useful in the early detection of disease based on the analysis of bodily fluids. Use of any multiplex mass spectroscopy based approach as in the analysis of bodily fluids to detect disease must be analyzed with great care due to the susceptibility of multiplex and mass spectroscopy methods to biases introduced via experimental design, patient samples, and/or methodology. Specific biases include those related to experimental design, patients, samples, protein chips, chip reader and spectral analysis. Contributions to biases based on patients include demographics (e.g., age, race, ethnicity, sex), homeostasis (e.g., fasting, medications, stress, time of sampling), and site of analysis (hospital, clinic, other). Biases in samples include conditions of sampling (type of sample container, time of processing, time to storage), conditions of storage, (time and temperature of storage), and prior sample manipulation (freeze thaw cycles). Also, there are many potential biases in methodology which can be avoided by careful experimental design including ensuring that cases and controls are analyzed randomly. All the above forms of biases affect any system based on analyzing multiple analytes and especially all mass spectroscopy based methods, not just SELDI-TOF-MS. Also, all current mass spectroscopy systems have relatively low sensitivity compared with immunoassays (e.g., ELISA). There are several problems which may be unique to the SELDI-TOF-MS system marketed by Ciphergen®. Of these, the most important is a relatively low resolution (±0.2%) of the bundled mass spectrometer which may cause problems with analysis of data. Foremost, this low resolution results in difficulties in determining what constitutes a “peak” if a peak matching approach is used in analysis. Also, once peaks are selected, the peaks may represent multiple proteins. In addition, because peaks may vary slightly in location due to instrumental drift, long term identification of the same peaks may prove to be a challenge. Finally, the Ciphergen® system has some “noise” of the baseline which results from the accumulation of charge in the detector system. Thus, we must be very aware of the factors that may affect the use of proteomics in the early detection of disease, in determining aggressive subsets of cancers, in risk assessment and in monitoring the effectiveness of novel therapies.

Biomarker development in the precision medicine era: lung cancer as a case study

Precision medicine relies on validated biomarkers with which to better classify patients by their probable disease risk, prognosis and/or response to treatment. Although affordable 'omics'-based technology has enabled faster identification of putative biomarkers, the validation of biomarkers is still stymied by low statistical power and poor reproducibility of results. This Review summarizes the successes and challenges of using different types of molecule as biomarkers, using lung cancer as a key illustrative example. Efforts at the national level of several countries to tie molecular measurement of samples to patient data via electronic medical records are the future of precision medicine research.

Invited Review: Toxicoproteomics: Proteomics Applied to Toxicology and Pathology

Global measurement of proteins and their many attributes in tissues and biofluids defines the field of proteomics. Toxicoproteomics, as part of the larger field of toxicogenomics, seeks to identify critical proteins and pathways in biological systems that are affected by and respond to adverse chemical and environmental exposures using global protein expression technologies. Toxicoproteomics integrates 3 disciplinary areas: traditional toxicology and pathology, differential protein and gene expression analysis, and systems biology. Key topics to be reviewed are the evolution of proteomics, proteomic technology platforms and their capabilities with exemplary studies from biology and medicine, a review of over 50 recent studies applying proteomic analysis to toxicological research, and the recent development of databases designed to integrate -Omics technologies with toxicology and pathology. Proteomics is examined for its potential in discovery of new biomarkers and toxicity signatures, in mapping serum, plasma, and other biofluid proteomes, and in parallel proteomic and transcriptomic studies. The new field of toxicoproteomics is uniquely positioned toward an expanded understanding of protein expression during toxicity and environmental disease for the advancement of public health.

Feature Selection for Classification of SELDI-TOF-MS Proteomic Profiles

BACKGROUND

Proteomic peptide profiling is an emerging technology harbouring great expectations to enable early detection, enhance diagnosis and more clearly define prognosis of many diseases. Although previous research work has illustrated the ability of proteomic data to discriminate between cases and controls, significantly less attention has been paid to the analysis of feature selection strategies that enable learning of such predictive models. Feature selection, in addition to classification, plays an important role in successful identification of proteomic biomarker panels.

METHODS

We present a new, efficient, multivariate feature selection strategy that extracts useful feature panels directly from the high-throughput spectra. The strategy takes advantage of the characteristics of surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF-MS) profiles and enhances widely used univariate feature selection strategies with a heuristic based on multivariate de-correlation filtering. We analyse and compare two versions of the method: one in which all feature pairs must adhere to a maximum allowed correlation (MAC) threshold, and another in which the feature panel is built greedily by deciding among best univariate features at different MAC levels.

RESULTS

The analysis and comparison of feature selection strategies was carried out experimentally on the pancreatic cancer dataset with 57 cancers and 59 controls from the University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania, USA. The analysis was conducted in both the whole-profile and peak-only modes. The results clearly show the benefit of the new strategy over univariate feature selection methods in terms of improved classification performance.

CONCLUSION

Understanding the characteristics of the spectra allows us to better assess the relative importance of potential features in the diagnosis of cancer. Incorporation of these characteristics into feature selection strategies often leads to a more efficient data analysis as well as improved classification performance.

Mass spectrometry-based proteomics in Chest Medicine, Gerontology, and Nephrology: subgroups omics for personalized medicine

Mass spectrometry (MS) is currently the most promising tool for studying proteomics to investigate largescale proteins in a specific proteome. Emerging MS-based proteomics is widely applied to decipher complex proteome for discovering potential biomarkers. Given its growing usage in clinical medicine for biomarker discovery to predict, diagnose and confer prognosis, MS-based proteomics can benefit study of personalized medicine. In this review we introduce some fundamental MS theory and MS-based quantitative proteomic approaches as well as several representative clinical MS-based proteomics issues in Chest Medicine, Gerontology, and Nephrology.

Transthyretin as a potential biomarker for the differential diagnosis between lung cancer and lung infection

Satisfactory biomarkers for screening and early diagnosis of lung cancer remain scarce and require further investigation. The aim of the present study was to examine the changes of the biochemical and protein composition in the serum and pleural effusion from lung cancer and lung infection (bacterial pneumonia) patients. A total of 92 patients with lung cancer, 38 with bacterial pneumonia and 42 healthy controls were enrolled in the study. The serum levels of cholesterol, apolipoprotein A and transthyretin (TTR) in the lung cancer patients were higher than that of the lung infection patients (P<0.05). The levels of TTR were higher, whereas the activity of adenosine deaminase (ADA) was lower in the pleural effusion from the lung cancer patients compared to the lung infection patients (P<0.05). Furthermore, the pleural effusion/serum TTR ratios in the lung cancer patients were higher, whereas the ratios of ADA were lower (P<0.05). By matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis, four major peaks corresponding to native TTR, Sul-TTR, Cys-TTR and Cysgly-TTR were observed in the serum of the lung cancer and lung infection patients. A significant increase was found in the proportion of Cysgly-TTR in the pleural effusion from the patients with lung cancer. The data indicated that a combination of pleural effusion/serum TTR ratios and modified TTR may be beneficial for the differential diagnosis between lung cancer and lung infection.

Mass spectrometric analysis of cerebrospinal fluid protein for glioma and its clinical application

Aim of the study

To establish and evaluate the fingerprint diagnostic models of cerebrospinal protein profile in glioma with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and bioinformatics analysis, in order to seek new tumor markers.

Material and methods

SELDI-TOF-MS was used to detect the cerebrospinal protein bond to ProteinChip H4. The cerebrospinal protein profiles were obtained and analyzed using the artificial neural network (ANN) method. Fingerprint diagnostic models of cerebrospinal protein profiles for distinguishing glioma from non-brain-tumor, and distinguishing glioma from benign brain tumor, were established. The support vector machine (SVM) algorithm was used for verification of established diagnostic models. The tumor markers were screened.

Results

In a fingerprint diagnostic model of cerebrospinal protein profiles for distinguishing glioma from non-brain tumor, the sensitivity and specificity of glioma diagnosis were 100% and 91.7%, respectively. Seven candidate tumor markers were obtained. In a fingerprint diagnostic model for distinguishing glioma from benign brain tumor, the sensitivity and specificity of glioma diagnosis were 88.9% and 100%, respectively, and 8 candidate tumor markers were gained.

Conclusions

The combination of SELDI-TOF-MS and bioinformatics tools is a very effective method for screening and identifying new markers of glioma. The established diagnostic models have provided a new way for clinical diagnosis of glioma, especially for qualitative diagnosis.

Protein profiling in respiratory disease: techniques and impact

Multifactorial diseases such as respiratory disease call for a global analysis of such disorders. Recent advances in protein profiling techniques may allow for early diagnosis of respiratory disease, which is crucial for intervention and treatment. In order to reduce false-positive rates, clinical diagnosis requires a high degree of sensitivity and specificity to be an effective screening tool. Protein profiles identified by ProteinChip (Ciphergen Biosystems) technology coupled with mass spectrometry affords a global analysis of clinical samples and is beginning to reach acceptable levels of sensitivity and specificity. Combining the profile with another diagnostic tool enhances the effectiveness of protein profiles to classify disease. Although current efforts have centered on serum protein profiling, the local environment of the lung may be better reflected in proteins of bronchoalveolar lavage or sputum. Identification of biomarkers of disease by protein profiling analyses may lead to an understanding of the mechanisms of this disease and contribute to the discovery of new therapeutics for the prevention and treatment of disease. Advancing these analyses are techniques such as ProteinChip mass spectrometry, laser capture microdissection, tissue microarrays and fluorescently labeled antibody bead arrays, which enable the direct global analysis of complex mixtures. Effective high-throughput and ease of use of clinical testing will arrive with improvements in bioinformatics and decreases in instrumentation costs.

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.

Proteomic assessment of markers for malignancy in the mucus of intraductal papillary mucinous neoplasms of the pancreas

OBJECTIVES

Intraductal papillary mucinous neoplasms (IPMN) of the pancreas evolve from dysplasia to invasive adenocarcinoma. The aims of this study were to look for candidate protein profiles in IPMN mucus according to histological grade, using a differential proteomic technique, and to highlight protein peaks associated with malignant transformation.

METHODS

Forty-three mucus samples obtained from surgically resected IPMN and categorized as benign (low/moderate dysplasia) or malignant (severe dysplasia/invasive adenocarcinoma) in 21 and 22 patients, respectively. A surface-enhanced laser desorption ionization time-of-flight mass spectrometry was used to determine candidate protein expression profiles. Protein peaks that significantly differed between benign/malignant IPMN (area under curve > 0.88; P < 10; high intensity) were identified using adapted software.

RESULTS

Among 952 protein peaks, 31 were differentially expressed in benign/malignant IPMN (P < 0.001). Among them, 5 candidate proteins of interest (mass-to-charge ratio [m/z]: 5217, 6326, 6719, 10,453, and 10,849 d) were selected by their high diagnostic accuracy and ability to distinguish between malignant and benign tumors. No correlation was found between peak profiles and duct involvement.

CONCLUSIONS

Carcinogenic process in IPMN is associated with changes in mucus proteome with characteristic peaks that could be potential candidate biomarkers of malignancy.

ABBREVIATIONS

IPMN - intraductal papillary mucinous neoplasm, EPC - extrapancreatic cancer, MRI - magnetic resonance imaging, ERCP - endoscopic retrograde cholangiopancreatography.

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.

Computational oncology

Oncology research has traditionally been conducted using techniques from the biological sciences. The new field of computational oncology has forged a new relationship between the physical sciences and oncology to further advance research. By applying physics and mathematics to oncologic problems, new insights will emerge into the pathogenesis and treatment of malignancies. One major area of investigation in computational oncology centers around the acquisition and analysis of data, using improved computing hardware and software. Large databases of cellular pathways are being analyzed to understand the interrelationship among complex biological processes. Computer-aided detection is being applied to the analysis of routine imaging data including mammography and chest imaging to improve the accuracy and detection rate for population screening. The second major area of investigation uses computers to construct sophisticated mathematical models of individual cancer cells as well as larger systems using partial differential equations. These models are further refined with clinically available information to more accurately reflect living systems. One of the major obstacles in the partnership between physical scientists and the oncology community is communications. Standard ways to convey information must be developed. Future progress in computational oncology will depend on close collaboration between clinicians and investigators to further the understanding of cancer using these new approaches.

Inconsistencies in findings from the early lung cancer action project studies of lung cancer screening

Long-standing guidelines against screening high-risk individuals for lung cancer may change following the publication of the randomized National Lung Screening Trial (NLST), which shows a benefit of computed tomography compared with chest x-ray screening. Guideline panels will likely also seek additional information from nonrandomized studies of computed tomography screening, such as the Early Lung Cancer Action Project (ELCAP). However, for the ELCAP findings to be incorporated into new guidelines, some inconsistencies in the published data should first be resolved. Specifically, some of the reports from ELCAP appear to contradict others in terms of important endpoints, and several findings from ELCAP appear to be statistically improbable or outliers when compared with analyses and studies by other research groups. Clarification of both internal and external inconsistencies is a prerequisite for evaluation of the body of work published by ELCAP investigators.

Proteomic analysis of resectable non-small cell lung cancer: post-resection serum samples may be useful in identifying potential markers

Surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF-MS) can be used to analyse peptides and proteins in clinical samples. A prospective study was undertaken on patients undergoing curative resection for non-small cell lung cancer (NSCLC): we used SELDI-TOF-MS to compare the proteomic profiles of serum from these patients both before surgical resection and after resection (disease-free) to identify potential biomarkers. Student t-tests were used, and a P-value of <0.01 was considered significant. Twenty-five patients with NSCLC [76% male, mean age 69 (range 53-81) years] were analysed. There were 13 squamous cell carcinomas, 10 adenocarcinomas and 2 large cell carcinomas with a stage distribution of four stage IA, 11 stage IB, five stage IIB, three stage IIIA, one stage IIIB and one stage IV. SELDI spectra generated with immobilised metal affinity chromatography arrays produced 170 peaks. Of these, 35 showed significant differences in their intensities between the preoperative and post-resection states (P<0.01). Postoperative samples in the disease-free state may represent good controls to identify biomarkers in NSCLC, avoiding the difficulties associated with cross-sectional studies. These pilot data need to be validated with larger numbers of patients.

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.

Modeling the mortality reduction due to computed tomography screening for lung cancer

BACKGROUND

The efficacy of computed tomography (CT) screening for lung cancer remains controversial because results from the National Lung Screening Trial are not yet available. In this study, the authors used data from a single-arm CT screening trial to estimate the mortality reduction using a modeling-based approach to construct a control comparison arm.

METHODS

To estimate the potential lung cancer mortality reduction because of CT screening, a previously developed and validated model was applied to the screening trial to predict the number of lung cancer deaths in the absence of screening. By using age, gender, and smoking characteristics matching those of the trial participants, the model was used to simulate 5000 trials in the absence of CT screening to produce the expected number of lung cancer deaths along with 95% confidence intervals (95% CIs), while adjusting for healthy volunteer bias.

RESULTS

There were 64 observed lung cancer deaths in the screening cohort (n = 7995), whereas the model predicted 117.7 deaths (95% CI, 98 deaths-139 deaths), indicating a mortality reduction of 45.6% (P < .001). When a more conservative healthy volunteer adjustment was applied, 111.3 lung cancer deaths were predicted (95% CI, 91 deaths-132 deaths), for a lung cancer-specific mortality reduction of 42.5% (P < .001).

CONCLUSIONS

The results of the current study indicate that CT screening along with early stage treatment can reduce lung cancer-specific mortality. This mortality reduction is greatly influenced by the protocol of nodule follow-up and treatment, and the length of follow-up.

MALDI-TOF MS combined with magnetic beads for detecting serum protein biomarkers and establishment of boosting decision tree model for diagnosis of colorectal cancer

The aim of present study is to study the serum protein fingerprint of patients with colorectal cancer (CRC) and to screen protein molecules that are closely related to colorectal cancer during the onset and progression of the disease with Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Serum samples from 144 patients with CRC and 120 healthy volunteers were adopted in present study. Weak cation exchange (WCX) magnetic beads and PBSII-C protein chips reader (Ciphergen Biosystems Ins.) were used. The protein fingerprint expression of all the Serum samples and the resulted profiles between cancer and normal groups were analyzed with Biomarker Wizard system. Several proteomic peaks were detected and four potential biomarkers with different expression profiles were identified with their relative molecular weights of 2870.7 Da, 3084 Da, 9180.5 Da, and 13748.8 Da, respectively. Among the four proteins, two proteins with m/z 2870.7 and 3084 were down-regulated, and the other two with m/z 9180.5 and 13748.8 were up-regulated in serum samples from CRC patients. The present diagnostic model could distinguish CRC from healthy controls with the sensitivity of 92.85% and the specificity of 91.25%. Blind test data indicated a sensitivity of 86.95% and a specificity of 85%. The result suggested that MALDI technology could be used to screen critical proteins with differential expression in the serum of CRC patients. These differentially regulated proteins were considered as potential biomarkers for the patients with CRC in the serum and of the potential value for further investigation.

Identification of esophageal carcinoma-associated proteins by proteomics in Han, Uygur and Kazakh patients with esophageal carcinoma in Xinjiang, China

AIM: To conduct a serum protein profile analysis in Han, Uygur and Kazakh patients with esophageal carcinoma (EC) in Xinjiang, China.

METHODS: Serum samples from patients with EC (43 Han, 43 Uygur and 41 Kazakh subjects) were detected by weak cation exchange (CM10) protein chip assay using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technology to screen differentially expressed serum markers for EC.

RESULTS: The peaks at the mass to charge ratios (M/Z) 4 310.0109, 8 713.0142 and 7 993.0223 were significantly different between Han and Uygur EC patients (P < 0.05). The peaks at M/Z 4 310.0184, 8 167.9277, 8 158.1117, 13 789.4864, 8 067.7056, 4 611.9098, 7 993.4422 and 16 146.8706 were significantly different between Han and Kazakh EC patients (P < 0.05). The peaks at M/Z 9 161.7944, 4 611.6342, 6 649.6163 and 4 979.3807 were significantly different between Uygur and Kazakh EC patients (P < 0.05). The peat at M/Z 4 310.0109 was highly expressed in Uygur and Kazakh patients but lowly expressed in Han patients.

CONCLUSION: The protein fingerprints are significantly different among Han, Uygur and Kazakh EC patients in Xinjiang, China, which can be used to build a diagnostic model of EC.

New biomarkers for lung cancer

IMPORTANCE OF THE FIELD

Despite many efforts to improve early detection, lung cancer remains the leading cause of cancer deaths. Stage is the main determinant of prognosis and the basis for deciding treatment options. Screening tests for lung cancer have not been successful so far.

AREAS COVERED IN THE REVIEW

The article reviews the available literature related to biomarkers in use at present and those that could be used for early diagnosis, staging, prognosis, response to therapy and prediction of recurrence. The single biomarkers are analysed, divided according to the technological methods used and the locations of sampling.

WHAT THE READER WILL GAIN

The reader will gain knowledge on biomarkers in use and those now under study. The reader will also gain insights into the difficulties pertaining to the development of biomarkers, results reproducibility and clinical application.

TAKE HOME MESSAGE

Although some markers seem to be promising, at present there is no consensus on the proven value of their clinical use in lung cancer. The future lies probably in a panel of biomarkers instead of individual assays, or in predictive models derived from the integration of clinical variables and gene expression profiles.

Differential expression of proteomics models of colorectal cancer, colorectal benign disease and healthy controls

BACKGROUND

Colorectal cancer (CRC) is often diagnosed at a late stage with concomitant poor prognosis. The hypersensitive analytical technique of proteomics can detect molecular changes before the tumor is palpable. The surface-enhanced laser desorption/ionization-time of flight-mass spectra (SELDI-TOF-MS) is a newly-developed technique of evaluating protein separation in recent years. The protein chips have established the expression of tumor protein in the serum specimens and become the newly discovered markers for tumor diagnosis. The objective of this study was to find new markers of the diagnosis among groups of CRC, colorectal benign diseases (CBD) and healthy controls. The assay of SELDI-TOF-MS with analytical technique of protein-chip bioinformatics was used to detect the expression of protein mass peaks in the sera of patients or controls. One hundred serum samples, including 52 cases of colorectal cancer, 27 cases of colorectal benign disease, and 21 cases of healthy controls, were examined by SELDI-TOF-MS with WCX2 protein-chips.

RESULTS

The diagnostic models (I, II and III) were setup by analyzed the data and sieved markers using Ciphergen - Protein-Chip-Software 5.1. These models were combined with 3 protein mass peaks to discriminate CRC, CBD, and healthy controls. The accuracy, the sensitivity and the particularity of cross verification of these models are all highly over 80%.

CONCLUSIONS

The SELDI-TOF-MS is a useful tool to help diagnose colorectal cancer, especially during the early stage. However, identification of the significantly differentiated proteins needs further study.

Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data

Background

Recent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduction and classification are considerable challenges in statistical machine learning. We therefore propose a novel approach for dimensionality reduction and tested it using published high-resolution SELDI-TOF data for ovarian cancer.

Results

We propose a method based on statistical moments to reduce feature dimensions. After refining and t-testing, SELDI-TOF data are divided into several intervals. Four statistical moments (mean, variance, skewness and kurtosis) are calculated for each interval and are used as representative variables. The high dimensionality of the data can thus be rapidly reduced. To improve efficiency and classification performance, the data are further used in kernel PLS models. The method achieved average sensitivity of 0.9950, specificity of 0.9916, accuracy of 0.9935 and a correlation coefficient of 0.9869 for 100 five-fold cross validations. Furthermore, only one control was misclassified in leave-one-out cross validation.

Conclusion

The proposed method is suitable for analyzing high-throughput proteomics data.

Lung cancer screening--are we there yet?

BACKGROUND

Lung cancer is the most lethal cancer and most cases are the result of cigarette smoking. Although a high risk target population for screening can be defined, and although early stage lung cancer has a much better prognosis than advanced disease, there is still no clear evidence that lung cancer screening decreases mortality. Accordingly, current guidelines suggest that there is no evidence to support routine screening. Although randomized studies in the 1970('s) which used chest x-ray and sputum for screening were clearly negative in the last 20 years more sensitive screening tools such as chest computed tomography have revolutionized the field. However, randomized controlled trials of computed tomography have only recently been launched. AIMS OF THIS REVIEW: Our objectives are to provide the reader with the rationale for screening for lung cancer, to review the older screening studies and their limitations, and to summarize the current knowledge and ongoing trials of lung cancer screening.

LITERATURE SEARCH

A literature search using Medline was conducted from 1966 onwards searching for articles with relevant key words such as lung cancer screening chest X - ray low dose computerized tomography cancer screening guideline. When appropriate additional references were found from the bibliographies of identified papers of interest.

CONCLUSIONS

Recent uncontrolled multicenter studies of chest computed tomography scans show encouraging results. However, until data from, large properly designed and appropriately analyzed randomized controlled trials which may overcome research biases is available, the benefit of lung cancer screening, if any is still unknown.

Efficient peak-labeling algorithms for whole-sample mass spectrometry proteomics

Whole-sample mass spectrometry (MS) proteomics allows for a parallel measurement of hundreds of proteins present in a variety of biospecimens. Unfortunately, the association between MS signals and these proteins is not straightforward. The need to interpret mass spectra demands the development of methods for accurate labeling of ion species in such profiles. To aid this process, we have developed a new peak-labeling procedure for associating protein and peptide labels with peaks. This computational method builds upon characteristics of proteins expected to be in the sample, such as the amino sequence, mass weight, and expected concentration within the sample. A new probabilistic score that incorporates this information is proposed. We evaluate and demonstrate our method's ability to label peaks first on simulated MS spectra and then on MS spectra from human serum with a spiked-in calibration mixture.

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.

Preliminary study of proteomic shift from normal to premalignant laryngeal lesions and to laryngeal squamous cell carcinoma

CONCLUSIONS

The malignant shift was discovered to begin even in the premalignant stage in the comparison of premalignant laryngeal lesions (PMLLs) with laryngeal squamous cell carcinoma (LSCC) and healthy controls. The differential expression of proteins among normal, PMLL, and cancer cells might provide the prediction for the changes from normal to PMLL and to malignant disease.

OBJECTIVES

To study the serum proteomic shift from normal control to PMLL and progression to LSCC.

MATERIALS AND METHODS

A total of 211 serum samples from patients with LSCC (n = 89 at stage I-II) or PMLL (n = 57), or normal controls (n = 65) were obtained with informed consent. Serum protein profiles on weak cationic exchange (WCX2) were performed by surface-enhanced laser desorption/ionization mass spectrometry (SELDI-TOF MS) and then analyzed by Biomarker Wizard software.

RESULTS

Peak intensities of serum from PMLLs were compared to normal controls and serum from patients with LSCC. Mean intensity differed significantly only for one peak (4532 Da, p = 0.032) between LSCC and precancerous diseases, while 13 peaks differed significantly between precancerous diseases and normal controls. Eighteen biomarkers were selected to separate stage I- II LSCC patients and healthy controls.

Mass Spectrometry in Diagnostic Oncoproteomics

Diagnostic oncoproteomics is the application of proteomic techniques for the diagnosis of malignancies. A new mass spectrometric technology involves surface enhanced laser desorption ionization combined with time-of flight mass analysis (SELDI-TOF-MS), using special protein chips. After the description of the relevant principles of the technique, including approaches to proteomic pattern diagnostics, applications are reviewed for the diagnosis of ovarian, breast, prostate, bladder, pancreatic, and head and neck cancers, and also several other malignancies. Finally, problems and prospects of the approach are discussed.

Discovery and identification of potential biomarkers of pediatric acute lymphoblastic leukemia

Background

Acute lymphoblastic leukemia (ALL) is a common form of cancer in children. Currently, bone marrow biopsy is used for diagnosis. Noninvasive biomarkers for the early diagnosis of pediatric ALL are urgently needed. The aim of this study was to discover potential protein biomarkers for pediatric ALL.

Methods

Ninety-four pediatric ALL patients and 84 controls were randomly divided into a "training" set (45 ALL patients, 34 healthy controls) and a test set (49 ALL patients, 30 healthy controls and 30 pediatric acute myeloid leukemia (AML) patients). Serum proteomic profiles were measured using surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy (SELDI-TOF-MS). A classification model was established by Biomarker Pattern Software (BPS). Candidate protein biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays.

Results

A total of 7 protein peaks (9290 m/z, 7769 m/z, 15110 m/z, 7564 m/z, 4469 m/z, 8937 m/z, 8137 m/z) were found with differential expression levels in the sera of pediatric ALL patients and controls using SELDI-TOF-MS and then analyzed by BPS to construct a classification model in the "training" set. The sensitivity and specificity of the model were found to be 91.8%, and 90.0%, respectively, in the test set. Two candidate protein peaks (7769 and 9290 m/z) were found to be down-regulated in ALL patients, where these were identified as platelet factor 4 (PF4) and pro-platelet basic protein precursor (PBP). Two other candidate protein peaks (8137 and 8937 m/z) were found up-regulated in the sera of ALL patients, and these were identified as fragments of the complement component 3a (C3a).

Conclusion

Platelet factor (PF4), connective tissue activating peptide III (CTAP-III) and two fragments of C3a may be potential protein biomarkers of pediatric ALL and used to distinguish pediatric ALL patients from healthy controls and pediatric AML patients. Further studies with additional populations or using pre-diagnostic sera are needed to confirm the importance of these findings as diagnostic markers of pediatric ALL.

Searching for Potential Ovarian Cancer Biomarkers with Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

Ovarian cancer is a common gynecological malignant disease, causing more deaths among women .The key objective in the treatment of ovarian cancer is early diagnosis. The objective of our study was to seek new ovarian cancer biomarkers based on a serum protein profile with the aim of discriminating ovarian cancer patients from healthy controls. An MB-WCX kit was used to analyze serum samples obtained from 20 ovarian cancer patients and 20 healthy controls and then we generated MALDI-TOF protein profiles from the analysis. After pre-processing of the spectra, linear analysis with ClinProTools bioinformatics software was used to classify protein profiles and search for prominent peaks that could be used as potential ovarian cancer biomarkers. Using ClinproTools bioinformatics and statistical software, we found 5 prominent expressed proteins in the ovarian cancer and healthy control groups. The mass to charge ratio were 4648.21(m/z), 9294.03(m/z), 3886.1(m/z), 9066.38(m/z) and 4254.71(m/z), respectively, and the former four proteins were expressed higher in the ovarian cancer patients, but the later one was expressed at lower levels in the cancer patients. The sensitivity and specificity were both more than 90%. From our study, we found that MALDI-TOF MS is a high-throughput sample preparation method and is a new potential tool for the diagnosis of human disease, not only to search for new early detection biomarkers in the ovarian cancer patients' serum samples, but also with a potential use for routine clinical work.

Analysis of the expression protein profiles of lung squamous carcinoma cell using shot-gun proteomics strategy

The aim of this study is to globally screen and identify the expression protein profiles of lung squamous carcinoma cell (SqCC) using shot-gun proteomics strategy and to further analyze function of individual proteins by bioinformatics, which may likely result in the identification of new biomarkers and provide helpful clues for pathogenesis, early diagnosis, and progression of lung SqCC. The specific tumor cells were isolated and collected from the tissues of six patients with lung SqCC by laser capture microdissection (LCM). Total proteins from the LCM cells were extracted, digested with trypsin. The sequence information of resulting peptides was acquired by high-performance liquid chromatography (HPLC) and tandem mass spectrometry (TMS). The global protein profiles of lung SqCC cell were identified with BioworksTM software in IPI human protein database. Cellular component, molecular function, and biological process of the all proteins were analyzed using gene ontology (GO). About 720,000 tumor cells were satisfactorily collected from tissues of six patients with lung SqCC by LCM and the homogeneities of cell population were estimated to be over 95% as determined by microscopic visualization. The high resolution profiles including HPLC, full mass spectrum, and tandem mass spectrum were successfully obtained. Database searching of the resulting bimolecular sequence information identified 1982 proteins in all samples. The bioinformatics of these proteins, including amino acids sequence, fraction of coverage, molecular weight, isoelectric point, etc., were analyzed in detail. Among them, the function of most proteins was recognized by using GO. Five candidate proteins, Prohibitin (PHB), Mitogen-activated protein kinase (MAPK), Heat shock protein27 (HSP27), Annexin A1(ANXA1), and High mobility group protein B1 (HMGB1), might play an important role in SqCC genesis, progression, recurrence, and metastasis according to relative literatures. We have successfully isolated the interesting cells and effectively solved the heterogeneous problem of lung SqCC using LCM. The globally expressional proteins of lung SqCC cell were identified by shot-gun proteomics strategy. The five proteins might be hopefully used as markers of lung SqCC.

Using SELDI-TOF MS to identify serum biomarkers of rheumatoid arthritis

OBJECTIVES

No satisfactory biomarkers are currently available to screen for rheumatoid arthritis (RA). We have developed and evaluated surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) for detection and analysis of multiple proteins for distinguishing individuals with RA from control individuals.

METHODS

A total of 156 serum samples from 90 RA patients, 30 patients with ankylosing spondylitis (AS), and 36 healthy individuals were examined by SELDI technology. Spectral data were analysed by the support vector machine (SVM) approach and potential biomarkers were chosen for system training and were used to construct a diagnostic model.

RESULTS

Pattern 1, consisting of four protein peaks with m/z values of 3899, 4594, 7566, and 13,842, distinguished RA from the healthy samples with sensitivity of 90.0% and a specificity of 91.7%. Pattern 2, consisting of m/z peaks 4287 and 6471, distinguished RA from AS with a sensitivity of 86.7% and a specificity of 85.0%.

CONCLUSION

The combination of SELDI-TOF MS and SVM could facilitate the discovery of better biomarkers for RA and also provide a useful tool for molecular diagnosis in the future.

EPO-KB: a searchable knowledge base of biomarker to protein links

The knowledge base EPO-KB (Empirical Proteomic Ontology Knowledge Base) is based on an OWL ontology that represents current knowledge linking mass-to-charge (m/z) ratios to proteins on multiple platforms including Matrix Assisted Laser/Desorption Ionization (MALDI) and Surface Enhanced Laser/Desorption Ionization (SELDI)--Time of Flight (TOF). At present, it contains information on m/z ratio to protein links that were extracted from 120 published research papers. It has a web interface that allows researchers to query and retrieve putative proteins that correspond to a user-specified m/z ratio. EPO-KB also allows automated entry of additional m/z ratio to protein links and is expandable to the addition of gene to protein and protein to disease links.

AVAILABILITY

http://www.dbmi.pitt.edu/EPO-KB

Use of SELDI-TOF mass spectrometry for identification of new biomarkers: potential and limitations

Surface-enhanced laser desorption time of flight mass spectrometry (SELDI-TOF-MS) is an important proteomic technology that is immediately available for the high throughput analysis of complex protein samples. Over the last few years, several studies have demonstrated that comparative protein profiling using SELDI-TOF-MS breaks new ground in diagnostic protein analysis particularly with regard to the identification of novel biomarkers. Importantly, researchers have acquired a better understanding also of the limitations of this technology and various pitfalls in biomarker discovery. Bearing these in mind, great emphasis must be placed on the development of rigorous standards and quality control procedures for the pre-analytical as well as the analytical phase and subsequent bioinformatics applied to analysis of the data. To avoid the risk of false-significant results studies must be designed carefully and control groups accurately selected. In addition, appropriate tools, already established for analysis of highly complex microarray data, need to be applied to protein profiling data. To validate the significance of any candidate biomarker derived from pilot studies in appropriately designed prospective multi-center studies is mandatory; reproducibility of the clinical results must be shown over time and in different diagnostic settings. SELDI-TOF-MS-based studies that are in compliance with these requirements are now required; only a few have been published so far. In the meantime, further evaluation and optimization of both technique and marker validation strategies are called for before MS-based proteomic algorithms can be translated into routine laboratory testing.

Study of distinct protein profiles for early diagnosis of NSCLC using LCM and SELDI-TOF-MS

No biomarker has been available to detect early lung cancer so far. The aim of this study is to screen biomarker patterns for early diagnosis of non-small cell lung cancer (NSCLC) using laser capture microdissection (LCM) and surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS). The 3 groups of the interested cells from 13 NSCLC tissues, 11 normal lung tissues (out of the 13 NSCLC patients), and 6 benign lung diseased tissues (BLD) were successfully separated by LCM, respectively, and the homogeneities of each type of the cell populations in the three groups were estimated to be over 95%. One-hundred- and twenty-three M/Z peaks were found in the NSCLCs and normal lungs, and between the two groups the relative intensity of 98 M/Z peaks was significantly different (P < 0.05) using SELDI-TOF-MS. The diagnostic pattern constructed using support vector machine (SVM) including three proteins, M/Z 4282, 3201, and 4252 Da, respectively, showed maximum Youden Index (YI). The pattern was validated by leave-one-out cross validation (LOOCV) and the results showed that the sensitivity was 100.0%, specificity 90.9%, and positive predictive value (PPV) 92.9%. In the NSCLCs and BLDs 188 M/Z peaks were determined and 54 showed statistically difference (P < 0.05). The sensitivity, specificity, and PPV of the diagnostic pattern consisting of two proteins, M/Z 3204 and 3701 Da, were all 100.0%. So, by using LCM we have successfully purified the interested cells and solved the problem of heterogeneity of lung cancer tissue. SELDI protein chip coupled with SVM could effectively screen the differentially expressional protein profiles and eventually establish biomarker patterns with high sensitivity and specificity.

Applications of SELDI-MS technology in oncology

Considerable interest, speculation and controversy have been generated utilising surface-enhanced laser desorption/ionization in conjunction with mass spectrometry (SELDI-MS) for the diagnosis, prognosis and therapeutic monitoring of cancer and offers an attractive approach to cancer biomarker discovery from tissues and biological fluids. This technology utilises a combination of mass spectrometry and chromatography to facilitate protein profiling of complex biological mixtures. Compared to some other more traditional proteomic platforms, such as 2D polyacrylamide gel electrophoresis, it has a high-throughput capability and can resolve low-mass proteins. However, a considerable number of challenging issues related to the design of studies, including reproducibility, sensitivity, specificity, variation in sample collection, processing and storage, have been reported as problematic with this technology; albeit some of these concerns could perhaps also be lauded against other proteomic approaches that have attempted to address complex protein mixtures, such as plasma. Applications, successes and limitations of SELDI-MS in both clinical and basic science arenas will be reviewed in this article.

Proteomics as a method for early detection of cancer: a review of proteomics, exhaled breath condensate, and lung cancer screening

The study of expressed proteins in neoplasia is undergoing a revolution with the advent of proteomic analysis. Unlike genomic studies where individual changes may have no functional significance, protein expression is closely aligned with cellular activity. This perspective will review proteomics as a method of detecting markers of neoplasia with a particular emphasis on lung cancer and the potential to sample the lung by exhaled breath condensate (EBC). EBC collection is a simple, new, and noninvasive technique, which allows sampling of lower respiratory tract fluid. EBC enables the study of a wide variety of biological markers from low molecular weight mediators to macromolecules, such as proteins, in a range of pulmonary diseases. EBC may be applied to the detection of lung cancer where it could be a tool in early diagnosis. This perspective will explore the potential of applying proteomics to the EBC from lung cancer patients as an example of detecting potential biomarkers of disease and progression.