Mass spectrometry as a diagnostic and a cancer biomarker discovery tool: opportunities and potential limitations

Proteomic cancer classification with mass spectrometry data

The ultimate goal of cancer proteomics is to adapt proteomic technologies for routine use in clinical laboratories for the purpose of diagnostic and prognostic classification of disease states, as well as in evaluating drug toxicity and efficacy. Analysis of tumor-specific proteomic profiles may also allow better understanding of tumor development and the identification of novel targets for cancer therapy. The biological variability among patient samples as well as the huge dynamic range of biomarker concentrations are currently the main challenges facing efforts to deduce diagnostic patterns that are unique to specific disease states. While several strategies exist to address this problem, we focus here on cancer classification using mass spectrometry (MS) for proteomic profiling and biomarker identification. Recent advances in MS technology are starting to enable high-throughput profiling of the protein content of complex samples. For cancer classification, the protein samples from cancer patients and noncancer patients or from different cancer stages are analyzed through MS instruments and the MS patterns are used to build a diagnostic classifier. To illustrate the importance of feature selection in cancer classification, we present a method based on support vector machine-recursive feature elimination (SVM-RFE), demonstrated on two cancer datasets from ovarian and lung cancer.

Proteomic Profiling in the Sera of Workers Occupationally Exposed to Arsenic and Lead: Identification of Potential Biomarkers

Arsenic (As) and lead (Pb) are important inorganic toxicants in the environment. Frequently, humans are exposed to the mixtures of As and Pb, but little is known about the expression of biomarkers resulting from such mixed exposures. In this study, we analyzed serum proteomic profiles in a group of smelter workers with the aim of identifying protein biomarkers of mixed As and Pb exposure. Forty-six male workers co-exposed to As and Pb were studied. Forty-five age-matched male office workers were chosen as controls. Urine As and blood Pb concentrations were determined. Serum proteomic profiles were analyzed by Surface-Enhanced Laser Desorption/Ionization Time-Of-Flight (SELDI-TOF) mass spectrometer on the WCX2 ProteinChip. Using Recursive support vector machine (RSVM) algorithm, a panel of five peptides/proteins (2097 Da, 2953 Da, 3941 Da, 5338 Da, and 5639 Da) was selected based on their collective contribution to the optional separation between higher metal mixture exposure and non-exposure controls. Among these five selected markers, the 3941 Da was down-regulated and the four other proteins were up-regulated. Descriptive statistics confirmed that these five proteins differed significantly between metal exposure and non-exposure. Interestingly, the combined use of the five selected biomarkers could achieve higher discriminative power than single marker. These results demonstrated that proteomic technology, in conjunction with bioinformatics tools, could facilitate the discovery of new and better biomarkers of mixed metal exposure.

Protein chip array profiling analysis of sera from neuroblastoma patients

Neuroblastoma, the most common extracranial solid tumour in children, is characterised by highly heterogeneous clinical behaviour; patients are stratified into risk categories according to a combination of clinical and biological markers. However, identifying non-invasive prognostic markers predicting outcome independently from current risk-stratification features remains critical for better disease monitoring. Using the SELDI-TOF-MS technology (surface-enhanced laser desorption/ionization time-of-flight mass spectrometry), we found a serum biomarker that strongly correlates with prognosis in neuroblastoma patients. Subsequent peptide mapping identified this biomarker as SAA protein. In support of this observation, high SAA levels were detected by ELISA in the sera of patients with poor prognosis neuroblastoma. Based on this finding, promises and limitations of the approach are discussed.

Proteomics: the next revolution in laboratory medicine?

BACKGROUND

The identification of specific genetic alterations and protein profiles associated with disease offers a unique opportunity to develop proteomics-based assays for early diagnosis. By identifying proteins in serum/plasma, a minimally invasive tool is used to assess the presence of disease and to monitor response to treatment and/or disease progression. The potential clinical applications of this tool are broad-based, including the diagnosis not only of cancer but also cardiovascular and neuromuscular diseases, organ transplantation associated conditions, and infertility.

METHODS

A number of competing chromatographic techniques have been proposed for overcoming the complexity and labor-intensive manipulations associated with the traditional technique for proteomic analysis, which is based on two-dimensional gel electrophoretic techniques. However, mass spectrometry has now assumed a central role in most proteomic workflows, and several combinations of ionization sources, analyzers and fragmentations devices have been described and developed.

RESULTS

Thanks to proteomic applications in the diagnosis of cancer, several research groups have identified proteomic patterns associated with ovarian, prostatic, colorectal and other cancers. While the sensitivity and specificity of these patterns are highly satisfactory, there are still some open questions concerning the standardization, reproducibility, and inter-laboratory agreement of these data.

CONCLUSIONS

Proteomics, and, in particular, serum mass spectroscopic proteomic pattern diagnostics, is a rapid expanding field of research. The plasma proteoma has an important position at the intersection between genes and diseases, and clinical laboratories must adapt to a new era of tests based on proteomics and genomics. In the future, mass spectrometry will become an essential tool in the clinical laboratory.

Place of pattern in proteomic biomarker discovery

The role of pattern in biomarker discovery and clinical diagnosis is examined in its historical context. The use of MS-derived pattern is treated as a logical extension of prior applications of non-MS-derived pattern. Criticisms pertaining to specific technology platforms and analytic methodologies are considered separately from the larger issues of pattern utility and deployment in biomarker discovery. We present a hybrid strategy that marries the desirable attributes of high-information content MS pattern with the capability to obtain identity, and explore the key steps in establishing a data analysis pipeline for pattern-based biomarker discovery.

Correcting common errors in identifying cancer-specific serum peptide signatures

"Molecular signatures" are the qualitative and quantitative patterns of groups of biomolecules (e.g., mRNA, proteins, peptides, or metabolites) in a cell, tissue, biological fluid, or an entire organism. To apply this concept to biomarker discovery, the measurements should ideally be noninvasive and performed in a single read-out. We have therefore developed a peptidomics platform that couples magnetics-based, automated solid-phase extraction of small peptides with a high-resolution MALDI-TOF mass spectrometric readout (Villanueva, J.; Philip, J.; Entenberg, D.; Chaparro, C. A.; Tanwar, M. K.; Holland, E. C.; Tempst, P. Anal. Chem. 2004, 76, 1560-1570). Since hundreds of peptides can be detected in microliter volumes of serum, it allows to search for disease signatures, for instance in the presence of cancer. We have now evaluated, optimized, and standardized a number of clinical and analytical chemistry variables that are major sources of bias; ranging from blood collection and clotting, to serum storage and handling, automated peptide extraction, crystallization, spectral acquisition, and signal processing. In addition, proper alignment of spectra and user-friendly visualization tools are essential for meaningful, certifiable data mining. We introduce a minimal entropy algorithm, "Entropycal", that simplifies alignment and subsequent statistical analysis and increases the percentage of the highly distinguishing spectral information being retained after feature selection of the datasets. Using the improved analytical platform and tools, and a commercial statistics program, we found that sera from thyroid cancer patients can be distinguished from healthy controls based on an array of 98 discriminant peptides. With adequate technological and computational methods in place, and using rigorously standardized conditions, potential sources of patient related bias (e.g., gender, age, genetics, environmental, dietary, and other factors) may now be addressed.

HUPO Plasma Proteome Project specimen collection and handling: towards the standardization of parameters for plasma proteome samples

There is a substantial list of pre-analytical variables that can alter the analysis of blood-derived samples. We have undertaken studies on some of these issues including choice of sample type, stability during storage, use of protease inhibitors, and clinical standardization. As there is a wide range of sample variables and a broad spectrum of analytical techniques in the HUPO PPP effort, it is not possible to define a single list of pre-analytical standards for samples or their processing. We present here a compendium of observations, drawing on actual results and sound clinical theories and practices. Based on our data, we find that (1) platelet-depleted plasma is preferable to serum for certain peptidomic studies; (2) samples should be aliquoted and stored preferably in liquid nitrogen; (3) the addition of protease inhibitors is recommended, but should be incorporated early and used judiciously, as some form non specific protein adducts and others interfere with peptide studies. Further, (4) the diligent tracking of pre-analytical variables and (5) the use of reference materials for quality control and quality assurance, are recommended. These findings help provide guidance on sample handling issues, with the overall suggestion being to be conscious of all possible pre-analytical variables as a prerequisite of any proteomic study.

Peptidomic analysis of human blood specimens: comparison between plasma specimens and serum by differential peptide display

The human Plasma Proteome Project pilot phase aims to analyze serum and plasma specimens to elucidate specimen characteristics by various proteomic techniques to ensure sufficient sample quality for the HUPO main phase. We used our proprietary peptidomics technologies to analyze the samples distributed by HUPO. Peptidomics summarizes technologies for visualization, quantitation, and identification of the low-molecular-weight proteome (<15 kDa), the "peptidome." We analyzed all four HUPO specimens (EDTA plasma, citrate plasma, heparin plasma, and serum) from African- and Asian-American donors and compared them to in-house collected Caucasian specimens. One main finding focuses on the most suitable method of plasma specimen collection. Gentle platelet removal from plasma samples is beneficial for improved specificity. Platelet contamination or activation of platelets by low temperature prior to their removal leads to distinct and multiple peptide signals in plasma samples. Two different specimen collection protocols for platelet-poor plasma are recommended. Further emphasis is placed on the differences between plasma and serum on a peptidomic level. A large number of peptides, many of them in rather high abundance, are only present in serum and not detectable in plasma. This ex vivo generation of multiple peptides hampers discovery efforts and is caused by a variety of factors: the release of platelet-derived peptides, other peptides derived from cellular components or the clot, enzymatic activities of coagulation cascades, and other proteases. We conclude that specimen collection is a crucial step for successful peptide biomarker discovery in human blood samples. For analysis of the low-molecular-weight proteome, we recommend the use of platelet-depleted EDTA or citrate plasma.

Direct tandem mass spectrometry reveals limitations in protein profiling experiments for plasma biomarker discovery

The low molecular weight plasma proteome and its biological relevance are not well defined; therefore, experiments were conducted to directly sequence and identify peptides observed in plasma and serum protein profiles. Protein fractionation, matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) profiling, and liquid-chromatography coupled to MALDI tandem mass spectrometry (MS/MS) sequencing were used to analyze the low molecular weight proteome of heparinized plasma. Four fractionation techniques using functionally derivatized 96-well plates were used to extract peptides from plasma. Tandem TOF was successful for identifying peptides up to m/z 5500 with no prior knowledge of the sequence and was also used to verify the sequence assignments for larger ion signals. The peptides (n>250) sequenced in these profiles came from a surprisingly small number of proteins (n approximately 20), which were all common to plasma, including fibrinogen, complement components, antiproteases, and carrier proteins. The cleavage patterns were consistent with those of known plasma proteases, including initial cleavages by thrombin, plasmin and complement proteins, followed by aminopeptidase and carboxypeptidase activity. On the basis of these data, we discuss limitations in biomarker discovery in the low molecular weight plasma or serum proteome using crude fractionation coupled to MALDI-MS profiling.

Effect of Immunoaffinity Depletion of Human Serum during Proteomic Investigations

Controversy exists regarding the proper mining of the human serum proteome. Because of the analytical challenges of accurately measuring samples containing a very large dynamic range of protein concentrations, current practices have employed depletion of the highly abundant housekeeping serum proteins, such as albumin and immunoglobins. There is question as to the selectivity of depletion, namely, is there loss of other non abundant serum proteins along with albumin, haptoglobin and other commonly depleted proteins. In this study, human serum was analyzed with and without immunoaffinity depletion of the six most abundant proteins by multidimensional liquid chromatography tandem mass spectrometry. Two replicates of each experiment were conducted and compared against one another. In both depleted and nondepleted replicates there was a 73% and 72% overlap of identified peptides and a 64% and 78% overlap of identified proteins, respectively. Of 262 unique proteins identified in the four experiments, 82 were found in common to all four experiments, 142 unique to the depleted serum, and 38 unique to the nondepleted serum. Although serum depletion of highly abundant proteins significantly increased the number of proteins identified, both the degree of sample complexity and this depletion method resulted in a nonselective loss of other proteins.

Analysis of mass spectral serum profiles for biomarker selection

MOTIVATION

Mass spectrometric profiles of peptides and proteins obtained by current technologies are characterized by complex spectra, high dimensionality and substantial noise. These characteristics generate challenges in the discovery of proteins and protein-profiles that distinguish disease states, e.g. cancer patients from healthy individuals. We present low-level methods for the processing of mass spectral data and a machine learning method that combines support vector machines, with particle swarm optimization for biomarker selection.

RESULTS

The proposed method identified mass points that achieved high prediction accuracy in distinguishing liver cancer patients from healthy individuals in SELDI-QqTOF profiles of serum.

AVAILABILITY

MATLAB scripts to implement the methods described in this paper are available from the HWR's lab website http://lombardi.georgetown.edu/labpage

Data pre-processing in liquid chromatography-mass spectrometry-based proteomics

MOTIVATION

In a liquid chromatography-mass spectrometry (LC-MS)-based expressional proteomics, multiple samples from different groups are analyzed in parallel. It is necessary to develop a data mining system to perform peak quantification, peak alignment and data quality assurance.

RESULTS

We have developed an algorithm for spectrum deconvolution. A two-step alignment algorithm is proposed for recognizing peaks generated by the same peptide but detected in different samples. The quality of LC-MS data is evaluated using statistical tests and alignment quality tests.

AVAILABILITY

Xalign software is available upon request from the author.

Application of Fast Fourier Transform Cross-Correlation for the Alignment of Large Chromatographic and Spectral Datasets

Preprocessing of chromatographic and spectral data is an important aspect of analytical sciences. In particular, recent advances in proteomics have resulted in the generation of large data sets that require analysis. To assist accurate comparison of chemical signals, we propose two methods for the alignment of multiple spectral data sets. Based on methods previously described, each chromatograph or spectrum to be aligned is divided and aligned as individual segments to a reference. However, our methods make use of fast Fourier transform for the rapid computation of a cross-correlation function that enables alignments between samples to be optimized. The proposed methods are demonstrated in comparison with an existing method on a chromatographic and a mass spectral data set. It is shown that our methods provide an advantage of speed and a reduction of the number of input parameters required. The software implementations for the proposed alignment methods are available under the downloads section at http://ptcl.chem.ox.ac.uk/~jwong/specalign.

Proteomics in prostate cancer

PURPOSE OF REVIEW

State-of-the-art proteomics technologies are currently being assessed for utility in the study of prostatic malignancy. This review aims to provide background information on the current proteomics techniques employed in prostate cancer research, recent reports showing the potential application of proteomics in urological practice, and the future direction of proteomics in prostate cancer research and management.

RECENT FINDINGS

Proteomic profiling of serum as a diagnostic tool and a platform for biomarker discovery in prostate cancer continues to draw favorable attention as well as close scrutiny as technological enhancements and multi-center study results are reported. In-vitro studies on prostate cell lines provide positive proof-of-principle results. The application of proteomics to query prostate tissue specimens yields novel prostate cancer biomarkers requiring further validation. The integration of proteomics with immunology also yields promising findings that may translate into clinically relevant biological assays.

SUMMARY

The study of proteomics is an emerging research field, and current studies continue to display potential future usage in prostate cancer management. Succeeding scientific investigations will probably yield new diagnostic and prognostic tools for prostate cancer, provide insights into its underlying biology, and contribute to the development of novel treatment strategies.

Nanoprobe-Based Affinity Mass Spectrometry for Selected Protein Profiling in Human Plasma

In recent decades, magnetic nanoparticles have emerged as a promising new platform in biomedical applications, particularly bioseparations. We have developed an immunoassay using antibody-conjugated magnetic nanoparticles as an efficient affinity probe to simultaneously preconcentrate and isolate targeted antigens from biological media. We combined this probe with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI MS) to profile proteins in diluted human plasma. The nanoparticles were designed to detect several disease-associated proteins and could be used directly in MALDI MS without an elution step, thereby facilitating multiple antigen screening and the characterization of antigen variants. Plasma antigens bound rapidly (approximately 10 min) to the antibody-conjugated nanoparticles, allowing the assay to be performed within 20 min. With sensitivity of detection in the femtomole range, the nanoscale immunoassay is superior to assays using microscale particles. We applied our method to comparative protein profiling of patients with gastric cancer and healthy individuals and found differential protein expression levels associated with the disease as well as individuals. Given the flexibility of manipulating functional groups on the nanoprobes, their low cost, robustness, and simplicity of the assay, our approach shows promise for targeted proteome profiling in clinical settings.

Protéomique clinique : vers la détection précoce des cancers ?

A key challenge in clinical proteomic of cancer is the identification of biomarkers that would allow early detection, diagnosis and monitor progression of the disease to improve long-term survival of patients. Recent advances in proteomic instrumentation and computational methodologies offer unique chance to rapidly identify these new candidate markers or pattern of markers. The combination of retentate affinity chromatography and surfaced-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry is one of the most interesting new approaches for cancer diagnostic using proteomic profiling. This review aims to summarize the results of studies that have used this new technology method for the early diagnosis of human cancer. Despite promising results, the use of the proteomic profiling as a diagnostic tool brought some controversies and technical problems and still requires some efforts to be standardised and validated.

Proteomics: From Basic Research to Diagnostic Application. A Review of Requirements & Needs†

For several years proteomics research has been expected to lead to the finding of new markers that will translate into clinical tests applicable to samples such as serum, plasma and urine: so-called in vitro diagnostics (IVDs). Attempts to implement technologies applied in proteomics, in particular protein arrays and surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS), as IVD instruments have initiated constructive discussions on opportunities and challenges inherent in such a translation process also with respect to the use of multi-marker profiling approaches and pattern signatures in IVD. Taking into account the role that IVD plays in health care, we describe IVD requirements and needs. Subject to stringent costs versus benefit analyses, IVD has to provide reliable information about a person's condition, prognosis or risk to suffer a disease, thus supporting decisions on treatment or prevention. It is mandatory to fulfill requirements in routine IVD, including disease prevention, diagnosis, prognosis, and treatment monitoring or follow up among others. To fulfill IVD requirements, it is essential to (1) provide diagnostic tests that allow for definite and reliable diagnosis tied to a decision on interventions (prevention, treatment, or nontreatment), (2) meet stringent performance characteristics for each analyte (in particular test accuracy, including both precision of the measurement and trueness of the measurement), and (3) provide adequate diagnostic accuracy, i.e., diagnostic sensitivity and diagnostic specificity, determined by the desired positive and negative predictive values which depend on disease frequency. The fulfillment of essential IVD requirements is mandatory in the regulated environment of modern diagnostics. Addressing IVD needs at an early stage can support a timely and effective transition of findings and developments into routine diagnosis. IVD needs reflect features that are useful in clinical practice. This helps to generate acceptance and assists the implementation process. On the basis of IVD requirements and needs, we outline potential implications for clinical proteomics focused on applied research activities.

Search for cancer markers from endometrial tissues using differentially labeled tags iTRAQ and cICAT with multidimensional liquid chromatography and tandem mass spectrometry

A total of nine potential markers for endometrial cancer (EmCa) have been discovered and identified from endometrial tissue homogenates using a combination of differentially labeled tags, iTRAQ and cICAT, with multidimensional liquid chromatography and tandem mass spectrometry. The tissues were snap frozen in liquid nitrogen within 15-20 min after devitalization. Samples for proteomic analysis were treated with protease inhibitors before processing. Marker proteins that were overexpressed in EmCa are chaperonin 10, pyruvate kinase M1 or M2 isozyme, calgizzarin, heterogeneous nuclear ribonucleoprotein D0, macrophage migratory inhibitory factor, and polymeric immunoglobulin receptor precursor; those that were underexpressed are alpha-1-antitrypsin precursor, creatine kinase B, and transgelin. The chaperonin 10 result confirms our earlier observation of overexpression in EmCa tissues using surface-enhanced laser desorption/ionization mass spectrometry, verified by Western analysis and immunohistochemistry [Yang, E. C. C. et al. J. Proteome Res. 2004, 3, 636-643]. Pyruvate kinase was observed to be overexpressed using both iTRAQ and cICAT labeling. All nine markers have been found to be associated with various forms of cancer. A panel of these plus other markers may confer sufficient selectivity for diagnosing and screening of EmCa. The use of cICAT led to identification of a higher proportion of lower-abundance signaling proteins; conversely, iTRAQ resulted in a higher percentage of the more abundant ribosomal proteins and transcription factors.

Investigating diversity in human plasma proteins

Plasma proteins represent an important part of the human proteome. Although recent proteomics research efforts focus largely on determining the overall number of proteins circulating in plasma, it is equally important to delineate protein variations among individuals, because they can signal the onset of diseases and be used as biological markers in diagnostics. To date, there has been no systematic proteomics effort to characterize the breadth of structural modifications in individual proteins in the general population. In this work, we have undertaken a population proteomics study to define gene- and protein-level diversity that is encountered in the general population. Twenty-five plasma proteins from a cohort of 96 healthy individuals were investigated through affinity-based mass spectrometric assays. A total of 76 structural forms/variants were observed for the 25 proteins within the samples cohort. Posttranslational modifications were detected in 18 proteins, and point mutations were observed in 4 proteins. The frequency of occurrence of these variations was wide-ranged, with some modifications being observed in only one sample, and others detected in all 96 samples. Even though a relatively small cohort of individuals was investigated, the results from this study illustrate the extent of protein diversity in the human population and can be of immediate aid in clinical proteomics/biomarker studies by laying a basal-level statistical foundation from which protein diversity relating to disease can be evaluated.

Influences of blood sample processing on low-molecular-weight proteome identified by surface-enhanced laser desorption/ionization mass spectrometry

BACKGROUND

Profiling approaches in proteomics, such as surface-enhanced laser desorption/ionization (SELDI) mass spectrometry, are used in disease marker discovery. The aim of this study was to investigate the potential influence of selected preanalytical factors on the results obtained.

METHODS

Plasma samples anticoagulated with EDTA, citrate, or heparin, and serum samples from healthy volunteers were profiled by SELDI on CM10, immobilized metal affinity capture (IMAC) array with copper, and H50 chip surfaces. Using linear mixed-effects models, we examined the influence of elapsed time between venipuncture and sample separation (immediate to 24 h) and the type of serum tube used (Greiner Vacuette activator, gel serum separator, or plain tubes). We analyzed purified platelets, as well as platelet-poor and platelet-rich plasma samples treated with calcium and/or thrombin to determine the platelet contribution, directly or via the clotting process, to the profiles generated. We then used cluster analysis to identify samples with similar peak profiles.

RESULTS

Different plasma types and sera could be distinguished on the basis of cluster analyses of their spectral profiles. Elapsed time between venipuncture and separation of plasma and serum from blood samples altered the profiles obtained, particularly for serum samples and particularly on IMAC chips. The type of serum collection tube also affected the profiles because of differences in clotting time. In vitro manipulation of platelets revealed that specific peaks in IMAC profiles of serum appeared to be derived directly from platelets. Several other peaks, including some of those exhibiting time-dependent changes, arose during the clotting process.

CONCLUSION

Preanalytical variables, such as sample handling, can markedly influence results.

Proteomics and the analysis of proteomic data: an overview of current protein-profiling technologies

In recent years, several proteomic methodologies have been developed that now make it possible to identify, characterize, and comparatively quantify the relative level of expression of hundreds of proteins that are coexpressed in a given cell type or tissue, or that are found in biological fluids such as serum. These advances have resulted from the integration of diverse scientific disciplines including molecular and cellular biology, protein/peptide chemistry, bioinformatics, analytical and bioanalytical chemistry, and the use of instrumental and software tools such as multidimensional electrophoretic and chromatographic separations and mass spectrometry. In this unit, some of the common protein-profiling technologies are reviewed, along with the accompanying data-analysis tools.

Mass profiling-directed isolation and identification of a stage-specific serologic protein biomarker of advanced prostate cancer

Carcinoma of the prostate (CaP) is the second leading cause of cancer-related mortality among American men. While high cure rates are associated with localized CaP, no cure exists for advanced recurrent disease. At present there are no known serologic biomarkers specific to this stage of the disease. Several groups have used mass spectrometry (MS) based mass profiling (MP) combined with multivariate analysis to identify diagnostically predictive protein peaks for CaP in serum and tissues. Nevertheless, an appreciable level of skepticism exists for MP attributed primarily to a lack of definitive protein characterization. To address this problem, we have applied an approach that combines MP with a whole-protein based top-down separation strategy for the identification of a stage-specific marker in a group comprising 16 patients with CaP (metastatic and localized disease) and 15 healthy individuals. MP, combined with multivariate analysis, yielded 17 serum proteins specific to metastatic disease. A single protein detected at m/z 7771 was found to be significantly decreased in the sera of all the metastatic CaP patients but not in localized CaP or healthy individuals. This protein was therefore chosen as the primary candidate for further analysis. The complex nature of the serologic proteome necessitated an isolation strategy that included a C18 prefractionation, followed by multidimensional liquid chromatography and, finally, two-dimensional gel electrophoresis. The separation process was monitored by UV-Vis and matrix-assisted laser desorption/ionization-time of flight MS analysis. This strategy was found to greatly facilitate subsequent MS characterization of the unknown protein, which was identified as platelet factor 4, a chemokine with prothrombolytic and antiangiogenic activities. Confirmation was achieved using both Western blot analysis and enzyme-linked immunosorbent assay. With the growing interest in using MP for patient classification and diagnosis, our approach and its variations should be powerful in the separation and characterization of proteins following MP.

MALDI-TOF mass spectrometry analysis of cerebrospinal fluid tryptic peptide profiles to diagnose leptomeningeal metastases in patients with breast cancer

Leptomeningeal metastasis (LM) is a devastating complication that occurs in 5% of patients with breast cancer. Early diagnosis and initiation of treatment are essential to prevent neurological deterioration. However, early diagnosis of LM remains challenging because 25% of cerebrospinal fluid (CSF) samples produce false-negative results at first cytological examination. We developed a new, MS-based method to investigate the protein expression patterns present in the CSF from patients with breast cancer with and without LM. CSF samples from 106 patients with active breast cancer (54 with LM and 52 without LM) and 45 control subjects were digested with trypsin. The resulting peptides were measured by MALDI-TOF MS. Then, the mass spectra were analyzed and compared between patient groups using newly developed bioinformatics tools. A total of 895 possible peak positions was detected, and 164 of these peaks discriminated between the patient groups (Kruskal-Wallis, p<0.01). The discriminatory masses were clustered, and a classifier was built to distinguish patients with breast cancer with and without LM. After bootstrap validation, the classifier had a maximum accuracy of 77% with a sensitivity of 79% and a specificity of 76%. Direct MALDI-TOF analysis of tryptic digests of CSF gives reproducible peptide profiles that can assist in diagnosing LM in patients with breast cancer. The same method can be used to develop diagnostic assays for other neurological disorders.

Proteomic approaches in the search for disease biomarkers

Significant technological advances in protein chemistry, physics and computer sciences in the last two decades have greatly improved protein separation methodologies, such as electrophoresis and chromatography, and have established mass spectrometry (MS) as an indispensable tool for protein study. The goal of this review is to provide a brief overview of the recent improvements in these methodologies and present examples from their application in proteome analysis and search for disease biomarkers.

A strategy for high-resolution protein identification in surface-enhanced laser desorption/ionization mass spectrometry: Calgranulin A and chaperonin 10 as protein markers for endometrial carcinoma

Surface-enhanced laser desorption/ionization-mass spectrometry (SELDI-MS) has conventionally been practiced on linear time of flight (TOF) which has low mass accuracy and resolution. Here we demonstrate in an examination of both malignant and nonmalignant endometrial tissue homogenates that high mass accuracy and resolution in the MS stage are crucial. Using a commercially available quadrupole/TOF (QqTOF), we were able to resolve two potential cancer markers, subsequently identified off-line as chaperonin 10 and calgranulin A, that differ by 8 Da in mass. Two off-line protein identification protocols were developed: the first was based on size-exclusion chromatography (SEC), sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), protein extraction, trypsin digestion, and matrix-assisted laser desorption/ionization-tandem MS (MALDI-MS/MS); the second on SEC and shotgun nano-liquid chromatography (nanoLC)-MS/MS. Analyses on a cohort of 44 endometrial homogenates showed 22 out of 23 nonmalignant samples had nondetectable to very low abundance of chaperonin 10 and calgranulin A; 17 of the 21 malignant samples had detectable to abundant levels of both proteins. Immunohistochemical staining of a tissue microarray of 32 samples showed that approximately half of malignant endometrial tissues exhibited positive staining for calgranulin A in the malignant epithelium, while 9 out of 10 benign tissues exhibited negative epithelial staining. In addition, macrophages/granulocytes in malignant as well as nonmalignant tissues showed positive staining. No immunostaining occurred in stroma or myometrium. Calgranulin A, in combination with chaperonin 10 and other proteins, may eventually constitute a panel of markers to permit diagnosis and screening of endometrial cancer.

Standardized approach to proteome profiling of human serum based on magnetic bead separation and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

BACKGROUND

Magnetic bead purification for the analysis of low-abundance proteins in body fluids facilitates the identification of potential new biomarkers by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The aims of our study were to establish a proteome fractionation technique and to validate a standardized blood sampling, processing, and storage procedure for proteomic pattern analysis.

METHODS

We used magnetic bead separation for proteome profiling of human blood by MALDI-TOF MS (mass range, 1000-10,000 Da) and studied the effects on the quality and reproducibility of the proteome analysis of anticoagulants, blood clotting, time and temperature of sample storage, and the number of freeze-thaw cycles of samples.

RESULTS

The proteome pattern of human serum was characterized by approximately 350 signals in the mass range of 1000-10,000 Da. The proteome profile showed time-dependent dynamic changes before and after centrifugation of the blood samples. Serum mass patterns differed between native samples and samples frozen once. The best reproducibility of proteomic patterns was with a single thawing of frozen serum samples.

CONCLUSION

Application of the standardized preanalytical blood sampling and storage procedure in combination with magnetic bead-based fractionation decreases variability of proteome patterns in human serum assessed by MALDI-TOF MS.

Multiclass cancer classification and biomarker discovery using GA-based algorithms

MOTIVATION

The development of microarray-based high-throughput gene profiling has led to the hope that this technology could provide an efficient and accurate means of diagnosing and classifying tumors, as well as predicting prognoses and effective treatments. However, the large amount of data generated by microarrays requires effective reduction of discriminant gene features into reliable sets of tumor biomarkers for such multiclass tumor discrimination. The availability of reliable sets of biomarkers, especially serum biomarkers, should have a major impact on our understanding and treatment of cancer.

RESULTS

We have combined genetic algorithm (GA) and all paired (AP) support vector machine (SVM) methods for multiclass cancer categorization. Predictive features can be automatically determined through iterative GA/SVM, leading to very compact sets of non-redundant cancer-relevant genes with the best classification performance reported to date. Interestingly, these different classifier sets harbor only modest overlapping gene features but have similar levels of accuracy in leave-one-out cross-validations (LOOCV). Further characterization of these optimal tumor discriminant features, including the use of nearest shrunken centroids (NSC), analysis of annotations and literature text mining, reveals previously unappreciated tumor subclasses and a series of genes that could be used as cancer biomarkers. With this approach, we believe that microarray-based multiclass molecular analysis can be an effective tool for cancer biomarker discovery and subsequent molecular cancer diagnosis.

Proteomics for biodefense applications: progress and opportunities

The increasing threat of bioterrorism and continued emergence of new infectious diseases has driven a major resurgence in biomedical research efforts to develop improved treatments, diagnostics and vaccines, as well as increase the fundamental understanding of the host immune response to infectious agents. The availability of multiple mass spectrometry platforms combined with multidimensional separation technologies and microbial genomic databases provides an unprecedented opportunity to develop these much needed resources. An overview of current proteomic strategies applied to microbes and viruses considered potential bioterrorism agents is presented. The emerging area of immunoproteomics as applied to the development of new vaccine targets is also summarized. These powerful research approaches can generate a multitude of potential new protein targets; however, translating these proteomic discoveries to useful counter-bioterrorism products will require large collaborative research efforts across multiple basic science and clinical disciplines. A translational proteomic research paradigm illustrating this approach using influenza virus as an example is discussed.

Mass Spectrometry-based Expression Profiling of Clinical Prostate Cancer

The maturation of MS technologies has provided a rich opportunity to interrogate protein expression patterns in normal and disease states by applying expression protein profiling methods. Major goals of this research strategy include the identification of protein biomarkers that demarcate normal and disease populations, and the identification of therapeutic biomarkers for the treatment of diseases such as cancer (Celis, J. E., and Gromov, P. (2003) Proteomics in translational cancer research: Toward an integrated approach. Cancer Cell 3, 9-151). Prostate cancer is one disease that would greatly benefit from implementing MS-based expression profiling methods because of the need to stratify the disease based on molecular markers. In this review, we will summarize the current MS-based methods to identify and validate biomarkers in human prostate cancer. Lastly, we propose a reverse proteomic approach implementing a quantitative MS research strategy to identify and quantify biomarkers implicated in prostate cancer development. With this approach, the absolute levels of prostate cancer biomarkers will be identified and quantified in normal and diseased samples by measuring the levels of native peptide biomarkers in relation to a chemically identical but isotopically labeled reference peptide. Ultimately, a centralized prostate cancer peptide biomarker expression database could function as a repository for the identification, quantification, and validation of protein biomarker(s) during prostate cancer progression in men.

Serum, salivary and tissue proteomics for discovery of biomarkers for head and neck cancers

Initial clinically oriented applications of emerging proteomic technologies that aim to identify biomarkers for head and neck squamous cell carcinoma diagnostics have yielded promising results. The development of new proteomic diagnostics remains critical for the early detection of head and neck squamous cell carcinoma at more treatable stages. Prognostic markers for disease recurrence and treatment sensitivities are also required. In this overview of current biomarker identification strategies for head and neck squamous cell carcinoma, different combinations of mass spectrometry platforms, laser capture microscopy and 2D gel electrophoresis procedures are summarized as applied to readily available clinical specimens (tissue, blood and saliva). Issues related to assay reproducibility, management of large data sets and future improvements in clinical proteomics are also addressed.

Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data

MOTIVATION

High-throughput and high-resolution mass spectrometry instruments are increasingly used for disease classification and therapeutic guidance. However, the analysis of immense amount of data poses considerable challenges. We have therefore developed a novel method for dimensionality reduction and tested on a published ovarian high-resolution SELDI-TOF dataset.

RESULTS

We have developed a four-step strategy for data preprocessing based on: (1) binning, (2) Kolmogorov-Smirnov test, (3) restriction of coefficient of variation and (4) wavelet analysis. Subsequently, support vector machines were used for classification. The developed method achieves an average sensitivity of 97.38% (sd = 0.0125) and an average specificity of 93.30% (sd = 0.0174) in 1000 independent k-fold cross-validations, where k = 2, ..., 10.

AVAILABILITY

The software is available for academic and non-commercial institutions.

ProteinChip technology reveals distinctive protein expression profiles in the urine of bladder cancer patients

OBJECTIVE

Since accurate biomarkers for the early diagnosis or individual prognosis of the bladder carcinoma are still not available, we used the ProteinChip technology, to search for discriminating protein expressions associated with this cancer and its subtypes.

METHODS

A training set consisting of 30 archival urine samples from bladder carcinoma patients and 30 urinary samples from healthy volunteers, was analyzed via ProteinChip technology and computer based data mining. Mass clusters of differentially expressed proteins were verified by a second set (test set) comprising 21 bladder carcinoma urine samples and 21 non-tumor urinary samples. Expression differences between carcinoma subtype sample groups of the initial training set were assessed by a trend test.

RESULTS

Bladder carcinoma was segregated from control with a sensitivity and specificity of 80% and 90 to 97% in the trainings set, as well as 52 to 57% and 57 to 62% in the test set, respectively. Segregation of pooled tumor stages pT2-pT3 from stages pT1 and pTa was possible at the 53.3 kDa cluster of the CM10-chip array data derived rule base.

CONCLUSION

ProteinChip technology together with adapted computer based data mining tools are useful for the rapid establishment of potential protein biomarkers.

Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry

The combined method of LC-MS/MS is increasingly being used to explore differences in the proteomic composition of complex biological systems. The reliability and utility of such comparative protein expression profiling studies is critically dependent on an accurate and rigorous assessment of quantitative changes in the relative abundance of the myriad of proteins typically present in a biological sample such as blood or tissue. In this review, we provide an overview of key statistical and computational issues relevant to bottom-up shotgun global proteomic analysis, with an emphasis on methods that can be applied to improve the dependability of biological inferences drawn from large proteomic datasets. Focusing on a start-to-finish approach, we address the following topics: 1) low-level data processing steps, such as formation of a data matrix, filtering, and baseline subtraction to minimize noise, 2) mid-level processing steps, such as data normalization, alignment in time, peak detection, peak quantification, peak matching, and error models, to facilitate profile comparisons; and, 3) high-level processing steps such as sample classification and biomarker discovery, and related topics such as significance testing, multiple testing, and choice of feature space. We report on approaches that have recently been developed for these steps, discussing their merits and limitations, and propose areas deserving of further research.

SELDI-TOF MS profiling of serum for detection of the progression of chronic hepatitis C to hepatocellular carcinoma

Proteomic profiling of serum is an emerging technique to identify new biomarkers indicative of disease severity and progression. The objective of our study was to assess the use of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) to identify multiple serum protein biomarkers for detection of liver disease progression to hepatocellular carcinoma (HCC). A cohort of 170 serum samples obtained from subjects in the United States with no liver disease (n = 39), liver diseases not associated with cirrhosis (n = 36), cirrhosis (n = 38), or HCC (n = 57) were applied to metal affinity protein chips for protein profiling by SELDI-TOF MS. Across the four test groups, 38 differentially expressed proteins were used to generate multiple decision classification trees to distinguish the known disease states. Analysis of a subset of samples with only hepatitis C virus (HCV)-related disease was emphasized. The serum protein profiles of control patients were readily distinguished from each HCV-associated disease state. Two-way comparisons of chronic hepatitis C, HCV cirrhosis, or HCV-HCC versus healthy had a sensitivity/specificity range of 74% to 95%. For distinguishing chronic HCV from HCV-HCC, a sensitivity of 61% and a specificity of 76% were obtained. However, when the values of known serum markers alpha fetoprotein, des-gamma carboxyprothrombin, and GP73 were combined with the SELDI peak values, the sensitivity and specifity improved to 75% and 92%, respectively. In conclusion, SELDI-TOF MS serum profiling is able to distinguish HCC from liver disease before cirrhosis as well as cirrhosis, especially in patients with HCV infection compared with other etiologies.

SpecAlign--processing and alignment of mass spectra datasets

SUMMARY

Pre-processing of chromatographic profile or mass spectral data is an important aspect of many types of proteomics and biomarker discovery experiments. Here we present a graphical computational tool, SpecAlign, that enables simultaneous visualization and manipulation of multiple datasets. SpecAlign not only provides all common processing functions, but also uniquely implements an algorithm that enables the complete alignment of each mass spectrum within a loaded dataset. We demonstrate its utility by aligning two datasets each containing six spectra; one set was acquired prior to instrument calibration and the other following calibration.

AVAILABILITY

The software is free of charge and available for download from http://ptcl.chem.ox.ac.uk/~jwong/specalign. Supports Windows operating systems including Windows 9X/NT/2000/XP.

Plasma Protein Profiling for Diagnosis of Pancreatic Cancer Reveals the Presence of Host Response Proteins

Plasma protein profiling using separations coupled to matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) has great potential in translational research; it can be used for biomarker discovery and contribute to disease diagnosis and therapy. Previously reported biomarker searches have been done solely by MS protein profiling followed by bioinformatics analysis of the data. To add to current methods, we tested an alternative strategy for plasma protein profiling using pancreatic cancer as the model. First, offline solid-phase extraction is done with 96-well plates to fractionate and partially purify the proteins. Then, multiple profiling and identification experiments can be conducted on the same protein fractions because only 5% of the fractions are used for MALDI MS profiling. After MALDI MS analysis, the mass spectra are normalized and subjected to a peak detection algorithm. Over three sets of mass spectra acquired using different instrument variables, ∼400 unique ion signals were detected. Classification schemes employing as many as eight individual peaks were developed using a training set with 123 members (82 cancer patients) and a blinded validation set with 125 members (57 cancer patients). The sensitivity of the study was 88%, but the specificity was significantly lower, 75%. The reason for the low specificity becomes apparent upon protein identification of the ion signals used for the classification. The identifications reveal only common serum proteins and components of the acute phase response, including serum amyloid A, α-1-antitrypsin, α-1-antichymotrypsin, and inter-α-trypsin inhibitor.

Proteomic profiling of pancreatic cancer for biomarker discovery

Pancreatic cancer is a uniformly lethal disease that is difficult to diagnose at early stage and even more difficult to cure. In recent years, there has been a substantial interest in applying proteomics technologies to identify protein biomarkers for early detection of cancer. Quantitative proteomic profiling of body fluids, tissues, or other biological samples to identify differentially expressed proteins represents a very promising approach for improving the outcome of this disease. Proteins associated with pancreatic cancer identified through proteomic profiling technologies could be useful as biomarkers for the early diagnosis, therapeutic targets, and disease response markers. In this article, we discuss recent progress and challenges for applying quantitative proteomics technologies for biomarker discovery in pancreatic cancer.

Diagnostic protein discovery using liquid chromatography/mass spectrometry for proteolytic peptide targeting

A peptide targeting method has been developed for diagnostic protein discovery, which combines proteolytic digestion of fractionated plasma proteins and liquid chromatography coupled to electrospray time-of-flight mass spectrometry (LC/ESI-TOFMS) profiling. Proteolysis prior to profiling overcomes molecular weight limitations and compensates for the poor sensitivity of matrix-assisted laser desorption/ionization (MALDI) protein profiling. LC/MS increases the peak capacity compared to crude fractionation techniques or single sample MALDI analysis. Differentially expressed peptides are targeted in the mass chromatograms using bioinformatic techniques and subsequently sequenced with MALDI tandem MS. In a model study comparing pancreatic cancer patients to controls, 74% of the peptide targets were successfully sequenced. This profiling method was superior to previous experiments using single sample MALDI analysis for protein profiling or proteolytic peptide profiling, because more potential protein markers were identified.

High throughput quantitative analysis of serum proteins using glycopeptide capture and liquid chromatography mass spectrometry

It is expected that the composition of the serum proteome can provide valuable information about the state of the human body in health and disease and that this information can be extracted via quantitative proteomic measurements. Suitable proteomic techniques need to be sensitive, reproducible, and robust to detect potential biomarkers below the level of highly expressed proteins, generate data sets that are comparable between experiments and laboratories, and have high throughput to support statistical studies. Here we report a method for high throughput quantitative analysis of serum proteins. It consists of the selective isolation of peptides that are N-linked glycosylated in the intact protein, the analysis of these now deglycosylated peptides by liquid chromatography electrospray ionization mass spectrometry, and the comparative analysis of the resulting patterns. By focusing selectively on a few formerly N-linked glycopeptides per serum protein, the complexity of the analyte sample is significantly reduced and the sensitivity and throughput of serum proteome analysis are increased compared with the analysis of total tryptic peptides from unfractionated samples. We provide data that document the performance of the method and show that sera from untreated normal mice and genetically identical mice with carcinogen-induced skin cancer can be unambiguously discriminated using unsupervised clustering of the resulting peptide patterns. We further identify, by tandem mass spectrometry, some of the peptides that were consistently elevated in cancer mice compared with their control littermates.

Towards discovery-driven translational research in breast cancer

Discovery-driven translational research in breast cancer is moving steadily from the study of cell lines to the analysis of clinically relevant samples that, together with the ever increasing number of novel and powerful technologies available within genomics, proteomics and functional genomics, promise to have a major impact on the way breast cancer will be diagnosed, treated and monitored in the future. Here we present a brief report on long-term ongoing strategies at the Danish Centre for Translational Breast Cancer Research to search for markers for early detection and targets for therapeutic intervention, to identify signalling pathways affected in individual tumours, as well as to integrate multiplatform 'omic' data sets collected from tissue samples obtained from individual patients. The ultimate goal of this initiative is to coalesce knowledge-based complementary procedures into a systems biology approach to fight breast cancer.

Proteomics for hepatocellular carcinoma marker discovery

Refinements of serological markers and screening of patients at high risk for developing hepatocellular carcinoma (HCC) may lead to better HCC detection, earlier intervention, and successful treatment, improving long-term outcomes. Proteomics promises the discovery of biomarkers for early HCC detection and diagnosis. Proteomic-based profiling uniquely allows delineation of global changes in expression patterns resulting from transcriptional and posttranscriptional control, posttranslational modifications, and shifts in proteins between cellular compartments. Approaches to that effect include direct serum protein profiling and comparative analysis of protein expression in normal, precancerous, and early-stage tumor tissues. Identification of panels of tumor antigens that elicit a humoral response also may contribute to the discovery of new markers for HCC screening and diagnosis. Today, 2-dimensional polyacrylamide gel electrophoresis, multidimensional liquid chromatography, mass spectrometry, and protein microarrays are among the proteomic tools available for biomarker and drug target discovery. We review these technologies and their application to the study of HCC. Our objective is to provide a framework for appreciating the promise, while at the same time understanding the challenges behind translating proteomics discovery into novel diagnostic tests.

The use of proteomics for the assessment of clinical samples in research

Proteomics, the analysis of expressed proteins, has been an important developing area of research for the past two decades [Anderson, NG, Anderson, NL. Twenty years of two-dimensional electrophoresis: past, present and future. Electrophoresis 1996;17:443-453]. Advances in technology have led to a rapid increase in applications to a wide range of samples; from initial experiments using cell lines, more complex tissues and biological fluids are now being assessed to establish changes in protein expression. A primary aim of clinical proteomics is the identification of biomarkers for diagnosis and therapeutic intervention of disease, by comparing the proteomic profiles of control and disease, and differing physiological states. This expansion into clinical samples has not been without difficulties owing to the complexity and dynamic range in plasma and human tissues including tissue biopsies. The most widely used techniques for analysis of clinical samples are surface-enhanced laser desorption/ionisation mass spectrometry (SELDI-MS) and 2-dimensional gel electrophoresis (2-DE) coupled to matrix-assisted laser desorption ionisation [Person, MD, Monks, TJ, Lau, SS. An integrated approach to identifying chemically induced posttranslational modifications using comparative MALDI-MS and targeted HPLC-ESI-MS/MS. Chem. Res. Toxicol. 2003;16:598-608]-mass spectroscopy (MALDI-MS). This review aims to summarise the findings of studies that have used proteomic research methods to analyse samples from clinical studies and to assess the impact that proteomic techniques have had in assessing clinical samples.