Proteomic technologies for identification of serum biomarkers of potential autoimmune demyelinating polyneuropathies

Time-of-flight MALDI mass spectrometry (MALDI-TOF MS) profiling of blood serum of patients with Guillain-Barré syndrome (GBS, 36 samples), chronic inflammatory demyelinating polyneuropathy (CIDP, 24 samples), and practically healthy donors (HD, 35 samples) was carried out in order to identify potential biomarkers of autoimmune demyelinating polyneuropathies (ADP). To simplify the peptide-protein mixture of serum prior to MALDI-TOF-MS analysis, samples were prefractionated on magnetic beads with a weak cation-exchange (MB-WCX) surface. Comparative analysis of mass spectrometric data using the classification algorithms (genetic and neural network-controlled) revealed a characteristic set of peaks and a correlating change area with a high specificity and sensitivity of the differentiated mass spectrometry profiles of the blood serum of patients with DPNP and healthy donors (for GBS, values of these characteristics reached 100 and 100%, and for CIDP, 94.1 and 100% respectively). Comparative analysis of mass spectrometric profiles of serum samples obtained from patients with GBS and CIDP allowed us to build a classification model to differentiate these diseases from each other, with a specificity of 88.9 and a sensitivity of 80%.     

Diagnostic application of serum proteomic patterns in gastric cancer patients by ProteinChip surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is one of thecurrently used techniques to identify biomarkers for cancers. This study was planned to establish a system to accurately distinguish gastric cancer patients by using SELDI-TOF-MS. A total of 100 serum samples obtained from 60 individuals with gastric cancer and 40 healthy individuals were screened. Protein expression profiles were expressed on CM10 ProteinChip arrays and analyzed. Peak intensities were analyzed with the Biomarker Wizard software to identify peaks showing significantly different intensities between normal and cancer groups. Classification analysis and construction of decision trees were done with the Biomarker Pattern software 5.0. Seventeen protein peaks showed significant differences between the two groups. The decision tree which gave the highest discrimination included four peaks at mass 5,919, 8,583, 10,286, and 13,758 as splitters. The sensitivity and specificity for classification of the decision tree were 96.7% (58/60) and 97.5% (39/40), respectively. When the protein biomarker pattern was tested on a blinded test set, it yielded a sensitivity of 93.3% (28/30) and a specificity of 90% (18/20). These results suggest that serum protein profiling by the SELDI system may distinguish gastric cancer patients from healthy controls with relatively high sensitivity and specificity.     

Combined experimental and statistical strategy for mass spectrometry based serum protein profiling for diagnosis of breast cancer: a case-control study

Serum protein profiling by mass spectrometry is a promising method for early detection of cancer. We have implemented a combined strategy based on matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) and statistical data analysis for serum protein profiling and applied it in a well-described breast cancer case-control study. A rigorous sample collection protocol ensured high quality specimen and reduced bias from preanalytical factors. Preoperative serum samples obtained from 48 breast cancer patients and 28 controls were used to generate MALDI MS protein profiles. A total of nine mass spectrometric protein profiles were obtained for each serum sample. A total of 533 common peaks were defined and represented a 'reference protein profile'. Among these 533 common peaks, we identified 72 peaks exhibiting statistically significant intensity differences ( p < 0.01) between cases and controls. A diagnostic rule based on these 72 mass values was constructed and exhibited a cross-validated sensitivity and specificity of approximately 85% for the detection of breast cancer. With this method, it was possible to distinguish early stage cancers from controls without major loss of sensitivity and specificity. We conclude that optimized serum sample handling and mass spectrometry data acquisition strategies in combination with statistical analysis provide a viable platform for serum protein profiling in cancer diagnosis.     

The use of the latex agglutination reaction for the diagnosis of a Brucella infection

The authors have developed the optimum conditions for the preparation of antigenic diagnosticum based on latex manufactured in the USSR. To sensitize latex with the diameter of microspheres equal to 0.83 microns, Brucella polysaccharide was used in a dose of 100 micrograms/ml. As stabilizer, polyvinylpyrrolidone at a concentration of 0.1% was used. The specificity and sensitivity of the diagnosticum were studied in analysis of serum samples taken from 102 healthy donors and patients with infectious diseases of nonbrucellar etiology and from 120 patients with different forms of brucellosis. The specificity of the diagnosticum was found to be 94.1% and its sensitivity, 77.5%. Comparative study of the latex agglutination test with other serological tests showed that the former test is highly effective both in acute and chronic forms of the disease. A high degree of correlation between the agglutination test, Coombs' test, the passive hemagglutination test and the latex agglutination test was established (r = 0.83, 0.72 and 0.62, respectively).     

Identification of gastric cancer patients by serum protein profiling

Using surface-enhanced laser desorption ionization mass spectrometry (SELDI/TOF-MS) and ProteinChip technology, coupled with a pattern-matching algorithm and serum samples, we screened for protein patterns to differentiate gastric cancer patients from noncancer patients. A classifier ensemble, consisting of 50 decision trees, correctly classified all gastric cancers and all controls of a training set (100% sensitivity and 100% specificity). Eight of 9 stage I gastric cancers (88.9% sensitivity for stage I) were correctly classified. In addition, 28 sera from gastric cancer patients taken in different hospitals were correctly classified (100% sensitivity). Furthermore, all 11 control sera obtained from patients without gastric cancer (100% specificity) were classified correctly and 29 of 30 healthy blood-donors were classified as noncancerous. ProteinChip technology in conjunction with bioinformatics allows the highly sensitive and specific recognition of gastric cancer patients.     

Serum metabolomics for the diagnosis and classification of myasthenia gravis

Myasthenia gravis (MG) is a chronic autoimmune neuromuscular disease with few reliable diagnostic measures. Therefore, it is great important to explore novel tools for the diagnosis of MG. In this study, a serum metabolomic approach based on LC?CMS in combination with multivariate statistical analyses was used to identify and classify patients with various grades of MG. Serum samples from 42 MG patients and 16 healthy volunteers were analyzed by liquid chromatography Fourier transform mass spectrometry (LC-FTMS). MG patients were clearly distinguished from healthy subjects based on their global serum metabolic profiles by using orthogonal partial least squares (OPLS) analysis. Moreover, different changes in metabolic profiles were observed between early- and late-stages MG patients. Nine biomarkers, including gamma-aminobutyric acid and sphingosine 1-phosphate were identified. In addition, 92.8% sensitivity, 83.3% specificity and 90% accuracy were obtained from the OPLS discriminant analysis (OPLS-DA) class prediction model in detecting MG. The results presented here illustrate that serum metabolomics exhibits great potential in the detecting and grading of MG, and it is potentially applicable as a new diagnostic approach for MG.     

A metabolic discrimination model for nasopharyngeal carcinoma and its potential role in the therapeutic evaluation of radiotherapy

Nasopharyngeal carcinoma (NPC) is one of the most common malignancies in Southeast Asia and radiotherapy or radiotherapy, in combination with chemotherapy is the primary treatment strategy. In this study, we adopted a metabolomic method to investigate the metabolic disorders in NPC and evaluate the effect of radiotherapy on metabolic profile alterations in NPC patients. To generate the NPC metabolic profiles, 402 serum samples were collected from 100 newly-diagnosed NPC patients and 100 healthy volunteers. Based on gas chromatography–mass spectrometry (GC–MS) metabolomics coupled with partial least squares-discriminant analysis, a NPC discrimination model was constructed with a sensitivity of 88 % (88/100) and a specificity of 92 % (92/100). Seven metabolites, including glucose, linoleic acid, stearic acid, arachidonic acid, proline, β-hydroxy butyrate and glycerol 1-hexadecanoate, were identified as contributing mostly to the discrimination of NPC serum from healthy controls. To validate if the model can be applied for therapeutic evaluation, 202 serum samples were collected from 20 patients receiving standard radiotherapy for up to a 3-year follow-up period. The metabolic footprints of 20 NPC patients treated with standard radiotherapy are visually presented. Based on the footprint trends of the sera samples in irradiation-treated NPC patients who were gradually closer to healthy controls or not, patients were divided into positive and negative groups, respectively. The coincident rate of the trends of metabolic footprints to the actual clinical prognosis trend was approximately 80 %. This study demonstrates that a GC–MS-based metabolic profiling approach as a novel strategy may be capable to delineating the potential of metabolite alterations in discrimination and therapeutic evaluation of NPC patients.     

Mass spectrometric and linear discriminant analysis of N-glycans of human serum alpha-1-acid glycoprotein in cancer patients and healthy individuals

N-glycan oligosaccharides of human serum alpha(1)-acid glycoprotein (AGP) samples isolated from 43 individuals (healthy individuals and patients with lymphoma and with ovarian tumor) were analyzed by MALDI-TOF mass spectrometry and a multivariate statistical method (linear discriminant analysis, LDA). 34 different glycan structures have been identified. From the glycosylation pattern determined by mass spectrometry fucosylation and branching indices have been calculated. These parameters show only small differences between the patient groups studied, but these differences are not sufficiently large to use as a potential biomarker. LDA analysis, on the other hand shows a very good separation between the three groups (with a classification of 88%). Cross-validation indicates that the method has predictive power: Identifying cancerous vs. healthy individuals shows 96% selectivity and 93% specificity; identification of lymphoma vs. the mixed group of healthy and ovarian tumor cases is also promising (72% selectivity and 84% specificity). The pilot study presented here demonstrates that mass spectrometry combined with linear discriminant analysis (LDA) may provide valuable data for identifying and studying the pathophysiology of malignant diseases.     

Probabilistic disease classification of expression-dependent proteomic data from mass spectrometry of human serum.

We have developed an algorithm called Q5 for probabilistic classification of healthy versus disease whole serum samples using mass spectrometry. The algorithm employs principal components analysis (PCA) followed by linear discriminant analysis (LDA) on whole spectrum surface-enhanced laser desorption/ionization time of flight (SELDI-TOF) mass spectrometry (MS) data and is demonstrated on four real datasets from complete, complex SELDI spectra of human blood serum. Q5 is a closed-form, exact solution to the problem of classification of complete mass spectra of a complex protein mixture. Q5 employs a probabilistic classification algorithm built upon a dimension-reduced linear discriminant analysis. Our solution is computationally efficient; it is noniterative and computes the optimal linear discriminant using closed-form equations. The optimal discriminant is computed and verified for datasets of complete, complex SELDI spectra of human blood serum. Replicate experiments of different training/testing splits of each dataset are employed to verify robustness of the algorithm. The probabilistic classification method achieves excellent performance. We achieve sensitivity, specificity, and positive predictive values above 97% on three ovarian cancer datasets and one prostate cancer dataset. The Q5 method outperforms previous full-spectrum complex sample spectral classification techniques and can provide clues as to the molecular identities of differentially expressed proteins and peptides.     

The rapid diagnosis of measles by using immunoenzyme analysis based on peroxidase-labelled viral antigens

The sensitivity and specificity of the enzyme immunoassay (the capture variant), based on the use of the complex of anti-IgM-IgM to purified measles virus, the peroxidase-labeled lysate and nucleocapsid antigens of measles virus, were evaluated. The advantages of the nucleocapsid conjugate were established. The study of 200 serum samples from measles patients revealed that antimeasles IgM antibodies could be detected in 100% of cases, and the assay of 15 serum samples from healthy donors and 5 serum samples from patients with autoimmune diseases did not yield a single false positive result.     

Comparative Proteomics Analysis of Cerebrospinal Fluid of Patients with Guillain–Barré Syndrome

To better understand the pathophysiologic mechanisms underlying Guillain-Barré syndrome (GBS), Comparative proteomic analysis of cerebrospinal fluid (CSF) between patients with GBS (the experiment group) and control subjects suffering from other neurological disorders (the control group) was carried out using two-dimensional gel electrophoresis (2-DE) technique, in combination with matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS) and database searching to determine abnormal CSF proteins in GBS patients. Image analysis of 2-DE gels silver stained revealed that 10 protein spots showed significant differential expression between the two groups of CSF samples. The expression of cystatin C, transthyretin, apolipoprotein E and heat shock protein 70 were decreased. However, haptoglobin, alpha-1-antitrypsin, apolipoprotein A-IV and neurofilaments were elevated. The subsequent ELISA measured the concentration of cystatin C and confirmed the result of the proteomic analysis. These identified proteins may be involved in the pathophysiological process of GBS and call for further studying the role of these proteins in the pathogenesis of the disease.     

Identification and validation of six proteins as marker for endemic nephropathy

Endemic nephropathy (EN) is defined as a slow progressive renal tubulointestitial disease that mainly occurs in the restricted areas of the Balkan Peninsula. The complexity of the pathogenesis of EN makes its earlier diagnosis very difficult. Urine samples from healthy volunteers from EN regions, EN patients with proteinuria less than 150 mg/L and EN patients with proteinuria more than 150 mg/L, patients with acute kidney injury, patients with diabetic nephropathy and healthy volunteers from Germany were collected. The urinary proteome analyses were performed using 2-D DIGE and mass spectrometry. The validation of biomarkers was investigated by two approaches (Western blot (WB) and dot blot) in successively increasing size - and partially overlapping - sample sets. Comparative and statistical analyses of the proteomics data from the different patient groups allowed the identification of six proteins (alpha-1-microglobulin, alpha-2-glycoprotein-1, beta-2-microglobulin, mannose-binding-lectin-2, protection-of-telomeres-protein-1, and superoxide-dismutase [Cu-Zn]), which were able to discriminate EN with low and high proteinuria from the other groups with high significance (p<0.05). The reliability of the identified proteins as EN marker was underlined with high statistical significance using WB analyses (sensitivity 66.7-98% and specificity 70-100%), whereas the dot blot analyses revealed a decrease in the sensitivity and specificity of these biomarkers.     

Evaluation of proteomics-identified CCL18 and CXCL1 as circulating tumor markers for differential diagnosis between ovarian carcinomas and benign pelvic masses

A lack of sensitive and specific tumor markers for early diagnosis and treatment is a major cause for the high mortality rate of ovarian cancer. The purpose of this study was to identify potential proteomics-based biomarkers useful for the differential diagnosis between ovarian cancer and benign pelvic masses. Serum samples from 41 patients with ovarian cancer, 32 patients with benign pelvic masses, and 41 healthy female blood donors were examined, and proteomic profiling of the samples was assessed by surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectroscopy (MS). A confirmatory study was also conducted with serum specimens from 58 patients with ovarian carcinoma, 37 patients with benign pelvic masses, and 48 healthy women. A classification tree was established using Biomarker Pattern Software. Six differentially expressed proteins (APP, CA 125, CCL18, CXCL1, IL-8, and ITIH4) were separated by high-performance liquid chromatography and identified by matrix-assisted laser desorption/ionization (MALDI)-MS/MS and database searches. Two of the proteins overexpressed in ovarian cancer patients, chemokine CC2 motif ligand 18 (CCL18) and chemokine CXC motif ligand 1 (CXCL1), were automatically selected in a multivariate predictive model. These two protein biomarkers were then validated and evaluated by enzyme-linked immunosorbent assay (ELISA) in 535 serum specimens (130 ovarian cancer, 64 benign ovarian masses, 36 lung cancer, 60 gastric cancer, 55 nasopharyngeal carcinoma, 48 hepatocellular carcinoma, and 142 healthy women). The combined use of CCL18 and CXCL1 as biomarkers for ovarian cancer had a sensitivity of 92% and a specificity of 97%. The multivariate ELISA analysis of the two putative markers in combination with CA 125 resulted in a sensitivity of 99% for healthy women and 94% for benign pelvic masses, and a specificity of 92% for both groups; these values were significantly higher than those obtained with CA 125 alone (p and lt;0.05). We conclude that serum CCL18 and CXCL1 are potentially useful as novel circulating tumor markers for the differential diagnosis between ovarian cancer and benign ovarian masses.     

Defining adult asthma endotypes by clinical features and patterns of volatile organic compounds in exhaled air

Background

Several classifications of adult asthma patients using cluster analyses based on clinical and demographic information has resulted in clinical phenotypic clusters that do not address molecular mechanisms. Volatile organic compounds (VOC) in exhaled air are released during inflammation in response to oxidative stress as a result of activated leukocytes. VOC profiles in exhaled air could distinguish between asthma patients and healthy subjects. In this study, we aimed to classify new asthma endotypes by combining inflammatory mechanisms investigated by VOC profiles in exhaled air and clinical information of asthma patients.

Methods

Breath samples were analyzed for VOC profiles by gas chromatography–mass spectrometry from asthma patients (n = 195) and healthy controls (n = 40). A total of 945 determined compounds were subjected to discriminant analysis to find those that could discriminate healthy from asthmatic subjects. 2-step cluster analysis based on clinical information and VOCs in exhaled air were used to form asthma endotypes.

Results

We identified 16 VOCs, which could distinguish between healthy and asthma subjects with a sensitivity of 100% and a specificity of 91.1%. Cluster analysis based on VOCs in exhaled air and the clinical parameters FEV1, FEV1 change after 3 weeks of hospitalization, allergic sensitization, Junipers symptoms score and asthma medications resulted in the formation of 7 different asthma endotype clusters. We identified asthma clusters with different VOC profiles but similar clinical characteristics and endotypes with similar VOC profiles, but distinct clinical characteristics.

Conclusion

This study demonstrates that both, clinical presentation of asthma and inflammatory mechanisms in the airways should be considered for classification of asthma subtypes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12931-014-0136-8) contains supplementary material, which is available to authorized users.     

From proteomic multimarker profiling to interesting proteins: thymosin-β(4) and kininogen-1 as new potential biomarkers for inflammatory hepatic lesions

Despite tremendous efforts in disclosing the pathophysiological and epidemiological factors associated with liver fibrogenesis, non-invasive diagnostic measures to estimate the clinical outcome and progression of liver fibrogenesis are presently limited. Therefore, there is a mandatory need for methodologies allowing the reasonable and reliable assessment of the severity and/or progression of hepatic fibrogenesis. We here performed proteomic serum profiling by matrix-assisted laser desorption ionization time-of-flight mass spectrometry in 179 samples of patients chronically infected with hepatitis C virus and 195 control sera. Multidimensional analysis of spectra allowed the definition of algorithms capable to distinguish class-specific protein expression profiles in serum samples. Overall about 100 peaks could be detected per single spectrum. Different algorithms including protein peaks in the range of 2000 and 10,000 Da were generated after pre-fractionation on a weak cation exchange surface. A specificity of 93% with a sensitivity of 86% as mean of the test set results was found, respectively. The nature of three of these protein peaks that belonged to kininogen-1 and thymosin-β(4) was further analysed by tandem mass spectrometry (MS)/MS. We further found that kininogen-1 mRNA was significantly down-regulated in cirrhotic livers. We have identified kininogen-1 and thymosin-β(4) as potential new biomarkers for human chronic hepatitis C and conclude that serum profiling is a reliable technique to identify hepatitis-associated expression patterns. Based on the high throughput capability, the identified differential protein panel may serve as a diagnostic marker and warrants further validation in larger cohorts.     

Cu isotope ratios are meaningful in ovarian cancer diagnosis

BackgroundOvarian cancer diagnosis is currently based on imaging and circulating CA-125 concentrations with well-known limits to sensitivity and specificity. New biomarkers are required to complement CA-125 testing to increase effectiveness. Increases in sensitivity of isotopic separation via multi collector inductively coupled plasma-mass spectrometry have recently allowed highly accurate measurement of copper (Cu) isotopic variations. Studies in breast cancer patients have revealed changes of serum copper isotopic composition demonstrating the potential for development as a cancer biomarker. Evaluating ⁶⁵Cu/⁶³Cu ratios (δ⁶⁵Cu) in serum samples from cancer patients has revealed a strong correlation with cancer development. In this study blood samples from forty-four ovarian cancer patients, and 13 ovarian biopsies were investigated.ResultsHere we demonstrate that changes in Cu isotopes also occurs in ovarian cancer patients. Copper composition determined by multiple collector inductively coupled plasma mass spectrometry revealed that the copper isotopic ratio δ⁶⁵Cu in the plasma of 44 ovarian cancer patient cohort was significantly lower than in a group of 48 healthy donors, and indicated that serum was enriched for ⁶³Cu. Further analysis revealed that the isotopic composition of tumour biopsies was enriched for ⁶⁵Cu compared with adjacent healthy ovarian tissues.ConclusionsWe propose that these changes are due to increase lactate and Cu transporter activities in the tumour. These observations demonstrate that, combined with existing strategies, δ⁶⁵Cu could be developed for use in ovarian cancer early detection.     

Intersession reproducibility of mass spectrometry profiles and its effect on accuracy of multivariate classification models

MOTIVATION: The 'reproducibility' of mass spectrometry proteomic profiling has become an intensely controversial topic. The mere mention of concern over the 'reproducibility' of data generated from any particular platform can lead to the anxiety over the generalizability of its results and its role in the future of discovery proteomics. In this study, we examine the reproducibility of proteomic profiles generated by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) across multiple data-generation sessions. We analyze the problem in terms of the reproducibility of signals, reproducibility of discriminative features and reproducibility of multivariate classification models on profiles for serum samples from early lung cancer and healthy control subjects. RESULTS: Proteomic profiles in individual data-generation sessions experience within-session variability. We show that combining data from multiple sessions introduces additional (inter-session) noise. While additional noise can affect the discriminative analysis, we show that its average effect on profiles in our study is relatively small. Moreover, for the purposes of prediction on future (previously unseen) data, classifiers trained on multi-session data are able to adapt to inter-session noise and improve their classification accuracy.     

Performance of a rapid immuno-chromatographic test (Schistosoma ICT IgG-IgM) for detecting Schistosoma-specific antibodies in sera of endemic and non-endemic populations

BackgroundSchistosomiasis, an acute and chronic parasitic disease caused by human pathogenic Schistosoma species, is a neglected tropical disease affecting more than 220 million people worldwide.For diagnosis of schistosomiasis, stool and urine microscopy for egg detection is still the recommended method, however sensitivity of these methods is limited. Therefore, other methods like molecular detection of DNA in stool, detection of circulating cathodic antigen in urine or circulating anodic antigen in urine and serum, as well as serological tests have gained more attention. This study examines the sensitivity and specificity of a rapid diagnostic test based on immunochromatography (Schistosoma ICT IgG-IgM, LD Bio, Lyon, France) for simultaneous detection of specific IgG and IgM antibodies in serum, against Schistosoma spp. in endemic and non-endemic populations.Methodology/Principal findingsFrozen banked serum samples from patients with confirmed schistosomiasis, patients with other helminth infections, patients with seropositive rheumatoid arthritis and healthy blood donors were used to assess the sensitivity and the specificity of the Schistosoma ICT IgG-IgM rapid diagnostic test.The test showed a sensitivity of 100% in patients with parasitologically confirmed schistosomiasis, irrespective of the species (S. mansoni, S. haematobium, S. japonicum, S. mekongi). In healthy blood donors and patients with rheumatoid factor positive rheumatoid arthritis from Europe, specificity was 100%. However, in serum samples of patients with other tissue invasive helminth infections, the test showed some cross-reactivity, resulting in a specificity of 85%.Conclusion/SignificanceWith its high sensitivity, the Schistosoma ICT IgG-IgM rapid diagnostic test is a suitable screening test for detection of Schistosoma specific antibodies, including S. mekongi. However, in populations with a high prevalence of co-infection with other tissue invasive helminths, positive results should be confirmed with other diagnostic assays due to the test’s imperfect specificity.     

Antibodies to Heteromeric Glycolipid Complexes in Guillain-Barré Syndrome

Autoantibodies are infrequently detected in the sera of patients with the demyelinating form of Guillain-Barré syndrome most commonly encountered in the Western world, despite abundant circumstantial evidence suggesting their existence. We hypothesised that antibody specificities reliant on the cis interactions of neighbouring membrane glycolipids could explain this discrepancy, and would not have been detected by traditional serological assays using highly purified preparations of single gangliosides. To assess the frequency of glycolipid complex antibodies in a Western European cohort of patients GBS we used a newly developed combinatorial glycoarray methodology to screen against large range of antigens (11 gangliosides, 8 other single glycolipids and 162 heterodimeric glycolipid complexes). Serum samples of 181 patients from a geographically defined, Western European cohort of GBS cases were analysed, along with 161 control sera. Serum IgG binding to single gangliosides was observed in 80.0% of axonal GBS cases, but in only 11.8% of cases with demyelinating electrophysiology. The inclusion of glycolipid complexes increased the positivity rate in demyelinating disease to 62.4%. There were 40 antigens with statistically significantly increased binding intensities in GBS as compared to healthy control sera. Of these, 7 complex antigens and 1 single ganglioside also produced statistically significantly increased binding intensities in GBS versus neurological disease controls. The detection of antibodies against specific complexes was associated with particular clinical features including disease severity, requirement for mechanical ventilation, and axonal electrophysiology. This study demonstrates that while antibodies against single gangliosides are often found in cases with axonal-type electrophysiology, antibodies against glycolipid complexes predominate in cases with demyelinating electrophysiology, providing a more robust serum biomarker than has ever been previously available for such cases. This work confirms the activation of the humoral immune system in the dysimmune disease process in GBS, and correlates patterns of antigen recognition with different clinical features.     

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.