Application of SELDI-TOF mass spectrometry for the identification of differentially expressed proteins in transformed follicular lymphoma

miR-31 and miR-17-5p levels change during transformation of follicular lymphoma

The 30% of patients whose indolent follicular lymphoma transforms to aggressive diffuse large B-cell lymphoma (DLBCL) have poor survival. Reliable predictors of follicular B-cell lymphoma transformation to DLBCL are lacking, and diagnosis of those that will progress is challenging. MicroRNA, which regulates gene expression, has critical functions in the growth and progression of many cancers and contributes to the pathogenesis of lymphoma. Using 5 paired samples from patients who presented with follicular lymphoma and progressed to DLBCL, we identified specific microRNA differentially expressed between the two. Specifically, miR-17-5p levels were low in follicular lymphoma and increased as the disease transformed. In contrast, miR-31 expression was high in follicular lymphoma and decreased as the lymphoma progressed. These results were confirmed in additional unpaired cases of low-grade follicular lymphoma (n = 13) and high-grade follicular lymphoma grade 3 or DLBCL (n = 17). Loss of miR-31 expression in DLBCL was not due to deletion of the locus. Changes in miR-17-5p and miR-31 were not correlated with immunophenotype, genetics, or status of the MYC oncogene. However, increased miR-17-5p expression did significantly correlate with increased expression of p53 protein, which is indicative of mutant TP53. Two pro-proliferative genes, E2F2 and PI3KC2A, were identified as direct messenger RNA targets of miR-31, suggesting that these may contribute to follicular lymphoma transformation. Our results indicate that changes in miR-31 and miR-17-5p reflect the transformation of follicular lymphoma to an aggressive large B-cell lymphoma and may, along with their targets, be viable markers for this process.

Proteomics in hematologic malignancies

Basic science research in hematology has been determining the functions of gene products using classical approaches that typically involve studying one or a few genes at a time. Proteomics, defined as the study of protein properties on a large scale, provides tools to globally analyze malignant hematologic cells. A major challenge in cancer therapy is the identification of drugs that kill tumor cells while preserving normal cells. Differential display via proteomics enables analysis of direct as well as side-effects of drugs at a molecular level. Proteomics also allows a better understanding of cell signaling pathways involved during apoptosis in hematologic cells. Storing the information in a 2D electrophoresis database enhances the efficiency of proteome research on malignant cells. Finally, the work needed to be carried out on proteomic analysis prior to routine clinical adoption is discussed, and the necessity for multi-institutional collaborations is emphasized.

Different protein profile in amniotic fluid with nervous system malformations by surface-enhanced laser desorption-ionization/time-of-flight mass spectrometry (SELDI-TOF-MS) technology

AIM

To detect the distinct proteins in amniotic fluid (AF) between nervous system malformations fetuses and normal fetuses.

MATERIAL AND METHODS

Surface-enhanced laser desorption-ionization/time-of-flight mass spectrometry was used to characterize AF peptides in AF between nervous system malformations fetuses and normal fetuses. WCX2 protein chips were used to characterize AF peptides in AF. Protein chips were examined in a PBSIIC protein reader, the protein profiling was collected by ProteinChip software version 3.1 (Ciphergen Biosystems, Fremont, CA, USA) and analyzed by Biomarker Wizard software (Ciphergen Biosystems). Nine distinct proteins were identified in AF between nervous system malformations fetuses and normal fetuses.

RESULTS

Compared with the control group, three proteins with m/z 4967.5 Da, 5258.0 Da, and 11,717.0 Da were down-regulated, and six proteins with m/z 2540.4 Da, 3107.1 Da, 3396.8 Da, 4590.965 Da, 5589.2 Da and 6429.4 Da up-regulated in nervous system malformations fetuses.

CONCLUSION

The results suggest that there are distinct proteins in protein profiling of AF between nervous system malformations fetuses and normal fetuses.

Application of MALDI imaging for the diagnosis of classical Hodgkin lymphoma

Hodgkin lymphoma (HL) is a distinctive lymphoma subtype characterized by rareness of tumor cells [Hodgkin's and Reed-Sternberg (HRS) cells in classical HL and lymphocytic and histiocytic cells in lymphocyte predominant HL] as well as the vast majority of the surrounding inflammatory-like cellular infiltrate. Still the onset of this highly variable disease is not completely understood. Proteome analysis can lead to the identification of potential proteins capable of elucidating malignant growth and survival in HL. Especially MALDI imaging could result in pinpointing differentially expressed proteins, which might represent potential marker molecules. In this study, we were able to distinguish between classical Hodgkin lymphoma and lymphadenitis with a sensitivity and specificity of 83.92 and 89.37%, respectively.

Protein mass spectra data analysis for clinical biomarker discovery: a global review

The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. In recent years there has been a growing interest in using high throughput technologies for the detection of such biomarkers. In particular, mass spectrometry appears as an exciting tool with great potential. However, to extract any benefit from the massive potential of clinical proteomic studies, appropriate methods, improvement and validation are required. To better understand the key statistical points involved with such studies, this review presents the main data analysis steps of protein mass spectra data analysis, from the pre-processing of the data to the identification and validation of biomarkers.

Protein profiling in pathology: analysis and evaluation of 239 frozen tissue biopsies for diagnosis of B-cell lymphomas

PURPOSE

We determined the potential value of protein profiling of tissue samples by assessing how precise this approach enables discrimination of B-cell lymphoma from reactive lymph nodes, and how well the profiles can be used for lymphoma classification.

EXPERIMENTAL DESIGN

Protein lysates from lymph nodes (n=239) from patients with the diagnosis of reactive hyperplasia (n=44), follicular lymphoma (n=63), diffuse large B-cell lymphoma (n=43), mantle cell lymphoma (n=47), and chronic lymphocytic leukemia/small lymphocytic B-cell lymphoma (n=42) were analysed by SELDI-TOF MS. Data analysis was performed by (i) classification and regression tree-based analysis and (ii) binary and polytomous logistic regression analysis.

RESULTS

After internal validation by the leave-one-out principle, both the classification and regression tree and logistic regression classification correctly identified the majority of the malignant (87 and 96%, respectively) and benign cases (73 and 75%, respectively). Classification was less successful since approximately one-third of the cases of each group were misclassified according to the histological classification. However, an additional mantle cell lymphoma case that was misclassified as chronic lymphocytic leukemia/small lymphocytic B-cell lymphoma initially was identified based on the protein profile.

CONCLUSIONS AND CLINICAL RELEVANCE

SELDI-TOF MS protein profiling allows for reliable identification of the majority of malignant lymphoma cases; however, further validation and testing robustness in a diagnostic setting is needed.

Heavy marijuana users show increased serum apolipoprotein C-III levels: evidence from proteomic analyses

Marijuana (MJ) is the most commonly used illicit drug in the United States. Its abuse is associated with cognitive dysfunctions and increased resistance to blood flow in the cerebral vasculature. In addition, MJ abuse is associated with increased risks of potentially serious cardiovascular disorders. In the present study, we used the protein chip platform based on surface-enhanced laser desorption/ionization time-of-flight mass spectroscopy (SELDI-TOF-MS) to test the possibility that MJ abuse might be associated with changes in serum protein levels. Indeed, MJ users showed significant increases in three protein peaks, which were identified as three isoforms of apolipoprotein (apo) C-III. Immunoprecipitation using an apoC-III antibody also validated the identification of the proteins. Marijuana-induced increases in apoC-III levels might occur through chronic stimulation of hepatic cannabinoid receptors (CB1 and/or CB2) by its active ingredient, Delta(9)tetrahydrocannibol (THC). Thus, chronic MJ abuse might cause increased transcription and/or translation of apoC-III in the liver with corresponding changes reflected in the plasma of these patients. In any case, because apoC-III is a cardiovascular risk factor, the increased levels observed in MJ users might explain, in part, the cardiac and cerebral abnormalities reported in these patients.

Proteomic assays for the detection of urothelial cancer

Bladder cancer is one of the most expensive cancers from diagnosis to death of the patient due to life-long surveillance involving upper tract imaging, urinary cytology, and cystoscopy. Cytology has been historically used in conjunction with cystoscopy to help detect disease that may be missed by routine cystoscopy (e.g., carcinoma in situ and upper tract disease). Urine cytology is highly cytopathologist dependent and has reasonable sensitivity for detecting high grade disease. However, its sensitivity drops precipitously with regard to well-differentiated low grade cancers. Intensive investigations have been undertaken using proteomics to find an alternative to cystoscopy and cytology. Urine proteomic markers currently evaluated critically in the literature include bladder tumor antigen, nuclear matrix protein 22, BLCA-4, hyaluronic acid, hyaluronidase, cytokeratin 8, cytokeratin 18, cytokeratin 19, tissue polypeptide antigen, and tissue polypeptide-specific antigen. Markers used as alternatives to cystoscopy must be accurate with high sensitivity and specificity, cost effective for life-long surveillance, and minimally invasive to minimize the burden to the patient. To date, no proteomic marker has been developed that can replace cystoscopy for the detection of bladder cancer. However, several urinary markers appear to have higher sensitivity albeit lower specificity than cytology and can be used to supplement cystoscopy. Some of those markers are herein described in this chapter. By defining and characterizing the current state of the art in protein based markers, we are poised to evaluate and benchmark newly discovered protein biomarkers that will be isolated through new proteomics based investigations of urine.

Mass Spectrometry in Diagnostic Oncoproteomics

Diagnostic oncoproteomics is the application of proteomic techniques for the diagnosis of malignancies. A new mass spectrometric technology involves surface enhanced laser desorption ionization combined with time-of flight mass analysis (SELDI-TOF-MS), using special protein chips. After the description of the relevant principles of the technique, including approaches to proteomic pattern diagnostics, applications are reviewed for the diagnosis of ovarian, breast, prostate, bladder, pancreatic, and head and neck cancers, and also several other malignancies. Finally, problems and prospects of the approach are discussed.

Serum diagnosis of diffuse large B-cell lymphomas and further identification of response to therapy using SELDI-TOF-MS and tree analysis patterning

BACKGROUND

Currently, there are no satisfactory biomarkers available to screen for diffuse large B cell lymphoma (DLBCL) or to identify patients who do not benefit from standard anti-cancer therapies. In this study, we used serum proteomic mass spectra to identify potential serum biomarkers and biomarker patterns for detecting DLBCL and patient responses to therapy.

METHODS

The proteomic spectra of crude sera from 132 patients with DLBCL and 75 controls were performed by SELDI-TOF-MS and analyzed by Biomarker Patterns Software.

RESULTS

Nine peaks were considered as potential DLBCL discriminatory biomarkers. Four peaks were considered as biomarkers for predicting the patient response to standard therapy. The proteomic patterns achieved a sensitivity of 94% and a specificity of 94% for detecting DLBCL samples in the test set of 85 samples, and achieved a sensitivity of 94% and a specificity of 92% for detecting poor prognosis patients in the test set of 66 samples.

CONCLUSION

These proteomic patterns and potential biomarkers are hoped to be useful in clinical applications for detecting DLBCL patients and predicting the response to therapy.

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.

How will haematologists use proteomics?

Proteomics technologies are emerging as a useful tool in the identification of disease biomarkers, and in defining and characterising both normal physiological and disease processes. Many cellular changes in protein expression in response to an external stimulus or mutation can only be characterised at the proteome level. In these cases protein expression is often controlled by altered rates of translation and/or degradation, making proteomics an important tool in the analysis of biological systems. In the leukaemias, post-translational modification of proteins (e.g. phosphorylation, acetylation) plays a key role in the molecular pathology of the disease: such modifications can now be detected with novel proteomic methods. In a clinical setting, serum remains a relatively un-mined source of information for prognosis and response to therapy. This protein rich fluid represents an opportunity for proteomics research to benefit hematologists and others. In this review, we discuss the technologies available for the study of the proteome that offer realistic opportunities in haematology.

Contributions of advanced proteomics technologies to cancer diagnosis

The ability of Medicine to effectively treat and cure cancer is directly dependent on their capability to detect cancers at their earliest stages. The advent of proteomics has brought with it the hope of discovering novel biomarkers in the early phases of tumorigenesis that can be used to diagnose diseases, predict susceptibility, and monitor progression. This discipline incorporates technologies that can be applied to complex biosystems such as serum and tissue in order to characterize the content of, and changes in, the proteome induced by physiological changes, benign or pathologic. These tools include 2-DE, 2D-DIGE, ICAT, protein arrays, MudPIT and mass spectrometries including SELDI-TOF. The application of these tools has assisted to uncover molecular mechanisms associated with cancer at the global level and may lead to new diagnostic tests and improvements in therapeutics. In this review these approaches are evaluated in the context of their contribution to cancer biomarker discovery. Particular attention is paid to the promising contribution of the ProteinChip/SELDI-TOF platform as a revolutionary approach in proteomic patterns analysis that can be applied at the bedside for discovering protein profiles that distinguish disease and disease-free states with high sensitivity and specificity. Understanding the basic concepts and tools used will illustrate how best to apply these technologies for patient benefit for the early cancer detection and improved patient care.

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

Recently, the focus of cancer research has expanded from genetic information in the human genome to protein expression analyses. Because this 'proteome' reflects the state of a cell, tissue or organism more accurately, much is expected from proteomics to yield better tumour markers for disease diagnosis and therapy monitoring. Some current proteomic technologies are particularly suitable for protein profiling in the search for new biomarkers. Surface-enhanced laser desorption ionization time-of-flight mass spectrometry has been used frequently, highlighting many new proteins as biomarkers (e.g. for ovarian, breast, prostate and colorectal cancer). However, it is becoming increasingly recognized that reproducibility and validation of these biomarkers should be addressed carefully, as should their origin and identity. If these efforts are made, protein profiling can contribute to the better diagnosis of patients and the optimization of their treatment.

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.

The molecular make-up of a tumour: proteomics in cancer research

The enormous progress in proteomics, enabled by recent advances in MS (mass spectrometry), has brought protein analysis back into the limelight of cancer research, reviving old areas as well as opening new fields of study. In this review, we discuss the basic features of proteomic technologies, including the basics of MS, and we consider the main current applications and challenges of proteomics in cancer research, including (i) protein expression profiling of tumours, tumour fluids and tumour cells; (ii) protein microarrays; (iii) mapping of cancer signalling pathways; (iv) pharmacoproteomics; (v) biomarkers for diagnosis, staging and monitoring of the disease and therapeutic response; and (vi) the immune response to cancer. All these applications continue to benefit from further technological advances, such as the development of quantitative proteomics methods, high-resolution, high-speed and high-sensitivity MS, functional protein assays, and advanced bioinformatics for data handling and interpretation. A major challenge will be the integration of proteomics with genomics and metabolomics data and their functional interpretation in conjunction with clinical results and epidemiology.

N/A

N/A

Proteomics in pathology research

Proteomics is a multifaceted approach to study various aspects of protein expression, post-translational modification, interactions, organization and function at a global level. While DNA constitutes the 'information archive of the genome', it is the proteins that actually serve as the functional effectors of cellular processes. Thus, analysis of protein derangements on a proteome-wide scale will reveal insights into deregulated pathways and networks involved in the pathogenesis of disease. Although the field of proteomics has advanced tremendously in recent years, there are significant technical challenges that pose limitations to the routine application of mass spectrometry to clinical research. Despite these challenges, proteomic studies have yielded unparalleled information and understanding of the cellular biology of diseased states. The application of mass spectrometry to the study of diseases will ultimately lead to identification of biomarkers that are critical for the detection, diagnosis, prognosis and treatment of specific disease entities.