Cancer-specific MALDI-TOF profiles of blood serum and plasma: biological meaning and perspectives. J Proteomics. 2010;73:537-551. [PMID: 19782778 DOI: 10.1016/j.jprot.2009.09.011]

Advances in Mass Spectrometry-Based Single Cell Analysis

Technological developments and improvements in single-cell isolation and analytical platforms allow for advanced molecular profiling at the single-cell level, which reveals cell-to-cell variation within the admixture cells in complex biological or clinical systems. This helps to understand the cellular heterogeneity of normal or diseased tissues and organs. However, most studies focused on the analysis of nucleic acids (e.g., DNA and RNA) and mass spectrometry (MS)-based analysis for proteins and metabolites of a single cell lagged until recently. Undoubtedly, MS-based single-cell analysis will provide a deeper insight into cellular mechanisms related to health and disease. This review summarizes recent advances in MS-based single-cell analysis methods and their applications in biology and medicine.

MALDI-TOF mass spectrometry profiling of bovine skim milk for subclinical mastitis detection

Introduction

Mastitis is one of most impacting health issues in bovine dairy farming that reduces milk yield and quality, leading to important economic losses. Subclinical forms of the disease are routinely monitored through the measurement of somatic cell count (SCC) and microbiological tests. However, their identification can be tricky, reducing the possibilities of early treatments. In this study, a MALDI-TOF mass spectrometry approach was applied to milk samples collected from cows classified according to the SCC, to identify differences in polypeptide/protein profiles.

Materials and methods

Twenty-nine raw milk samples with SCC >200,000 cell/ml (group H) and 91 samples with SCC lower than 200,000 (group L) were randomly collected from 12 dairy farms. Spectral profiles from skim milk were acquired in the positive linear mode within the 4,000–20,000 m/z mass acquisition range.

Results and discussion

Based on signal intensity, a total of 24 peaks emerged as significant different between the two groups. The most discriminant signals (4,218.2 and 4,342.98 m/z) presented a ROC curve with AUC values higher than 0.8. Classification algorithms (i.e., quick classifier, genetic algorithm, and supervised neural network) were applied for generating models able to classify new spectra (i.e., samples) into the two classes. Our results support the MALDI-TOF mass spectrometry profiling as a tool to detect mastitic milk samples and to potentially discover biomarkers of the disease. Thanks to its rapidity and low-cost, such method could be associated with the SCC measurement for the early diagnosis of subclinical mastitis.

Circulating Biomarkers in Predicting Pathological Response to Neoadjuvant Therapy for Colorectal Cancer

Circulating biomarkers show promise in the management of many cancers. They have become the novel non-invasive approach to complement the current strategies in colorectal cancer (CRC) management. Their ability in guiding diagnosis, evaluating response to treatment, screening and prognosis is phenomenal, especially when it comes to their minimally invasive nature. These “liquid biopsies,” which show potential for replacing invasive surgical biopsies, provide useful information on the primary and metastatic disease by providing an insight into cancer biology. Analysis of blood and body fluids for circulating tumour DNA (ctDNA), carcinoembryonic antigen (CEA), circulating tumour cells (CTC), or circulating micro RNA (miRNA) shows potential for improving CRC management. Recognizing a predictive model to assess response to neoadjuvant chemotherapy would help in better patient selection. This review was conducted with the aim of outlining the use of circulatory biomarkers in current practice and their effectiveness in the management of patients having CRC with a focus on response to neoadjuvant therapy.

Evaluation of the diagnostic value of serum-based proteomics for colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a highly malignant cancer with a high incidence and mortality in China. It is urgent to find a diagnostic marker with higher sensitivity and specificity than the traditional approaches for CRC diagnosis.

AIM

To provide new ideas for the diagnosis of CRC based on serum proteomics.

METHODS

Specimens from 83 healthy people, 62 colon polyp (CRP) patients, and 101 CRC patients were analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The diagnostic value of the profiles of differentially expressed proteins was then analyzed.

RESULTS

Compared with the healthy control group, CRC patients had elevated expression of 5 proteins and reduced expression of 14 proteins. The area under the curve (AUC) for a differentially expressed protein with a mass-to-charge ratio of 2022.34 was the largest; the AUC was 0.843, which was higher than the AUC of 0.717 observed with carcinoembryonic antigen (CEA), and the sensitivity and specificity of this identified marker were 75.3% and 79.5%, respectively. After cross-validation, the accuracy of diagnosis using levels of this differentially expressed protein was 82.37%. Compared with the CRP group, the expression of 3 proteins in the serum of CRC patients was elevated and 11 proteins were expressed at reduced levels. Proteins possessing mass-to-charge ratio values of 2899.38 and 877.3 were selected to establish a classification tree model. The results showed that the accuracy of CRC diagnosis was 89.5%, the accuracy of CRP diagnosis was 81.6%, and the overall accuracy of this approach was 86.3%. The overall sensitivity and specificity of diagnosis using the proteomics approach were 81.8% and 66.75%, respectively. The sensitivities and specificities of diagnoses based on CEA and carbohydrate antigen 19-9 expression were 55.6% and 91.3% and 65.4% and 65.2%, respectively.

CONCLUSION

We demonstrated that serum proteomics may be helpful for the detection of CRC, and it may assist clinical practice for CRC diagnosis.

Detection of Hepatocellular Carcinoma in a High-Risk Population by a Mass Spectrometry-Based Test

Simple Summary

Liver cancer is one of the most common causes of cancer worldwide, but unfortunately, current technology has a limited ability to detect it early in high-risk patients. This study investigates a machine learning algorithm based on protein levels in the blood that can be used to help with diagnosis. The test shows promising results, especially in patients with smaller tumors and compared to current blood detection tests. This research suggests an important role in the future for machine learning algorithm-based blood detection tests.

Abstract

Hepatocellular carcinoma (HCC) is one of the fastest growing causes of cancer-related death. Guidelines recommend obtaining a screening ultrasound with or without alpha-fetoprotein (AFP) every 6 months in at-risk adults. AFP as a screening biomarker is plagued by low sensitivity/specificity, prompting interest in discovering alternatives. Mass spectrometry-based techniques are promising in their ability to identify potential biomarkers. This study aimed to use machine learning utilizing spectral data and AFP to create a model for early detection. Serum samples were collected from three separate cohorts, and data were compiled to make Development, Internal Validation, and Independent Validation sets. AFP levels were measured, and Deep MALDI^® analysis was used to generate mass spectra. Spectral data were input into the VeriStrat^® classification algorithm. Machine learning techniques then classified each sample as “Cancer” or “No Cancer”. Sensitivity and specificity of the test were >80% to detect HCC. High specificity of the test was independent of cause and severity of underlying disease. When compared to AFP, there was improved cancer detection for all tumor sizes, especially small lesions. Overall, a machine learning algorithm incorporating mass spectral data and AFP values from serum samples offers a novel approach to diagnose HCC. Given the small sample size of the Independent Validation set, a further independent, prospective study is warranted.

Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways

Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.

Mass Spectrometry: A Powerful Method for Monitoring Various Type of Leukemia, Especially MALDI-TOF in Leukemia's Proteomics Studies Review

Recent success in studying the proteome, as a source of biomarkers, has completely changed our understanding of leukemia (blood cancer). The identification of differentially expressed proteins, such as relapse and drug resistance proteins involved in leukemia by using various ionization sources and mass analyzers of mass spectrometry techniques, has helped scientists find better diagnosis, prognosis, and treatment strategies. With the aid of this powerful analytical technique, we can investigate the qualification/quantification of proteins, protein-protein interactions, post-translational modifications, and find the correlation between proteins and their genes with the hope of finding the missing parts of the successful therapy puzzle. In this review, we followed different MS sources and analyzers which used for monitoring various type of leukemia, then focused on MALDI-TOF MS as a quick and reliable method for studying proteins. Due to several review published for other techniques, the present review is the first work in this field. Also, by classifying more than 400 proteins, we have found 42 proteins are involved in two or three different stages of leukemia. Finally, we have suggested six specific biomarkers for AML, one for ALL, three biomarkers with a role in the etiology of leukemia and 13 markers with the potential for further studies.

Extending the information content of the MALDI analysis of biological fluids via multi-million shot analysis

INTRODUCTION

Reliable measurements of the protein content of biological fluids like serum or plasma can provide valuable input for the development of personalized medicine tests. Standard MALDI analysis typically only shows high abundance proteins, which limits its utility for test development. It also exhibits reproducibility issues with respect to quantitative measurements. In this paper we show how the sensitivity of MALDI profiling of intact proteins in unfractionated human serum can be substantially increased by exposing a sample to many more laser shots than are commonly used. Analytical reproducibility is also improved.

METHODS

To assess what is theoretically achievable we utilized spectra from the same samples obtained over many years and combined them to generate MALDI spectral averages of up to 100,000,000 shots for a single sample, and up to 8,000,000 shots for a set of 40 different serum samples. Spectral attributes, such as number of peaks and spectral noise of such averaged spectra were investigated together with analytical reproducibility as a function of the number of shots. We confirmed that results were similar on MALDI instruments from different manufacturers.

RESULTS

We observed an expected decrease of noise, roughly proportional to the square root of the number of shots, over the whole investigated range of the number of shots (5 orders of magnitude), resulting in an increase in the number of reliably detected peaks. The reproducibility of the amplitude of these peaks, measured by CV and concordance analysis also improves with very similar dependence on shot number, reaching median CVs below 2% for shot numbers > 4 million. Measures of analytical information content and association with biological processes increase with increasing number of shots.

CONCLUSIONS

We demonstrate that substantially increasing the number of laser shots in a MALDI-TOF analysis leads to more informative and reliable data on the protein content of unfractionated serum. This approach has already been used in the development of clinical tests in oncology.

RNA sequencing for research and diagnostics in clinical oncology

Molecular diagnostics is becoming one of the major drivers of personalized oncology. With hundreds of different approved anticancer drugs and regimens of their administration, selecting the proper treatment for a patient is at least nontrivial task. This is especially sound for the cases of recurrent and metastatic cancers where the standard lines of therapy failed. Recent trials demonstrated that mutation assays have a strong limitation in personalized selection of therapeutics, consequently, most of the drugs cannot be ranked and only a small percentage of patients can benefit from the screening. Other approaches are, therefore, needed to address a problem of finding proper targeted therapies. The analysis of RNA expression (transcriptomic) profiles presents a reasonable solution because transcriptomics stands a few steps closer to tumor phenotype than the genome analysis. Several recent studies pioneered using transcriptomics for practical oncology and showed truly encouraging clinical results. The possibility of directly measuring of expression levels of molecular drugs' targets and profiling activation of the relevant molecular pathways enables personalized prioritizing for all types of molecular-targeted therapies. RNA sequencing is the most robust tool for the high throughput quantitative transcriptomics. Its use, potentials, and limitations for the clinical oncology will be reviewed here along with the technical aspects such as optimal types of biosamples, RNA sequencing profile normalization, quality controls and several levels of data analysis.

Identification of newly developed advanced schistosomiasis with MALDI-TOF mass spectrometry and ClinProTools analysis

Cases of newly developed advanced schistosomiasis (NDAS) have occurred in areas where schistosomiasis transmission has been blocked for more than 25 years. The causes and pathogenesis of NDAS are still unknown. Diagnosis of NDAS relies on historical investigation and clinical symptoms, such as liver fibrosis, hepatic ascites and abnormal biochemical indexes in serum. It is important but difficult at this stage to develop a new tool for early screening and rapid diagnosis. In this study, serum peptides from thirty patients with NDAS and thirty healthy controls were captured with weak cation exchange magnetic beads, and subjected to MALDI-TOF mass spectrometry and ClinProTools analysis. Eleven peaks with m/z 924, 2661, 2953, 2991, 3241, 3884, 5337, 5905, 5943, 7766 and 9289 were decreased and three peaks with m/z 1945, 2082 and 4282 were increased in the NDAS group. The proteomic detection pattern (PDP) was established with 14 different peptide peaks, and its sensitivity and specificity were investigated with a blind test. The peptide mass fingerprints of sera from 50 NDAS patients and 100 healthy controls were double-blind subjected to the PDP method, and 50 patients and 92 healthy controls were classified as NDAS and healthy separately, which showed 100% sensitivity and 92% specificity. Our results showed that the PDP could be a new and useful method to detect NDAS.

[Omics biomarkers and early diagnostics]

One of main goals for omics sciences, such as transcriptomics, proteomics and metabolomics, in medicine is biomarker discovery for diagnostics of common non-infectious diseases. The opinion paper discusses diagnostic parameters, which limit the use of the biomarkers, as well as a positive predictive value, and conditions providing possible application of the biomarkers for early diagnostics. Using some examples from proteomics, it is stated that omics technologies, which measure gene expression products, are more often used to discover prognostic and predictive biomarkers. These biomarkers help to classify already diagnosed patients to groups with different disease management.

Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure

BACKGROUND

Current risk prediction models in heart failure (HF) including clinical characteristics and biomarkers only have moderate predictive value. The aim of this study was to use matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling to determine if a combination of peptides identified with MALDI-MS will better predict clinical outcomes of patients with HF.

METHODS

A cohort of 100 patients with HF were recruited in the biomarker discovery phase (50 patients who died or had a HF hospital admission vs. 50 patients who did not have an event). The peptide extraction from plasma samples was performed using reversed phase C18. Then samples were analysed using MALDI-MS. A multiple peptide biomarker model was discovered that was able to predict clinical outcomes for patients with HF. Finally, this model was validated in an independent cohort with 100 patients with HF.

RESULTS

After normalisation and alignment of all the processed spectra, a total of 11,389 peptides (m/z) were detected using MALDI-MS. A multiple biomarker model was developed from 14 plasma peptides that was able to predict clinical outcomes in HF patients with an area under the receiver operating characteristic curve (AUC) of 1.000 (p = 0.0005). This model was validated in an independent cohort with 100 HF patients that yielded an AUC of 0.817 (p = 0.0005) in the biomarker validation phase. Addition of this model to the BIOSTAT risk prediction model increased the predictive probability for clinical outcomes of HF from an AUC value of 0.643 to an AUC of 0.823 (p = 0.0021). Moreover, using the prediction model of fourteen peptides and the composite model of the multiple biomarker of fourteen peptides with the BIOSTAT risk prediction model achieved a better predictive probability of time-to-event in prediction of clinical events in patients with HF (p = 0.0005).

CONCLUSIONS

The results obtained in this study suggest that a cluster of plasma peptides using MALDI-MS can reliably predict clinical outcomes in HF that may help enable precision medicine in HF.

MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance

Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell dyscrasia that can progress to malignant multiple myeloma (MM). Specific molecular biomarkers to classify the MGUS status and discriminate the initial asymptomatic phase of MM have not been identified. We examined the serum peptidome profile of MGUS patients and healthy volunteers using MALDI-TOF mass spectrometry and developed a predictive model for classifying serum samples. The predictive model was built using a support vector machine (SVM) supervised learning method tuned by applying a 20-fold cross-validation scheme. Predicting class labels in a blinded test set containing randomly selected MGUS and healthy control serum samples validated the model. The generalization performance of the predictive model was evaluated by a double cross-validation method that showed 88% average model accuracy, 89% average sensitivity and 86% average specificity. Our model, which classifies unknown serum samples as belonging to either MGUS patients or healthy individuals, can be applied to clinical diagnosis.

Proteins are potent biomarkers to detect colon cancer progression

Colon cancer is the most common type of cancer and major cause of death worldwide. The detection of colon cancer is difficult in early stages. However, the secretory proteins have been used as ideal biomarker for the detection of colon cancer progress in cancer patients. Serum/tissue protein expression could help general practitioners to identify colon cancer at earlier stages. By this way, we use the biomarkers to evaluate the anticancer drugs and their response to therapy in cancer models. Recently, the biomarker discovery is important in cancer biology and disease management. Also, many measurable specific molecular components have been studied in colon cancer therapeutics. The biomolecules are mainly DNA, RNA, metabolites, enzymes, mRNA, aptamers and proteins. Thus, in this review we demonstrate the important protein biomarker in colon cancer development and molecular identification of protein biomarker discovery.

Characterization of the Low-Molecular-Weight Human Plasma Peptidome

The human plasma proteome represents an important secreted sub-proteome. Proteomic analysis of blood plasma with mass spectrometry is a challenging task. The high complexity and wide dynamic range of proteins as well as the presence of several proteins at very high concentrations complicate the profiling of the human plasma proteome. The peptidome (or low-molecular-weight fraction, LMF) of the human plasma proteome is an invaluable source of biological information, especially in the context of identifying plasma-based markers of disease. Peptides are generated by active synthesis and proteolytic processing, often yielding proteolytic fragments that mediate a variety of physiological and pathological functions. As such, degradomic studies, investigating cleavage products via peptidomics and top-down proteomics in particular, have warranted significant research interest. However, due to their molecular weight, abundance, and solubility, issues with identifying specific cleavage sites and coverage of peptide fragments remain challenging. Peptidomics is currently focused toward comprehensively studying peptides cleaved from precursor proteins by endogenous proteases. This protocol outlines a standardized rapid and reproducible procedure for peptidomic profiling of human plasma using centrifugal ultrafiltration and mass spectrometry. Ultrafiltration is a convective process that uses anisotropic semipermeable membranes to separate macromolecular species on the basis of size. We have optimized centrifugal ultrafiltration (cellulose triacetate membrane) for plasma fractionation with respect to buffer and solvent composition, centrifugal force, duration, and temperature to facilitate recovery >95% and enrichment of the human plasma peptidome. This method serves as a comprehensive and facile process to enrich and identify a key, underrepresented sub-proteome of human blood plasma.

New detection method in experimental mice for schistosomiasis: ClinProTool and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Oncomelania hupensis snails along the Yangtze River and the low positive rate and infectiosity of human and livestock schistosomiasis still pose a threat to public health in China. Adult blood flukes were recognized as Schistosoma japonicum, which are found in the portal system of the sentinel mice bred in the laboratory for 35 days after contact with the water. However, 35 days was too long from the field test to dissection, and the dissection in the laboratory was also time-consuming and labor-intensive. Serum peptides in mice at different times after infection were measured by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. ClinProTool was used to establish the proteomic detection pattern (PDP), based on the differentially expressed peptide between the infection and healthy control groups. Under experimental conditions, characteristic PDP were detected in 5 % (3/60), 35 % (21/60), 75 % (45/60), 87.93 % (51/58), and 98.15 % (53/54) of infected mice from weeks 1 to 5 post-infection, whereas ELISA and dissection examination for adult blood flukes missed the first 2 weeks. At 35 days post-infection, the infectiosity assay showed 40 % (4/10), 50 % (5/10), and 80 % (8/10) positivity with the PDP test in mice infected with 4, 6, and 10 cercariae, respectively, as well as 100 % (10/10) positivity in mice infected with 14, 18, and 22 cercariae. Five stored sera of positive sentinel mice with parasite detection were verified correctly in the PDP test. The results confirm that PDP can be used as a rapid and early detection method for S. japonicum infection in experimental mice, which are expected to apply in early surveillance for schistosomiasis.

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.

Identification of protein markers for the occurrence of defrosted material in milk through a MALDI-TOF-MS profiling approach

Mozzarella di Bufala Campana is a soft, stretched curd Italian cheese made from fresh buffalo milk that obtained the Protected Designation of Origin (PDO) registration in EU legislation. Seasonality of buffalo milk production, rapid cheese decay and transport of its preserving liquid have relevant practical/economic consequences for mozzarella production; consequently, a progressive diffusion of cheese products realized with frozen curd or frozen milk has recently been observed. In order to meet the demand of the dairy producers and consumers for a reduction of starting material adulterations and for the certification of the raw milk used for cheese manufacturing, we have developed a rapid/robust MALDI-TOF-MS polypeptide profiling procedure that assays material quality through the identification of specific markers of its freshness. Massive analysis of fresh and frozen buffalo milks (stored for different times) was realized to this purpose; a tough statistical evaluation of the resulting data ultimately permitted the typing of milk samples. We identified 28 polypeptide markers of the milk freezing storage, among which 13 and 15 showed down- and over-representation, respectively. Quantitative data were confirmed by an independent analytical approach on selected markers. GLYCAM1-derived phosphopeptides (1-53), β-casein-derived phosphopeptides (1-68), β-casein-derived γ2-, γ3- and γ4-fragments, α-lactalbumin and β-lactoglobulin were components showing the highest significance. The occurrence of the first compounds in buffalo milk is here described for the first time; their formation in the frozen material was ascribed to the activity of plasmin or of unknown bacterial proteases/peptidases stable at low temperatures. In conclusion, data reported here suggest the application of this MALDI-TOF-MS polypeptide profiling platform to other high-quality dairy productions, in which milk freshness has important consequences on final product organoleptic properties.

BIOLOGICAL SIGNIFICANCE

In the last decades, several studies have provided the molecular basis underlying the relation between food quality and human wellness/health. In this context, Foodomics emerged as a novel scientific discipline studying food and nutrition domains through the application of advanced omics technologies, including genomics, transcriptomics, proteomics and/or metabolomics. Above-mentioned technologies have been used in an integrated, holistic way to study foods for: i) compound profiling, authenticity, and/or biomarker-detection related to product quality or safety; ii) contaminants and their whole toxicity; iii) bioactivity and general effects on human health; iv) their digestion and assumption in human body; v) development of new transgenic products; and vi) evaluation of their modifications within the digestive tract. In the first context, a highly reproducible MALDI-TOF-MS polypeptide profiling procedure is here presented, which provides information on buffalo milk quality through the identification of specific markers of its freshness. Among identified markers, some were indicative of the action of various proteolytic enzymes and the resulting occurrence of specific defense components in buffalo milk having the physiological role to limit bacterial/virus content in this biological fluid. Data suggest the possible application of similar MALDI-TOF-based platforms to other high-quality food productions, where storage conditions of the starting materials may have important consequences on final product characteristics.

Alterations of the Plasma Peptidome Profiling in Colorectal Cancer Progression

Early detection of colorectal cancer (CRC) remains a challenge. It has been highlighted that the pathological alterations within an organ and tissues might be reflected in serum or plasma proteomic/peptidic patterns. The aim of the study was to follow the changes in the plasma peptides associated to colorectal cancer progression by mass spectrometry. This study included 27 adenoma, 67 CRC (n = 33 I-II stage and n = 34 III-IV stage), 23 liver metastasis from CRC patients and 34 subjects disease-free as controls. For plasma peptides analysis, samples purification was performed on the Nanoporous Silica Chips technology followed by matrix-assisted laser desorption/ionisation-time of flight analysis. Since the high complexity of the obtained dataset, multivariate statistical analysis, and discriminant pattern recognition were performed for study groups classification. Forty-four of 88 ionic species were successfully identified as fragments of peptides and proteins physiologically circulating in the blood and belonging to immune and coagulation systems and inflammatory mediators. Many peptides clustered into sets of overlapping sequences with ladder-like truncation clearly associated to proteolytic processes of both endo- and exoproteases activity. Comparing to controls, a different median ion intensity of the group-type fragments distribution was observed. Moreover, the degradation pattern obtained by proteolytic cleavage was different into study groups. This pattern was specific and characteristic of each group: controls, colon tumour disease (including adenoma and CRC), and liver metastasis, revealing a role as biomarker in early diagnosis and prognosis. Our findings highlighted peculiar changes in protease activity characteristic of CRC progression from pre-cancer lesion to metastatic disease. J. Cell. Physiol. 231: 915-925, 2016. © 2015 Wiley Periodicals, Inc.

Clinical predictive circulating peptides in rectal cancer patients treated with neoadjuvant chemoradiotherapy

Preoperative chemoradiotherapy is worldwide accepted as a standard treatment for locally advanced rectal cancer. Current standard of treatment includes administration of ionizing radiation for 45-50.4 Gy in 25-28 fractions associated with 5-fluorouracil administration during radiation therapy. Unfortunately, 40% of patients have a poor or absent response and novel predictive biomarkers are demanding. For the first time, we apply a novel peptidomic methodology and analysis in rectal cancer patients treated with preoperative chemoradiotherapy. Circulating peptides (Molecular Weight <3 kDa) have been harvested from patients' plasma (n = 33) using nanoporous silica chip and analyzed by Matrix-Assisted Laser Desorption/Ionization-Time of Flight mass spectrometer. Peptides fingerprint has been compared between responders and non-responders. Random Forest classification selected three peptides at m/z 1082.552, 1098.537, and 1104.538 that were able to correctly discriminate between responders (n = 16) and non-responders (n = 17) before therapy (T0) providing an overall accuracy of 86% and an area under the receiver operating characteristic (ROC) curve of 0.92. In conclusion, the nanoporous silica chip coupled to mass spectrometry method was found to be a realistic method for plasma-based peptide analysis and we provide the first list of predictive circulating biomarker peptides in rectal cancer patients underwent preoperative chemoradiotherapy.

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

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

Serum peptidome based biomarkers searching for monitoring minimal residual disease in adult acute lymphocytic leukemia

BACKGROUND

The persistence of minimal residual disease (MRD) during therapy is the strongest adverse prognostic factor in acute lymphocytic leukemia (ALL). This study was to identify serum candidate peptides for monitoring MRD in adult ALL.

RESULTS

A total of 33 peptides in the molecular weight range of 1000-10000 Da were detected using ClinProt system and statistically different between adult patients with ALL and healthy controls. Quick classifier (QC) algorithm was used to obtain a diagnostic model consisting of five peptides that could discriminate patients with ALL from controls with a high sensitivity (100%) and specificity (96.67%). The peptides in the QC model were identified as fibrinogen alpha chain (FGA), glutathione S-transferase P1 (GSTP1), isoform 1 of fibrinogen alpha chain precursor, platelet factor 4 (PF4) by high pressure/performance liquid chromatography mass spectrometry/mass spectrometry. Relative intensities of the five peptides were compared among ALL different groups for the potential importance of MRD evaluation in ALL. The peptides with increased relative intensities in newly diagnosed (ND) ALL patients were found to be decreased in their relative intensities after complete remission (CR) of adult ALL. When ALL patients were refractory & relapsed (RR), relative intensities of the peptides were elevated again. Peptides with decreased relative intensities in ND and RR ALL patients were found to be increased in their relative intensities when ALL patients achieved CR. The findings were validated by ELISA and western blot. Further linear regression analyses were performed to eliminate the influence of platelet and white blood cell counts on serum protein contents and indicated that there were no correlations between the contents of all four proteins (PF4, connective tissue active peptide III, FGA and GSTP1) and white blood cell or platelet counts in ALL different groups and healthy control.

CONCLUSIONS

We speculate the five peptides, FGA, isoform 1 of fibrinogen alpha chain precursor, GSTP1, PF4 and connective tissue active peptide III would be potential biomarkers for forecasting relapse, monitoring MRD and evaluating therapeutic response in adult ALL.

Mass spectrometry-based serum and plasma peptidome profiling for prediction of treatment outcome in patients with solid malignancies

INTRODUCTION

Treatment selection tools are needed to enhance the efficacy of targeted treatment in patients with solid malignancies. Providing a readout of aberrant signaling pathways and proteolytic events, mass spectrometry-based (MS-based) peptidomics enables identification of predictive biomarkers, whereas the serum or plasma peptidome may provide easily accessible signatures associated with response to treatment. In this systematic review, we evaluate MS-based peptide profiling in blood for prompt clinical implementation.

METHODS

PubMed and Embase were searched for studies using a syntax based on the following hierarchy: (a) blood-based matrix-assisted or surface-enhanced laser desorption/ionization time-of-flight MS peptide profiling (b) in patients with solid malignancies (c) prior to initiation of any treatment modality, (d) with availability of outcome data.

RESULTS

Thirty-eight studies were eligible for review; the majority were performed in patients with non-small cell lung cancer (NSCLC). Median classification prediction accuracy was 80% (range: 66%-93%) in 11 models from 14 studies reporting an MS-based classification model. A pooled analysis of 9 NSCLC studies revealed clinically significant median progression-free survival in patients classified as "poor outcome" and "good outcome" of 2.0 ± 1.06 months and 4.6 ± 1.60 months, respectively; median overall survival was also clinically significant at 4.01 ± 1.60 months and 10.52 ± 3.49 months, respectively.

CONCLUSION

Pretreatment MS-based serum and plasma peptidomics have shown promising results for prediction of treatment outcome in patients with solid tumors. Limited sample sizes and absence of signature validation in many studies have prohibited clinical implementation thus far. Our pooled analysis and recent results from the PROSE study indicate that this profiling approach enables treatment selection, but additional prospective studies are warranted.

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.

Identification of autoantibodies against transthyretin for the screening and diagnosis of rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic, autoimmune, systemic and inflammatory rheumatic disease that leads to inflammation of the joints and surrounding tissues. Identification of novel protein(s) associated with severity of RA is a prerequisite for better understanding of pathogenesis of this disease that may also have potential to serve as novel biomarkers in the diagnosis of RA. Present study was undertaken to compare the amount of autoantigens and autoantibodies in the plasma of RA patients in comparison to healthy controls. Plasma samples were collected from the patients suffering from RA, Osteoarthritis (OA), Systemic lupus erythematosus (SLE) and healthy volunteers. The screening of plasma proteins were carried out using 2-dimensional gel electrophoresis followed by identification of differentially expressed protein by MALDI-TOF MS/MS. Among several differentially expressed proteins, transthyretin (TTR) has been identified as one of the protein that showed significantly up regulated expression in the plasma of RA patients. The results were further validated by Western blot analysis and ELISA. In comparison to OA synovium, an exclusive significantly high expression of TTR in RA has been validated through IHC, Western blotting and IEM studies. Most importantly, the increase in expression of TTR with the progression of severity of RA condition has been observed. The autoantibodies against TTR present in the RA plasma were identified using immunoprecipitation-Western methods. The significant production of autoantibodies was validated by ELISA and Western blot analysis using recombinant pure protein of TTR. Hence, these novel observations on increase in TTR expression with the increase in severity of RA conditions and significant production of autoantibodies against TTR clearly suggest that a systematic studies on the role TTR in the pathogenesis of RA is immediately required and TTR may be used as a serum diagnostic marker together with other biochemical parameters and clinical symptoms for RA screening and diagnosis.

Divide and conquer: subproteomic approaches toward gastric cancer biomarker and drug target discovery

The discovery of biomarkers for early detection and treatment for gastric cancer are two important gaps that proteomics have the potential to fill. Advancements in mass spectrometry, sample preparation and separation strategies are crucial to proteomics-based discoveries and subsequent translations from bench to bedside. A great number of studies exploiting various subproteomic approaches have emerged for higher-resolution analysis (compared with shotgun proteomics) that permit interrogation of different post-translational and subcellular compartmentalized forms of the same proteins as determinants of disease phenotypes. This is a unique and key strength of proteomics over genomics. In this review, the salient features, competitive edges and pitfalls of various subproteomic approaches are discussed. We also highlight valuable insights from several subproteomic studies that have increased our understanding of the molecular etiology of gastric cancer and the findings that led to the discovery of potential biomarkers/drug targets that were otherwise not revealed by conventional shotgun expression proteomics.

MALDI-TOF MS as evolving cancer diagnostic tool: a review

Recent developments in mass spectrometry have introduced clinical proteomics to the forefront of diseases diagnosis, offering reliable, robust and efficient analytical method for biomarker discovery and monitoring. MALDI-TOF is a powerful tool for surveying proteins and peptides comprising the realm for clinical analysis. MALDI-TOF has the potential to revolutionize cancer diagnostics by facilitating biomarker discovery, enabling tissue imaging and quantifying biomarker levels. Healthy (control) and cancerous tissues can be analyzed on the basis of mass spectrometry (MALDI-TOF) imaging to identify cancer-specific changes that may prove to be clinically useful. We review MALDI-TOF profiling techniques as tools for detection of cancer biomarkers in various cancers. We mainly discuss recent advances including period from 2011 to 2013.

Selective isolation and analysis of glycoprotein fractions and their glycomes from hepatocellular carcinoma sera

As one of the most important post-translational modifications, the discovery, isolation, and identification of glycoproteins are becoming increasingly important. In this study, a Con A-magnetic particle conjugate-based method was utilized to selectively isolate the glycoproteins and their glycomes from the healthy donor and hepatocellular carcinoma (HCC) case sera. The isolated glycoproteins and their N-linked glycans were identified by LC-ESI-MS/MS and MALDI-TOF/TOF-MS, respectively. A total of 93 glycoproteins from the healthy donors and 85 glycoproteins from the HCC cases were identified. There were 34 different glycoproteins shown between the healthy donors (21/34) and the HCC cases (13/34). Twenty-eight glycans from the healthy donors and 30 glycans from the HCC cases were detected and there were 22 different glycans shown between the healthy donors (10/22) and HCC cases (12/22). Among these glycoproteins, 50 were known to be N-linked glycoproteins and three novel glycopeptides from two predicted potential glycoproteins were discovered. Moreover, lectin blotting, Western blotting and lectin/glyco-antibody microarrays were applied to definitely elucidate the change of selective protein expressions and their glycosylation levels, the results indicated that the differences of the identified glycoproteins between the healthy donors and HCC cases were caused by the change of both protein expression and their glycosylation levels.

Inhibition of Raf-MEK-ERK and hypoxia pathways by Phyllanthus prevents metastasis in human lung (A549) cancer cell line

BACKGROUND

Lung cancer constitutes one of the malignancies with the greatest incidence and mortality rates with 1.6 million new cases and 1.4 million deaths each year. Prognosis remains poor due to deleterious development of multidrug resistance resulting in less than 15% lung cancer patients reaching five years survival. We have previously shown that Phyllanthus induced apoptosis in conjunction with its antimetastastic action. In the current study, we aimed to determine the signaling pathways utilized by Phyllanthus to exert its antimetastatic activities.

METHODS

Cancer 10-pathway reporter array was performed to screen the pathways affected by Phyllanthus in lung carcinoma cell line (A549) to exert its antimetastatic effects. Results from this array were then confirmed with western blotting, cell cycle analysis, zymography technique, and cell based ELISA assay for human total iNOS. Two-dimensional gel electrophoresis was subsequently carried out to study the differential protein expressions in A549 after treatment with Phyllanthus.

RESULTS

Phyllanthus was observed to cause antimetastatic activities by inhibiting ERK1/2 pathway via suppression of Raf protein. Inhibition of this pathway resulted in the suppression of MMP2, MMP7, and MMP9 expression to stop A549 metastasis. Phyllanthus also inhibits hypoxia pathway via inhibition of HIF-1α that led to reduced VEGF and iNOS expressions. Proteomic analysis revealed a number of proteins downregulated by Phyllanthus that were involved in metastatic processes, including invasion and mobility proteins (cytoskeletal proteins), transcriptional proteins (proliferating cell nuclear antigen; zinc finger protein), antiapoptotic protein (Bcl2) and various glycolytic enzymes. Among the four Phyllanthus species tested, P. urinaria showed the greatest antimetastatic activity.

CONCLUSIONS

Phyllanthus inhibits A549 metastasis by suppressing ERK1/2 and hypoxia pathways that led to suppression of various critical proteins for A549 invasion and migration.

Intact protein profiling in breast cancer biomarker discovery: protein identification issue and the solutions based on 3D protein separation, bottom-up and top-down mass spectrometry

Proteomics profiling of intact proteins based on MALDI-TOF MS and derived platforms has been used in cancer biomarker discovery studies. This approach suffers from a number of limitations such as low resolution, low sensitivity, and that no knowledge is available on the identity of the respective proteins in the discovery mode. Nevertheless, it remains the most high-throughput, untargeted mode of clinical proteomics studies to date. Here we compare key protein separation and MS techniques available for protein biomarker identification in this type of studies and define reasons of uncertainty in protein peak identity. As a result of critical data analysis, we consider 3D protein separation and identification workflows as optimal procedures. Subsequently, we present a new protocol based on 3D LC-MS/MS with top-down at high resolution that enabled the identification of HNRNP A2/B1 intact peptide as correlating with the estrogen receptor expression in breast cancer tissues. Additional development of this general concept toward next generation, top-down based protein profiling at high resolution is discussed.

Detection of ricin intoxication in mice using serum peptide profiling by MALDI-TOF/MS

Ricin toxin has been regarded as one of the most potent poisons in the plant kingdom, and there is no effective therapeutic countermeasure or licensed vaccine against it. Consequently, early detection of ricin intoxication is necessary. In this study, we took mice as test subjects, and used the technique of Matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF/MS) and ClinProt™ microparticle beads to set up an effective detection model with an accuracy of almost 100%. Eighty-two peaks in the mass range 1000-10,000 m/z were detected by ClinProTools software, and five different peaks with m/z of 4982.49, 1333.25, 1537.86, 4285.05 and 2738.88 had the greatest contribution to the accuracy and sensitivity of this model. They may therefore provide biomarkers for ricin intoxication.

Detection of murine toxoplasmosis using magnetic bead-based serum peptide profiling by MALDI-TOF MS

Establishment of a rapid, highly specific, and accurate method for diagnosis of Toxoplasma gondii infection is essential to control and prevent zoonotic toxoplasmosis. In this study, a novel diagnostic strategy using magnetic bead-based serum peptide profiling by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was developed. The serum peptides (samples I, II, and III) from T. gondii RH strain-infected mice at days 3, 6, and 9 post-infection (p.i.), and healthy mice were enriched by the optimized magnetic bead-based hydrophobic interaction (MB-HIC8). The mass spectrograms were acquired by MALDI-TOF MS, and analyzed by ClinProTools bioinformatics software from Bruker Daltonics. The diagnostic models from T. gondii RH-infected serum peptide profiling of samples I, II, and III were produced by genetic algorithms, and verified by cross-validation. The sample II model could correctly recognize T. gondii RH strain infection in mice at days 3, 6, and 9 p.i. with a sensitivity of 91.1% and a specificity of 96.7%., and also detect T. gondii ME49 strain-infected serum samples at days 3, 6, 9, and 12 p.i. with a sensitivity of 91.7%. The results of the present study suggest that serum peptide profiling by MALDI-TOF MS is a novel potential tool for the clinical diagnosis of acute T. gondii infection.

Serum profiling by MALDI-TOF mass spectrometry as a diagnostic tool for domoic acid toxicosis in California sea lions

BACKGROUND

There are currently no reliable markers of acute domoic acid toxicosis (DAT) for California sea lions. We investigated whether patterns of serum peptides could diagnose acute DAT. Serum peptides were analyzed by MALDI-TOF mass spectrometry from 107 sea lions (acute DAT n = 34; non-DAT n = 73). Artificial neural networks (ANN) were trained using MALDI-TOF data. Individual peaks and neural networks were qualified using an independent test set (n = 20).

RESULTS

No single peak was a good classifier of acute DAT, and ANN models were the best predictors of acute DAT. Performance measures for a single median ANN were: sensitivity, 100%; specificity, 60%; positive predictive value, 71%; negative predictive value, 100%. When 101 ANNs were combined and allowed to vote for the outcome, the performance measures were: sensitivity, 30%; specificity, 100%; positive predictive value, 100%; negative predictive value, 59%.

CONCLUSIONS

These results suggest that MALDI-TOF peptide profiling and neural networks can perform either as a highly sensitive (100% negative predictive value) or a highly specific (100% positive predictive value) diagnostic tool for acute DAT. This also suggests that machine learning directed by populations of predictive models offer the ability to modulate the predictive effort into a specific type of error.

Reproducibility of mass spectrometry based protein profiles for diagnosis of ovarian cancer across clinical studies: A systematic review

The focus of this systematic review is to give an overview of the current status of clinical protein profiling studies using MALDI and SELDI MS platforms in the search for ovarian cancer biomarkers. A total of 34 profiling studies were qualified for inclusion in the review. Comparative analysis of published discriminatory peaks to peaks found in an original MALDI MS protein profiling study was made to address the key question of reproducibility across studies. An overlap was found despite substantial heterogeneity between studies relating to study design, biological material, pre-analytical treatment, and data analysis. About 47% of the peaks reported to be associated to ovarian cancer were also represented in our experimental study, and 34% of these redetected peaks also showed a significant difference between cases and controls in our study. Thus, despite known problems related to reproducibility an overlap in peaks between clinical studies was demonstrated, which indicate convergence toward a set of common discriminating, reproducible peaks for ovarian cancer. The potential of the discriminating protein peaks for clinical use as ovarian cancer biomarkers will be discussed and evaluated. This article is part of a Special Issue entitled: Proteomics: The clinical link.

Mass spectrometry-based salivary proteomics for the discovery of head and neck squamous cell carcinoma

The 5-year survival rates for cases of head and neck squamous cell carcinoma (HNSCC) are only some 60%, mainly because 20%-40% of the patients develop a local relapse in the same or an adjacent anatomic region, even when the surgical margins are histologically tumour-free. Tumours are often discovered in an advanced stage because of the lack of specific symptoms and the diagnostic difficulties. The more advanced the stage of the tumour, the more invasive the diagnostic and treatment interventions needed. An early molecular diagnosis is therefore of vital importance in order to increase the survival rate. The aim of this study was to develop an efficient rapid and sensitive mass spectrometric method for the detection of differentially expressed proteins as tumour-specific biomarkers in saliva from HNSCC patients. Whole saliva samples were collected from patients with HNSCC and from healthy subjects. The proteins were profiled by using SDS PAGE, MALDI TOF/TOF mass spectrometry and the Mascot database search engine. Several potential tumour markers were identified, including annexin A1, beta- and gamma-actin, cytokeratin 4 and 13, zinc finger proteins and P53 pathway proteins. All of these proteins play a proven role in tumour genesis, and have not been detected previously in saliva. Salivary proteomics is a non-invasive specific method for cancer diagnosis and follow-up treatment. It provides facilities for the readily reproducible and reliable detection of tumours in early stages.

Detection of Leishmania donovani infection using magnetic beads-based serum peptide profiling by MALDI-TOF MS in mice model

Leishmaniasis is an important parasitic disease, and definite diagnosis using a specific and sensitive method is the first step to cure the disease. Here, we present a novel diagnostic strategy based on serum peptide profiling by magnetic beads and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The serum peptides from the Leishmani donovani-infected and healthy mice were enriched by the optimized magnetic beads. The mass spectrograms were acquired by MALDI-TOF MS and analyzed by the ClinProTools bioinformatics software from Bruker Daltonics. The diagnostic model of serum peptide profiling produced by the ClinProTools software could correctly detect L. donovani infection in mice from the third day post-infection, with the accuracy of 94.1%, sensitivity of 92.4%, and specificity of 97.1%, respectively. The results of the present study suggested that the serum peptide profiling by MALDI-TOF MS is a novel potential tool for the clinical diagnosis of leishmaniasis.

The application of atmospheric pressure matrix-assisted laser desorption/ionization to the analysis of long-term cryopreserved serum peptidome

Although most time-of-flight (TOF) mass spectrometers come equipped with vacuum matrix-assisted laser desorption/ionization (MALDI) sources, the atmospheric pressure MALDI (API-MALDI) source is an attractive option because of its ability to be coupled to a wide range of analyzers. This article describes the use of an API-MALDI source coupled to a TOF mass spectrometer for evaluation of the effects of medium- and long-term storage on peptidomic profiles of cryopreserved serum samples from healthy women. Peptides were purified using superparamagnetic beads either from fresh sera or after serum storage at -80°C for 18 months or at -20°C for 8 years. Data were preprocessed using newly developed bioinformatic tools and then were subjected to statistical analysis and class prediction. The analyses showed a dramatic effect of storage on the abundance of several peptides such as fibrinopeptides A and B, complement fractions, bradykinin, and clusterin, indicated by other authors as disease biomarkers. Most of these results were confirmed by shadow clustering analysis, able to classify each sample in the correct group. In addition to demonstrating the suitability of the API-MALDI technique for peptidome profiling studies, our data are of relevance for retrospective studies that involve frozen sera stored for many years in biobanks.

The application of SELDI-TOF-MS in clinical diagnosis of cancers

Cancer diagnosis is important, and the early diagnosis of cancers could predict a more successful treatment. The proteomic studies emerged to be useful in combined analyses of samples from patients and provide more accurate diagnosis when compared to the single-factor-based diagnosis. In recent years, cancer detection with surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS) is flourishing and brought significant progress in this area. This paper summarizes some recent results with this technique for cancer diagnosis.

MALDI-TOF serum profiling using semiautomated serum peptide capture with magnetic reversed phase (C18) beads

Mass spectrometry can be used to generate diagnostic peptide peak profiles "signatures" of serum samples. Peak profiles can be used to compare different sera and correlate samples (i.e., patient groups) with clinical data to assist in diagnosis, monitoring, and/or prediction. We describe the semiautomated capture of serum peptides/small proteins with magnetic beads that harbor C18 alkyl chains, the deposition of captured material on a target plate for MALDI-TOF mass spectrometry, as well as, general guidelines for data acquisition. We also include, in a separate note, a short manual version of the capture procedure. Overall, the serum sample processing protocol, reported here, is reproducible and robust. In conjunction with MALDI-TOF mass spectrometry, this protocol allows for the profiling of several hundreds of serum peptides.