SELDI-TOF-MS proteomics of breast cancer

A journey from omics to clinicomics in solid cancers: Success stories and challenges

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

Circulating proteins as predictive and prognostic biomarkers in breast cancer

Breast cancer (BC) is the most common cancer and among the leading causes of cancer death in women. It is a heterogeneous group of tumours with numerous morphological and molecular subtypes, making predictions of disease evolution and patient outcomes difficult. Therefore, biomarkers are needed to help clinicians choose the best treatment for each patient. For the last years, studies have increasingly focused on biomarkers obtainable by liquid biopsy. Circulating proteins (from serum or plasma) can be used for inexpensive and minimally invasive determination of disease risk, early diagnosis, treatment adjusting, prognostication and disease progression monitoring. We provide here a review of the main published studies on serum proteins in breast cancer and elaborate on the potential of circulating proteins to be predictive and/or prognostic biomarkers in breast cancer.

Diagnostic and prognostic role of serum protein peak at 6449 m/z in gastric adenocarcinoma based on mass spectrometry

Background:

Gastric cancer (GC) is a highly aggressive cancer type associated with significant mortality owing to delayed diagnosis and non-specific symptoms observed in the early stages. Therefore, identification of novel specific GC serum biomarkers for screening purposes is an urgent clinical requirement.

Methods:

This study recruited a total of 432 serum samples from 296 GC patients split into the mining and testing sets. We aimed to screen for reliable protein biomarkers from matched serum samples based on mass spectrometry, followed by comparison with three representative conventional markers using receiver operating characteristic and survival curve analyses to ascertain their potential values as diagnostic and prognostic biomarkers for GC.

Results:

We identified an apoC-III fragment with confirmation in an independent test set from a second hospital. We found that the diagnostic ability of this fragment performed better than current standard GC diagnostic biomarkers both individually and in combination in distinguishing patients with GC from healthy individuals. Moreover, we found that this apoC-III protein fragment represents a more robust potential prognostic factor for GC than the three conventional markers.

Conclusions:

In view of these findings, we suggest that apoC-III protein fragment is a novel diagnostic and prognostic biomarker, a complement to conventional biomarkers in detecting GC.

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.

Proteomic identification of tumor biomarkers associated with primary gallbladder cancer

AIM: To identify potential biomarkers of primary gallbladder cancer (PGC).

METHODS: Fresh PGC, cholecystitis and normal gallbladder tissue specimens collected from 10 patients, respectively, were subjected to comparative proteomic analysis. The proteomic patterns of PGC were compared with those of cholecystitis and normal gallbladder tissues using two-dimensional gel electrophoresis (2-DE). The differentially expressed proteins were then identified using a MALDI-TOF mass spectrometer (MS) and database searches. To further validate these proteins, 20 samples of PGC tissues and normal tumor-adjacent tissues were collected for Western blot, quantitative real-time PCR, and immunohistochemical staining assay.

RESULTS: Seven differentially expressed protein spots were detected by 2-ED analysis by comparing the average maps of PGC, cholecystitis and normal gallbladder tissues. Six of the seven differentially expressed proteins were identified using MALDI-TOF MS, with three overexpressed and three underexpressed in PGC tissue. Protein levels of annexin A4 (ANXA4) were significantly elevated, and heat shock protein 90-beta (Hsp90β) and dynein cytoplasmic 1 heavy chain 1 (Dync1h1) were decreased in PGC tissues relative to the normal tumor-adjacent tissues as shown by Western blot analysis. However, levels of actin, aortic smooth muscle and gamma-actin were unchanged. In addition, the mRNA levels of all 5 proteins showed similar changes to those of the protein levels (P < 0.01). Further validation by immunohistochemical analysis showed the upregulated expression of ANXA4 and decreased expression of Hsp90β and Dync1h1 in the cytoplasm of PGC tissues relative to the normal tumor-adjacent tissues.

CONCLUSION: Three proteins are identified as potential biomarkers of PGC using proteomic analysis. The functions of these proteins in the carcinogenesis of PGC remain to be studied.

A proteomics view of the molecular mechanisms and biomarkers of glaucomatous neurodegeneration

Despite improving understanding of glaucoma, key molecular players of neurodegeneration that can be targeted for treatment of glaucoma, or molecular biomarkers that can be useful for clinical testing, remain unclear. Proteomics technology offers a powerful toolbox to accomplish these important goals of the glaucoma research and is increasingly being applied to identify molecular mechanisms and biomarkers of glaucoma. Recent studies of glaucoma using proteomics analysis techniques have resulted in the lists of differentially expressed proteins in human glaucoma and animal models. The global analysis of protein expression in glaucoma has been followed by cell-specific proteome analysis of retinal ganglion cells and astrocytes. The proteomics data have also guided targeted studies to identify post-translational modifications and protein-protein interactions during glaucomatous neurodegeneration. In addition, recent applications of proteomics have provided a number of potential biomarker candidates. Proteomics technology holds great promise to move glaucoma research forward toward new treatment strategies and biomarker discovery. By reviewing the major proteomics approaches and their applications in the field of glaucoma, this article highlights the power of proteomics in translational and clinical research related to glaucoma and also provides a framework for future research to functionally test the importance of specific molecular pathways and validate candidate biomarkers.

The proteomics big challenge for biomarkers and new drug-targets discovery

In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets.

The current status of cancer biomarker research using tumour-associated antigens for minimal invasive and early cancer diagnostics

Tumour-associated antigens (TAA) can be detected prior to clinical diagnosis and thus would be ideal biomarkers for early detection of cancer using only a few microliters of a patient's serum. In this article we provide a summary of TAA screening and serum-profiling conducted for breast, prostate, lung and colon cancers. Different methodological approaches, including SEREX, SERPA, and phage display for TAA identification and TAA panels are summarised, and a revision of array based techniques is provided. The most promising studies performed on these cancers (performed with 80-400 serum samples, including controls) obtained sensitivities in a range of 44-95% and specificities of 80-100%. From the various studies reviewed, only one performed cross validation (AUC=0.71) in a prostate cancer study. Thus, albeit receiver operation characteristics are very promising, cross validation of most studies is still missing. Additionally, the concerted action of research groups for standardization of serum-TAA testing and cross validation is required. Along with today's technological options, the chances of establishing TAA biomarkers are now higher than ever before. This may also be true for confirmation and validation of already existing data, which is a prerequisite for implementation of TAA biomarkers into clinical diagnostics. This article is part of a Special Issue entitled: Integrated omics.

Screening and identification of serum proteomic biomarkers for gastric adenocarcinoma

The aim of the present study was to screen for possible serum biomarkers for gastric adenocarcinoma. Surface-enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS) was used to screen serum samples from 109 cases of gastric adenocarcinoma and 106 control subjects (60 healthy subjects, 30 patients with chronic superficial gastritis and 16 cases of chronic atrophic gastritis). The differentially expressed protein peaks were selected and isolated using high performance liquid chromatography (HPLC) and processed with enzyme prior to liquid chromatography-mass spectrometry tandem mass spectrometry (LC-MS/MS) analysis and data mining with software XCalibur program components BioWorks 3.2. Among the gastric cancer cases, three differentially expressed protein peaks were selected as potential serum biomarkers: the m/z peaks at 5,906.5 showed increased expression (8.53±4.33 in the cancer group, and 0.88±0.31 in the control group); the m/z peaks at 6,635.7 and 8,716.3 showed decreased expression (6.54±2.44 and 0.93±0.29, respectively, in the cancer group and 17.56±4.43 and 2.16±0.98, respectively, in the control group) (P<0.01). The m/z peaks at 5,906.5, 6,635.7 and 8,716.3, were identified as fibrinogen α-chain, apolipo-protein A-II and apolipoprotein C-I. The combined use of the three biomarkers distinguished the cancer group patients from the control group samples at a sensitivity of 93.85% (61/65) and a specificity of 94.34% (50/53). In conclusion, fibrinogen α-chain, apolipoprotein A-II and apolipoprotein C-I were identified as potential markers for gastric cancer and appear to have diagnostic value for clinical applications.

Contributions of mass spectrometry-based proteomics to defining cellular mechanisms and diagnostic markers for systemic lupus erythematosus

Systematic lupus erythematosus (SLE) is a complex disease for which molecular diagnostics are limited and pathogenesis is not clearly understood. Important information is provided in this regard by identification and characterization of more specific molecular and cellular targets in SLE immune cells and target tissue and markers of early-onset and effective response to treatment of SLE complications. In recent years, advances in proteomic technologies and applications have facilitated such discoveries. Here we provide a review of insights into SLE pathogenesis, diagnosis and treatment that have been provided by mass spectrometry-based proteomic approaches.

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.

Serum amyloid a protein in clinical cancer diagnosis

The serum amyloid A (SAA) protein is an acute phase protein that is synthesized under the regulation of inflammatory cytokines during both acute and chronic inflammation. It is suggested that the SAA increases correlate with many types of carcinogenesis and neoplastic diseases. Th changes in SAA in serum could therefore indicate the progress and malignancy of the disease, as well as the host responses. The present paper reviewed the rationale of using SAA as potential cancer biomarker in clinical diagnosis, including the contribution and involvement of SAA in cancer growth and development. Then we discussed the current applications of SAA in diagnosis and tracing of different types of cancers. Finally the proteomics techniques, especially the SELDI-TOF MS to identify SAA in serum from patients were appreciated as an important manner in clinical diagnosis.

Ovine serum biomarkers of early and late phase scrapie

Background

Transmissible spongiform encephalopathies are fatal neurodegenerative disease occurring in animals and humans for which no ante-mortem diagnostic test in biological fluids is available. In such pathologies, detection of the pathological form of the prion protein (i.e., the causative factor) in blood is difficult and therefore identification of new biomarkers implicated in the pathway of prion infection is relevant.

Methods

In this study we used the SELDI-TOF MS technology to analyze a large number of serum samples from control sheep and animals with early phase or late phase scrapie. A few potential low molecular weight biomarkers were selected by statistical methods and, after a training analysis, a protein signature pattern, which discriminates between early phase scrapie samples and control sera was identified.

Results

The combination of early phase biomarkers showed a sensitivity of 87% and specificity of 90% for all studied sheep in the early stage of the disease. One of these potential biomarkers was identified and validated in a SELDI-TOF MS kinetic study of sera from Syrian hamsters infected by scrapie, by western blot analysis and ELISA quantitation.

Conclusions

Differential protein expression profiling allows establishing a TSE diagnostic in scrapie sheep, in the early phase of the disease. Some proteic differences observed in scrapie sheep exist in infected hamsters. Further studies are being performed to identify all the discriminant biomarkers of interest and to test our potential markers in a new cohort of animals.

Synovial fluid proteomic fingerprint: S100A8, S100A9 and S100A12 proteins discriminate rheumatoid arthritis from other inflammatory joint diseases

OBJECTIVE

We investigated SF and serum proteomic fingerprints of patients suffering from RA, OA and other miscellaneous inflammatory arthritides (MIAs) in order to identify RA-specific biomarkers.

METHODS

SF profiles of 65 patients and serum profiles of 31 patients were studied by surface-enhanced laser desorption and ionization-time-of-flight-mass spectrometry technology. The most discriminating RA biomarkers were identified by matrix-assisted laser desorption ionization-time of flight and their overexpression was confirmed by western blotting and ELISA.

RESULTS

Three biomarkers of 10 839, 10 445 and 13 338 Da, characterized as S100A8, S100A12 and S100A9 proteins, were the most up-regulated proteins in RA SF. Their expression was about 10-fold higher in RA SF vs OA SF. S100A8 exhibited a sensitivity of 82% and a specificity of 69% in discriminating RA from other MIAs, whereas S100A12 displayed a sensitivity of 79% and a specificity of 64%. Three peptides of 3351, 3423 and 3465 Da, corresponding to the alpha-defensins-1, -2 and -3, were also shown to differentiate RA from other MIAs with weaker sensitivity and specificity. Levels of S100A12, S100A8 and S100A9 were statistically correlated with the neutrophil count in MIA SF but not in the SF of RA patients. S100A8, S100A9, S100A12 and alpha-defensin expression in serum was not different in the three populations.

CONCLUSION

The most enhanced proteins in RA SF, the S100A8, S100A9 and S00A12 proteins, distinguished RA from MIA with high accuracy. Possible implication of resident cells in this increase may play a role in RA physiopathology.

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.

Depletion of one, six, twelve or twenty major blood proteins before proteomic analysis: the more the better?

Depletion of major blood proteins is one of the most promising approaches to access low abundant biomarkers using proteomics. Immunocapture columns often used for this purpose exist in different formats depending on the number of major proteins removed. In this article, we compared the relative interest of depleting either one (albumin), six (albumin, IgG, IgA, transferrin, alpha1-antitrypsin, and haptoglobin), twelve (the previous six and apo A-I and -II, orosomucoid, alpha2-macroglobulin, fibrinogen, IgM) or twenty blood proteins (the previous twelve and IgD, ceruloplasmin, apo B, complement C1q, C3, C4, plasminogen, and prealbumin). Such study raises interesting issues related to the reproducibility, practicability, specificity of the immunocapture, and to the impact of removing not only the selected molecules, but also associated peptides and proteins. Depleted sera were here analysed using different proteomic approaches, including two dimensional electrophoresis and SELDI-TOF. Altogether, our results clearly confirmed the interest of depleting major blood proteins for the proteomic detection of low abundant components. However, we observed that increasing the number of depleted proteins from twelve to twenty had a limited beneficial impact and might increase drawbacks in removing associated peptides and proteins. This conclusion is however related to the technologies that we have used, and we believe that it is necessary to adapt the immunocapture to the analytical method employed, and to the ratio between wanted and unwanted proteins removed.

Comparative proteomics analysis of human gastric cancer

AIM: To isolate and identify differentially expressed proteins between cancer and normal tissues of gastric cancer by two-dimensional electrophoresis (2-DE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS).

METHODS: Soluble fraction proteins of gastric cancer tissues and paired normal tissues were separated by 2-DE. The differentially expressed proteins were selected and identified by MALDI-TOF-MS and database search.

RESULTS: 2-DE profiles with high resolution and reproducibility were obtained. Twenty-three protein spots were excised from sliver staining gel and digested in gel by trypsin, in which fifteen protein spots were identified successfully. Among the identified proteins, there were ten over-expressed and five under-expressed proteins in stomach cancer tissues compared with normal tissues.

CONCLUSION: In this study, the well-resolved, reproducible 2-DE patterns of human gastric cancer tissue and paired normal tissue were established and optimized and certain differentially-expressed proteins were identified. The combined use of 2-DE and MS provides an effective approach to screen for potential tumor markers.

Apolipoprotein A-I and platelet factor 4 are biomarkers for infliximab response in rheumatoid arthritis

OBJECTIVES

The use of biologicals such as infliximab has dramatically improved the treatment of rheumatoid arthritis (RA). However, factors predictive of therapeutic response need to be identified. A proteomic study was performed prior to infliximab therapy to identify a panel of candidate protein biomarkers of RA predictive of treatment response.

METHODS

Plasma profiles of 60 patients with RA (28 non-responders (as defined by the American College of Rheumatology 20% improvement criteria (ACR20)) negative and 32 responders (ACR70 positive) to infliximab) were studied by surface enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF MS) technology on two types of arrays, an anion exchange array (SAX2) and a nickel affinity array (IMAC3-Ni). Biomarker characterisation was carried out using classical biochemical methods (purification by ammonium sulfate precipitation or metal affinity chromatography) and identification by matrix assisted laser desorption/ionisation time-of-flight (MALDI-TOF) MS analysis.

RESULTS

Two distinct protein profiles were observed on both arrays and several proteins were differentially expressed in both patient populations. Five proteins at 3.86, 7.77, 7.97, 8.14 and 74.07 kDa were overexpressed in the non-responder group, whereas one at 28 kDa was increased in the responder population (sensitivity>56%, specificity>77.5%). Moreover, combination of several biomarkers improved the sensitivity and specificity of the detection of patient response to over 97%. The 28 kDa protein was characterised as apolipoprotein A-I and the 7.77 kDa biomarker was identified as platelet factor 4.

CONCLUSIONS

Six plasma biomarkers are characterised, enabling the detection of patient response to infliximab with high sensitivity and specificity. Apolipoprotein A-1 was predictive of a good response to infliximab, whereas platelet factor 4 was associated with non-responders.

Moving forward in colorectal cancer research, what proteomics has to tell

Colorectal cancer is the third most common cancer and is highly fatal. During the last several years, research has been primarily based on the study of expression profiles using microarray technology. But now, investigators are putting into practice proteomic analyses of cancer tissues and cells to identify new diagnostic or therapeutic biomarkers for this cancer. Because the proteome reflects the state of a cell, tissue or organism more accurately, much is expected from proteomics to yield better tumor markers for disease diagnosis and therapy monitoring. This review summarizes the most relevant applications of proteomics the biomarker discovery for colorectal cancer.

Deep proteome profiling of sera from never-smoked lung cancer patients

Previous studies on the serum proteome are hampered by the huge dynamic range of concentration of different protein species. The use of Equalizer Beads coupled with a combinatorial library of ligands has been shown to allow access to many low-abundance proteins or polypeptides undetectable by classical analytical methods. This study focused on never-smoked lung cancer, which is considered to be more homogeneous and distinct from smoking-related cases both clinically and biologically. Serum samples obtained from 42 never-smoked lung cancer patients (28 patients with active untreated disease and 14 patients with tumor resected) were compared with those from 30 normal control subjects using the pioneering Equalizer Beads technology followed by subsequent analysis by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Eighty-five biomarkers were significantly different between lung cancer and normal control. The application of classification algorithms based on significant biomarkers achieved good accuracy of 91.7%, 80% and 87.5% in class-prediction with respect to presence or absence of disease, subsequent development of metastasis and length of survival (longer or shorter than median) respectively. Support vector machine (SVM) performed best overall. We have proved the feasibility and convenience of using the Equalizer Beads technology to study the deep proteome of the sera of lung cancer patients in a rapid and high-throughput fashion, and which enables detection of low abundance polypeptides/proteins biomarkers. Coupling with classification algorithms, the technologies will be clinically useful for diagnosis and prediction of prognosis in lung cancer.

A method for improving SELDI-TOF mass spectrometry data quality

Background

Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is a powerful tool for rapidly generating high-throughput protein profiles from a large number of samples. However, the events that occur between the first and last sample run are likely to introduce technical variation in the results.

Methods

We fractionated and analyzed quality control and investigational serum samples on 3 Protein Chips and used statistical methods to identify poor-quality spectra and to identify and reduce technical variation.

Results

Using diagnostic plots, we were able to visually depict all spectra and to identify and remove those that were of poor quality. We detected a technical variation associated with when the samples were run (referred to as batch effect) and corrected for this variation using analysis of variance. These corrections increased the number of peaks that were reproducibly detected.

Conclusion

By removing poor-quality, outlier spectra, we were able to increase peak detection, and by reducing the variance introduced when samples are processed and analyzed in batches, we were able to increase the reproducibility of peak detection.

Insights into molecular medicine: development of new diagnostic and prognostic parameters

Molecular medicine leads us towards an understanding of some diseases at the molecular level. Examples are the analysis of immune complexes and receptor-antireceptor compounds used in clinical medicine. Structural changes of some serum proteins occur in inflammation, neoplasia and autoimmunity. The detection and analysis of such structural modifications may offer a new field for the diagnosis, prognosis and therapy of some diseases. Modern medicine requires new technologies with high sensitivity, specificity and applicability. For the first time in Austria we have combined fluorescence correlation spectroscope (FCS), surface enhanced laser desorption ionisation--time of flight (SELDI-TOF) and the molecular modelling and visualization system according to the computer enhanced programs. Experimental and computational methods are combined in such a way that clinical data can be interpreted by theoretical methods at a molecular level or vice versa, the computational output delivers input for new investigations. One method brings us single results. In view of the spectrum of parameters relevant to clinical entities, multiplexing is a new way of development. Since the technologies are new, the scientifically interested reader should be informed about the matters arising.

Mass spectrometry for enzyme assays and inhibitor screening: an emerging application in pharmaceutical research

Robust methods that monitor enzyme activity and inhibitor potency are crucial to drug discovery and development. Over the past 20 years, mass spectrometric methods have increasingly been used to measure enzyme activity and kinetics. However, for rapid screening of inhibitory compounds, various forms of fluorescence and chemiluminscence readout have continued to dominate the market. As the sensitivity, speed, and miniaturization of mass spectrometry methods continue to advance, opportunities to couple mass spectrometry with screening will continue to come to the forefront. To appreciate the tremendous potential for MS-based screening assays, it becomes necessary to understand the current state of capabilities in this arena. Thus, this review is intended to capture how mass spectrometry for studying enzymes activity has progressed from simple qualitative questions (i.e., is the product detected?) to quantitative measures of enzyme activity and kinetics and then as a tool for rapidly screening inhibitory compounds as an alternative to current methods of high throughput drug screening.

Identification and characterization of DNA-binding proteins by mass spectrometry

Mass spectrometry is the most sensitive and specific analytical technique available for protein identification and quantification. Over the past 10 years, by the use of mass spectrometric techniques hundreds of previously unknown proteins have been identified as DNA-binding proteins that are involved in the regulation of gene expression, replication, or DNA repair. Beyond this task, the applications of mass spectrometry cover all aspects from sequence and modification analysis to protein structure, dynamics, and interactions. In particular, two new, complementary ionization techniques have made this possible: matrix-assisted laser desorption/ionization and electrospray ionization. Their combination with different mass-over-charge analyzers and ion fragmentation techniques, as well as specific enzymatic or chemical reactions and other analytical techniques, has led to the development of a broad repertoire of mass spectrometric methods that are now available for the identification and detailed characterization of DNA-binding proteins. These techniques, how they work, what their requirements and limitations are, and selected examples that document their performance are described and discussed in this chapter.

Interest of major serum protein removal for Surface-Enhanced Laser Desorption/Ionization - Time Of Flight (SELDI-TOF) proteomic blood profiling

BACKGROUND

Surface-Enhanced Laser Desorption/Ionization - Time Of Flight (SELDI-TOF) has been proposed as new approach for blood biomarker discovery. However, results obtained so far have been often disappointing as this technique still has difficulties to detect low-abundant plasma and serum proteins.

RESULTS

We used a serum depletion scheme using chicken antibodies against various abundant proteins to realized a pre-fractionation of serum prior to SELDI-TOF profiling. Depletion of major serum proteins by immunocapture was confirmed by 1D and 2D gel electrophoresis. SELDI-TOF analysis of bound and unbound (depleted) serum fractions revealed that this approach allows the detection of new low abundant protein peaks with satisfactory reproducibility.

CONCLUSION

The combination of immunocapture and SELDI-TOF analysis opens new avenues into proteomic profiling for the discovery of blood biomarkers.

Proteomic technology for biomarker profiling in cancer: an update

The progress in the understanding of cancer progression and early detection has been slow and frustrating due to the complex multifactorial nature and heterogeneity of the cancer syndrome. To date, no effective treatment is available for advanced cancers, which remain a major cause of morbidity and mortality. Clearly, there is urgent need to unravel novel biomarkers for early detection. Most of the functional information of the cancer-associated genes resides in the proteome. The later is an exceptionally complex biological system involving several proteins that function through posttranslational modifications and dynamic intermolecular collisions with partners. These protein complexes can be regulated by signals emanating from cancer cells, their surrounding tissue microenvironment, and/or from the host. Some proteins are secreted and/or cleaved into the extracellular milieu and may represent valuable serum biomarkers for diagnosis purpose. It is estimated that the cancer proteome may include over 1.5 million proteins as a result of posttranslational processing and modifications. Such complexity clearly highlights the need for ultra-high resolution proteomic technology for robust quantitative protein measurements and data acquisition. This review is to update the current research efforts in high-resolution proteomic technology for discovery and monitoring cancer biomarkers.

Proteomics in clinical trials and practice: present uses and future promise

The study of clinical proteomics is a promising new field that has the potential to have many applications, including the identification of biomarkers and monitoring of disease, especially in the field of oncology. Expression proteomics evaluates the cellular production of proteins encoded by a particular gene and exploits the differential expression and post-translational modifications of proteins between healthy and diseased states. These biomarkers may be applied towards early diagnosis, prognosis, and prediction of response to therapy. Functional proteomics seeks to decipher protein-protein interactions and biochemical pathways involved in disease biology and targeted by newer molecular therapeutics. Advanced spectrometry technologies and new protein array formats have improved these analyses and are now being applied prospectively in clinical trials. Further advancement of proteomics technology could usher in an era of personalized molecular medicine, where diseases are diagnosed at earlier stages and where therapies are more effective because they are tailored to the protein expression of a patient's malignancy.

Proteomics of breast cancer: principles and potential clinical applications

Progresses in screening, early diagnosis, prediction of aggressiveness and of therapeutic response or toxicity, and identification of new targets for therapeutic will improve survival of breast cancer. These progresses will likely be accelerated by the new proteomic techniques. In this review, we describe the different techniques currently applied to clinical samples of breast cancer and the most important results obtained with the two most popular proteomic approaches in translational research (tissue microarrays and SELDI-TOF).