Applications of SELDI-MS technology in oncology

Recent Contributions of Mass Spectrometry-Based "Omics" in the Studies of Breast Cancer

Breast cancer (BC) is one of the most heterogeneous groups of cancer. As every biotype of BC is unique and presents a particular "omic" signature, they are increasingly characterized nowadays with novel mass spectrometry (MS) strategies. BC therapeutic approaches are primarily based on the two features of human epidermal growth factor receptor 2 (HER2) and estrogen receptor (ER) positivity. Various strategic MS implementations are reported in studies of BC also involving data independent acquisitions (DIAs) of MS which report novel differential proteomic, lipidomic, proteogenomic, phosphoproteomic, and metabolomic characterizations associated with the disease and its therapeutics. Recently many "omic" studies have aimed to identify distinct subsidiary biotypes for diagnosis, prognosis, and targets of treatment. Along with these, drug-induced-resistance phenotypes are characterized by "omic" changes. These identifying aspects of the disease may influence treatment outcomes in the near future. Drug quantifications and characterizations are also done regularly and have implications in therapeutic monitoring and in drug efficacy assessments. We report these studies, mentioning their implications toward the understanding of BC. We briefly provide the MS instrumentation principles that are adopted in such studies as an overview with a brief outlook on DIA-MS strategies. In all of these, we have chosen a model cancer for its revelations through MS-based "omics".

Fin field-effect-transistor engineered sensor for detection of MDA-MB-231 breast cancer cells: A switching-ratio-based sensitivity analysis

The present study describes the utilization of a gallium-arsenide gate-stack gate-all-around (GaAs-GS-GAA) fin field-effect transistor (FinFET) to accomplish the electrical identification of the breast cancer cell MDA-MB-231 by monitoring the device switching ratio. The proposed sensor uses four nanocavities carved beneath the gate electrodes for enhanced detection sensitivity. MDA-MB-231 (cancerous) and MCF-10A (healthy) breast cells have a distinct dielectric constant, and it changes when exposed to microwave frequencies spanning across 200 MHz and 13.6 GHz, which modifies the electrical characteristics, allowing for early diagnosis. First, a percentage shift in the primary DC characteristics is presented to demonstrate the advantage of GS-GAA FinFET over conventional FinFET. The sensor measures the switching-ratio-based sensitivity, which comes out to be 99.72% for MDA-MB-231 and 47.78% for MCF-10A. The sensor was tested for stability and reproducibility and found to be repeatable and sufficiently stable with settling times of 55.51, 60.80, and 71.58 ps for MDA-MB-231 cells, MCF-10A cells, and air, respectively. It can distinguish between viable and nonviable cells based on electrical response alterations. The possibility of early detection of cancerous breast cells using Bruggeman's model is also discussed. Further, the impact of biomolecule occupancy and frequency variations on the device sensitivity is carried out. This study also explains how to maximize the sensing performance by adjusting the fin height, fin width, work function, channel doping, temperature, and drain voltage. Lastly, this article compared the proposed breast cancer cell detectors to existing literature to evaluate their performance and found considerable improvement. The findings of this research have the potential to establish GaAs-GS-GAA FinFET as a promising contender for MDA-MB-231 breast cancer cell detection.

An efficient Pt@MXene platform for the analysis of small-molecule natural products

Small-molecule (m/z<500) natural products have rich biological activity and significant application value thus need to be effectively detected. Surface-assisted laser desorption/ionization mass spectrometry (SALDI MS) has become a powerful detection tool for small-molecule analysis. However, more efficient substrates need to be developed to improve the efficiency of SALDI MS. Thus, platinum nanoparticle-decorated Ti₃C₂ MXene (Pt@MXene) was synthesized in this study as an ideal substrate for SALDI MS in positive ion mode and exhibited excellent performance for the high-throughput detection of small molecules. Compared with using MXene, GO, and CHCA matrix, a stronger signal peak intensity and wider molecular coverage was obtained using Pt@MXene in the detection of small-molecule natural products, with a lower background, excellent salt and protein tolerance, good repeatability, and high detection sensitivity. The Pt@MXene substrate was also successfully used to quantify target molecules in medicinal plants. The proposed method has potentially wide application.

Understanding Cervical Cancer through Proteomics

Cancer is one of the leading public health issues worldwide, and the number of cancer patients increases every day. Particularly, cervical cancer (CC) is still the second leading cause of cancer death in women from developing countries. Thus, it is essential to deepen our knowledge about the molecular pathogenesis of CC and propose new therapeutic targets and new methods to diagnose this disease in its early stages. Differential expression analysis using high-throughput techniques applied to biological samples allows determining the physiological state of normal cells and the changes produced by cancer development. The cluster of differential molecular profiles in the genome, the transcriptome, or the proteome is analyzed in the disease, and it is called the molecular signature of cancer. Proteomic analysis of biological samples of patients with different grades of cervical intraepithelial neoplasia (CIN) and CC has served to elucidate the pathways involved in the development and progression of cancer and identify cervical proteins associated with CC. However, several cervical carcinogenesis mechanisms are still unclear. Detecting pathologies in their earliest stages can significantly improve a patient's survival rate, prognosis, and recurrence. The present review is an update on the proteomic study of CC.

Gingival Crevicular Fluid Peptidome Profiling in Healthy and in Periodontal Diseases

Given its intrinsic nature, gingival crevicular fluid (GCF) is an attractive source for the discovery of novel biomarkers of periodontal diseases. GCF contains antimicrobial peptides and small proteins which could play a role in specific immune-inflammatory responses to guarantee healthy gingival status and to prevent periodontal diseases. Presently, several proteomics studies have been performed leading to increased coverage of the GCF proteome, however fewer efforts have been done to explore its natural peptides. To fill such gap, this review provides an overview of the mass spectrometric platforms and experimental designs aimed at GCF peptidome profiling, including our own data and experiences gathered from over several years of matrix-assisted laser desorption ionization/time of flight mass spectrometry (MALDI-TOF MS) based approach in this field. These tools might be useful for capturing snapshots containing diagnostic clinical information on an individual and population scale, which may be used as a specific code not only for the diagnosis of the nature or the stage of the inflammatory process in periodontal disease, but more importantly, for its prognosis, which is still an unmet medical need. As a matter of fact, current peptidomics investigations suffer from a lack of standardized procedures, posing a serious problem for data interpretation. Descriptions of the efforts to address such concerns will be highlighted.

Liquid Atmospheric Pressure Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Adds Enhanced Functionalities to MALDI MS Profiling for Disease Diagnostics

A liquid matrix-assisted laser desorption/ionization (liquid MALDI) method has been developed for high-throughput atmospheric pressure (AP) mass spectrometry (MS) analysis of the molecular content of crude bioliquids for disease diagnostics. The presented method is rapid and highly robust, enabling its application in environments where speed and low-cost high-throughput analyses are required. Importantly, because of the creation of multiply charged analyte ions, it provides additional functionalities that conventional solid MALDI MS profiling is lacking, including the use of high-performance mass analyzers with limited m/z range. The concomitant superior MS/MS performance that is achieved similar to ESI MS/MS adds greater analytical power and specificity to MALDI MS profiling while retaining the advantages of a fast laser-based analysis system and off-line large-scale sample preparation. The potential of this MALDI MS profiling method is demonstrated on the detection of dairy cow mastitis, which is a substantial economic burden on the dairy industry with losses of hundreds of dollars per diseased cow per year, equating to a total annual loss of billions of dollars, as well as leading to the use of large quantities of antibiotics, adding to the proliferation of antimicrobial resistance. Only small amounts of aliquots obtained from the daily farm milking process were prepared for liquid MALDI MS profiling using a simple one-pot/two-step analyte extraction. Automated analysis was performed using a custom-built AP-MALDI ion source, enabling the simultaneous detection of lipids, peptides, and proteins. Diagnostic, multiply charged, proteinaceous ions were easily sequenced and identified by MS/MS experiments. Samples were classified according to mastitis status using multivariate analysis, achieving 98.5% accuracy (100% specificity) determined by "leave 20% out" cross-validation. The methodology is generally applicable to AP-MALDI MS profiling on most commercial high-resolution mass spectrometers, with the potential for expansion into hospitals for rapid assessment of human and other biofluids.

Urinary proteomics and metabolomics studies to monitor bladder health and urological diseases

Background

Assays of molecular biomarkers in urine are non-invasive compared to other body fluids and can be easily repeated. Based on the hypothesis that the secreted markers from the diseased organs may locally release into the body fluid in the vicinity of the injury, urine-based assays have been considered beneficial to monitoring bladder health and urological diseases. The urine proteome is much less complex than the serum and tissues, but nevertheless can contain biomarkers for diagnosis and prognosis of diseases. The urine metabolome has a much higher number and concentration of low-molecular metabolites than the serum or tissues, with a far lower lipid concentration, yet informs directly about dietary and microbial metabolism.

Discussion

We here discuss the use of mass spectrometry-based proteomics and metabolomics for urine biomarker assays, specifically with respect to the underlying mechanisms that trigger the pathological condition.

Conclusion

Molecular biomarker profiles, based on proteomics and metabolomics studies, reliably distinguish patients from healthy controls, stratify sub-populations with respect to treatment options, and predict therapeutic response of patients with urological disease.

Biomarker Characterization by MALDI-TOF/MS

Mass spectrometric techniques frequently used in clinical diagnosis, such as gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, ambient ionization mass spectrometry, and matrix-assisted laser desorption ionization/time-of-flight mass spectrometry (MALDI-TOF/MS), are discussed. Due to its ability to rapidly detect large biomolecules in trace amounts, MALDI-TOF/MS is an ideal tool for characterizing disease biomarkers in biologic samples. Clinical applications of MS for the identification and characterization of microorganisms, DNA fragments, tissues, and biofluids are introduced. Approaches for using MALDI-TOF/MS to detect various disease biomarkers including peptides, proteins, and lipids in biological fluids are further discussed. Finally, various sample pretreatment methods which improve the detection efficiency of disease biomarkers are introduced.

The C-terminal fragment of prostate-specific antigen, a 2331 Da peptide, as a new urinary pathognomonic biomarker candidate for diagnosing prostate cancer

Background and Objectives

Prostate cancer (PCa) is one of the most common cancers and leading cause of cancer-related deaths in men. Mass screening has been carried out since the 1990s using prostate-specific antigen (PSA) levels in the serum as a PCa biomarker. However, although PSA is an excellent organ-specific marker, it is not a cancer-specific marker. Therefore, the aim of this study was to discover new biomarkers for the diagnosis of PCa.

Materials and Methods

We focused on urine samples voided following prostate massage (digital rectal examination [DRE]) and conducted a peptidomic analysis of these samples using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MSⁿ). Urinary biomaterials were concentrated and desalted using CM-Sepharose prior to the following analyses being performed by MALDI-TOF/MSⁿ: 1) differential analyses of mass spectra; 2) determination of amino acid sequences; and 3) quantitative analyses using a stable isotope-labeled internal standard.

Results

Multivariate analysis of the MALDI-TOF/MS mass spectra of urinary extracts revealed a 2331 Da peptide in urine samples following DRE. This peptide was identified as a C-terminal PSA fragment composed of 19 amino acid residues. Moreover, quantitative analysis of the relationship between isotope-labeled synthetic and intact peptides using MALDI-TOF/MS revealed that this peptide may be a new pathognomonic biomarker candidate that can differentiate PCa patients from non-cancer subjects.

Conclusion

The results of the present study indicate that the 2331 Da peptide fragment of PSA may become a new pathognomonic biomarker for the diagnosis of PCa. A further large-scale investigation is currently underway to assess the possibility of using this peptide in the early detection of PCa.

Cancer proteomics

Cancer presents high mortality and morbidity globally, largely due to its complex and heterogenous nature, and lack of biomarkers for early diagnosis. A proteomics study of cancer aims to identify and characterize functional proteins that drive the transformation of malignancy, and to discover biomarkers to detect early-stage cancer, predict prognosis, determine therapy efficacy, identify novel drug targets, and ultimately develop personalized medicine. The various sources of human samples such as cell lines, tissues, and plasma/serum are probed by a plethora of proteomics tools to discover novel biomarkers and elucidate mechanisms of tumorigenesis. Innovative proteomics technologies and strategies have been designed for protein identification, quantitation, fractionation, and enrichment to delve deeper into the oncoproteome. In addition, there is the need for high-throughput methods for biomarker validation, and integration of the various platforms of oncoproteome data to fully comprehend cancer biology.

Elucidating the role of metals in Alzheimer's disease through the use of Surface-Enhanced Laser Desorption/Ionisation time-of-flight mass spectrometry

Alzheimer's disease (AD) is a highly heterogeneous and progressive dementia which is characterised by a progressive decline in cognitive functioning, selective neuronal atrophy, and loss of cortical volume in areas involved in learning and memory. However, recent research has indicated that the AD-affected brain is also besieged by increases in oxidative stress as well as perturbations to the homeostasis of biometals, such as copper and iron. These metals are known to interact with the neuropathological hallmark of AD, the β-amyloid peptide (Aβ), in a manner which increases Aβ's neurotoxic effects. This knowledge has led to the development of therapeutic measures which act to restore biometal homeostasis within the AD brain. This chapter outlines how Surface-Enhanced Laser Desorption/Ionisation Time-of-Flight Mass Spectrometry can be used to monitor Aβ levels within biological systems as well as describing the use of immobilised metal affinity capture in the observation of synthetic Aβ peptides.

The application of a three-step proteome analysis for identification of new biomarkers of pancreatic cancer

We searched for novel tumor markers of pancreatic cancer by three-step serum proteome analysis. Twelve serum abundant proteins were depleted using immunoaffinity columns followed by fractionation by reverse-phase high-performance liquid chromatography. Proteins in each fraction were separated by two-dimensional gel electrophoresis. Then the gel was stained by Coomassie Brilliant Blue. Protein spots in which the expression levels were significantly different between cancer and normal control were identified by LC-MS/MS. One hundred and two spots were upregulated, and 84 spots were downregulated in serum samples obtained from patients with pancreatic cancers, and 58 proteins were identified by mass spectrometry. These candidate proteins were validated using western blot analysis and enzyme-linked immunosorbent assay (ELISA). As a result of these validation process, we could confirm that the serum levels of apolipoprotein A-IV, vitamin D-binding protein, plasma retinol-binding protein 4, and tetranectin were significantly decreased in patients with pancreatic cancer.

Enriched sera protein profiling for detection of non-small cell lung cancer biomarkers

Background

Non Small Cell Lung Cancer (NSCLC) is the major cause of cancer related-death. Many patients receive diagnosis at advanced stage leading to a poor prognosis. At present, no satisfactory screening tests are available in clinical practice and the discovery and validation of new biomarkers is mandatory. Surface Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-ToF-MS) is a recent high-throughput technique used to detect new tumour markers. In this study we performed SELDI-ToF-MS analysis on serum samples treated with the ProteoMiner™ kit, a combinatorial library of hexapeptide ligands coupled to beads, to reduce the wide dynamic range of protein concentration in the sample. Serum from 44 NSCLC patients and 19 healthy controls were analyzed with IMAC30-Cu and H50 ProteinChip Arrays.

Results

Comparing SELDI-ToF-MS protein profiles of NSCLC patients and healthy controls, 28 protein peaks were found significantly different (p < 0.05), and were used as predictors to build decision classification trees. This statistical analysis selected 10 protein peaks in the low-mass range (2-24 kDa) and 6 in the high-mass range (40-80 kDa). The classification models for the low-mass range had a sensitivity and specificity of 70.45% (31/44) and 68.42% (13/19) for IMAC30-Cu, and 72.73% (32/44) and 73.68% (14/19) for H50 ProteinChip Arrays.

Conclusions

These preliminary results suggest that SELDI-ToF-MS protein profiling of serum samples pretreated with ProteoMiner™ can improve the discovery of protein peaks differentially expressed between NSCLC patients and healthy subjects, useful to build classification algorithms with high sensitivity and specificity. However, identification of the significantly different protein peaks needs further study in order to provide a better understanding of the biological nature of these potential biomarkers and their role in the underlying disease process.

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.

Protein expression profiles distinguish between experimental invasive pulmonary aspergillosis and Pseudomonas pneumonia

We hypothesized that invasive pulmonary aspergillosis (IPA) may generate a distinctive proteomic signature in plasma and bronchoalveolar lavage (BAL). Proteins in plasma and BAL from two neutropenic rabbit models of IPA and Pseudomonas pneumonia were analyzed by SELDI-TOF MS. Hierarchical clustering analysis of plasma time course spectra demonstrated two clusters of peaks that were differentially regulated between IPA and Pseudomonas pneumonia (57 and 34 peaks, respectively, p<0.001). PCA of plasma proteins demonstrated a time-dependent separation of the two infections. A random forest analysis that ranked the top 30 spectral points distinguished between late Aspergillus and Pseudomonas pneumonias with 100% sensitivity and specificity. Based on spectral data analysis, three proteins were identified using SDS-PAGE and LC/MS and quantified using reverse phase arrays. Differences in the temporal sequence of plasma haptoglobin (p<0.001), apolipoprotein A1 (p<0.001) and transthyretin (p<0.038) were observed between IPA and Pseudomonas pneumonia, as was C-reactive protein (p<0.001). In summary, proteomic analysis of plasma and BAL proteins of experimental Aspergillus and Pseudomonas pneumonias demonstrates unique protein profiles with principal components and spectral regions that are shared in early infection and diverge at later stages of infection. Haptoglobin, apolipoprotein A1, transthyretin, and C-reactive protein are differentially expressed in these infections suggesting important contributions to host defense against IPA.

Proteomics and biomarkers for ovarian cancer diagnosis

Ovarian cancer remains a leading cause of death from gynecological malignancy. Early diagnosis is the most important determinant of survival. Current diagnostic tools have had very limited success in early detection. In recent years, the advancing techniques for proteomics have accelerated the discovery of ovarian cancer biomarkers. Numerous proteomics-based molecular biomarkers/panels have been identified and hold great potential for diagnostic applications, but they need further development and validation. This article reviews recently published data on the diagnosis of ovarian cancer with proteomics, including the major proteomics technologies and promising strategies for biomarker discovery and development.

Imaging of meningioma progression by matrix-assisted laser desorption ionization time-of-flight mass spectrometry

Often considered benign, meningiomas represent 32% of intracranial tumors with three grades of malignancy defined by the World Health Organization (WHO) histology based classification. Malignant meningiomas are associated with less than 2 years median survival. The inability to predict recurrence and progression of meningiomas induces significant anxiety for patients and limits physicians in implementing prophylactic treatment approaches. This report presents an analytical approach to tissue characterization based on matrix-assisted laser desorption ionization time-of-flight (MALDI TOF) mass spectrometry imaging (MSI) which is introduced in an attempt to develop a reference database for predictive classification of brain tumors. This pilot study was designed to evaluate the potential of such an approach and to begin to address limitations of the current methodology. Five recurrent and progressive meningiomas for which surgical specimens were available from the original and progressed grades were selected and tested against nonprogressive high-grade meningiomas, high-grade gliomas, and nontumor brain specimens. The common profiling approach of data acquisition was compared to imaging and revealed significant benefits in spatially resolved acquisition for improved spectral definition. A preliminary classifier based on the support vector machine showed the ability to distinguish meningioma image spectra from the nontumor brain and from gliomas, a different type of brain tumor, and to enable class imaging of surgical tissue. Although the development of classifiers was shown to be sensitive to data preparation parameters such as recalibration and peak picking criteria, it also suggested the potential for maturing into a predictive algorithm if provided with a larger series of well-defined cases.

Identification of a beta-casein-like peptide in breast nipple aspirate fluid that is associated with breast cancer

AIMS

Nipple aspirate fluid was collected prospectively from women scheduled for diagnostic breast surgery in order to determine protein masses associated with breast cancer, subsets of women with a unique proteomic profile and a breast cancer predictive model.

MATERIALS & METHODS

Breast nipple aspirate fluid was collected preoperatively in 163 breasts from 125 women and analyzed for changes in cell morphology and by SELDI-TOF mass spectrometry over approximately a 44 kDa range (1.5-45 kDa) using IMAC30, CM10 and Q10 ProteinChips.

RESULTS

Considering all samples, 16 protein masses were associated with the presence of cancer, the most discriminating being 3592, 6570/6580 and 15870 Da. Excluding women with pathologic nipple discharge or those with a papilloma identified an additional protein of 6383 Da. The best cancer detection models included Breast Imaging Reporting and Data System, age, and either the 4262 (best sensitivity: >87%) or 3592 (best specificity: >94%) peak. MALDI-TOF mass spectrometry demonstrated the 3592 peak, which was most discriminating in many of our cancer prediction models, to be a beta-casein-like peptide.

CONCLUSION

Differential nipple aspirate fluid proteomic expression exists between women with/without breast cancer. The most discriminating protein identified is a beta-casein-like peptide not previously described. Combining proteomic and clinical information, which are available before surgery, optimizes the prediction of which women have breast cancer.

Association of serum amyloid A protein and peptide fragments with prognosis in renal cancer

BACKGROUND

In renal cell carcinoma (RCC), the discovery of biomarkers for clinical use is a priority. This study aimed to identify and validate diagnostic and prognostic serum markers using proteomic profiling.

METHODS

Pre-operative sera from 119 patients with clear cell RCC and 69 healthy controls was analysed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry with stringent in-house quality control and analysis routines. Following identification of one prognostic peak as a fragment of serum amyloid A (SAA), total serum SAA and CRP were also determined by immunoassay for further validation.

RESULTS

Several peptides were identified as having independent prognostic but not diagnostic significance on multivariable analysis. One was subsequently identified as a 1525 Da fragment of SAA (hazard ratio (HR)=0.26, 95% CI 0.08-0.85, P=0.026). This was weakly negatively correlated with total SAA, which was also of independent prognostic significance (HR=2.46, 95% CI 1.17-5.15, P=0.017). Both potentially strengthened prognostic models based solely on pre-operative variables.

CONCLUSIONS

This is the first description of the prognostic value of this peptide in RCC and demonstrates proof of principle of the approach. The subsequent examination of SAA protein considerably extends previous studies, being the first study to focus solely on pre-operative samples and describing potential clinical utility in pre-operative prognostic models.

Comparison of three methods for fractionation and enrichment of low molecular weight proteins for SELDI-TOF-MS differential analysis

In most diseases, the clinical need for serum/plasma markers has never been so crucial, not only for diagnosis, but also for the selection of the most efficient therapies, as well as exclusion of ineffective or toxic treatment. Due to the high sample complexity, prefractionation is essential for exploring the deep proteome and finding specific markers. In this study, three different sample preparation methods (i.e., highly abundant protein precipitation, restricted access materials (RAM) combined with IMAC chromatography and peptide ligand affinity beads) were investigated in order to select the best fractionation step for further differential proteomic experiments focusing on the LMW proteome (MW inferior to 40,000 Da). Indeed, the aim was not to cover the entire plasma/serum proteome, but to enrich potentially interesting tissue leakage proteins. These three methods were evaluated on their reproducibility, on the SELDI-TOF-MS peptide/protein peaks generated after fractionation and on the information supplied. The studied methods appeared to give complementary information and presented good reproducibility (below 20%). Peptide ligand affinity beads were found to provide efficient depletion of HMW proteins and peak enrichment in protein/peptide profiles.

Identification of novel diagnostic serum biomarkers for Chagas' disease in asymptomatic subjects by mass spectrometric profiling

More than 10 million people are thought to be infected with Trypanosoma cruzi, primarily in the Americas. The clinical manifestations of Chagas' disease (CD) are variable, but most subjects remain asymptomatic for decades. Only 15 to 30% eventually develop terminal complications. All current diagnostic tests have limitations. New approaches are needed for blood bank screening as well as for improved diagnosis and prognosis. Sera from subjects with asymptomatic CD (n = 131) were compared to those from uninfected controls (n = 164) and subjects with other parasitic diseases (n = 140), using protein array mass spectrometry. To identify biomarkers associated with CD, sera were fractionated by anion-exchange chromatography and bound to two commercial ProteinChip array chemistries: WCX2 and IMAC3. Multiple candidate biomarkers were found in CD sera (3 to 75.4 kDa). Algorithms employing 3 to 5 of these biomarkers achieved up to 100% sensitivity and 98% specificity for CD. The biomarkers most useful for diagnosis were identified and validated. These included MIP1 alpha, C3a anaphylatoxin, and unusually truncated forms of fibronectin, apolipoprotein A1 (ApoA1), and C3. An antipeptide antiserum against the 28.9-kDa C terminus of the fibronectin fragment achieved good specificity (90%) for CD in a Western blot format. We identified full-length ApoA1 (28.1 kDa), the major structural and functional protein component of high-density lipoprotein (HDL), as an important negative biomarker for CD, and relatively little full-length ApoA1 was detected in CD sera. This work provides proof of principle that both platform-dependent (i.e., mass spectrometry-based) and platform-independent (i.e., Western blot) tests can be generated using high-throughput mass profiling.

Challenges for biomarker discovery in body fluids using SELDI-TOF-MS

Protein profiling using SELDI-TOF-MS has gained over the past few years an increasing interest in the field of biomarker discovery. The technology presents great potential if some parameters, such as sample handling, SELDI settings, and data analysis, are strictly controlled. Practical considerations to set up a robust and sensitive strategy for biomarker discovery are presented. This paper also reviews biological fluids generally available including a description of their peculiar properties and the preanalytical challenges inherent to sample collection and storage. Finally, some new insights for biomarker identification and validation challenges are provided.

Cancer-specific MALDI-TOF profiles of blood serum and plasma: biological meaning and perspectives

MALDI-TOF mass-spectrometry has become a popular tool of cancer research during the last decade. High throughput and relative simplicity of this technology have made it attractive for biomarker discovery and validation across various platforms in blood serum/plasma. Many technical approaches have been developed for plasma/serum profiling including protein-chip based SELDI-TOF mass-spectrometry, purification of serum on magnetic beads, analysis of carrier-associated fraction and mass-spectrometric immunoassays. Extensive data about the identity of differential features detected on mass-spectra up to now makes it possible to draw conclusions about potency and perspectives of MALDI-TOF mass-spectrometry in this field. A great majority of identified differentially expressed proteins are either house-keeping or inflammatory proteins as well as their modifications or fragments. Discriminating ability of mass-spectra is likely to be based on differential modification and fragmentation patterns of abundant serum proteins reflecting activity of enzymes including proteases and their inhibitors.

Proteomics of apolipoproteins and associated proteins from plasma high-density lipoproteins

Proteomics studies have extended the list of identified apolipoproteins and associated proteins present in HDL and its subclasses. These proteins appear to cluster around specific functions related to lipid metabolism, inflammation, the immune system, hormone-binding, hemostasis, and antioxidant properties. Small studies suggest that there are substantial differences between the HDL proteome from cardiovascular disease patients and that from controls. Furthermore, dyslipidemia therapy shifts the HDL proteome from patients toward the profile observed in healthy controls. In addition, the proteome of HDL and LDL from patients with insulin resistance and peripheral atherosclerosis show significant differences with that of matched healthy controls. The proteome of HDL and LDL density subclasses have apolipoproteins and associated proteins profiles that suggest subclass-specific functions. However, proteomics studies of lipoproteins are few and small and should be interpreted with caution. Nonetheless rapid technical progress in proteomic platforms suggest that soon analysis time will be reduced and precise measurement of identified proteins will be possible. This, combined with controlled purification steps of HDL and its subclasses should provide further information about proteins involved in the particles postulated spectrum of functions, including those believed to be atheroprotective.

Proteomic profiling of growth hormone-responsive proteins in human peripheral blood leukocytes

CONTEXT

GH is a known modulator of the immune system, but the effect of exogenous GH administration on white blood cell proteins has not been investigated. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is a powerful platform for the study of GH effects on immune system proteins.

OBJECTIVE

Our objective was to explore a novel approach for the detection of GH-responsive proteins in human leukocytes by proteomic analysis using SELDI-TOF MS.

DESIGN

We conducted a randomized double-blind, placebo-controlled GH administration study of 8 wk treatment followed by 6 wk washout. Pre- and posttreatment samples from 30 subjects were used for biomarker discovery.

SETTING

The study was performed at a clinical research facility.

PARTICIPANTS

We studied 30 recreationally trained healthy athletes.

INTERVENTION

Subjects received either recombinant human GH (2 mg/d sc; n = 22) or placebo (n = 8) for 8 wk.

MAIN OUTCOME MEASURES

Proteomic profiles were determined using CM10 weak cation-exchange protein chips, and some GH-regulated proteins were purified and identified by mass spectrometry and/or immunoblotting.

RESULTS

SELDI-TOF analysis revealed a number of GH-regulated peptides/proteins in the 3- to 22-kDa range that are either up- or down-regulated by GH. Several of these may be useful as biomarkers of GH action. The calcium-binding, proinflammatory calgranulins S100A8, S100A9, and S100A12 were all significantly down-regulated in response to GH treatment.

CONCLUSION

This study illustrates the novel use of human leukocyte proteomic profiling by SELDI-TOF MS and reveals the negative regulation of proinflammatory S100 proteins by GH in human white blood cells.

An integrative multi-platform analysis for discovering biomarkers of osteosarcoma

BACKGROUND

SELDI-TOF-MS (Surface Enhanced Laser Desorption/Ionization-Time of Flight-Mass Spectrometry) has become an attractive approach for cancer biomarker discovery due to its ability to resolve low mass proteins and high-throughput capability. However, the analytes from mass spectrometry are described only by their mass-to-charge ratio (m/z) values without further identification and annotation. To discover potential biomarkers for early diagnosis of osteosarcoma, we designed an integrative workflow combining data sets from both SELDI-TOF-MS and gene microarray analysis.

METHODS

After extracting the information for potential biomarkers from SELDI data and microarray analysis, their associations were further inferred by link-test to identify biomarkers that could likely be used for diagnosis. Immuno-blot analysis was then performed to examine whether the expression of the putative biomarkers were indeed altered in serum from patients with osteosarcoma.

RESULTS

Six differentially expressed protein peaks with strong statistical significances were detected by SELDI-TOF-MS. Four of the proteins were up-regulated and two of them were down-regulated. Microarray analysis showed that, compared with an osteoblastic cell line, the expression of 653 genes was changed more than 2 folds in three osteosarcoma cell lines. While expression of 310 genes was increased, expression of the other 343 genes was decreased. The two sets of biomarkers candidates were combined by the link-test statistics, indicating that 13 genes were potential biomarkers for early diagnosis of osteosarcoma. Among these genes, cytochrome c1 (CYC-1) was selected for further experimental validation.

CONCLUSION

Link-test on datasets from both SELDI-TOF-MS and microarray high-throughput analysis can accelerate the identification of tumor biomarkers. The result confirmed that CYC-1 may be a promising biomarker for early diagnosis of osteosarcoma.