Clinical proteomics: searching for better tumour markers with SELDI-TOF mass spectrometry

Novel Sample Preparation for Mass Spectral Analysis of Complex Biological Samples

The ability to combine a selective capture strategy with on chip MALDI-TOF analysis allows for rapid, sensitive analysis of a variety of different analytes. In this overview a series of applications of capture enhanced laser desorption ionization time of flight (CELDI-TOF) mass spectrometry are described. The key feature of the assay is an off-chip capture step that utilizes high affinity bacterial binding proteins to capture a selected ligand. This allows large volumes of sample to be used and provides for a concentration step prior to transfer to a gold chip for traditional mass spectral analysis. The approach can also be adapted to utilize specific antibody as the basis of the capture step. The direct and indirect CELDI-TOF assays are rapid, reproducible and can be a valuable proteomic tool for analysis of low abundance molecules present in complex mixtures like blood plasma.

Characterization of discriminatory urinary proteomic biomarkers for severe preeclampsia using SELDI-TOF mass spectrometry

OBJECTIVE

To analyze the proteomic pattern in urine for distinguishing severe preeclampsia from mild preeclampsia and normotensive controls using surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS).

STUDY DESIGN

Urine samples were collected from women with severe preeclampsia (n=11 [sPE]), mild preeclampsia (n=7 [mPE]), and normotensive controls (n=8) and analyzed by SELDI-TOF-MS to identify discriminatory protein peaks in the sPE cohort. A scoring system was constructed--designated as Preeclampsia Proteomic Score of Urine (PPSU)--to differentiate sPE from mPE and normotensive controls.

RESULTS

Four discriminatory protein peaks were identified (m/z ratio: 4155, 6044, 6663, and 7971), all of which were down-regulated in women with sPE. PPSU scores in women with sPE were significantly lower than that in both mPE and controls (sPE 0 [0-4] vs. mPE 3 [0-4] vs. controls 4 [2-4]; median [range]; P<0.05). PPSU<2 had a sensitivity of 90.9% and specificity of 93.3% in discriminating patients with sPE from mPE and controls.

CONCLUSION

Proteomic analysis of urine can accurately distinguish sPE from mPE and normotensive controls.

Identification of serum protein markers for breast cancer relapse with SELDI-TOF MS

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) was used to screen serum samples to identify protein markers for early breast cancer relapse. We collected 67 serum samples from patients with breast cancer (24 preoperative; 23 postoperative without breast cancer relapse; 20 postoperative with breast cancer relapse). Eight protein peaks varied between the presurgical group and the postsurgical group without breast cancer relapse; 4 protein peaks were differentially expressed between the postsurgical patients without relapse and patients with relapse. The peak at 3964 m/z dropped after surgery and rebounded after relapse (P < 0.01). These results indicate that there are differences in serum protein expression among the three different groups of patients. SELDI-TOF MS could be used to screen blood samples for the early detection of relapse in primary breast cancer patients. Specifically, protein peak at 3964 m/z is a potential biomarker for the detection of early breast cancer relapse.

Improving Detection Accuracy of Lung Cancer Serum Proteomic Profiling via Two-Stage Training Process

Background

Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for cancer biomarker research. The specificity of biomarkers detected by SELDI can be influenced by concomitant inflammation. This study aimed to increase detection accuracy using a two-stage analysis process.

Methods

Sera from 118 lung cancer patients, 72 healthy individuals, and 31 patients with inflammatory disease were randomly divided into training and testing groups by 3:2 ratio. In the training group, the traditional method of using SELDI profile analysis to directly distinguish lung cancer patients from sera was used. The two-stage analysis of distinguishing the healthy people and non-healthy patients (1^st-stage) and then differentiating cancer patients from inflammatory disease patients (2^nd-stage) to minimize the influence of inflammation was validated in the test group.

Results

In the test group, the one-stage method had 87.2% sensitivity, 37.5% specificity, and 64.4% accuracy. The two-stage method had lower sensitivity (> 70.1%) but statistically higher specificity (80%) and accuracy (74.7%). The predominantly expressed protein peak at 11480 Da was the primary splitter regardless of one- or two-stage analysis. This peak was suspected to be SAA (Serum Amyloid A) due to the similar m/z countered around this area. This hypothesis was further tested using an SAA ELISA assay.

Conclusions

Inflammatory disease can severely interfere with the detection accuracy of SELDI profiles for lung cancer. Using a two-stage training process will improve the specificity and accuracy of detecting lung cancer.

Tissue and serum proteomic profiling for diagnostic and prognostic bladder cancer biomarkers

A panel of biomarkers for the early detection of bladder cancer has not yet been identified. Many different molecules, including DNA, RNA or proteins have been reported but none have provided adequate sensitivity for a single-tier screening test or a test to replace cystoscopy. Therefore, multimarker panels are discussed at present to give a more-precise answer to the biomarker quest. Mass spectrometry or 2D gel-electrophoresis have evolved greatly within recent years and are capable of analyzing multiple proteins or peptides in parallel with high sensitivity and specificity. However, transmission of screening results from one laboratory to another is still the main pitfall of those methods; a fact that emphasizes the need for consistent and standardized procedures as suggested by the Human Proteome Organization (HUPO). In this article, recent results in screening approaches and other proteomic techniques used for biomarker evaluation in bladder cancer are discussed with a focus on serum and tissue biomarkers.

Is SELDI-TOF a valid tool for diagnostic biomarkers?

The genome revolution is providing fresh insights into host and parasite genomes, and new tools are becoming available for examining host-parasite interactions at the proteome level. Technologies such as surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) can be applied to discover biomarkers (alterations in both host and parasite proteomes) associated with parasitic diseases. Such biomarkers can represent host proteins, fragments of host proteins or parasite proteins that appear in body fluids or tissues following infection. Individual biomarkers or biomarker patterns not only have diagnostic utility (e.g. in active disease, prognosis, tests of cure) but can also provide unique insights into the mechanisms underlying host responses and pathogenesis.

The discovery and validation of colorectal cancer biomarkers

Colorectal cancer is currently the third most common malignancy in the world. Patients have excellent prognosis following surgical resection if their tumour is still localized at diagnosis. By contrast, once the tumour has started to metastasize, prognosis is much poorer. Accurate early detection can therefore significantly reduce the mortality from this disease. However, current tests either lack the required sensitivity and selectivity or are costly and invasive. Improved biomarkers, or panels of biomarkers, are therefore urgently required. We have addressed current screening strategies and potential protein biomarkers that have been proposed. The role of both discovery and hypothesis-driven proteomics approaches for biomarker discovery and validation is discussed. Using such approaches we show how multiple reaction monitoring (MRM) can be successfully developed and used for quantitative multiplexed analysis of potential faecal biomarkers.

SILAC-based quantitative proteomic approach to identify potential biomarkers from the esophageal squamous cell carcinoma secretome

The identification of secreted proteins that are differentially expressed between non-neoplastic and esophageal squamous cell carcinoma (ESCC) cells can provide potential biomarkers of ESCC. We used a SILAC-based quantitative proteomic approach to compare the secretome of ESCC cells with that of non-neoplastic esophageal squamous epithelial cells. Proteins were resolved by SDS-PAGE, and tandem mass spectrometry analysis (LC-MS/MS) of in-gel trypsin-digested peptides was carried out on a high-accuracy qTOF mass spectrometer. In total, we identified 441 proteins in the combined secretomes, including 120 proteins with > 2-fold upregulation in the ESCC secretome vs. that of non-neoplastic esophageal squamous epithelial cells. In this study, several potential protein biomarkers previously known to be increased in ESCC including matrix metalloproteinase 1, transferrin receptor, and transforming growth factor beta-induced 68 kDa were identified as overexpressed in the ESCC-derived secretome. In addition, we identified several novel proteins that have not been previously reported to be associated with ESCC. Among the novel candidate proteins identified, protein disulfide isomerase family a member 3 (PDIA3), GDP dissociation inhibitor 2 (GDI2), and lectin galactoside binding soluble 3 binding protein (LGALS3BP) were further validated by immunoblot analysis and immunohistochemical labeling using tissue microarrays. This tissue microarray analysis showed overexpression of protein disulfide isomerase family a member 3, GDP dissociation inhibitor 2, and lectin galactoside binding soluble 3 binding protein in 93%, 93% and 87% of 137 ESCC cases, respectively. Hence, we conclude that these potential biomarkers are excellent candidates for further evaluation to test their role and efficacy in the early detection of ESCC.

Reference spectra from squamous epithelium and connective tissue allow whole section proteomics analysis

We reasoned that micro-dissection of tumour cells for protein expression studies should be omitted since tumour-stroma interactions are an important part of the biology of solid tumours. To study such interactions in head and neck squamous cell carcinoma (HNSCC) development, we generated reference protein spectra for normal squamous epithelium and connective tissue by SELDI-TOF-MS. Calgranulins A and B, Annexin1 and Histone H4 were found to be strongly enriched in the epithelium. The alpha-defensins 1-3 and the haemoglobin subunits were identified in the connective tissue. Tumour-distant epithelia, representing early pre-malignant lesions, showed up-regulated expression of the stromal alpha-defensins, whereas the epithelial Annexin 1 was down-regulated. Thus, tumour microenvironment interactions occur very early in the carcinogenic process. These data demonstrate that omitting micro-dissection is actually beneficial for studying changes in protein expression during development and progression of solid tumours.

SELDI-TOF serum proteomics and colorectal cancer: a current overview

Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is a widely used technology platform for diagnostic biomarker discovery in tissue, plasma and serum. High-throughput and simplicity of experimental procedures have allowed this technology to become an important research tool for biomarker discovery during the last years. This review provides an overview of SELDI-TOF functionality, its advantages and drawbacks and gives a current literature overview of colorectal cancer based serum biomarker detection. Further improvements in instrumentation sensitivity and labelling chemistries will enable detection of novel, tissue-leakage biomarkers in serum. However, major emphasis should be given on subsequent identification of differentially observed protein peaks detected by SELDI-TOF. Clinical validation in large patient cohorts will then allow transferring novel biomarkers into clinical use.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Non-invasive proteomics-thinking about personalized breast cancer screening and treatment

The early diagnosis of breast cancer in potentially curable stages improves prognosis and consecutively reduces mortality of breast cancer patients. Established screening programs have an unfavorable connotation due to significant rates of false negative as well as false positive results leading to overdiagnosis and overtherapy. The combination of a non-invasive breast-cancer-suspectability-biomarker with established clinical diagnostics could help to increase the acceptance of population based breast cancer screening programs by creating an individual risk profile, which is irrespective of mammography quality and interpretation. Recently, non-invasive proteomic biomarkers obtained from blood, saliva or nipple aspiration fluid have been extensively investigated and might play a future role in the personalized management of breast cancer screening. A simple, robust and inexpensive, non-invasive test for screening and diagnosis could easily be performed in every medical practice leading to an affordable, high-throughput instrument. This review describes recently investigated proteomic screening biomarkers that could improve the early diagnosis of breast cancer in the following years.

Proteomics and the search for biomarkers of female reproductive diseases

Over the past decade, high-throughput proteomics technologies have evolved considerably and have become increasingly more commonly applied to the investigation of female reproductive diseases. Proteomic approaches facilitate the identification of new disease biomarkers by comparing the abundance of hundreds of proteins simultaneously to find those specific to a particular clinical condition. Some of the best studied areas of female reproductive biology applying proteomics include gynaecological cancers, endometriosis and endometrial infertility. This review will discuss the progress that has been made in these areas and will highlight some of the emerging technologies that promise to contribute to better understanding of the female reproductive disease.

Serum proteomics and disease-specific biomarkers of patients with advanced gastric cancer

Gastric cancer is a commonly diagnosed solid tumor which is associated with a dismal prognosis making early diagnosis essential. Thus, this study aimed to identify novel biomarkers in gastric cancer. Serum of patients with advanced gastric cancer was collected according to a predefined schedule: prior to first-line chemotherapy with epirubicin (50 mg/m(2), day 1), cisplatin (60 mg/m(2), day 1) and capecitabine (1,000 mg/m(2), twice daily on days 1-14). The serum was collected serially before the treatment cycles and then analyzed by SELDI-TOF MS. Normal control subjects were matched according to age, gender and serum collection. Serum proteomic mass spectrometry data of all subjects were processed using the tbimass R-package and compared. We analyzed i) whether proteomic profile changes were associated with a response to chemotherapy and survival, and ii) whether changes in proteomic profiles occurring during the time period of chemotherapy were associated with tumor response. In total, 82 patients with adenocarcinoma of the stomach (mean age 57 years, males 69.5%) were treated with a mean number of five chemotherapy cycles. The overall tumor response rate, complete and partial remission combined, was 37%, median time to progression was 7 months (95% CI, 6-8) and median overall survival 11 months (95% CI, 9.5-12). By comparing 77 serum samples of patients with normal matched controls, we identified 32 proteins which discriminated the two groups. By selecting the most differentiating proteins, we built a classification model that correctly categorized 81% of the gastric cancer patients and 90% of the normal controls. Furthermore, we found a statistically significant correlation between the pre-treatment intensity of serum amyloid-α (SAA) and overall survival in gastric cancer patients, whereby a low intensity of SAA predicted a longer patient survival. A classification model, based on the 32 most discriminating proteins differentiating gastric cancer from normal controls, correctly classified subjects with relatively high sensitivity and specificity.

A FQHPSFI peptide selectively binds to LPS-activated alveolar macrophages and inhibits LPS-induced MIP-2 production

OBJECTIVE

The goal of this study was to identify peptides selectively binding to lipopolysaccharide (LPS)-activated alveolar macrophages (AMs) and to characterize their effects on the production of LPS-induced cytokines.

METHODS

A phage display library was sequentially screened by binding phages to unmanipulated AMs and then to LPS-activated AMs. Individual phage clones were identified by cell-based ELISA. Positive phage clones were characterized by DNA sequencing and bioinformatics analysis. Binding specificity of the selected phage to LPS-activated AMs was tested using immunofluorescent staining. The selected candidate peptide was chemically synthesized to determine whether it could modulate LPS-induced cytokine production in AMs.

RESULTS

Twenty-two out of 40 phage clones selected randomly after four rounds of biopanning bound selectively to LPS-activated AMs, and 12 of them displayed novel peptides. A phage clone displaying FQHPSFI peptide bound effectively to LPS-activated AMs, but not to other cells tested. Furthermore, the synthetic FQHPSFI peptide, but not seven point mutants tested, competitively inhibited the binding of the phage clone to LPS-activated AMs. Importantly, the FQHPSFI peptide significantly inhibited LPS-stimulated microphage inflammatory protein 2 (MIP-2) production in vitro.

CONCLUSIONS

Our data demonstrate that phage display technology is a powerful tool for the identification of bioactive peptides. The identified FQHPSFI peptide may be used for the modulation of LPS-stimulated MIP-2 production in AMs.

[Variation in protein expression depending on the severity of sleep apnoea-hypopnoea syndrome]

OBJECTIVE

A prospective study with a consecutive sample and a control group to determine whether protein expression in patients with sleep apnoea-hypopnoea syndrome (SAHS) is different from that of the control group (IAH < or =5).

PATIENTS AND METHODS

A total of 32 patients aged between 35 and 60 years who had a polysomnograph performed were included. Patients with an acute or chronic were excluded. The first dimension of the proteomic study was carried out on IPG strips (18cm, pH 4-7) and the second on SDS-PAGE gels in triplicate for each group. The gels were stained with SYPRO-Ruby (Bio-Rad((R))), the images obtained with an FX-Imager laser scanner and the spots were analysed using ProteomWeaver v. 4.0 (Bio-Rad((R))) software. Significant changes between the gels were analysed by replicates and separately, being considered a significant change if the relative intensity of the spots was three times higher or lower than that of the control and if it was observed in 2 of the 3 replicates of each group, with a coefficient of variation of <20%.

RESULTS

The patients were divided into 8 subjects per group (control, mild, moderate and severe). The comparison of the gels showed significant differences between the control group and the 3 clinical groups, with significant over-expression being observed in 3 spots, and under-expression in 7 spots in the control group.

CONCLUSION

There are significant changes in protein expression between a control group and patients in different stages of disease. The proteomic study can identify biomarkers associated with the diagnosis and severity of the SAHS.

The use of proteomics to identify novel therapeutic targets for the treatment of disease

The completion of the Human Genome Project has revealed a multitude of potential avenues for the identification of therapeutic targets. Extensive sequence information enables the identification of novel genes but does not facilitate a thorough understanding of how changes in gene expression control the molecular mechanisms underlying the development and regulation of a cell or the progression of disease. Proteomics encompasses the study of proteins expressed by a population of cells, and evaluates changes in protein expression, post-translational modifications, protein interactions, protein structure and splice variants, all of which are imperative for a complete understanding of protein function within the cell. From the outset, proteomics has been used to compare the protein profiles of cells in healthy and diseased states and as such can be used to identify proteins associated with disease development and progression. These candidate proteins might provide novel targets for new therapeutic agents or aid the development of assays for disease biomarkers. This review provides an overview of the current proteomic techniques available and focuses on their application in the search for novel therapeutic targets for the treatment of disease.

Five serum proteins identified using SELDI-TOF-MS as potential biomarkers of gastric cancer

OBJECTIVE

The aims of this study were to detect serum proteomic patterns in gastric cancer serum samples using Surface-enhanced Laser Desorption/ionization-Time-of-flight-Mass Spectrometry ProteinChip array technology, to screen biomarker candidates, to build diagnostic models and to evaluate their clinical significance.

METHODS

Serum samples from patients with gastric cancer and normal healthy control subjects (n = 125) were analysed using surface-enhanced laser desorption/ionization technology. The spectra were generated on weak cation exchange (WCX2) chips, and protein peak clustering and classification analyses were established using Ciphergen Biomarker Wizard and Biomarker Pattern software, respectively. The diagnostic models were developed and validated by discriminant analysis. In addition, the results of the surface-enhanced laser desorption/ionization model were compared with the biomarkers carcinoembryonic antigen and carbohydrate antigen 199 in a subset of samples using a microparticle enzyme immunoassay.

RESULTS

Five protein peaks at 2046, 3179, 1817, 1725 and 1929 m/z were automatically chosen as components of the best biomarker pattern for diagnosis of gastric cancer. In addition, we identified a single protein peak at 4665 m/z, which could distinguish between stage I/II and stage III/IV gastric cancer with a specificity and sensitivity of 91.6% (11/12) and 95.4% (21/22), respectively. When this biomarker was validated in the second set of samples, the specificity and sensitivity were 91.7% (11/12) and 86.3% (19/22), respectively.

CONCLUSIONS

The present results suggest that serum surface-enhanced laser desorption/ionization protein profiling can distinguish patients with gastric cancer, and in particular stage I/II patients, from normal subjects with a relatively high sensitivity and specificity. Surface-enhanced Laser Desorption/ionization-Time-of-flight-Mass Spectrometry is a potential new diagnostic tool for the screening of gastric cancer.

Identification of serum biomarkers for pancreatic adenocarcinoma by proteomic analysis

Diagnosis of pancreatic adenocarcinoma (PaCa) at an early stage is important for successful treatment and improving the prognosis of patients. Serum samples were applied to strong anionic exchange chromatography (SAX) protein chips for protein profiling by surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) to distinguish PaCa from noncancer. The Wilcoxon rank-sum test, decision tree algorithm, and logistic regression were used to statistically analyze the multiple protein peaks. Sixty-one protein peaks between 2000 and 30,000 m/z ratios were detected to establish multiple decision classification trees for differentiating the known disease states. A sensitivity of 0.833 and a specificity of 1.000 were obtained in distinguishing PaCa from healthy controls and benign pancreatic diseases. Six protein biomarkers related to different PaCa TNM stages were detected (P < 0.01). One protein biomarker (m/z 4016) rich in PaCa had a down-regulated trend when preoperative and postoperative samples (P < 0.05) were compared. Three protein biomarkers (m/z 4155, 4791, and 28,068) were detected in the differential diagnosis of the three test groups (P < 0.05). A peak m/z 28 068 was identified as C14orf16 using ProteinChip immunoassay. C14orf166 levels were significantly higher in the serum of patients with PaCa compared with the control group using a sandwich immunoenzymatic system. Immunolabeling of tissue sections revealed that the C14orf166 protein was strongly expressed in tumor cells. The results suggest that SELDI-TOF-MS serum profiling is helpful for the diagnostic, prognostic or therapeutic effects of PaCa, which is superior to CA 19-9. The identified protein biomarker C14orf166 is a potential biomarker of PaCa.

Optimal preparation methods for automated matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling of low molecular weight proteins and peptides

Mass spectrometry (MS) profiling of the proteome and peptidome for disease-associated patterns is a new concept in clinical diagnostics. The technique, however, is highly sensitive to external sources of variation leading to potentially unacceptable numbers of false positive and false negative results. Before MS profiling can be confidently implemented in a medical setting, standard experimental methods must be developed that minimize technical variance. Past studies of variance have focused largely on pre-analytical variation (i.e., sample collection, handling, etc.). Here, we examined how factors at the analytical stage including the matrix and solid-phase extraction influence MS profiling. Firstly, a standard peptide/protein sample was measured automatically by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS across five consecutive days using two different preparation methods, dried droplet and sample/matrix, of four types of matrix: alpha-cyano-4-hydroxycinnamic acid (HCCA), sinapinic acid (SA), 2,5-dihydroxybenzoic acid (DHB) and 2,5-dihydroxyacetophenone (DHAP). The results indicated that the matrix preparation greatly influenced a number of key parameters of the spectra including repeatability (within-day variability), reproducibility (inter-day variability), resolution, signal strength, background intensity and detectability. Secondly, an investigation into the variance associated with C8 magnetic bead extraction of the standard sample prior to automated MS profiling demonstrated that the process did not adversely affect these same parameters. In fact, the spectra were generally more robust following extraction. Thirdly, the best performing matrix preparations were evaluated using C8 magnetic bead extracted human plasma. We conclude that the DHAP prepared according to the dried-droplet method is the most appropriate matrix to use when performing automated MS profiling.

SELDI-TOF MS profiling of serum for detection of nasopharyngeal carcinoma

Background

No satisfactory biomarkers are currently available to screen for nasopharyngeal carcinoma (NPC). We have developed and evaluated surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) for detection and analysis of multiple proteins for distinguishing individuals with NPC from control individuals.

Methods

A preliminary learning set and a classification tree of spectra derived from 24 patients with NPC and a group of 24 noncancer controls were used to develop a proteomic model that discriminated cancer from noncancer effectively. Then, the validity of the classification tree was challenged with a blind test set, which included another 20 patients with NPC and 12 noncancer controls.

Results

A panel of 3 biomarkers ranging m/z 3–20 k was selected to establish Decision Tree model by BPS with sensitivity of 91.66% and specificity of 95.83%. The ability to detect NPC patients was evaluated, a sensitivity of 95.0% and specificity of 83.33% were validated in blind testing set.

Conclusion

This high-flux proteomic classification system will provide a highly accurate and innovative approach for the detection/diagnosis of NPC.

Identification of a novel potential biomarker in a model of hemorrhagic shock and valproic acid treatment

BACKGROUND

The initial management of a poly-trauma patient requires evaluation for potential hemorrhage and ongoing monitoring to assess the efficacy of treatment and avoid complications related to massive blood loss. Certain serum protein levels may be altered in response to hemorrhagic shock, and may serve as useful biomarkers to guide diagnosis, prognosis, and therapeutics in traumatic hemorrhagic shock (HS). Treatment with valproic acid (VPA) has been shown to up-regulate various survival pathways and improve outcome. Here we determine whether these changes would result in altered serum biomarkers.

METHODS

Wistar-Kyoto rats underwent hemorrhagic shock (60% blood loss) followed by treatment with or without VPA (300 mg/kg). Using surface enhanced laser desorption-time of flight mass spectrometry (SELDI or SELDI-TOF MS) technology, we screened serum samples obtained from five rats at different time points (baseline, post-hemorrhagic shock, and post-VPA treatment) in a lethal model of hemorrhagic shock (HS). Additionally, we used isobaric tag labeling for relative quantitation (iTRAQ) to identify potential biomarkers in the serum. Western blots were performed to validate iTRAQ-identified biomarker from independent serum samples, and to analyze protein biomarker levels in the intestine during hemorrhagic shock and treatment.

RESULTS

HS and treatment with VPA affected serum levels of many proteins. One such protein with a mass spectrum around 22.7 kDa was detected in all five rats. The same serum samples subjected to iTRAQ resulted in our identification of claudin-3, a 23 kDa tight junction protein. HS elevated serum claudin-3 protein levels, which was reversed by VPA treatment in a pattern similar to the SELDI-TOF MS studies. Further validation with independent serum and intestine samples from individual rats by Western blots confirmed that HS increased the protein level of claudin-3 in serum and decreased its level in the intestine. Treatment with VPA reversed the hemorrhagic shock-induced alteration in claudin-3 to sham levels.

CONCLUSIONS

HS causes an acute rise in serum claudin-3 protein levels and a concurrent decrease in intestinal claudin-3 protein expression. VPA treatment attenuates these alterations and stabilizes intestinal claudin-3 levels. Our results demonstrate for the first time that claudin-3 is a potential biomarker in HS and treatment.

Comparative analysis of differential serum low molecular weight proteins between colorectal cancer and other malignant tumors

AIM: To evaluate the diagnostic specificity of differentially expressed serum low molecular proteins for colorectal cancer by comparative analysis between the patients with colorectal cancer and with other malignant tumors.

METHODS: A total of 218 serum samples from patients with colorectal cancer (n = 68) and other malignant tumors (breast, gastric, esophagus, liver, lung and kidney, 25 cases each) were collected. Their serum protein fingerprinting was read by surface-enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS). Biomarker Wizard software was used to analyze the distinct proteins between colorectal cancer and other malignant tumors and to create receiver operating characteristic (ROC) curves. Then Fisher's discriminant model was established using the distinct proteins with the area under the curve (AUC) > 0.8.

RESULTS: Relatively 12 proteins were highly expressed and 102 proteins were lowly expressed in the sera of colorectal cancer compared with that of the other malignant tumors. Of all the 75 proteins with AUC > 0.5 by ROC analysis, six proteins (AUC > 0.8) were selected to establish the Fisher's discriminant model. The accuracy of distinguishing colorectal cancer from other malignant tumors was 91.7%, and the sensitivity and specificity were 88.2% and 93.3%, respectively.

CONCLUSION: The significantly differentially expressed serum low molecular proteins have a high diagnostic specificity for distinguishing colorectal cancer from other malignant tumors, which deserves further research.

Diagnostic model of saliva protein finger print analysis of patients with gastric cancer

AIM: To explore the method for early diagnosis of gastric cancer by screening the expression spectrum of saliva protein in gastric cancer patients using mass spectrometry for proteomics.

METHODS: Proportional peptide mass fingerprints were obtained by analysis based on proteomics matrix-assisted laser desorption ionization time-of-flight/mass spectrometry. A diagnosis model was established using weak cation exchange magnetic beads to test saliva specimens from gastric cancer patients and healthy subjects.

RESULTS: Significant differences were observed in the mass to charge ratio (m/z) peaks of four proteins (1472.78 Da, 2936.49 Da, 6556.81 Da and 7081.17 Da) between gastric cancer patients and healthy subjects.

CONCLUSION: The finger print mass spectrum of saliva protein in patients with gastric cancer can be established using gastric cancer proteomics. A diagnostic model for distinguishing protein expression mass spectra of gastric cancer from non-gastric-cancer saliva can be established according to the different expression of proteins 1472.78 Da, 2936.49 Da, 6556.81 Da and 7081.17 Da. The method for early diagnosis of gastric cancer is of certain value for screening special biological markers.

Noninvasive molecular biomarkers for the detection of colorectal cancer

Colorectal cancer (CRC) is the third most common malignancy in the world. Because CRC develops slowly from removable precancerous lesions, detection of the disease at an early stage during regular health examinations can reduce both the incidence and mortality of the disease. Although sigmoidoscopy offers significant improvements in the detection rate of CRC, its diagnostic value is limited by its high costs and inconvenience. Therefore, there is a compelling need for the identification of noninvasive biomarkers that can enable earlier detection of CRC. Accordingly, many validation studies have been conducted to evaluate genetic, epigenetic or protein markers that can be detected in the stool or in serum. Currently, the fecal-occult blood test is the most widely used method of screening for CRC. However, advances in genomics and proteomics combined with developments in other relevant fields will lead to the discovery of novel non invasive biomarkers whose usefulness will be tested in larger validation studies. Here, noninvasive molecular biomarkers that are currently used in clinical settings and have the potential for use as CRC biomarkers are discussed.

Clinical proteomics in breast cancer: a review

Breast cancer imposes a significant healthcare burden on women worldwide. Early detection is of paramount importance in reducing mortality, yet the diagnosis of breast cancer is hampered by the lack of an adequate detection method. In addition, better breast cancer prognostication may improve selection of patients eligible for adjuvant therapy. Hence, new markers for early diagnosis, accurate prognosis and prediction of response to treatment are warranted to improve breast cancer care. Since proteomics can bridge the gap between the genetic alterations underlying cancer and cellular physiology, much is expected from proteome analyses for the detection of better protein biomarkers. Recent technical advances in mass spectrometry, such as matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) and its variant surface-enhanced laser desorption/ionisation (SELDI-) TOF MS, have enabled high-throughput proteome analysis. In the current review, we give a comprehensive overview of the results of expression proteomics (i.e. protein profiling) research performed in breast cancer using these two platforms. Many protein peaks have been reported to bear significant diagnostic, prognostic or predictive value, however, only few candidate markers have been structurally identified yet. In addition, although of pivotal importance in preventing overfitting of data and systematic bias by pre-analytical parameters, validation of biomarker candidates by other, quantitative, methods and/or in new populations is very limited. Moreover, none of the identified candidate biomarkers has been investigated for their utility as breast cancer markers in large, prospective, clinical settings. As such, the candidate biomarkers discussed in this overview have not been validated sufficiently to be used for clinical patient care. Nonetheless, regarding the promising results up to now, MALDI- and SELDI-TOF MS protein profiling studies could eventually fulfil the great promise that protein biomarkers have for improving cancer patient outcome, provided that these studies are performed with adequate statistical power and analytical rigour.

Optimization of SELDI-TOF protein profiling for analysis of cervical mucous

Cervical mucous, produced in the region where cervical neoplasia occurs, is thought to be a good choice for discovery of biomarkers to improve cervical cancer screening. In this study, SELDI-TOF MS analysis was used to evaluate parameters for protein profiling of mucous. Proteins were extracted from mucous collected with Weck-Cel sponges. Several parameters like extraction reagent, loading protein concentration, matrix type, bind/wash conditions and sample fractionation, on different protein chip surfaces were evaluated. SELDI peak number and consistency in the resulting spectra were used to evaluate each condition. Analysis of spectra generated by different protein chips revealed an average of 30 peaks in the 2.5-30 kDa mass range using sinnapinic acid in the unfractionated sample. Sample concentration and buffer conditions evaluated did not lead to large alterations in the profiles. Quality control spectra were reproducible with intra- and inter-assay intensity CV for CM10, H50 and Q10 arrays being less than 20% and 30% respectively. IMAC30-Cu chips had higher intra- and inter-assay CV's at 25% and 35%. Current data showed that optimizing pre-analytical parameters can help in standardization and reproducibility of protein profiles produced by cervical mucous, and thus can be used for protein biomarker discovery with the SELDI platform.

The cancer secretome: a reservoir of biomarkers

Biomarkers are pivotal for cancer detection, diagnosis, prognosis and therapeutic monitoring. However, currently available cancer biomarkers have the disadvantage of lacking specificity and/or sensitivity. Developing effective cancer biomarkers becomes a pressing and permanent need. The cancer secretome, the totality of proteins released by cancer cells or tissues, provides useful tools for the discovery of novel biomarkers. The focus of this article is to review the recent advances in cancer secretome analysis. We aim to elaborate the approaches currently employed for cancer secretome studies, as well as its applications in the identification of biomarkers and the clarification of carcinogenesis mechanisms. Challenges encountered in this newly emerging field, including sample preparation, in vivo secretome analysis and biomarker validation, are also discussed. Further improvements on strategies and technologies will continue to drive forward cancer secretome research and enable development of a wealth of clinically valuable cancer biomarkers.

Cancer screening: a mathematical model relating secreted blood biomarker levels to tumor sizes

BACKGROUND

Increasing efforts and financial resources are being invested in early cancer detection research. Blood assays detecting tumor biomarkers promise noninvasive and financially reasonable screening for early cancer with high potential of positive impact on patients' survival and quality of life. For novel tumor biomarkers, the actual tumor detection limits are usually unknown and there have been no studies exploring the tumor burden detection limits of blood tumor biomarkers using mathematical models. Therefore, the purpose of this study was to develop a mathematical model relating blood biomarker levels to tumor burden.

METHODS AND FINDINGS

Using a linear one-compartment model, the steady state between tumor biomarker secretion into and removal out of the intravascular space was calculated. Two conditions were assumed: (1) the compartment (plasma) is well-mixed and kinetically homogenous; (2) the tumor biomarker consists of a protein that is secreted by tumor cells into the extracellular fluid compartment, and a certain percentage of the secreted protein enters the intravascular space at a continuous rate. The model was applied to two pathophysiologic conditions: tumor biomarker is secreted (1) exclusively by the tumor cells or (2) by both tumor cells and healthy normal cells. To test the model, a sensitivity analysis was performed assuming variable conditions of the model parameters. The model parameters were primed on the basis of literature data for two established and well-studied tumor biomarkers (CA125 and prostate-specific antigen [PSA]). Assuming biomarker secretion by tumor cells only and 10% of the secreted tumor biomarker reaching the plasma, the calculated minimally detectable tumor sizes ranged between 0.11 mm(3) and 3,610.14 mm(3) for CA125 and between 0.21 mm(3) and 131.51 mm(3) for PSA. When biomarker secretion by healthy cells and tumor cells was assumed, the calculated tumor sizes leading to positive test results ranged between 116.7 mm(3) and 1.52 x 10(6) mm(3) for CA125 and between 27 mm(3) and 3.45 x 10(5) mm(3) for PSA. One of the limitations of the study is the absence of quantitative data available in the literature on the secreted tumor biomarker amount per cancer cell in intact whole body animal tumor models or in cancer patients. Additionally, the fraction of secreted tumor biomarkers actually reaching the plasma is unknown. Therefore, we used data from published cell culture experiments to estimate tumor cell biomarker secretion rates and assumed a wide range of secretion rates to account for their potential changes due to field effects of the tumor environment.

CONCLUSIONS

This study introduced a linear one-compartment mathematical model that allows estimation of minimal detectable tumor sizes based on blood tumor biomarker assays. Assuming physiological data on CA125 and PSA from the literature, the model predicted detection limits of tumors that were in qualitative agreement with the actual clinical performance of both biomarkers. The model may be helpful in future estimation of minimal detectable tumor sizes for novel proteomic biomarker assays if sufficient physiologic data for the biomarker are available. The model may address the potential and limitations of tumor biomarkers, help prioritize biomarkers, and guide investments into early cancer detection research efforts.

Proteomic studies of early-stage and advanced ovarian cancer patients

OBJECTIVES

The objectives of this study were to evaluate the diagnostic value for ovarian cancer using proteomic pattern established by surface-enhanced laser desorption/ionization (SELDI-TOF-MS) profiling of plasma proteins coupled with support vector machine (SVM) data analysis, and to investigate whether the proteomic pattern established by advanced ovarian cancer could be used for diagnosis of early-stage ovarian cancer patients.

METHODS

The study included 44 ovarian cancer patients (11 early-stage and 33 advanced ovarian cancer patients) and 31 age-matched non-cancer controls. SELDI-TOF-MS coupled with SVM analysis was performed to establish a proteomic pattern to discriminate 33 advanced ovarian cancer patients from 31 non-cancer controls. A blind test, including 11 early-stage ovarian cancer cases, was performed to investigate whether proteomic pattern established by advanced ovarian cancer could be used for diagnosis of early-stage ovarian cancer patients.

RESULTS

A seven-peak proteomic pattern was established which discriminated 33 advanced ovarian cancer patients from 31 non-cancer controls effectively. A sensitivity of 93.94% (31/33) and a specificity of 93.55% (29/31) were yielded from the proteomic pattern. Among the 7 protein peaks, 5 with mass charge ratio (m/z) 4099 Da, 5488 Da, 4144 Da, 4479 Da and 3940 Da were up-regulated, while 2 peaks, with m/z 13 783 Da and 8588 Da were down-regulated in the advanced ovarian cancer group compared with non-cancer control group. After blind test, 9 of 11 early-stage ovarian cancer patients were successfully diagnosed with the accuracy of 81.82% (9/11).

CONCLUSIONS

This study demonstrated that SELDI-TOF-MS coupled with SVM is effective in distinguishing protein expression between ovarian cancer and non-cancer plasma and it may be feasible to diagnose early-stage ovarian cancer using proteomic pattern established by advanced ovarian cancer. The gained and lost protein peaks in plasma may exist in both early-stage and advanced ovarian cancer plasma. Further studies should be performed using larger sample numbers.

Detection of Colorectal Cancer by Serum and Tissue Protein Profiling: A Prospective Study in a Population at Risk

Colorectal cancer (CRC) is the second most common cause of cancer-related death in Europe and its prognosis is largely dependent on stage at diagnosis. Currently, there are no suitable tumour markers for early detection of CRC. In a retrospective study we previously found discriminative CRC serum protein profiles with surface enhanced laser desorption ionisation—time of flight mass spectrometry (SELDI-TOF MS). We now aimed at prospective validation of these profiles. Additionally, we assessed their applicability for follow-up after surgery and investigated tissue protein profiles of patients with CRC and adenomatous polyps (AP). Serum and tissue samples were collected from patients without known malignancy with an indication for colonoscopy and patients with AP and CRC during colonoscopy. Serum samples of controls (CON; n = 359), patients with AP (n = 177) and CRC (n = 73), as well as tissue samples from AP (n = 52) and CRC (n = 47) were analysed as described previously. Peak intensities were compared by non-parametric testing. Discriminative power of differentially expressed proteins was assessed with support vector machines (SVM). We confirmed the decreased serum levels of apolipoprotein C-1 in CRC in the current population. No differences were observed between CON and AP. Apolipoprotein C-I levels did not change significantly within 1 month post-surgery, although a gradual return to normal levels was observed. Several proteins differed between AP and CRC tissue, among which a peak with similar mass as apolipoprotein C-1. This peak was increased in CRC compared to AP. Although we prospectively validated the serum decrease of apolipoprotein C-1 in CRC, serum protein profiles did not yield SVM classifiers with suitable sensitivity and specificity for classification of our patient groups.

Proteomic profiling of urine for the detection of colon cancer

Background

Colorectal cancer is the second most common cause of cancer related death in the developed world. To date, no blood or stool biomarkers with both high sensitivity and specificity for potentially curable early stage disease have been validated for clinical use. SELDI and MALDI profiling are being used increasingly to search for biomarkers in both blood and urine. Both techniques provide information predominantly on the low molecular weight proteome (<15 kDa). There have been several reports that colorectal cancer is associated with changes in the serum proteome that are detectable by SELDI and we hypothesised that proteomic changes would also be detectable in urine.

Results

We collected urine from 67 patients with colorectal cancer and 72 non-cancer control subjects, diluted to a constant protein concentration and generated MALDI and SELDI spectra. The intensities of 19 peaks differed significantly between cancer and non-cancer patients by both t-tests and after adjusting for confounders using multiple linear regressions. Logistic regression classifiers based on peak intensities identified colorectal cancer with up to 78% sensitivity at 87% specificity. We identified and independently quantified 3 of the discriminatory peaks using synthetic stable isotope peptides (an 1885 Da fragment of fibrinogen and hepcidin-20) or ELISA (β2-microglobulin).

Conclusion

Changes in the urine proteome may aid in the early detection of colorectal cancer.

Surface-enhanced laser desorption/ionization mass spectrometry for protein and Peptide profiling of body fluids

Recently, Ciphergen Biosystems (Fremont, CA, USA) has developed a technique called surface-enhanced laser desorption/ionization (SELDI). This technology is based on ProteinChips(R) with chemically or biochemically modified surfaces for the selective retention and enrichment of protein subsets from a complex protein mixture. The proteins or peptides of interest can then be identified by using mass spectrometry (SELDI-MS). This highly sensitive and high-throughput technique can detect minute differences in proteins and peptide profiles between biological samples. The versatility of this technology enables a wide variety of applications in basic research, clinical, proteomic, and drug discovery such as identification of novel biomarkers associated with certain diseases or treatments. Many disease biomarkers or biomarker patterns have been identified using SELDI-MS in various laboratories. In this chapter, we provide a general guide to the profiling of proteins or peptides as well as biomarker discovery using the most common body fluids such as serum/plasma, nipple aspiration fluid, and urine.

Population proteomics: investigation of protein diversity in human populations

Outlined in this review is the concept of population proteomics, its aspects, enabling approaches, and significance in understanding proteins' roles in physiological processes and diseases. Population proteomics addresses the need for individual assessment of proteins across large populations to delineate the existence of structural variations, determine their frequency, and explore the association of the modifications with specific diseases. Besides the basic concepts and underlying reasons for such protein diversity studies, also reviewed here are the results of two fundamental studies that investigated human plasma protein diversity across the healthy population in the United States. Such studies of protein diversity are needed to map all the post-expression protein modifications and determine the wild-type protein profiles, similar to the human diversity studies at the genome level that have helped redefine the "normal" human genome.

Use of SELDI-TOF mass spectrometry for identification of new biomarkers: potential and limitations

Surface-enhanced laser desorption time of flight mass spectrometry (SELDI-TOF-MS) is an important proteomic technology that is immediately available for the high throughput analysis of complex protein samples. Over the last few years, several studies have demonstrated that comparative protein profiling using SELDI-TOF-MS breaks new ground in diagnostic protein analysis particularly with regard to the identification of novel biomarkers. Importantly, researchers have acquired a better understanding also of the limitations of this technology and various pitfalls in biomarker discovery. Bearing these in mind, great emphasis must be placed on the development of rigorous standards and quality control procedures for the pre-analytical as well as the analytical phase and subsequent bioinformatics applied to analysis of the data. To avoid the risk of false-significant results studies must be designed carefully and control groups accurately selected. In addition, appropriate tools, already established for analysis of highly complex microarray data, need to be applied to protein profiling data. To validate the significance of any candidate biomarker derived from pilot studies in appropriately designed prospective multi-center studies is mandatory; reproducibility of the clinical results must be shown over time and in different diagnostic settings. SELDI-TOF-MS-based studies that are in compliance with these requirements are now required; only a few have been published so far. In the meantime, further evaluation and optimization of both technique and marker validation strategies are called for before MS-based proteomic algorithms can be translated into routine laboratory testing.

Ovarian cancer in the proteomics era

Ovarian cancer presents a diagnostic challenge because of its subtle clinical presentation and elusive cell of origin. Two new technologies of proteomics have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of ovarian cancer: mass spectrometry and protein array analysis. Mass spectrometry can provide a snapshot of a proteome in time and space, with sensitivity and resolution that may allow identification of the elusive "needle in the haystack" heralding ovarian cancer. Proteomic profiling of tumor tissue samples can survey molecular targets during treatment and quantify changes using reverse phase protein arrays generated from tumor samples captured by microdissection, lysed and spotted in serial dilutions for high-throughput analysis. This approach can be applied to identify the optimal biological dose of a targeted agent and to validate target to outcome link. The evolution of proteomic technologies has the capacity to advance rapidly our understanding of ovarian cancer at a molecular level and thus elucidate new directions for the treatment of this disease.