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.     

SELDI-TOF MS Proteomics in Breast Cancer

Background

Proteomic profiling is a rapidly developing technology that may enable early disease screening and diagnosis. Surface-enhanced laser desorption ionization–time of flight mass spectrometry (SELDI-TOF MS) has demonstrated promising results in screening and early detection of many diseases. In particular, it has emerged as a high-throughput tool for detection and differentiation of several cancer types. This review aims to appraise published data on the impact of SELDI-TOF MS in breast cancer.

Methods

A systematic literature search between 1965 and 2009 was conducted using the PubMed, EMBASE, and Cochrane Library databases. Studies covering different aspects of breast cancer proteomic profiling using SELDI-TOF MS technology were critically reviewed by researchers and specialists in the field.

Results

Fourteen key studies involving breast cancer biomarker discovery using SELDI-TOF MS proteomic profiling were identified. The studies differed in their inclusion and exclusion criteria, biologic samples, preparation protocols, arrays used, and analytical settings. Taken together, the numerous studies suggest that SELDI-TOF MS methodology may be used as a fast and robust approach to study the breast cancer proteome and enable the analysis of the correlations between proteomic expression patterns and breast cancer.

Conclusion

SELDI-TOF MS is a promising high-throughput technology with potential applications in breast cancer screening, detection, and prognostication. Further studies are needed to resolve current limitations and facilitate clinical utility.     

Use of serological proteomic methods to find biomarkers associated with breast cancer

New technologies for the detection and therapy of early stage breast cancer are urgently needed. Pathological changes in breast might be reflected in proteomic patterns in serum. A proteomic tool was used to identify proteomic patterns in serum that distinguishes neoplastic from non-neoplastic disease within the breast. Preliminary results derived from the serum analysis from 54 unaffected women and 76 patients with breast cancer were analyzed by two-dimensional (2-D) electrophoresis and matrix-assisted laser desorption/ionization-time of flight mass spectrometry, HSP27 was found up-regulated while 14-3-3 sigma was down-regulated in the serum of breast cancer patients. The two protein biomarkers were then used to classify an independent set of 104 masked serum samples. The results showed that the protein pattern on 2-D gels can completely segregate the serum of breast cancer from non-cancer. The discriminatory pattern correctly identified all 69 breast cancer cases in the masked set. Of the 35 cases of non-malignant disease, 34 were recognized as non-cancer. These findings justify a prospective population-based assessment of proteomic technology as a screening or diagnostic tool for breast cancer in high-risk and general populations. These two protein biomarkers could also be used as targets for further study in drug design and breast cancer therapy.     

Proteomics in prostate cancer biomarker discovery

Despite advances in molecular medicine, genomics, proteomics and translational research, prostate cancer remains the second most common cause of cancer-related mortality for men in the Western world. Clearly, early detection, targeted treatment and post-treatment monitoring are vital tools to combat this disease. Tumor markers can be useful for diagnosis and early detection of cancer, assessment of prognosis, prediction of therapeutic effect and treatment monitoring. Such tumor markers include prostate-specific antigen (prostate), cancer antigen (CA)15.3 (breast), CA125 (ovarian), CA19.9 (gastrointestinal) and serum α-fetoprotein (testicular cancer). However, all of these biomarkers lack sensitivity and specificity and, therefore, there is a large drive towards proteomic biomarker discovery. Current research efforts are directed towards discovering biosignatures from biological samples using novel proteomic technologies that provide high-throughput, in-depth analysis and quantification of the proteome. Several of these studies have revealed promising biomarkers for use in diagnosis, assessment of prognosis, and targeting treatment of prostate cancer. This review focuses on prostate cancer proteomic biomarker discovery and its future potential.     

Advances in clinical cancer proteomics: SELDI-ToF-mass spectrometry and biomarker discovery.

For most cancers, survival rates depend on the early detection of the disease. So far, no biomarkers exist to cope with this difficult task. New proteomic technologies have brought the hope of discovering novel early cancer-specific biomarkers in complex biological samples and/or of the setting up of new clinically relevant test systems. Novel mass spectrometry-(MS) based technologies in particular, such as surface-enhanced laser desorption/ionisation time of flight (SELDI-ToF-MS), have shown promising results in the recent literature. Here, proteomic profiles of control and disease states are compared to find biomarkers for diagnosis. This paper aims to address the authors' own work and that of other groups in clinical cancer proteomics based on SELDI-ToF-MS. Shortcomings and hopes for the future are discussed.     

Biomarkers of breast cancer apoptosis induced by chemotherapy and TRAIL

Treatment of breast cancer is complex and challenging due to the heterogeneity of the disease. To avoid significant toxicity and adverse side-effects of chemotherapy in patients who respond poorly, biomarkers predicting therapeutic response are essential. This study has utilized a proteomic approach integrating 2D-DIGE, LC-MS/MS, and bioinformatics to analyze the proteome of breast cancer (ZR-75-1 and MDA-MB-231) and breast epithelial (MCF-10A) cell lines induced to undergo apoptosis using a combination of doxorubicin and TRAIL administered in sequence (Dox-TRAIL). Apoptosis induction was confirmed using a caspase-3 activity assay. Comparative proteomic analysis between whole cell lysates of Dox-TRAIL and control samples revealed 56 differentially expressed spots (≥2-fold change and p < 0.05) common to at least two cell lines. Of these, 19 proteins were identified yielding 11 unique protein identities: CFL1, EIF5A, HNRNPK, KRT8, KRT18, LMNA, MYH9, NACA, RPLP0, RPLP2, and RAD23B. A subset of the identified proteins was validated by selected reaction monitoring (SRM) and Western blotting. Pathway analysis revealed that the differentially abundant proteins were associated with cell death, cellular organization, integrin-linked kinase signaling, and actin cytoskeleton signaling pathways. The 2D-DIGE analysis has yielded candidate biomarkers of response to treatment in breast cancer cell models. Their clinical utility will depend on validation using patient breast biopsies pre- and post-treatment with anticancer drugs.     

Clinical proteomics: present and future prospects

Advances in proteomics technology offer great promise in the understanding and treatment of the molecular basis of disease. The past decade of proteomics research, the study of dynamic protein expression, post-translational modifications, cellular and sub-cellular protein distribution, and protein-protein interactions, has culminated in the identification of many disease-related biomarkers and potential new drug targets. While proteomics remains the tool of choice for discovery research, new innovations in proteomic technology now offer the potential for proteomic profiling to become standard practice in the clinical laboratory. Indeed, protein profiles can serve as powerful diagnostic markers, and can predict treatment outcome in many diseases, in particular cancer. A number of technical obstacles remain before routine proteomic analysis can be achieved in the clinic; however the standardisation of methodologies and dissemination of proteomic data into publicly available databases is starting to overcome these hurdles. At present the most promising application for proteomics is in the screening of specific subsets of protein biomarkers for certain diseases, rather than large scale full protein profiling. Armed with these technologies the impending era of individualised patient-tailored therapy is imminent. This review summarises the advances in proteomics that has propelled us to this exciting age of clinical proteomics, and highlights the future work that is required for this to become a reality.     

Identification and analysis of signaling networks potentially involved in breast carcinoma metastasis to the brain

Brain is a common site of breast cancer metastasis associated with significant neurologic morbidity, decreased quality of life, and greatly shortened survival. However, the molecular and cellular mechanisms underpinning brain colonization by breast carcinoma cells are poorly understood. Here, we used 2D-DIGE (Difference in Gel Electrophoresis) proteomic analysis followed by LC-tandem mass spectrometry to identify the proteins differentially expressed in brain-targeting breast carcinoma cells (MB231-Br) compared with parental MDA-MB-231 cell line. Between the two cell lines, we identified 12 proteins consistently exhibiting greater than 2-fold (p<0.05) difference in expression, which were associated by the Ingenuity Pathway Analysis (IPA) with two major signaling networks involving TNFα/TGFβ-, NFκB-, HSP-70-, TP53-, and IFNγ-associated pathways. Remarkably, highly related networks were revealed by the IPA analysis of a list of 19 brain-metastasis-associated proteins identified recently by the group of Dr. A. Sierra using MDA-MB-435-based experimental system (Martin et al., J Proteome Res 2008 7:908-20), or a 17-gene classifier associated with breast cancer brain relapse reported by the group of Dr. J. Massague based on a microarray analysis of clinically annotated breast tumors from 368 patients (Bos et al., Nature 2009 459: 1005-9). These findings, showing that different experimental systems and approaches (2D-DIGE proteomics used on brain targeting cell lines or gene expression analysis of patient samples with documented brain relapse) yield highly related signaling networks, suggest strongly that these signaling networks could be essential for a successful colonization of the brain by metastatic breast carcinoma cells.     

蛋白质芯片SELDI-TOFMS技术的研究进展及其在临床中的应用

蛋白质芯片为新一代的蛋白质组研究技术,由美国Ciphergen生物系统公司引进,表面增强激光解吸电离-飞行时间质谱(SELDI-TOFMS)提供一个高通量和高灵敏度的检测平台。投放至今虽短短10来年,但卓越的成果已广为医学科学界重视,尤其在恶性肿瘤的早期诊断、监控和预后研究上。蛋白质是细胞内执行生物功能的最终分子,蛋白质组学研究让人类更深入了解疾病和生命的本源,不断发现的特异性肿瘤标志物更为攻克癌症带来新希望。这里除对表面增强激光解吸电离_飞行时间质谱作较详尽的介绍外,更重点阐述其近年来蛋白质芯片近期的研究进展和在临床中的应用,并就其优劣和发展前景作出评估。     

Modern Tumor Marker Discovery in Urology: Surface Enhanced Laser Desorption and Ionization (SELDI)

During the last two decades, biomarker research has benefited from the introduction of new proteomic analytical techniques. In this article, we review the application of surface enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectroscopy in urologic cancer research. After reviewing the literature from MEDLINE on proteomics and urologic oncology, we found that SELDI-TOF is an emerging proteomic technology in biomarker discovery that allows for rapid and sensitive analysis of complex protein mixtures. SELDI-TOF is a novel proteomic technology that has the potential to contribute further to the understanding and clinical exploitation of new, clinically relevant biomarkers.     

Proteomic analysis in human breast cancer: identification of a characteristic protein expression profile of malignant breast epithelium

Gene expression analysis has become a promising tool in predicting the clinical course of malignant disease and the response to antineoplastic therapy. Surprisingly, only little is known about the protein expression pattern of human tumors. Recent advances in proteomic analysis allow proteins of interest to be identified by their expression and/or modification pattern in 2-DE rather than using the traditional approach of translating gene expression data. To identify a proteomic pattern that is characteristic for malignant breast epithelium, we performed differential 2-DE analysis in sets of microdissected malignant breast epithelia and corresponding adjacent normal breast epithelia from five patients with invasive breast carcinoma. Thirty-two protein spots were found to be selectively regulated in malignant epithelium, and were subjected to MALDI-TOF and/or immunoblotting for protein identification. Thirteen of the identified proteins had previously not been associated with breast cancer. The validity of these findings was confirmed by literature review and immunohistochemistry for identified proteins in an independent cohort of 50 breast cancer specimens. We here describe, for the first time, a proteomic analysis of matched normal and malignant epithelia from invasive breast carcinomas. This strategy leads to a better understanding of oncogenesis at an operational level and helps to characterize the malignant phenotype of individual tumors, and thereby to identify novel targets for antineoplastic therapy.     

Clinical proteomics: revolutionizing disease detection and patient tailoring therapy

The evolving discipline of Clinical Proteomics is more than simply describing and enumerating the systematic changes in the protein constituency of a cell, or just generating lists of proteins that increase or decrease in expression as a cause or consequence of disease. Clinical applications of proteomics involve the use of proteomic technologies at the bedside with the ultimate goal to characterize the information flow through the intra- and extracellular molecular protein networks that interconnect organ and circulatory systems together. These networks are both new targets for therapeutics themselves as well as underpin the dynamic changes that give rise to cascades of new diagnostic biomarkers. The analysis of human cancer can be used as a model for how clinical proteomics is having an impact at the bedside for early detection, rational therapeutic targeting, and patient-tailored therapy.     

Cancer biomarker development and two-dimensional difference gel electrophoresis (2D-DIGE)

Cancer results from the accumulation of genomic alterations. As the genome is functionally translated to the proteome and regulates tumor cell behavior, proteomics studies are expected to further the current understanding of the molecular mechanisms underlying carcinogenesis and cancer progression. Biomarkers are potential tools to classify cancers for therapy, predict responses to treatments, and support treatment-related decision-making. Biomarker development has been actively pursued in oncology by proteomic approaches. Two-dimensional difference gel electrophoresis (2D-DIGE) is a proteomics technique based on two-dimensional polyacrylamide gel electrophoresis (2D-PAGE). In 2D-DIGE, protein samples are labeled with distinct fluorescent dyes before fractionation via 2D-PAGE. 2D-DIGE offers advantages to identify biomarker candidates, including reproducibility, high sensitivity, comprehensiveness, and high throughput. 2D-DIGE has contributed to the establishment of tissue biomarkers, which potentially facilitate precision medicine. 2D-DIGE is thus expected to yield major advancements in cancer biomarker identification and development.     

Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data

Background  

Recent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduction and classification are considerable challenges in statistical machine learning. We therefore propose a novel approach for dimensionality reduction and tested it using published high-resolution SELDI-TOF data for ovarian cancer.     

植物蛋白质组学研究若干重要进展

植物蛋白质组学近年来正从定性向精确定量蛋白质组学的方向发展。国际上近两年发表的约160篇研究论文报道了利用不断改进的双向电泳结合生物质谱技术、多维蛋白质鉴定技术,以及包括双向荧光差异凝胶电泳、幅N体内代谢标记、同位素标记的亲和标签、同位素标记相对和绝对定量等在内的第2代蛋白质组学技术,对植物组织（器官）与细胞器、植物发育过程和植物响应环境胁迫的蛋白质组特征,以及植物蛋白质翻译后修饰和蛋白质相互作用等方面的研究成果。该文对上述报道进行总结,综述了2007年以来植物蛋白质组学若干重要问题研究的新进展。     

植物蛋白质组学研究若干重要进展

植物蛋白质组学近年来正从定性向精确定量蛋白质组学的方向发展。国际上近两年发表的约160篇研究论文报道了利用不断改进的双向电泳结合生物质谱技术、多维蛋白质鉴定技术, 以及包括双向荧光差异凝胶电泳、15N体内代谢标记、同位素标记的亲和标签、同位素标记相对和绝对定量等在内的第2代蛋白质组学技术, 对植物组织(器官)与细胞器、植物发育过程和植物响应环境胁迫的蛋白质组特征, 以及植物蛋白质翻译后修饰和蛋白质相互作用等方面的研究成果。该文对上述报道进行总结, 综述了2007年以来植物蛋白质组学若干重要问题研究的新进展。     

Serum protein profile in systemic-onset juvenile idiopathic arthritis differentiates response versus nonresponse to therapy

Systemic-onset juvenile idiopathic arthritis (SJIA) is a disease of unknown etiology with an unpredictable response to treatment. We examined two groups of patients to determine whether there are serum protein profiles reflective of active disease and predictive of response to therapy. The first group (n = 8) responded to conventional therapy. The second group (n = 15) responded to an experimental antibody to the IL-6 receptor (MRA). Paired sera from each patient were analyzed before and after treatment, using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Despite the small number of patients, highly significant and consistent differences were observed before and after response to therapy in all patients. Of 282 spectral peaks identified, 23 had mean signal intensities significantly different (P < 0.001) before treatment and after response to treatment. The majority of these differences were observed regardless of whether patients responded to conventional therapy or to MRA. These peaks represent potential biomarkers of active disease. One such peak was identified as serum amyloid A, a known acute-phase reactant in SJIA, validating the SELDI-TOF MS platform as a useful technology in this context. Finally, profiles from serum samples obtained at the time of active disease were compared between the two patient groups. Nine peaks had mean signal intensities significantly different (P < 0.001) between active disease in patients who responded to conventional therapy and in patients who failed to respond, suggesting a possible profile predictive of response. Collectively, these data demonstrate the presence of serum proteomic profiles in SJIA that are reflective of active disease and suggest the feasibility of using the SELDI-TOF MS platform used as a tool for proteomic profiling and discovery of novel biomarkers in autoimmune diseases.     

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).