Detection of colorectal cancer using MALDI-TOF serum protein profiling

Next-generation pathology practices with mass spectrometry imaging

Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.

Molecular Pathways, Targeted Therapies, and Proteomic Investigations of Colorectal Cancer

According to the GLOBOCAN 2020 data, colorectal cancer is the third most commonly diagnosed cancer and the second leading cause of cancer-related death. The risk factors for colorectal cancer include a diet abundant with fat, refined carbohydrates, animal protein, low fiber content, alcoholism, obesity, long-term cigarette smoking, low physical activity, and aging. Colorectal carcinomas are classified as adenocarcinoma, neuroendocrine, squamous cell, adenosquamous, spindle cell, and undifferentiated carcinomas. In addition, many variants of colorectal carcinomas have been recently distinguished based on histological, immunological, and molecular characteristics. Recently developed targeted molecules in conjunction with standard chemotherapeutics or immune checkpoint inhibitors provide promising treatment protocols for colorectal cancer. However, the benefit of targeted therapies is strictly dependent on the mutational status of signaling molecules (e.g., KRAS) or mismatch repair systems. Here it is aimed to provide a comprehensive view of colorectal cancer types, molecular pathways associated, recently developed targeted therapies, as well as proteomic investigations applied to colorectal cancer for the discovery of novel biomarkers and new targets for treatment protocols.

MALDI-TOF MS: application in diagnosis, dereplication, biomolecule profiling and microbial ecology

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has revolutionized scientific research over the past few decades and has provided a unique platform in ongoing technological developments. Undoubtedly, there has been a bloom chiefly in the field of biological sciences with this emerging technology, and has enabled researchers to generate critical data in the field of disease diagnoses, drug development, dereplication. It has received well acceptance in the field of microbial identification even at strain level, as well as diversified field like biomolecule profiling (proteomics and lipidomics) has evolved tremendously. Additionally, this approach has received a lot more attention over conventional technologies due to its high throughput, speed, and cost effectiveness. This review aims to provide a detailed insight regarding the application of MALDI-TOF MS in the context of medicine, biomolecule profiling, dereplication, and microbial ecology. In general, the expansion in the application of this technology and new advancements it has made in the field of science and technology has been highlighted.

Matrix-assisted laser desorption ionisation (MALDI) mass spectrometry (MS): basics and clinical applications

Background Matrix-assisted laser desorption ionisation (MALDI) mass spectrometry (MS) has been used for more than 30 years. Compared with other analytical techniques, it offers ease of use, high throughput, robustness, cost-effectiveness, rapid analysis and sensitivity. As advantages, current clinical techniques (e.g. immunoassays) are unable to directly measure the biomarker; rather, they measure secondary signals. MALDI-MS has been extensively researched for clinical applications, and it is set for a breakthrough as a routine tool for clinical diagnostics. Content This review reports on the principles of MALDI-MS and discusses current clinical applications and the future clinical prospects for MALDI-MS. Furthermore, the review assesses the limitations currently experienced in clinical assays, the advantages and the impact of MALDI-MS to transform clinical laboratories. Summary MALDI-MS is widely used in clinical microbiology for the screening of microbial isolates; however, there is scope to apply MALDI-MS in the diagnosis, prognosis, therapeutic drug monitoring and biopsy imaging in many diseases. Outlook There is considerable potential for MALDI-MS in clinic as a tool for screening, profiling and imaging because of its high sensitivity and specificity over alternative techniques.

Prognostic accuracy of MALDI-TOF mass spectrometric analysis of plasma in COVID-19

SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that predict high (hospitalized) and low-risk (outpatients) cases of COVID-19 identified by a platform that combines machine learning with matrix-assisted laser desorption ionization mass spectrometry analysis. Sample preparation, MS, and data analysis parameters were optimized to achieve an overall accuracy of 92%, sensitivity of 93%, and specificity of 92% in dataset without feature selection. We identified two distinct regions in the MALDI-TOF profile belonging to the same proteoforms. A combination of SDS-PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins, both already described as biomarkers for viral infections in the acute phase. Unbiased discrimination of high- and low-risk COVID-19 patients using a technology that is currently in clinical use may have a prompt application in the noninvasive prognosis of COVID-19. Further validation will consolidate its clinical utility.

Identification of several plasma proteins whose levels in colorectal cancer patients differ depending on outcome

Colorectal cancer (CRC) stands for 10% of the worldwide cancer burden and has recently become the second most common cause of cancer death. The 5-year survival rate depends mainly on stage at diagnosis. Mass spectrometric proteomic analysis is widely used to study the plasma proteome, which is complex and contains multitudes of proteins. In this study, we have used Ultra Performance Liquid Chromatography-Ultra Definition Mass Spectrometry (UPLC-UDMSE)-based proteomics to analyze plasma samples from 76 CRC patients. We identified several plasma proteins, such as CP, TVP23C, FETUB, and IGFBP3, of which altered levels led to significant differences in survival, as seen by Cox regression and Kaplan-Meier analysis. Additionally, during Cox regression analysis, samples were adjusted for age and/or tumor stage, enabling stringent analysis. These proteins, although in need of further validation, could be of use during patient follow-up, as their levels can non-invasively be measured from blood samples, and could be of use in predicting patient outcome. Several of these proteins additionally have roles in metabolism and inflammation, two processes central to the development and progression of cancer, further indicating their importance in cancer.

Colorectal carcinoma screening: Established methods and emerging technology

Colorectal carcinoma screening programs have shown success in lowering both the incidence and mortality rate of colorectal carcinoma at a population level, in part because this carcinoma is relatively slow growing and has an identifiable premalignant lesion. Still, many patients do not undergo the recommended screening for colorectal carcinoma, and of those who do, a subset may be over- or under-diagnosed by the currently available testing methods. The primary purpose of this article is to review the data regarding currently available colorectal cancer screening modalities, which include fecal occult blood testing, direct colonic visualization, and noninvasive imaging techniques. In addition, readers will be introduced to a variety of biomarkers that may serve as stand-alone or adjunct tests in the future. Finally, there is a brief discussion of the current epidemiologic considerations that public health officials must address as they create population screening guidelines. The data we provide as laboratory physicians and scientists are critical to the construction of appropriate recommendations that ultimately decrease the burden of disease from colorectal carcinoma.

Plasma Peptidome Pattern of Breast Cancer Using Magnetic Beads-Based Plasma Fractionation and MALDI-TOF MS: A Case Control Study in Egypt

Objective: The present study aimed to determine peptidome patterns in breast cancer (BC). Methods: We analyzed the plasma proteomic profiling of 80 BC patients and 50 healthy controls, using hydrophobic interaction chromatography magnetic beads (MB-HIC8) separation followed by Matrix assisted laser desorption ionization/ time of flight mass spectrometry (MALDI-TOF MS). Results: ClinProTools software identified 92 peaks that differed among the analyzed groups, 33 peaks were significantly different (P < 0.05). Of those, 22 peaks were up-regulated while 11 peaks were down-regulated in BC patients compared with the healthy controls. Three peptide ion signatures (m/z 1,570.31, 1,897.4 and 2,568.17) were provided by the Quick Classifier model to discriminate BC patients from healthy control subjects with 96.4% accuracy. External validation was performed by an independent group and this achieved a sensitivity of 100% and a specificity of 76.9%. Conclusion: MALDI-TOF MS has good analytical performance in distinguishing BC patients from healthy controls.

Mass spectrometry and multivariate analysis to classify cervical intraepithelial neoplasia from blood plasma: an untargeted lipidomic study

Cervical cancer is still an important issue of public health since it is the fourth most frequent type of cancer in women worldwide. Much effort has been dedicated to combating this cancer, in particular by the early detection of cervical pre-cancerous lesions. For this purpose, this paper reports the use of mass spectrometry coupled with multivariate analysis as an untargeted lipidomic approach to classifying 76 blood plasma samples into negative for intraepithelial lesion or malignancy (NILM, n = 42) and squamous intraepithelial lesion (SIL, n = 34). The crude lipid extract was directly analyzed with mass spectrometry for untargeted lipidomics, followed by multivariate analysis based on the principal component analysis (PCA) and genetic algorithm (GA) with support vector machines (SVM), linear (LDA) and quadratic (QDA) discriminant analysis. PCA-SVM models outperformed LDA and QDA results, achieving sensitivity and specificity values of 80.0% and 83.3%, respectively. Five types of lipids contributing to the distinction between NILM and SIL classes were identified, including prostaglandins, phospholipids, and sphingolipids for the former condition and Tetranor-PGFM and hydroperoxide lipid for the latter. These findings highlight the potentiality of using mass spectrometry associated with chemometrics to discriminate between healthy women and those suffering from cervical pre-cancerous lesions.

Proteomics and Mass Spectrometry for Cancer Biomarker Discovery

Proteomics is a rapidly advancing field not only in the field of biology but also in translational cancer research. In recent years, mass spectrometry and associated technologies have been explored to identify proteins or a set of proteins specific to a given disease, for the purpose of disease detection and diagnosis. Such biomarkers are being investigated in samples including cells, tissues, serum/plasma, and other types of body fluids. When sufficiently refined, proteomic technologies may pave the way for early detection of cancer or individualized therapy for cancer. Mass spectrometry approaches coupled with bioinformatic tools are being developed for biomarker discovery and validation. Understanding basic concepts and application of such technology by investigators in the field may accelerate the clinical application of protein biomarkers in disease management.

Serum peptidome profiling for the diagnosis of colorectal cancer: discovery and validation in two independent cohorts

Colorectal cancer (CRC) is one of the most common malignant neoplasms worldwide. Except for the existing fecal occult blood test, colonoscopy and sigmoidoscopy, no widely accepted in vitro diagnostic methods have been available. To identify potential peptide biomarkers for CRC, serum samples from a discovery cohort (100 CRC patients and 100 healthy controls) and an independent validation cohort (91 CRC patients and 91 healthy controls) were collected. Peptides were fractionated by weak cation exchange magnetic beads (MB-WCX) and analysed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Five peptides (peaks at m/z 1895.3, 2020.9, 2080.7, 2656.8 and 3238.5) were identified as candidate biomarkers for CRC. A diagnostic panel based on the five peptides can discriminate CRC patients from healthy controls, with an accuracy of 91.8%, sensitivity of 95.6%, and specificity of 87.9% in the validation cohort. Peptide peaks at m/z 1895.3, 2020.9 and 3238.5 were identified as the partial sequences of complement component 4 (C4), complement component 3 (C3) and fibrinogen α chain (FGA), respectively. This study potentiated peptidomic analysis as a promising in vitro diagnostic tool for diagnosis of CRC. The identified peptides suggest the involvement of the C3, C4 and FGA in CRC pathogenesis.

iTRAQ-based quantitative analysis of cancer-derived secretory proteome reveals TPM2 as a potential diagnostic biomarker of colorectal cancer

Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. We aimed to find novel molecules as potential biomarkers for the early diagnosis of CRC. A serum-free conditioned medium was successfully collected from three pairs of CRC tissue and adjacent normal tissue. iTRAQ-based quantitative proteomic analysis was applied to compare the differences in secretome between primary CRC mucosa and adjacent normal mucosa. A total of 145 kinds of proteins were identified. Of these proteins, 29 were significantly different between CRC and normal tissue. Tropomyosin 2 β (TPM2) exhibited the most significant differences; as such, this protein was selected for further validation. Quantitative real-time PCR indicated that the mRNA expression of TPM2 significantly decreased in the CRC tissue compared with the paired adjacent normal tissue. Immunohistochemical analysis also confirmed that TPM2 was barely detected at protein levels in the CRC tissue. In summary, this study revealed potential molecules for future biomarker applications and provided an efficient approach for the differential analysis of cancer-associated secretome. TPM2 may be valuable for the early diagnosis of CRC.

Rapid Diagnosis of Lung Tumors, a Feasability Study Using Maldi-Tof Mass Spectrometry

OBJECTIVE

Despite recent advances in imaging and core or endoscopic biopsies, a percentage of patients have a major lung resection without diagnosis. We aimed to assess the feasibility of a rapid tissue preparation/analysis to discriminate cancerous from non-cancerous lung tissue.

METHODS

Fresh sample preparations were analyzed with the Microflex LTTM MALDI-TOF analyzer. Each main reference spectra (MSP) was consecutively included in a database. After definitive pathological diagnosis, each MSP was labeled as either cancerous or non-cancerous (normal, inflammatory, infectious nodules). A strategy was constructed based on the number of concordant responses of a mass spectrometry scoring algorithm. A 3-step evaluation included an internal and blind validation of a preliminary database (n = 182 reference spectra from the 100 first patients), followed by validation on a whole cohort database (n = 300 reference spectra from 159 patients). Diagnostic performance indicators were calculated.

RESULTS

127 cancerous and 173 non-cancerous samples (144 peripheral biopsies and 29 inflammatory or infectious lesions) were processed within 30 minutes after biopsy sampling. At the most discriminatory level, the samples were correctly classified with a sensitivity, specificity and global accuracy of 92.1%, 97.1% and 95%, respectively.

CONCLUSIONS

The feasibility of rapid MALDI-TOF analysis, coupled with a very simple lung preparation procedure, appears promising and should be tested in several surgical settings where rapid on-site evaluation of abnormal tissue is required. In the operating room, it appears promising in case of tumors with an uncertain preoperative diagnosis and should be tested as a complementary approach to frozen-biopsy analysis.

Challenges in biomarker discovery with MALDI-TOF MS

MALDI-TOF MS technique is commonly used in system biology and clinical studies to search for new potential markers associated with pathological conditions. Despite numerous concerns regarding a sample preparation or processing of complex data, this strategy is still recognized as a popular tool and its awareness has risen in the proteomic community over the last decade. In this review, we present comprehensive application of MALDI mass spectrometry with special focus on profiling research. We also discuss major advantages and disadvantages of universal sample preparation methods such as micro-SPE columns, immunodepletion or magnetic beads, and we show the potential of nanostructured materials in capturing low molecular weight subproteomes. Furthermore, as the general protocol considerably affects spectra quality and interpretation, an alternative solution for improved ion detection, including hydrophobic constituents, data processing and statistical analysis is being considered in up-to-date profiling pattern. In conclusion, many reports involving MALDI-TOF MS indicated highly abundant proteins as valuable indicators, and at the same time showed the inaccuracy of available methods in the detection of low abundant proteome that is the most interesting from the clinical perspective. Therefore, the analytical aspects of sample preparation methods should be standardized to provide a reproducible, low sample handling and credible procedure.

In silico discovery of significant pathways in colorectal cancer metastasis using a two-stage optimisation approach

Accurate and reliable modelling of protein-protein interaction networks for complex diseases such as colorectal cancer can help better understand mechanism of diseases and potentially discover new drugs. Different machine learning methods such as empirical mode decomposition combined with least square support vector machine, and discrete Fourier transform have been widely utilised as a classifier and for automatic discovery of biomarkers for the diagnosis of the disease. The existing methods are, however, less efficient as they tend to ignore interaction with the classifier. In this study, the authors propose a two-stage optimisation approach to effectively select biomarkers and discover interactions among them. At the first stage, particle swarm optimisation (PSO) and differential evolution (DE) are used to optimise parameters of support vector machine recursive feature elimination algorithm, and dynamic Bayesian network is then used to predict temporal relationship between biomarkers across two time points. Results show that 18 and 25 biomarkers selected by PSO and DE-based approach, respectively, yields the same accuracy of 97.3% and F1-score of 97.7 and 97.6%, respectively. The stratified analysis reveals that Alpha-2-HS-glycoprotein was a dominant hub gene with multiple interactions to other genes including Fibrinogen alpha chain, which is also a potential biomarker for colorectal cancer.

Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry

Mixture - modeling of mass spectra is an approach with many potential applications including peak detection and quantification, smoothing, de-noising, feature extraction and spectral signal compression. However, existing algorithms do not allow for automated analyses of whole spectra. Therefore, despite highlighting potential advantages of mixture modeling of mass spectra of peptide/protein mixtures and some preliminary results presented in several papers, the mixture modeling approach was so far not developed to the stage enabling systematic comparisons with existing software packages for proteomic mass spectra analyses. In this paper we present an efficient algorithm for Gaussian mixture modeling of proteomic mass spectra of different types (e.g., MALDI-ToF profiling, MALDI-IMS). The main idea is automated partitioning of protein mass spectral signal into fragments. The obtained fragments are separately decomposed into Gaussian mixture models. The parameters of the mixture models of fragments are then aggregated to form the mixture model of the whole spectrum. We compare the elaborated algorithm to existing algorithms for peak detection and we demonstrate improvements of peak detection efficiency obtained by using Gaussian mixture modeling. We also show applications of the elaborated algorithm to real proteomic datasets of low and high resolution.

Establishment of a novel diagnostic model for Sjögren's syndrome by proteomic fingerprinting

Primary Sjögren's syndrome (pSS) is a systemic autoimmune disease that lacks sensitive and specific diagnostic methods. The aim of this study was to identify potential biomarkers specific for pSS and to establish a diagnostic model. Serum samples from patients with pSS, disease controls (DC, patients with systemic lupus erythematosus (SLE), rheumatoid arthritis (RA)), and healthy controls (HC)) were randomly divided into a training set (35 pSS, 50 DC, and 26 HC) and a testing set (25 pSS, 50 DC, and 25 HC). Weak cationic exchange (WCX) magnetic beads were used to differentially capture serum proteins prior to proteomic analysis. Proteomic mass spectra were generated by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS). One hundred differential M/Z peaks associated with pSS were identified, and the m/z peaks at 8,133.85, 11,972.8, 2,220.81, and 4,837.66 were used to establish a diagnostic model for pSS. This diagnostic model was able to distinguish pSS from non-pSS controls with a sensitivity of 77.1 % and a specificity of 85.5 %, and its efficacy was confirmed in our blinded testing set with good sensitivity and specificity of 95.5 and 88 %, respectively. The results indicated that the proteomic fingerprinting model was effective in the diagnosis of pSS.

Current and future molecular diagnostics in colorectal cancer and colorectal adenoma

Colorectal cancer (CRC) is one of the most prevalent cancers in developed countries. On the other hand, CRC is also one of the most curable cancers if it is detected in early stages through regular colonoscopy or sigmoidoscopy. Since CRC develops slowly from precancerous lesions, early detection can reduce both the incidence and mortality of the disease. Fecal occult blood test is a widely used non-invasive screening tool for CRC. Although fecal occult blood test is simple and cost-effective in screening CRC, there is room for improvement in terms of the accuracy of the test. Genetic dysregulations have been found to play an important role in CRC development. With better understanding of the molecular basis of CRC, there is a growing expectation on the development of diagnostic tests based on more sensitive and specific molecular markers and those tests may provide a breakthrough to the limitations of current screening tests for CRC. In this review, the molecular basis of CRC development, the characteristics and applications of different non-invasive molecular biomarkers, as well as the technologies available for the detection were discussed. This review intended to provide a summary on the current and future molecular diagnostics in CRC and its pre-malignant state, colorectal adenoma.

MALDI MS imaging as a tool for biomarker discovery: methodological challenges in a clinical setting

MALDI MS imaging (MSI) is an analytical tool capable of providing spatial distribution and relative abundance of biomolecules directly in tissue. After 15 years of intense efforts to improve the acquisition and quality of molecular images, MSI has matured into an asset of the proteomic toolbox. The power of MSI lies in the ability to differentiate tissue regions that are not histologically distinct but are characterized by different MS profiles. Recently, MSI has been gaining momentum in biomedical research and has found applications in disease diagnosis and prognosis, biomarker discovery, and drug therapy. Although the technology holds great promise, MSI is still faced with a set of methodological challenges presented by the clinical setting. There is a growing awareness regarding this topic and efforts are being taken to develop clear and practical standards to overcome these challenges. This review presents an overview of MALDI MSI as a biomarker discovery tool and recent methodological progress in the field.

Changes in the electron paramagnetic resonance spectra of albumin-associated spin-labeled stearic acid as a diagnostic parameter of colorectal cancer

Background

With more than 940,000 new colorectal cancer cases worldwide each year, there is no better way for colorectal cancer routine screening. The aim of this study was to investigate whether the fatty acid binding to albumin is detectably and significantly altered in colorectal cancer patients when compared with healthy people, in order to find a better way for colorectal cancer diagnosis.

Methods

One hundred and forty-one patients operatively treated for colorectal cancer were included in the examination, and 180 healthy people were also enrolled as controls. Commercial 16-doxyl stearic acid was used as spin probe. Serum albumin was analyzed by electron paramagnetic resonance (EPR) with spin probe. Discriminant analysis was carried out using the measured EPR spectra by SPSS 20.0.

Results

Of the original grouped cases, 89.4% were correctly classified. Of the cross-validated grouped cases, 86.9% were correctly classified. Using Fisher linear discriminant analysis we were able to develop a mathematical model allowing for identification of colorectal cancer patients based on five values (both relative intensity and peak width) which are obtained from the EPR spectrum.

Conclusions

Cancer-associated alterations to albumin can be assessed by spin-label EPR. The potential applications for this diagnostic technique are significant and represent a cost-effective means for screening patients with cancer. Spin probe for diagnosis of colorectal cancer might be a useful tool and further studies should take place in order to investigate all stages of colorectal cancer patients.

Potential biomarkers for adult acute myeloid leukemia minimal residual disease assessment searched by serum peptidome profiling

BACKGROUND

Post treatment minimal residual disease (MRD) determination contributes to impending relapse prediction, chemotherapy response and clinical outcomes assessment, guiding clinicians to develop reasonable and effective individual chemotherapy options after induction/consolidation. This study was to identify serum candidate peptides for monitoring adult acute myeloid leukemia (AML) MRD.

RESULTS

47 statistically different expressed peptide peaks were obtained in the molecular weight range of 700-10000 Da. Quick classifier (QC) model had optimal distinction efficiency, in the training set with a sensitivity of 90% and a specificity of 93.33%. Peptides were identified as ubiquitin-like modifier activating enzyme 1(UBA1), isoform 1 of fibrinogen alpha chain precursor and platelet factor 4(PF4). The peptide up-regulated in newly diagnosed AML patients were decreased to the normal level after CR. When refractory & relapsed, relative intensity was elevated again. Results were contrary to down-regulated peptide peaks. Western blot demonstrated that levels of the UBA1 protein did not differ between the leukemia and normal cells. Levels of isoform 1 of fibrinogen alpha chain precursor protein and PF4 protein were both decreased in leukemia cells comparing with normal cells. The serum levels of the PF4 in the newly diagnosed AML patients and healthy controls were significantly different. Further correlation analysis did not indicate the correlated relation between platelet counts and PF4 content, the correlation coefficient was 0.097. Kaplan-Meier analyses of overall survival showed that relative intensity of peptides was correlated with patient's clinical outcome.

CONCLUSIONS

We speculate the peptides can be used as potential markers for monitoring minimal residual disease and clinical outcome assessment.

Detection of pancreatic cancer using serum protein profiling

BACKGROUND

Currently, no suitable biomarkers for the early detection of pancreatic cancer (PC) are available. Proteins present in the serum could reflect a state of the disease. In this study, these profiles as a diagnostic marker for PC were evaluated.

METHODS

Serum samples were obtained from PC patients (n = 50 calibration set, n = 39 validation set) and healthy volunteers (n = 110 and n = 75 respectively) according to a uniform standardized collection and processing protocol. For peptide and protein isolation, automated solid-phase extraction (SPE) with Weak Cation Exchange (WCX) magnetic beads (MB) was performed using a 96-channel liquid handling platform. Protein profiles were obtained by mass spectrometry (MS) and evaluated by linear discriminant analysis with double cross-validation.

RESULTS

A discriminating profile for PC has been identified, with a sensitivity of 78% and a specificity of 89% in the calibration set with an area under the curve (AUC) of 90%. These results were validated with a sensitivity of 74% and a specificity of 91% (AUC 90%).

CONCLUSION

Serum profiles of healthy controls and PC can be discrimated between. Further research is warranted to evaluate specificity and whether this biosignature can be used for early detection in a high risk population.

An automated RP-SCX solid-phase extraction procedure for urinary peptidomics biomarker discovery studies

Urine represents the most easily obtainable body fluid and consequently one of the most common samples in clinical chemistry. The majority of pathological changes in human organs may well be reflected in urine. In this way, urine analysis can aid in disease diagnosis, treatment monitoring, and prognosis. Currently, the most commonly used method for identification of new urine biomarkers involves centrifugation of the urine sample to collect either the soluble urine proteins or the urinary exosomes followed by 1 or 2 protein purification and separation steps before visualization and finally identification of potential biomarkers, usually by mass spectrometry. Here we present a generally applicable, rapid, and robust method for screening large number of urine samples, resulting in a broad spectrum of native peptides, as a tool to be used for biomarker discovery. The method combines online sample pretreatment with a well-established mass spectrometric technique. Native peptides are extracted from urine samples on a miniaturized reverse-phase-strong cation exchange cartridge system. As the proper identification of native peptides often requires combination of data acquired on different mass analyzers, we have aimed at a procedure providing us with sufficient material to identify and characterize the differentially expressed markers.

Improving colorectal cancer management: the potential of proteomics

Colorectal cancer (CRC) is the third most common cancer worldwide. Successful treatment is heavily dependent on tumor stage at the time of detection, but unfortunately CRC is often only detected in advanced stages. New biomarkers in the form of genes or proteins that can be used for diagnosis, prognostication, follow-up, and treatment selection and monitoring could be of great benefit for the management of CRC. Furthermore, proteins could prove valuable new targets for therapy. Therefore, clinical proteomics has gained a lot of scientific interest in this regard. To get an overall insight into the extent to which this research has contributed to a better management of CRC, we give a comprehensive overview of the results of proteomics research on CRC, focusing on expression proteomics, in other words, protein profiling studies. Furthermore, we evaluate the potential of the discriminating proteins identified in this research for clinical use as biomarkers for (early) diagnosis, prognosis and follow-up of CRC or as targets for new therapeutic regimens.

Diagnostic biomarkers differentiating metastatic melanoma patients from healthy controls identified by an integrated MALDI-TOF mass spectrometry/bioinformatic approach

The prognosis of advanced metastatic melanoma (American Joint Committee on Cancer (AJCC) stage IV) remains dismal with a 5-year survival rate of 6-18%. In the present study, an integrated MALDI mass spectrometric approach combined with artificial neural networks (ANNs) analysis and modeling has been used for the identification of biomarker ions in serum from stage IV melanoma patients allowing the discrimination of metastatic disease from healthy status with high specificities of 92% for protein ions and 100% for peptide biomarkers. Our ANNs model also correctly classified 98% of a blind validation set of AJCC stage I melanoma samples as nonstage IV samples, emphasizing the power of the newly defined biomarkers to identify patients with late-stage metastatic melanoma. Sequence analysis identified peptides derived from metastasis-associated proteins; alpha 1-acid glycoprotein precursor-1/2 (AAG-1/2) and complement C3 component precursor-1 (CCCP-1). Furthermore, quantitation of serum AAG by an immunoassay showed a significant (p<0.001) increase in AAG serum concentration in stage IV patients in comparison with healthy volunteers; moreover; the quantity of AAG plotted against MALDI-MS peak intensity classified the groups into two distinct clusters. Ongoing studies of other disease stages will provide evidence whether our strategy is sufficiently robust to give rise to stage-specific protein/peptide signatures in melanoma.

Improved classification of breast cancer peptide and protein profiles by combining two serum workup procedures

Purpose

Detection of breast cancer at early stage increases patient’s survival. Mass spectrometry-based protein analysis of serum samples is a promising approach to obtain biomarker profiles for early detection. A combination of commonly applied solid-phase extraction procedures for clean-up may increase the number of detectable peptides and proteins. In this study, we have evaluated whether the classification performance of breast cancer profiles improves by using two serum workup procedures.

Methods

Serum samples from 105 breast cancer patients and 202 healthy volunteers were processed according to a standardized protocol implemented on a high-end liquid-handling robot. Peptide and protein enrichments were carried out using weak-cation exchange (WCX) and reversed-phase (RP) C18 magnetic beads. Profiles were acquired on a matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometer. In this way, two different biomarker profiles were obtained for each serum sample, yielding a WCX- and RPC18-dataset.

Results

The profiles were statistically evaluated with double cross-validation. Classification results of WCX- and RPC18-datasets were determined for each set separately and for the combination of both sets. Sensitivity and specificity were 82 and 87 % (WCX) and 73 and 93 % (RPC18) for the individual workup procedures. These values increased up to 84 and 95 %, respectively, upon combining the data.

Conclusion

It was found that MALDI-TOF peptide and protein profiles can be used for classification of breast cancer with high sensitivity and specificity. The classification performance even improved when two workup procedures were applied, since these provide a greater number of features (proteins).

Electronic supplementary material

The online version of this article (doi:10.1007/s00432-012-1273-4) contains supplementary material, which is available to authorized users.

Mutual information optimization for mass spectra data alignment

"Signal" alignments play critical roles in many clinical setting. This is the case of mass spectrometry data, an important component of many types of proteomic analysis. A central problem occurs when one needs to integrate (mass spectrometry) data produced by different sources, e.g., different equipment and/or laboratories. In these cases some form of "data integration'" or "data fusion'" may be necessary in order to discard some source specific aspects and improve the ability to perform a classification task such as inferring the "disease classes'" of patients. The need for new high performance data alignments methods is therefore particularly important in these contexts. In this paper we propose an approach based both on an information theory perspective, generally used in a feature construction problem, and on the application of a mathematical programming task (i.e. the weighted bipartite matching problem). We present the results of a competitive analysis of our method against other approaches. The analysis was conducted on data from plasma/ethylenediaminetetraacetic acid (EDTA) of "control" and Alzheimer patients collected from three different hospitals. The results point to a significant performance advantage of our method with respect to the competing ones tested.

Screening for colorectal cancer: established and emerging modalities

It has been estimated that >95% of cases of colorectal cancer (CRC) would benefit from curative surgery if diagnosis was made at an early or premalignant polyp stage of disease. Over the past 10 years, most developed nation states have implemented mass population screening programs, which are typically targeted at the older (at-risk) age group (>50-60 years old). Conventional screening largely relies on periodic patient-centric investigation, particularly involving colonoscopy and flexible sigmoidoscopy, or else on the fecal occult blood test. These methods are compromised by either low cost-effectiveness or limited diagnostic accuracy. Advances in the development of diagnostic molecular markers for CRC have yielded an expanding list of potential new screening modalities based on investigations of patient stool (for colonocyte DNA mutations, epigenetic changes or microRNA expression) or blood specimens (for plasma DNA mutations, epigenetic changes, heteroplasmic mitochondrial DNA mutations, leukocyte transcriptome profile, plasma microRNA expression or protein and autoantibody expression). In this Review, we present a critical evaluation of the performance data and relative merits of these various new potential methods. None of these molecular diagnostic methods have yet been evaluated beyond the proof-of-principle and pilot-scale study stage and it could be some years before they replace existing methods for population screening in CRC.

Molecular tests for colorectal cancer screening

Detecting and removing high-risk adenomas and early colorectal cancer (CRC) can reduce mortality of this disease. The noninvasive fecal occult blood test (FOBT; guaiac-based or immunochemical) is widely used in screening programs and although effective, it leaves room for improvement in terms of test accuracy. Molecular tests are expected to be more sensitive, specific and informative than current detection tests, and are promising future tools for CRC screening. This review provides an overview of the performances of DNA, RNA, and protein markers for CRC detection in stool and blood. Most emphasis currently is on DNA and protein markers. Among DNA markers there is trend to move away from mutation markers in favor of methylation markers. The recent boost in proteomics research leads to many new candidate protein markers. Usually in small series, some markers show better performance than the present FOBT. Evaluation in large well-controlled randomized trials is the next step needed to take molecular markers for CRC screening to the next level and warrant implementation in a screening setting.

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.

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Background

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multi-factorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics.

Results

We present a comprehensive work-flow tailored for analyzing complex data including data from multi-factorial studies. The developed approach aims at revealing effects caused by a distinct combination of experimental factors, in our case genotype and diet. Applying the developed work-flow to the analysis of an established polygenic mouse model for diet-induced type 2 diabetes, we found peptides with significant fold changes exclusively for the combination of a particular strain and diet. Exploitation of redundancy enables the visualization of peptide correlation and provides a natural way of feature selection for classification and prediction. Classification based on the features selected using our approach performs similar to classifications based on more complex feature selection methods.

Conclusions

The combination of ANOVA and redundancy exploitation allows for identification of biomarker candidates in multi-dimensional MALDI-TOF MS profiling studies with complex experimental design. With respect to feature selection our method provides a fast and intuitive alternative to global optimization strategies with comparable performance. The method is implemented in R and the scripts are available by contacting the corresponding author.

Serum biomarkers which correlate with failure to respond to immunotherapy and tumor progression in a murine colorectal cancer model

PURPOSE

To advance our understanding of mechanisms involved in tumor progression/regression, a CT26 colorectal mouse model treated intra-tumorally with DISC-herpes simplex virus as immunotherapy was used in the discovery and validation phases to investigate and ultimately identify biomarkers correlating with the failure to respond to immunotherapy.

EXPERIMENTAL DESIGN

For the discovery phase, serum protein/peptide profiles of a retrospective sample collection (total n=70) were analyzed using MALDI-TOF-MS combined with artificial neural networks. Following identification of the key predictive peptides using ESI-MS/MS, validation of the identified proteins was carried out on serum and tissues collected in an independent sample set (total n=60).

RESULTS

Artificial neural network analysis resulted in four discriminatory peaks with an accuracy of 86%, sensitivity of 90% and specificity of 81% between the progressor/regressor groups. Three of the identified discriminatory markers were upregulated and demonstrated a positive correlation with tumor progression following DISC-herpes simplex virus therapy. Immunovalidation studies corroborated the MALDI-TOF-MS findings. Immunohistochemistry revealed that serum amyloid A-1 and serum amyloid P produced in the liver localized intracellularly in CT26 tumor tissue.

CONCLUSIONS

MALDI-TOF-MS and BI analysis of the serum proteome of tumor-bearer mice undergoing immunotherapy, identified biomarkers associating with "failure to respond" and biological arrays confirmed these findings.

Identification of biomarkers for colorectal cancer through proteomics-based approaches

The early detection of colorectal cancer is one of the great challenges in the battle against this disease. However, owing to its heterogeneous character, single markers are not likely to provide sufficient diagnostic power to be used in colorectal cancer population screens. This review provides an overview of recent studies aimed at the discovery of new diagnostic protein markers through proteomics-based approaches. It indicates that studies that start with the proteomic analysis of tumor tissue or tumor cell lines (near the source) have a high potential to yield novel and colorectal cancer-specific biomarkers. In the next step, the diagnostic accuracy of these candidate markers can be assessed by a targeted ELISA assay using serum from colorectal cancer patients and healthy controls. Instead, direct proteomic analysis of serum yields predominantly secondary markers composed of fragments of abundant serum proteins that may be associated with tumor-associated protease activity, and alternatively, immunoproteomic analysis of the serum antibody repertoire provides a valuable tool to identify the molecular imprint of colorectal cancer-associated antigens directly from patient serum samples. The latter approach also allows a relatively easy translation into targeted assays. Eventually, multimarker assays should be developed to reach a diagnostic accuracy that meets the stringent criteria for colorectal cancer screening at the population level.

Proteomic serum biomarkers and their potential application in cancer screening programs

Early diagnosis of cancer is of pivotal importance to reduce disease-related mortality. There is great need for non-invasive screening methods, yet current screening protocols have limited sensitivity and specificity. The use of serum biomarkers to discriminate cancer patients from healthy persons might be a tool to improve screening programs. Mass spectrometry based proteomics is widely applied as a technology for mapping and identifying peptides and proteins in body fluids. One commonly used approach in proteomics is peptide and protein profiling. Here, we present an overview of profiling methods that have the potential for implementation in a clinical setting and in national screening programs.

Use of anchorchip-time-of-flight spectrometry technology to screen tumor biomarker proteins in serum for small cell lung cancer

BACKGROUND

The purpose of this study is to discover potential biomarkers in serum for the detection of small cell lung cancer (SCLC).

METHODS

74 serum samples including 30 from SCLC patients and 44 from healthy controls were analyzed using ClinProt system combined with matrix-assisted laser desorption/ionization time-of-flight masss spectrometry (MALDI-TOF-MS). ClinProt software and genetic algorithm analysis selected a panel of serum markers that most efficiently predicted which patients had SCLC.

RESULTS

The diagnostic pattern combined with 5 potential biomarkers could differentiate SCLC patients from healthy persons, with a sensitivity of 90%, specificity of 97.73%. Remarkably, 88.89% of stage I/II patients were accurately assigned to SCLC.

CONCLUSIONS

Anchorchip-time-of-flight spectrometry technology will provide a highly accurate approach for discovering new biomarkers for the detection of SCLC.

Quality control based on isotopic distributions for high-throughput MALDI-TOF and MALDI-FTICR serum peptide profiling

In this study, we have implemented a new quality control (QC) parameter for peptide profiling based on isotopic distributions. This QC parameter is an objective measure and facilitates automatic sorting of large numbers of peptide spectra. Peptides in human serum samples were enriched using reversed-phase C(18)-functionalized magnetic beads using a high-throughput robotic platform. High-resolution MALDI-TOF and ultrahigh resolution MALDI-FTICR mass spectra were obtained and a workflow was developed for automated analysis and evaluation of these profiles. To this end, the isotopic distributions of multiple peptides were quantified from both MALDI-TOF and MALDI-FTICR spectra. Odd peptide isotope distributions in TOF spectra could be rationalized from ultrahigh resolution FTICR spectra that showed overlap of different peptides. The comparison of isotope patterns with estimated polyaveragine distributions was used to calculate a QC value for each single mass spectrum. Sorting these QC values enabled the best MALDI spectrum to be selected from replicate spots. Moreover, using this approach spectra containing high intensities of polymers or other contaminants and lacking peptides of interest can be efficiently removed from a clinical dataset. In general, this method simplifies the exclusion of low quality spectra from further statistical analysis.

Serum proteomic profile analysis for endometrial carcinoma detection with MALDI-TOF MS

INTRODUCTION

Endometrial carcinoma is the one of the leading causes of terminal cancer death in women, and the best way to solve it is through an early diagnosis. Serum proteomic profiling is a promising approach to classify disease versus normal and matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF). This analysis is a new potential tool for the diagnosis of human diseases. The objective of our study was to assess the feasibility of mass spectrometry, based on serum proteomic pattern for the discrimination of endometrial carcinoma patients from healthy control patients.

MATERIAL AND METHODS

By using 30 pre-operative endometrial carcinoma serum samples and 30 healthy controls, we generated MALDI-TOF protein profiles, established the serum detection model, and analyzed the data. Furthermore, we validated the data and got the total sensitivity (97.6%) and specificity (100%), which could classify all the samples well and demonstrated the high, significant separation ability.

RESULTS

Our results indicated that high sensitivity and specificity classify endometrial carcinoma by the serum detection model and showed the potential usefulness of serum protein profiles.

CONCLUSIONS

From these results, we could tell that MALDI-TOF MS is a potential tool for diagnosing diseases by the use of serum samples and will be widely used in the future for clinical work.

Analysis of post-operative changes in serum protein expression profiles from colorectal cancer patients by MALDI-TOF mass spectrometry: a pilot methodological study

BACKGROUND

Mass spectrometry-based protein expression profiling of blood sera can be used to discriminate colorectal cancer (CRC) patients from unaffected individuals. In a pilot methodological study, we have evaluated the changes in protein expression profiles of sera from CRC patients that occur following surgery to establish the potential of this approach for monitoring post-surgical response and possible early prediction of disease recurrence.

METHODS

In this initial pilot study, serum specimens from 11 cancer patients taken immediately prior to surgery and at approximately 6 weeks following surgery were analysed alongside 10 normal control sera by matrix-assisted laser desorption ionisation time of-flight-mass spectrometry (MALDI-TOF MS). Using a two-sided t-test the top 20 ranked protein peaks that discriminate normal from pre-operative sera were identified. These were used to classify post-operative sera by hierarchical clustering analysis (Spearman's Rank correlation) and, as an independent 'test' dataset, by k-nearest neighbour and weighted voting supervised learning algorithms.

RESULTS

Hierarchical cluster analysis classified post-operative sera from all six early Dukes' stage (A and B) patients as normal. The remaining five post-operative sera from more advanced Dukes' stages (C1 and C2) were classified as cancer. Analysis by supervised learning algorithms similarly grouped all advanced Dukes' stages as cancer, with four of the six post-operative sera from early Dukes' stages being classified as normal (P = 0.045; Fisher's exact test).

CONCLUSIONS

The results of this pilot methodological study illustrate the proof-of-concept of using protein expression profiling of post-surgical blood sera from individual patients to monitor disease course. Further validation on a larger patient cohort and using an independent post-operative sera dataset would be required to evaluate the potential clinical relevance of this approach. Prospective data, including follow-up on patient survival, could in the future, then be evaluated to inform decisions on individualised treatment modalities.

Serum protein profiling and proteomics in autistic spectrum disorder using magnetic bead-assisted mass spectrometry

The pathophysiology of autistic spectrum disorder (ASD) is not fully understood and there are no diagnostic or predictive biomarkers. Proteomic profiling has been used in the past for biomarker research in several non-psychiatric and psychiatric disorders and could provide new insights, potentially presenting a useful tool for generating such biomarkers in autism. Serum protein pre-fractionation with C8-magnetic beads and protein profiling by matrix-assisted laser desorption/ionisation-time of flight-mass spectrometry (MALDI-ToF-MS) were used to identify possible differences in protein profiles in patients and controls. Serum was obtained from 16 patients (aged 8-18) and age-matched controls. Three peaks in the MALDI-ToF-MS significantly differentiated the ASD sample from the control group. Sub-grouping the ASD patients into children with and without comorbid Attention Deficit and Hyperactivity Disorder, ADHD (ASD/ADHD+ patients, n = 9; ASD/ADHD- patients, n = 7), one peak distinguished the ASD/ADHD+ patients from controls and ASD/ADHD- patients. Our results suggest that altered protein levels in peripheral blood of patients with ASD might represent useful biomarkers for this devastating psychiatric disorder.

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.