Electroless Ionization Mass Spectrometry Using a Compact Electrokinetic Ionization Source

We introduce a new ionization technique for compact, portable mass spectrometers. It consists of a syringe with sample liquid capped by a self-ionizing spray nozzle containing a microfabricated nozzle chip. Interaction of the sample liquid with the nozzle wall results in electrical charging without the need for electronics. Elaborate cleaning procedures are redundant when disposable syringes and mass-fabricated spray nozzles are used. This self-named electroless spray ionization (ELI) technique shows comparable performance to conventional ionization techniques. In contrast to commonly used electrospray ionization, ELI exhibits excellent ionization efficiency for low-conductive solutions such as water or acetonitrile. Due to its compact size and the absence of high-voltage electronics, it can also be readily integrated in other ionization sources. Besides reviewing the main properties of ELI, we showcase the technique's potential for two on-site, ambient mass spectroscopy applications: perfume fingerprinting and fast screening of fungicides on citrus fruits.

Rapid detection of serum free light chains by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Introduction: Serum free light chain (FLC) measurements are increasingly prominent for patients with plasma cell disorders (PCDs) in screening, prognostic stratification, and monitoring therapy responses. Objectives: We aimed to develop a sensitive, reliable, and accurate method for diagnosing PCDs that can notably decrease the time and cost of current methods. Methods: Here, we present a novel approach for FLC measurement using immunoenrichment on micro-affinity chromatography in combination with matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) detection. In this study, serum free kappa (κ) and free lambda (λ) light chain (LC) levels in the serum of 105 patients were compared between the nephelometric serum FLC quantification and MALDI-TOF MS detection. Results: Cohen's kappa coefficient between the MALDI-TOF MS-based method and the FLC assay revealed an almost perfect agreement in the case of normal (negative) results (κ = 0.92; 95% confidence interval (CI): 0.837 to 0.968) and a good agreement in the case of increased (positive) results (κ = 0.76; 95% CI: 0.608 to 0.870). In Spearman's correlation analysis, the best correlation was found between serum free κ/λ ratios (r = 0.628, 0.496 to 0.732; p <0.0001). Our method showed sensitivity (92.5%) and specificity (76.3%) for discrimination between the κ/λ FLC ratio compared to the serum FLC assay. Conclusion: The proposed method can significantly contribute to diagnosing and monitoring PCDs as it can significantly be time-saving, cost-effective in FLC measurement.

Absolute Quantitative Targeted Proteomics Assays for Plasma Proteins

Preclinical and clinical trials require rapid, precise, and multiplexed analytical methods to characterize the complex samples and to allow high-throughput biomarker monitoring with low consumption of sample material. Targeted proteomics has been used to address these challenges when quantifying protein abundances in complex biological matrices. In many of these studies, blood plasma is collected either as the main research or diagnostic sample or in combination with other specimens. Mass spectrometry (MS)-based targeted proteomics using multiple reaction monitoring (MRM) or parallel reaction monitoring (PRM) with stable isotope-labeled internal standard (SIS) peptides allows robust characterization of blood plasma protein via absolute quantification. Compared to other commonly used technologies like enzyme-linked immunosorbent assay (ELISA), targeted proteomics is faster, more sensitive, and more cost-effective. Here we describe a protocol for the quantification of proteins in blood plasma using targeted MRM proteomics with heavy-labeled internal standards. The 270-protein panel allows rapid and robust absolute quantitative proteomic characterization of blood plasma in a 1 h gradient. The method we describe here works for non-depleted plasma, which makes it simple and easy to implement. Moreover, the protocol works with the two most commonly used blood plasma collection methods used in practice, namely, either K₂EDTA or sodium citrate as anticoagulants.

Revealing the Hidden Diagnostic Clues of Male Infertility from Human Seminal Plasma by Dispersive Solid Phase Extraction and MALDI-TOF MS

Seminal plasma (SP) mirrors the local pathophysiology of the male reproductive system and represents a non-invasive fluid for the study of infertility. Matrix-Assisted Laser Desorption/Ionization-Time-of-Flight Mass Spectrometry (MALDI-TOF-MS) provides a high-throughput platform to rapidly extrapolate the diagnostic profiles of information-rich patterns. In this study, dispersive solid phase extraction (d-SPE) combined with MALDI-TOF-MS was applied for the first time to the human SP, with the aim of revealing a diagnostic signature for male infertility. Commercially available octadecyl (C₁₈)-, octyl (C₈)-bonded silica sorbents and hexagonal mesoporous silica (HMS) were tested and the robustness of MALDI-TOF peptide profiling was evaluated. Best performances were obtained for C₁₈-bonded silica with the highest detection of peaks and the lowest variation of spectral features. To assess the diagnostic potential of the method, C₁₈-bonded silica d-SPE and MALDI-TOF-MS were used to generate enriched endogenous peptide profiles of SP from 15 fertile and 15 non-fertile donors. Principal component analysis (PCA) successfully separated fertile from non-fertile men into two different clusters. An array of seven semenogelin-derived peptides was found to distinguish the two groups, with high statistical significance. These findings, while providing a rapid and convenient route to selectively enrich native components of SP peptidome, strongly reinforce the prominent role of semenogelins in male infertility.

Isoform 1 of Fibrinogen Alpha Chain Precursor is a Potential Biomarker for Steroid-Induced Osteonecrosis of the Femoral Head

PURPOSE

Early diagnosis is crucial to increase the chances of conservation treatment for patients with steroid-induced osteonecrosis of the femoral head (SIONFH). This study aimed to identify serum peptides as potential biomarkers to diagnose SIONFH.

EXPERIMENTAL DESIGN

The serum proteome of 32 SIONFH patients and 24 healthy controls are analyzed using magnetic bead-based weak cation exchange (MB-WCX) and matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF-MS). Next, candidate biomarkers are identified using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS). Candidate biomarkers are then validated using ELISA and western blotting.

RESULTS

39 peaks are identified and the expression fold changes of seven peaks in the two groups are greater than 1.5. Three peaks (m/z: 1077.84 Da; m/z: 1061.78 Da; m/z: 1099.56 Da) tend to be upregulated, while four peaks (m/z: 3973.92 Da; m/z: 7766.53 Da; m/z: 3957.31 Da; m/z: 4212.02 Da) tend to be down-regulated in SIONFH patients. The peak for a 1077.84 Da peptide is identified as Isoform 1 of the Fibrinogen alpha chain precursor (FGA). ELISAs and western blot analyses reveal that the expression of FGA is significantly higher in SIONFH patients than healthy controls.

CONCLUSION AND CLINICAL RELEVANCE

FGA is overexpressed in SIONFH patients, and thus, is a novel potential biomarker for SIONFH.

High Sensitive Immunoelectrochemical Measurement of Lung Cancer Tumor Marker ProGRP Based on TiO₂-Au Nanocomposite

Progastrin-releasing peptide (ProGRP), which is known to be highly specific and sensitive to small cell lung cancer (SCLC), has been proven to be a valuable substitute for neuron-specific enolase in SCLC diagnostics and monitoring, especially in its early stages. The detection of ProGRP levels also facilitates a selection of therapeutic treatments. For the fabrication of our proposed biosensor, titanium (IV) oxide microparticles were first used, followed by dispersing gold nanoparticles into chitosan and immobilizing them onto a carbon paste electrode (CPE) surface. The developed immunosensor exhibits a much higher biosensing performance in comparison with current methods, when it comes to the detection of ProGRP. Therefore, the proposed CPE/TiO₂/(CS+AuNPs)/anti-ProGRP/BSA/ProGRP is excellent for the development of a compact diagnostics apparatus.

Identification of serum proteins AHSG, FGA and APOA-I as diagnostic biomarkers for gastric cancer

Background

The development of clinically accessible biomarkers is critical for the early diagnosis of gastric cancer (GC) in patients. High-throughput proteomics techniques could not only effectively generate a serum peptide profile but also provide a new approach to identify potentially diagnostic and prognostic biomarkers for cancer patients.

Methods

In this study, we aim to identify potentially discriminating serum biomarkers for GC. In the discovery cohort, we screened potential biomarkers using magnetic-bead-based purification and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry in 64 samples from 32 GC patients that were taken both pre- and post-operatively and 30 healthy volunteers that served as controls. In the validation cohort, the expression patterns and diagnostic values of serum FGA, AHSG and APOA-I were further confirmed by ELISA in 42 paired GC patients (pre- and post-operative samples from 16 patients with pathologic stage I/II and 26 with stage III/IV), 30 colorectal cancer patients, 30 hepatocellular carcinoma patients, and 28 healthy volunteers.

Results

ClinProTools software was used and annotated 107 peptides, 12 of which were differentially expressed among three groups (P < 0.0001, fold > 1.5). These 12 peptide peaks were further identified as FGA, AHSG, APOA-I, HBB, TXNRD1, GSPT2 and CAKP5. ELISA data suggested that the serum levels of FGA, AHSG and APOA-I in GC patients were significantly different compared with healthy controls and had favorable diagnostic values for GC patients. Moreover, we found that the serum levels of these three proteins were associated with TNM stages and could reflect tumor burden.

Conclusion

Our findings suggested that FGA, AHSG and APOA-I might be potential serum biomarkers for GC diagnosis.

ExSTA: External Standard Addition Method for Accurate High-Throughput Quantitation in Targeted Proteomics Experiments

PURPOSE

Targeted proteomics using MRM with stable-isotope-labeled internal-standard (SIS) peptides is the current method of choice for protein quantitation in complex biological matrices. Better quantitation can be achieved with the internal standard-addition method, where successive increments of synthesized natural form (NAT) of the endogenous analyte are added to each sample, a response curve is generated, and the endogenous concentration is determined at the x-intercept. Internal NAT-addition, however, requires multiple analyses of each sample, resulting in increased sample consumption and analysis time.

EXPERIMENTAL DESIGN

To compare the following three methods, an MRM assay for 34 high-to-moderate abundance human plasma proteins is used: classical internal SIS-addition, internal NAT-addition, and external NAT-addition-generated in buffer using NAT and SIS peptides. Using endogenous-free chicken plasma, the accuracy is also evaluated.

RESULTS

The internal NAT-addition outperforms the other two in precision and accuracy. However, the curves derived by internal vs. external NAT-addition differ by only ≈3.8% in slope, providing comparable accuracies and precision with good CV values.

CONCLUSIONS AND CLINICAL RELEVANCE

While the internal NAT-addition method may be "ideal", this new external NAT-addition can be used to determine the concentration of high-to-moderate abundance endogenous plasma proteins, providing a robust and cost-effective alternative for clinical analyses or other high-throughput applications.

Systemic effects of ionizing radiation at the proteome and metabolome levels in the blood of cancer patients treated with radiotherapy: the influence of inflammation and radiation toxicity

PURPOSE

Blood is the most common replacement tissue used to study systemic responses of organisms to different types of pathological conditions and environmental insults. Local irradiation during cancer radiotherapy induces whole body responses that can be observed at the blood proteome and metabolome levels. Hence, comparative blood proteomics and metabolomics are emerging approaches used in the discovery of radiation biomarkers. These techniques enable the simultaneous measurement of hundreds of molecules and the identification of sets of components that can discriminate different physiological states of the human body. Radiation-induced changes are affected by the dose and volume of irradiated tissues; hence, the molecular composition of blood is a hypothetical source of biomarkers for dose assessment and the prediction and monitoring of systemic responses to radiation. This review aims to provide a comprehensive overview on the available evidence regarding molecular responses to ionizing radiation detected at the level of the human blood proteome and metabolome. It focuses on patients exposed to radiation during cancer radiotherapy and emphasizes effects related to radiation-induced toxicity and inflammation.

CONCLUSIONS

Systemic responses to radiation detected at the blood proteome and metabolome levels are primarily related to the intensity of radiation-induced toxicity, including inflammatory responses. Thus, several inflammation-associated molecules can be used to monitor or even predict radiation-induced toxicity. However, these abundant molecular features have a rather limited applicability as universal biomarkers for dose assessment, reflecting the individual predisposition of the immune system and tissue-specific mechanisms involved in radiation-induced damage.

Glycans and glycoproteins as specific biomarkers for cancer

Protein glycosylation and other post-translational modifications are involved in potentially all aspects of human growth and development. Defective glycosylation has adverse effects on human physiological conditions and accompanies many chronic and infectious diseases. Altered glycosylation can occur at the onset and/or during tumor progression. Identifying these changes at early disease stages may aid in making decisions regarding treatments, as early intervention can greatly enhance survival. This review highlights some of the efforts being made to identify N- and O-glycosylation profile shifts in cancer using mass spectrometry. The analysis of single or panels of potential glycoprotein cancer markers are covered. Other emerging technologies such as global glycan release and site-specific glycosylation analysis and quantitation are also discussed. Graphical Abstract Steps involved in the biomarker discovery.

Developments in FTICR-MS and Its Potential for Body Fluid Signatures

Fourier transform mass spectrometry (FTMS) is the method of choice for measurements that require ultra-high resolution. The establishment of Fourier transform ion cyclotron resonance (FTICR) MS, the availability of biomolecular ionization techniques and the introduction of the Orbitrap™ mass spectrometer have widened the number of FTMS-applications enormously. One recent example involves clinical proteomics using FTICR-MS to discover and validate protein biomarker signatures in body fluids such as serum or plasma. These biological samples are highly complex in terms of the type and number of components, their concentration range, and the structural identity of each species, and thus require extensive sample cleanup and chromatographic separation procedures. Clearly, such an elaborate and multi-step sample preparation process hampers high-throughput analysis of large clinical cohorts. A final MS read-out at ultra-high resolution enables the analysis of a more complex sample and can thus simplify upfront fractionations. To this end, FTICR-MS offers superior ultra-high resolving power with accurate and precise mass-to-charge ratio (m/z) measurement of a high number of peptides and small proteins (up to 20 kDa) at isotopic resolution over a wide mass range, and furthermore includes a wide variety of fragmentation strategies to characterize protein sequence and structure, including post-translational modifications (PTMs). In our laboratory, we have successfully applied FTICR “next-generation” peptide profiles with the purpose of cancer disease classifications. Here we will review a number of developments and innovations in FTICR-MS that have resulted in robust and routine procedures aiming for ultra-high resolution signatures of clinical samples, exemplified with state-of-the-art examples for serum and saliva.

Topics in Study Design and Analysis for Multistage Clinical Proteomics Studies

This chapter discusses the design issues in clinical proteomics study and provides specific suggestions for addressing these questions when using the standard guidelines for the planning. It provides two methods for the sample size estimation in study design. The first method is used for the planning of a clinical proteomic study at the discovery or verification stage; the second method is proposed for the systematic planning of a multistage study. The second part of the chapter introduces three approaches to analyzing the clinical proteomic study and provides analyses for two case studies of clinical proteomic discoveries.

Identification of serum proteome signatures of locally advanced and metastatic gastric cancer: a pilot study

BACKGROUND

The gastric cancer is one of the most common and mortal cancer worldwide. The initial asymptomatic development and further nonspecific symptoms result in diagnosis at the advanced stage with poor prognosis. Yet, no clinically useful biomarkers are available for this malignancy, and invasive gastrointestinal endoscopy remains the only reliable option at the moment. Hence, there is a need for discovery of clinically useful noninvasive diagnostic and/or prognostic tool as an alternative (or complement) for current diagnostic tools. Here we aimed to search for serum proteins characteristic for local and invasive gastric cancer.

METHODS

Pre-treatment blood samples were collected from patients with diagnosed gastric adenocarcinoma at the different stage of disease: 35 patients with locally advanced cancer and 18 patients with metastatic cancer; 50 healthy donors were also included as a control group. The low-molecular-weight fraction of serum proteome (i.e., endogenous peptidome) was profiled by the MALDI-ToF mass spectrometry, and the whole proteome components were identified and quantified by the LC-MS/MS shotgun approach.

RESULTS

Multicomponent peptidome signatures were revealed that allowed good discrimination between healthy controls and cancer patients, as well as between patients with locally advanced and metastatic cancer. Moreover, a LC-MS/MS approach revealed 49 serum proteins with different abundances between healthy donors and cancer patients (predominantly proteins associated with inflammation and acute phase response). Furthermore, 19 serum proteins with different abundances between patients with locally advanced and metastatic cancer were identified (including proteins associated with cytokine/chemokine response and metabolism of nucleic acids). However, neither peptidome profiling nor shotgun proteomics approach allowed detecting serum components discriminating between two subgroups of patients with local disease who either developed or did not develop metastases during follow-up.

CONCLUSIONS

The molecular differences between locally advanced and metastatic gastric cancer, as well as more obvious differences between healthy individuals and cancer patients, have marked reflection at the level of serum proteome. However, we have no evidence that features of pre-treatment serum proteome could predict a risk of cancer dissemination in patients treated due to local disease. Nevertheless, presented data confirmed potential applicability of a serum proteome signature-based biomarker in diagnostics of gastric cancer.

Partial-Body Irradiation in Patients with Prostate Cancer Treated with IMRT Has Little Effect on the Composition of Serum Proteome

Partial body irradiation during cancer radiotherapy (RT) induces a response of irradiated tissues that could be observed at the level of serum proteome. Here we aimed to characterize the response to RT in group of patients treated because of prostate cancer. Five consecutive blood samples were collected before, during, and after the end of RT in a group of 126 patients who received definitive treatment with a maximum dose of 76 Gy. Serum peptidome, which was profiled in the 2000–16,000 Da range using MALDI-MS. Serum proteins were identified and quantified using the shotgun LC-MS/MS approach. The majority of changes in serum peptidome were detected between pre-treatment samples and samples collected after 3–4 weeks of RT (~25% of registered peptides changed their abundances significantly), yet the intensity of observed changes was not correlated significantly with the degree of acute radiation toxicity or the volume of irradiated tissues. Furthermore, there were a few serum proteins identified, the abundances of which were different in pre-RT and post-RT samples, including immunity and inflammation-related factors. Observed effects were apparently weaker than in comparable groups of head and neck cancer patients in spite of similar radiation doses and volumes of irradiated tissues in both groups. We concluded that changes observed at the level of serum proteome were low for this cohort of prostate cancer patients, although the specific components involved are associated with immunity and inflammation, and reflect the characteristic acute response of the human body to radiation.

Mass spectrometric characterization of limited proteolysis activity in human plasma samples under mild acidic conditions

We developed a limited proteolysis assay for estimating dynamics in plasma-borne protease activities using MALDI ToF MS analysis as readout. A highly specific limited proteolysis activity was elicited in human plasma by shifting the pH to 6. Mass spectrometry showed that two singly charged ion signals at m/z 2753.44 and m/z 2937.56 significantly increased in abundance under mild acidic conditions as a function of incubation time. For proving that a provoked proteolytic activity in mild acidic solution caused the appearance of the observed peptides, control measurements were performed (i) with pepstatin as protease inhibitor, (ii) with heat-denatured samples, (iii) at pH 1.7, and (iv) at pH 7.5. Mass spectrometric fragmentation analysis showed that the observed peptides encompass the amino acid sequences 1-24 and 1-26 from the N-terminus of human serum albumin. Investigations on peptidase specificities suggest that the two best candidates for the observed serum albumin cleavages are cathepsin D and E. Reproducibility, robustness, and sensitivity prove the potential of the developed limited proteolysis assay to become of clinical importance for estimating dynamics of plasma-borne proteases with respect to associated pathophysiological tissue conditions.

Using data-independent, high-resolution mass spectrometry in protein biomarker research: perspectives and clinical applications

In medicine, there is an urgent need for protein biomarkers in a range of applications that includes diagnostics, disease stratification, and therapeutic decisions. One of the main technologies to address this need is MS, used for protein biomarker discovery and, increasingly, also for protein biomarker validation. Currently, data-dependent analysis (also referred to as shotgun proteomics) and targeted MS, exemplified by SRM, are the most frequently used mass spectrometric methods. Recently developed data-independent acquisition techniques combine the strength of shotgun and targeted proteomics, while avoiding some of the limitations of the respective methods. They provide high-throughput, accurate quantification, and reproducible measurements within a single experimental setup. Here, we describe and review data-independent acquisition strategies and their recent use in clinically oriented studies. In addition, we also provide a detailed guide for the implementation of SWATH-MS (where SWATH is sequential window acquisition of all theoretical mass spectra)-one of the data-independent strategies that have gained wide application of late.

Identification of peptide regions of SERPINA1 and ENOSF1 and their protein expression as potential serum biomarkers for gastric cancer

This study aimed to detect potential serum biomarkers for gastric cancer. In the present study, we used magnetic bead-based purification and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry to detect potential serum markers in 70 gastric cancer (GC) patients compared with 72 healthy controls. On average, up to 81 peaks, of which 11 were significantly different m/z peaks (fold change >1.5; P < 0.001, Wilcoxon rank sum test) between GC group and healthy controls were detected. Two potential gastric serum biomarkers (m/z values of 1546.02 and 5335.08), with higher and specific expression in GC patients were further identified as peptide regions of SERPINA1 and ENOSF1. Enzyme-linked immunosorbent assays (ELISAs) were used to analyze 210 additional serum samples obtained from 36 healthy volunteers, 36 GC patients, 30 GU patients, 36 nonsmall-cell lung cancer (NSCLC) patients, 36 clear-cell renal cell carcinoma (CCRCC) patients, and 36 pancreatic cancer patients to verify the expression of SERPINA1 and ENOSF1 in GC sera. The suitability of the present method for gastric serum proteomic analysis was demonstrated and led to the identification of two peptide regions and their corresponding proteins as potential serum biomarkers for the serum detection of GC.

Advances in tissue section preparation for MALDI imaging MS

Enriched by a decade of remarkable developments, matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI IMS) has witnessed a phenomenal expansion. Initially introduced for the mapping of peptides and intact proteins from mammalian tissue sections, MALDI IMS applications now extend to a wide range of molecules including peptides, lipids, metabolites and xenobiotics. Technology and methodology are quickly evolving to push the limits of the technique forward. Within a short period of time, numerous protocols and concepts have been developed and introduced in tissue section preparation, nonexhaustively including in situ tissue chemistries and solvent-free matrix depositions. Considering the past progress and current capabilities, this Review aims to cover the different aspects and challenges of tissue section preparation for MALDI IMS.

Proteomics for discovery of candidate colorectal cancer biomarkers

Colorectal cancer (CRC) is the second most common cause of cancer-related deaths in Europe and other Western countries, mainly due to the lack of well-validated clinically useful biomarkers with enough sensitivity and specificity to detect this disease at early stages. Although it is well known that the pathogenesis of CRC is a progressive accumulation of mutations in multiple genes, much less is known at the proteome level. Therefore, in the last years many proteomic studies have been conducted to find new candidate protein biomarkers for diagnosis, prognosis and as therapeutic targets for this malignancy, as well as to elucidate the molecular mechanisms of colorectal carcinogenesis. An important advantage of the proteomic approaches is the capacity to look for multiple differentially expressed proteins in a single study. This review provides an overview of the recent reports describing the different proteomic tools used for the discovery of new protein markers for CRC such as two-dimensional electrophoresis methods, quantitative mass spectrometry-based techniques or protein microarrays. Additionally, we will also focus on the diverse biological samples used for CRC biomarker discovery such as tissue, serum and faeces, besides cell lines and murine models, discussing their advantages and disadvantages, and summarize the most frequently identified candidate CRC markers.

Biomarkers screening between preoperative and postoperative patients in pancreatic cancer

OBJECTIVE

To investigate discriminating protein patterns and potential biomarkers in serum samples between pre/postoperative pancreatic cancer patients and healthy controls.

METHODS

23 serum samples from PC patients (12 preoperative and 11 postoperative) and 76 from healthy controls were analyzed using matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS) technique combined with magnetic beads-based weak cation-exchange chromatography (MB-WCX). ClinProTools software selected several markers that made a distinction between pancreatic cancer patients and healthy controls.

RESULTS

49 m/z distinctive peaks were found among the three groups, of which 33 significant peaks with a P < 0.001 were detected. Two proteins could distinguish the preoperative pancreatic cancer patients from the healthy controls. About 15 proteins may be potential biomarkers in assessment of pancreatic cancer resection.

CONCLUSION

MB-MALDI-TOF-MS method could generate serum peptidome profiles of pancreatic cancer and provide a new approach to identify potential biomarkers for diagnosis and prognosis of this malignancy.

Radiotherapy-related changes in serum proteome patterns of head and neck cancer patients; the effect of low and medium doses of radiation delivered to large volumes of normal tissue

Background

Conformal intensity-modulated radiation therapy (IMRT) involves irradiation of large volume of normal tissue with low and medium doses, biological relevance of which is not clear yet. Serum proteome features were used here to study the dose-volume effects in patients irradiated with IMRT due to head and neck cancer.

Methods

Blood samples were collected before and during RT, and also about one month and one year after the end of RT in a group of 72 patients who received definitive treatment. Serum proteome profiles were analyzed using MALDI-ToF mass spectrometry in 800–14,000 Da range.

Results

Major changes in serum proteome profiles were observed between pre-treatment samples and samples collected one month after RT. Radiation-related changes in serum proteome features were affected by low-to-medium doses delivered to a large fraction of body mass. Proteome changes were associated with intensity of acute radiation toxicity, indicating collectively that RT-related features of serum proteome reflected general response of patient’s organism to irradiation. However, short-term dose-related changes in serum proteome features were not associated significantly with the long-term efficacy of the treatment.

Conclusions

The effects of low and medium doses of radiation have been documented at the level of serum proteome, which is a reflection of the patient’s whole body response.

Bottom-Up Mass Spectrometry-Based Proteomics as an Investigative Analytical Tool for Discovery and Quantification of Proteins in Biological Samples

OBJECTIVE

The objective of this overview is to introduce bottom-up mass spectrometry (MS)-based proteomics approaches and strategies, widely used in other biomedical research fields, to the wound-healing research community.

APPROACHES

TWO MAJOR PROTEOMICS WORKFLOWS ARE DISCUSSED: gel-based and gel-free chromatographic separation to reduce the complexity of the sample at protein and peptide level, respectively, prior to nano-liquid chromatography-tandem mass spectrometry analysis. Other strategies to discover less abundant proteins present in the sample, are also briefly discussed along with label-free and label-incorporated methods for protein quantification. Overall, the experimental workflows are designed and continually improved to increase the number of proteins identifiable and quantifiable.

DISCUSSION

Recent advances and improvements in all areas of proteomics workflow from sample preparation, to acquisition of massive amounts of data, to bioinformatics analysis have made this technology an indispensable tool for in-depth large-scale characterization of complex proteomes. This technology has been successfully applied in studies focusing on biomarker discovery, differential protein expression, protein-protein interactions, and post-translational modifications in complex biological samples such as cerebrospinal fluid, serum and plasma, and urine from patients. The publications from these studies have reported greater number of identified proteins, novel biomarker candidates, and post-translational modifications previously unknown.

CONCLUSIONS

The qualitative and quantitative protein analysis of the protein population of wound tissues or fluids at different stages is important in wound healing research. Given the complexities and analytical challenges of these samples, MS-based proteomic workflows further improved with recent advances offer a powerful and attractive technology for this purpose.

Laser ionization mass spectrometry in oral squamous cell carcinoma

Biomarker research in oral squamous cell carcinoma (OSCC) aims for screening/early diagnosis and in predicting its recurrence, metastasis and overall prognosis. This article reviews the current molecular perspectives and diagnosis of oral cancer with proteomics using matrix-assisted laser desorption ionization (MALDI) and surface-enhanced laser desorption ionization (SELDI) mass spectrometry (MS). This method shows higher sensitivity, accuracy, reproducibility and ability to handle complex tissues and biological fluid samples. However, the data interpretation tools of contemporary mass spectrometry still warrant further improvement. Based on the data available with laser-based mass spectrometry, biomarkers of OSCC are classified as (i) diagnosis and prognosis, (ii) secretory, (iii) recurrence and metastasis, and (iv) drug targets. Majority of these biomarkers are involved in cell homeostasis and are either physiologic responders or enzymes. Therefore, proteins directly related to tumorigenesis have more diagnostic value. Salivary secretory markers are another group that offers a favourable and easy strategy for non-invasive screening and early diagnosis in oral cancer. Key molecular inter-related pathways in oral carcinogenesis are also intensely researched with software analysis to facilitate targeted drug therapeutics. The review suggested the need for incorporating 'multiple MS or tandem approaches' and focusing on a 'group of biomarkers' instead of single protein entities, for making early diagnosis and treatment for oral cancer a reality.

Availability of MudPIT data for classification of biological samples

Background

Mass spectrometry is an important analytical tool for clinical proteomics. Primarily employed for biomarker discovery, it is increasingly used for developing methods which may help to provide unambiguous diagnosis of biological samples. In this context, we investigated the classification of phenotypes by applying support vector machine (SVM) on experimental data obtained by MudPIT approach. In particular, we compared the performance capabilities of SVM by using two independent collection of complex samples and different data-types, such as mass spectra (m/z), peptides and proteins.

Results

Globally, protein and peptide data allowed a better discriminant informative content than experimental mass spectra (overall accuracy higher than 87% in both collection 1 and 2). These results indicate that sequencing of peptides and proteins reduces the experimental noise affecting the raw mass spectra, and allows the extraction of more informative features available for the effective classification of samples. In addition, proteins and peptides features selected by SVM matched for 80% with the differentially expressed proteins identified by the MAProMa software.

Conclusions

These findings confirm the availability of the most label-free quantitative methods based on processing of spectral count and SEQUEST-based SCORE values. On the other hand, it stresses the usefulness of MudPIT data for a correct grouping of sample phenotypes, by applying both supervised and unsupervised learning algorithms. This capacity permit the evaluation of actual samples and it is a good starting point to translate proteomic methodology to clinical application.

Processing of Mass Spectrometry Data in Clinical Applications

Mass spectrometry-based proteomics has become the leading approach for analyzing complex biological samples at a large-scale level. Its importance for clinical applications is more and more increasing, thanks to the development of high-performing instruments which allow the discovery of disease-specific biomarkers and an automated and rapid protein profiling of the analyzed samples. In this scenario, the large-scale production of proteomic data has driven the development of specific bioinformatic tools to assist researchers during the discovery processes. Here, we discuss the main methods, algorithms, and procedures to identify and use biomarkers for clinical and research purposes. In particular, we have been focused on quantitative approaches, the identification of proteotypic peptides, and the classification of samples, using proteomic data. Finally, this chapter is concluded by reporting the integration of experimental data with network datasets, as valuable instrument for identifying alterations that underline the emergence of specific phenotypes. Based on our experience, we show some examples taking into consideration experimental data obtained by multidimensional protein identification technology (MudPIT) approach.

Effects of sample delivery on analyte capture in porous bead sensors

Sample delivery is a crucial aspect of point-of-care applications where sample volumes need to be low and assay times short, while providing high analytical and clinical sensitivity. In this paper, we explore the influence of the factors surrounding sample delivery on analyte capture in an immunoassay-based sensor array manifold of porous beads resting in individual wells. We model using computational fluid dynamics and a flow-through device containing beads sensitized specifically to C-reactive protein (CRP) to explore the effects of volume of sample, rate of sample delivery, and use of recirculation vs. unilateral delivery on the effectiveness of the capture of CRP on and within the porous bead sensor. Rate of sample delivery lends to the development of a time-dependent, shrinking depletion region around the bead exterior. Our findings reveal that at significantly high rates of delivery, unique to porous bead substrates, capture at the rim of the bead is reaction-limited, while capture in the interior of the bead is transport-limited. While the fluorescence signal results from the aggregate of captured material throughout the bead, multiple kinetic regimes exist within the bead. Further, under constant pressure conditions dictated by the array architecture, we reveal the existence of an optimal flow rate that generates the highest signal, under point-of-care constraints of limited-volume and limited-time. When high sensitivity is needed, recirculation can be implemented to overcome the analyte capture limitations due to volume and time constraints. Computational simulations agree with experimental results performed under similar conditions.

Digging deeper into the field of the small cell lung cancer tumor marker ProGRP: a method for differentiation of its isoforms

In this paper, we have used a newly developed immunocapture and LC-MS method to demonstrate for the first time the presence of protein isoforms 1 and 3 of the small cell lung cancer (SCLC) marker progastrin-releasing peptide (ProGRP) in sera. In addition, the method allows for indirect determination of the relative presence of the other known isoform of ProGRP, also known as ProGRP isoform 2. This new method is able to determine total ProGRP as a marker in sera at clinically relevant levels and to differentiate between isoforms at the low-pM level through combining selective sample preparation by immunoextraction, tryptic digestion, and separation followed by detection with LC-SRM-MS of the signature peptides, NLLGLIEAK (total ProGRP), LSAPGSQR (ProGRP isoform 1), and DLVDSLLQVLNVK (ProGRP isoform 3), with accuracies ≤ 25% for lower limit of quantification (LLOQ) and precisions ≤ 33%. By analyzing serum samples from four patients diagnosed with SCLC using the here described new and fully validated method, the ability is shown to both determine total ProGRP concentration and to differentiate between ProGRP isoforms 1 and 3 in one single run. Quantification of various ProGRP isoforms in one single run may be helpful for further understanding of the underlying biochemical processes in SCLC and differentiation of small cell lung cancer.

A mass spectrometric multicenter study supports classification of preeclampsia as heterogeneous disorder

OBJECTIVE

The diagnostic value of affinity-based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis to distinguish preeclampsia (PE) from matched controls was tested in a multicenter setting.

METHODS

Serum samples of preeclamptic (n = 60) and healthy pregnant women (n = 66) from four centers were prospectively analyzed with predefined rule sets.

RESULTS

Overall sample classification reached sensitivity of 0.88 and specificity of 0.73. Separate calculations for early-onset PEs only (before 34 weeks of gestation) revealed sensitivity of 0.88 and specificity of 0.89.

CONCLUSION

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry serum-profiling with center-wise standardization offers a fast and robust method to classify PE and contributes to the theory of PE being a heterogeneous disorder that ought to be subclassified.

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.

Serum protein profiles as potential biomarkers for infectious disease status in pigs

Background

In veterinary medicine and animal husbandry, there is a need for tools allowing the early warning of diseases. Preferably, tests should be available that warn farmers and veterinarians during the incubation periods of disease and before the onset of clinical signs. The objective of this study was to explore the potential of serum protein profiles as an early biomarker for infectious disease status. Serum samples were obtained from an experimental pig model for porcine circovirus-associated disease (PCVAD), consisting of Porcine Circovirus type 2 (PCV2) infection in combination with either Porcine Parvovirus (PPV) or Porcine Reproductive and Respiratory Syndrome virus (PRRSV). Sera were collected before and after onset of clinical signs at day 0, 5 and 19 post infection. Serum protein profiles were evaluated against sera from non-infected control animals.

Results

Protein profiles were generated by SELDI-TOF mass spectrometry in combination with the Proteominer™ technology to enrich for low-abundance proteins. Based on these protein profiles, the experimentally infected pigs could be classified according to their infectious disease status. Before the onset of clinical signs 88% of the infected animals could be classified correctly, after the onset of clinical sigs 93%. The sensitivity of the classification appeared to be high. The protein profiles could distinguish between separate infection models, although specificity was moderate to low. Classification of PCV2/PRRSV infected animals was superior compared to PCV2/PPV infected animals. Limiting the number of proteins in the profiles (ranging from 568 to 10) had only minor effects on the classification performance.

Conclusions

This study shows that serum protein profiles have potential for detection and identification of viral infections in pigs before clinical signs of the disease become visible.

Precision profiling and identification of human serum peptides using Fourier transform ion cyclotron resonance mass spectrometry

Many biomarker discovery studies are based on matrix-assisted laser desorption/ionisation (MALDI) peptide profiles. In this study, 96 human serum samples were analysed on a Bruker solariX(TM) MALDI Fourier transform ion cyclotron resonance (FTICR) system equipped with a 15 tesla magnet. Isotopically resolved peptides were observed in ultrahigh resolution FTICR profiles up to m/z 6500 with mass measurement errors (MMEs) of previously identified peptides at a sub-ppm level. For comparison with our previous platform for peptide profile mass analysis (i.e. Ultraflex II) the corresponding time-of-flight (TOF) spectra were obtained with isotopically resolved peptides up to m/z 3500. The FTICR and TOF systems performed rather similar with respect to the repeatability of the signal intensities. However, the mass measurement precision improved at least 10-fold in ultrahigh resolution data and thus simplified spectral alignment necessary for robust and quantitatively precise comparisons of profiles in large-scale clinical studies. From each single MALDI-FTICR spectrum an m/z-list was obtained with sub-ppm precision for all different species, which is beneficial for identification purposes and interlaboratory comparisons. Furthermore, the FTICR system allowed new peptide identifications from collision-induced dissociation (CID) spectra using direct infusion of reversed-phase (RP) C(18)-fractionated serum samples on an electrospray ionisation (ESI) source.

Radiation-related changes in serum proteome profiles detected by mass spectrometry in blood of patients treated with radiotherapy due to larynx cancer

The study aimed to detect features of human serum proteome that were associated with exposure to ionizing radiation. The analyzed group consisted of 46 patients treated with radical radiotherapy for larynx cancer; patients were irradiated with total doses in a range from 51 to 72 Gy. Three consecutive blood samples were collected from each patient: before the start, 2 weeks after the start, and 4-6 weeks after the end of radiotherapy. The low-molecular-weight fraction of the serum proteome (2,000-13,000 Da) was analyzed by the MALDI-ToF mass spectrometry. Proteome profiles of serum samples collected before the start of radiotherapy and during the early stage of the treatment were similar. In marked contrast, mass profiles of serum samples collected several weeks after the end of the treatment revealed clear changes. We found that 41 out of 312 registered peptide ions changed their abundance significantly when serum samples collected after the final irradiation were compared with samples collected at the two earlier time points. We also found that abundances of certain serum peptides were associated with total doses of radiation received by patients. The results of this pilot study indicate that features of serum proteome analyzed by mass spectrometry have potential applicability as a retrospective marker of exposure to ionizing radiation.

Serum peptidome profiling in patients with gastric cancer

To identify discriminating protein patterns in serum samples between gastric cancer patients (early and advanced stages) and healthy controls. We used magnetic bead-based separation followed by matrix-assisted laser desorption ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS) to identify patients with gastric cancer. In total, serum samples from 62 gastric cancer patients (32 in the training set and 30 in the test set; 19 of which had early-stage tumors and 43 of which had advanced-stage tumors) and 64 healthy controls (32 in the training set and 32 in the test set) were analyzed. The mass spectra, analyzed using ClinProTools software, distinguished between cancer patients and healthy individuals based on three different algorithm models. In the training set, patients with gastric cancer could be identified with a mean sensitivity of 94.7% and a mean specificity of 99%. Similar results were obtained with the test set, showing 79.3% sensitivity and 86.5% specificity. Our study demonstrates the high sensitivity and specificity of screening serum protein patterns using MALDI-TOF MS for the identification of patients with gastric cancer.

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.

Proteomic patterns analysis with multivariate calculations as a promising tool for prompt differentiation of early stage lung tissue with cancer and unchanged tissue material

Background

Lung cancer diagnosis in tissue material with commonly used histological techniques is sometimes inconvenient and in a number of cases leads to ambiguous conclusions. Frequently advanced immunostaining techniques have to be employed, yet they are both time consuming and limited. In this study a proteomic approach is presented which may help provide unambiguous pathologic diagnosis of tissue material.

Methods

Lung tissue material found to be pathologically changed was prepared to isolate proteome with fast and non selective procedure. Isolated peptides and proteins in ranging from 3.5 to 20 kDa were analysed directly using high resolution mass spectrometer (MALDI-TOF/TOF) with sinapic acid as a matrix. Recorded complex spectra of a single run were then analyzed with multivariate statistical analysis algorithms (principle component analysis, classification methods). In the applied protocol we focused on obtaining the spectra richest in protein signals constituting a pattern of change within the sample containing detailed information about its protein composition. Advanced statistical methods were to indicate differences between examined groups.

Results

Obtained results indicate changes in proteome profiles of changed tissues in comparison to physiologically unchanged material (control group) which were reflected in the result of principle component analysis (PCA). Points representing spectra of control group were located in different areas of multidimensional space and were less diffused in comparison to cancer tissues. Three different classification algorithms showed recognition capability of 100% regarding classification of examined material into an appropriate group.

Conclusion

The application of the presented protocol and method enabled finding pathological changes in tissue material regardless of localization and size of abnormalities in the sample volume. Proteomic profile as a complex, rich in signals spectrum of proteins can be expressed as a single point in multidimensional space and than analysed using advanced statistical methods. This approach seems to provide more precise information about a pathology and may be considered in futer evaluation of biomarkers for clinical applications in different pathology. Multiparameter statistical methods may be helpful in elucidation of newly expressed sensitive biomarkers defined as many factors "in one point".

Multifactorial analysis of affinity-mass spectrometry data from serum protein samples: a strategy to distinguish patients with preeclampsia from matching control individuals

A multifactorial differential analysis of serum proteins using mass spectrometry distinguished samples from pregnant women with severe early-onset preeclampsia (n = 11) from those of control individuals with uneventful pregnancies (n = 13). Serum proteins were fractionated by either their affinities to reversed-phase material coated magnetic beads or by fractionated precipitation. The on-average most abundant ion signals were observed at m/z 9390, 9103, and 8886. The best differentiating ion signals between the two sample groups were found at m/z 13,715, 13,834, and 13,891. The normalized intensities of these ion signals were on-average lower in the preeclampsia group than in the control group. The six ion signal intensities enabled sorting of the individual spectra with high accuracy. Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) analysis showed that a protein band migrating just above the 14 kDa marker band contained transthyretin (P02766; M(r) (avg.): 13,761). Densitometric analysis of the transthyretin bands showed lower intensities in the preeclampsia samples with respect to those of the controls. Nephelometric analysis of the serum samples determined the mean concentration of transthyretin in the preeclampsia group were lower (0.16 mg/mL; range: 0.13 to 0.20; SD: 0.03) than that in the control group (0.19 mg/mL; range: 0.14 to 0.22; SD: 0.02), substantiating the role of transthyretin concentration differences in the comparison of the two groups. Altogether, our findings support the theory of preeclampsia being a heterogeneous disorder that might be sub-classified by a defined proteome signature in maternal blood using multifactorial analysis of affinity-fractionated serum samples.

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.

Imaging and 'omic' methods for the molecular diagnosis of cancer

Molecular imaging methods can noninvasively detect specific biological processes that are aberrant in cancer, including upregulated glycolytic metabolism, increased cellular proliferation and altered receptor expression. PET using the glucose analogue 18F-fluoro-2-deoxyglucose, which detects the increased glucose uptake that is a characteristic of tumor cells, has been widely used in the clinic to detect tumors and their responses to treatment; however, there are many new PET tracers being developed for a wide range of biological targets. Magnetic resonance spectroscopy (MRS), which can be used to detect cellular metabolites, can also provide prognostic information, particularly in brain, breast and prostate cancers. An emerging technique, which by hyperpolarizing 13C-labeled cell substrates dramatically enhances their sensitivity to detection, could further extend the use of MRS in molecular imaging in the clinic. Molecular diagnostics applied to serum samples or tumor samples obtained by biopsy, can measure changes at the individual cell level and the underlying changes in gene or protein expression. DNA microarrays enable high-throughput gene-expression profiling, while mass spectrometry can detect thousands of proteins that may be used in the future as biomarkers of cancer. Probing molecular changes will aid not only cancer diagnosis, but also provide tumor grading, based on gene-expression analysis and imaging measurements of cell proliferation and changes in metabolism; staging, based on imaging of metastatic spread and elevation of protein biomarkers; and the detection of therapeutic response, using serial molecular imaging measurements or monitoring of serum markers. The present article provides a summary of the molecular diagnostic methods that are currently being trialed in the clinic.

Mass spectrometry protein expression profiles in colorectal cancer tissue associated with clinico-pathological features of disease

Background

Studies of several tumour types have shown that expression profiling of cellular protein extracted from surgical tissue specimens by direct mass spectrometry analysis can accurately discriminate tumour from normal tissue and in some cases can sub-classify disease. We have evaluated the potential value of this approach to classify various clinico-pathological features in colorectal cancer by employing matrix-assisted laser desorption ionisation time of-flight-mass spectrometry (MALDI-TOF MS).

Methods

Protein extracts from 31 tumour and 33 normal mucosa specimens were purified, subjected to MALDI-Tof MS and then analysed using the 'GenePattern' suite of computational tools (Broad Institute, MIT, USA). Comparative Gene Marker Selection with either a t-test or a signal-to-noise ratio (SNR) test statistic was used to identify and rank differentially expressed marker peaks. The k-nearest neighbours algorithm was used to build classification models either using separate training and test datasets or else by using an iterative, 'leave-one-out' cross-validation method.

Results

73 protein peaks in the mass range 1800-16000Da were differentially expressed in tumour verses adjacent normal mucosa tissue (P ≤ 0.01, false discovery rate ≤ 0.05). Unsupervised hierarchical cluster analysis classified most tumour and normal mucosa into distinct cluster groups. Supervised prediction correctly classified the tumour/normal mucosa status of specimens in an independent test spectra dataset with 100% sensitivity and specificity (95% confidence interval: 67.9-99.2%). Supervised prediction using 'leave-one-out' cross validation algorithms for tumour spectra correctly classified 10/13 poorly differentiated and 16/18 well/moderately differentiated tumours (P = < 0.001; receiver-operator characteristics - ROC - error, 0.171); disease recurrence was correctly predicted in 5/6 cases and disease-free survival (median follow-up time, 25 months) was correctly predicted in 22/23 cases (P = < 0.001; ROC error, 0.105). A similar analysis of normal mucosa spectra correctly predicted 11/14 patients with, and 15/19 patients without lymph node involvement (P = 0.001; ROC error, 0.212).

Conclusions

Protein expression profiling of surgically resected CRC tissue extracts by MALDI-TOF MS has potential value in studies aimed at improved molecular classification of this disease. Further studies, with longer follow-up times and larger patient cohorts, that would permit independent validation of supervised classification models, would be required to confirm the predictive value of tumour spectra for disease recurrence/patient survival.