Reproducibility of mass spectrometry based protein profiles for diagnosis of ovarian cancer across clinical studies: A systematic review

The focus of this systematic review is to give an overview of the current status of clinical protein profiling studies using MALDI and SELDI MS platforms in the search for ovarian cancer biomarkers. A total of 34 profiling studies were qualified for inclusion in the review. Comparative analysis of published discriminatory peaks to peaks found in an original MALDI MS protein profiling study was made to address the key question of reproducibility across studies. An overlap was found despite substantial heterogeneity between studies relating to study design, biological material, pre-analytical treatment, and data analysis. About 47% of the peaks reported to be associated to ovarian cancer were also represented in our experimental study, and 34% of these redetected peaks also showed a significant difference between cases and controls in our study. Thus, despite known problems related to reproducibility an overlap in peaks between clinical studies was demonstrated, which indicate convergence toward a set of common discriminating, reproducible peaks for ovarian cancer. The potential of the discriminating protein peaks for clinical use as ovarian cancer biomarkers will be discussed and evaluated. This article is part of a Special Issue entitled: Proteomics: The clinical link.     

Combined experimental and statistical strategy for mass spectrometry based serum protein profiling for diagnosis of breast cancer: a case-control study

Serum protein profiling by mass spectrometry is a promising method for early detection of cancer. We have implemented a combined strategy based on matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) and statistical data analysis for serum protein profiling and applied it in a well-described breast cancer case-control study. A rigorous sample collection protocol ensured high quality specimen and reduced bias from preanalytical factors. Preoperative serum samples obtained from 48 breast cancer patients and 28 controls were used to generate MALDI MS protein profiles. A total of nine mass spectrometric protein profiles were obtained for each serum sample. A total of 533 common peaks were defined and represented a 'reference protein profile'. Among these 533 common peaks, we identified 72 peaks exhibiting statistically significant intensity differences ( p < 0.01) between cases and controls. A diagnostic rule based on these 72 mass values was constructed and exhibited a cross-validated sensitivity and specificity of approximately 85% for the detection of breast cancer. With this method, it was possible to distinguish early stage cancers from controls without major loss of sensitivity and specificity. We conclude that optimized serum sample handling and mass spectrometry data acquisition strategies in combination with statistical analysis provide a viable platform for serum protein profiling in cancer diagnosis.     

Dampening Enthusiasm for Circulating MicroRNA in Breast Cancer

Genome-wide platforms for high-throughput profiling of circulating miRNA (oligoarray or miR-Seq) offer enormous promise for agnostic discovery of circulating miRNA biomarkers as a pathway for development in breast cancer detection. By harmonizing data from 15 previous reports, we found widespread inconsistencies across prior studies. Whether this arises from differences in study design, such as sample source or profiling platform, is unclear. As a reproducibility experiment, we generated a genome-wide plasma miRNA dataset using the Illumina oligoarray and compared this to a publically available dataset generated using an identical sample size, substrate and profiling platform. Samples from 20 breast cancer patients, 20 mammography-screened controls, as well as 20 breast cancer patients after surgical resection and 10 female lung or colorectal cancer patients were included. After filtering for miRNAs derived from blood cells, and for low abundance miRNAs (non-detectable in over 10% of samples), a set of 522 plasma miRNAs remained, of which 46 were found to be differentially expressed between breast cancer patients and healthy controls (p<0.05), of which only 3 normalized to baseline levels in post-resection cases and were unique to breast cancer vs. lung or colorectal cancer (miR-708*, miR-92b* and miR-568, none previously reported). We were unable to demonstrate reproducibility by various measures between the two datasets. This finding, along with widespread inconsistencies across prior studies, highlight the need for better understanding of factors influencing circulating miRNA levels as prerequisites to progress in this area of translational research.     

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.     

A well‐characterised peak identification list of MALDI MS profile peaks for human blood serum

MALDI MS profiling, using easily available body fluids such as blood serum, has attracted considerable interest for its potential in clinical applications. Despite the numerous reports on MALDI MS profiling of human serum, there is only scarce information on the identity of the species making up these profiles, particularly in the mass range of larger peptides. Here, we provide a list of more than 90 entries of MALDI MS profile peak identities up to 10 kDa obtained from human blood serum. Various modifications such as phosphorylation were detected among the peptide identifications. The overlap with the few other MALDI MS peak lists published so far was found to be limited and hence our list significantly extends the number of identified peaks commonly found in MALDI MS profiling of human blood serum.     

Intact protein profiling in breast cancer biomarker discovery: Protein identification issue and the solutions based on 3D protein separation,bottom‐up and top‐down mass spectrometry

Proteomics profiling of intact proteins based on MALDI‐TOF MS and derived platforms has been used in cancer biomarker discovery studies. This approach suffers from a number of limitations such as low resolution, low sensitivity, and that no knowledge is available on the identity of the respective proteins in the discovery mode. Nevertheless, it remains the most high‐throughput, untargeted mode of clinical proteomics studies to date. Here we compare key protein separation and MS techniques available for protein biomarker identification in this type of studies and define reasons of uncertainty in protein peak identity. As a result of critical data analysis, we consider 3D protein separation and identification workflows as optimal procedures. Subsequently, we present a new protocol based on 3D LC‐MS/MS with top‐down at high resolution that enabled the identification of HNRNP A2/B1 intact peptide as correlating with the estrogen receptor expression in breast cancer tissues. Additional development of this general concept toward next generation, top‐down based protein profiling at high resolution is discussed.     

Breast tumor microenvironment: proteomics highlights the treatments targeting secretome

Tumor secreted substances (secretome), including extracellular matrix (ECM) components, act as mediators of tumor-host communication in the breast tumor microenvironment. Proteomic analysis has emphasized the value of the secretome as a source of prospective markers and drug targets for the treatment of breast cancers. Utilizing bioinformatics, our recent studies revealed global changes in protein expression after the activation of ECM-mediated signaling in breast cancer cells. A newly designed technique integrating a capillary ultrafiltration (CUF) probe with mass spectrometry was demonstrated to dynamically sample and identify in vivo and pure secretome from the tumor microenvironment. Such in vivo profiling of breast cancer secretomes may facilitate the development of novel drugs specifically targeting secretome.     

Quality control and quality assessment of data from surface-enhanced laser desorption/ionization (SELDI) time-of flight (TOF) mass spectrometry (MS)

Background

Proteomic profiling of complex biological mixtures by the ProteinChip technology of surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry (MS) is one of the most promising approaches in toxicological, biological, and clinic research. The reliable identification of protein expression patterns and associated protein biomarkers that differentiate disease from health or that distinguish different stages of a disease depends on developing methods for assessing the quality of SELDI-TOF mass spectra. The use of SELDI data for biomarker identification requires application of rigorous procedures to detect and discard low quality spectra prior to data analysis.

Results

The systematic variability from plates, chips, and spot positions in SELDI experiments was evaluated using biological and technical replicates. Systematic biases on plates, chips, and spots were not found. The reproducibility of SELDI experiments was demonstrated by examining the resulting low coefficient of variances of five peaks presented in all 144 spectra from quality control samples that were loaded randomly on different spots in the chips of six bioprocessor plates. We developed a method to detect and discard low quality spectra prior to proteomic profiling data analysis, which uses a correlation matrix to measure the similarities among SELDI mass spectra obtained from similar biological samples. Application of the correlation matrix to our SELDI data for liver cancer and liver toxicity study and myeloma-associated lytic bone disease study confirmed this approach as an efficient and reliable method for detecting low quality spectra.

Conclusion

This report provides evidence that systematic variability between plates, chips, and spots on which the samples were assayed using SELDI based proteomic procedures did not exist. The reproducibility of experiments in our studies was demonstrated to be acceptable and the profiling data for subsequent data analysis are reliable. Correlation matrix was developed as a quality control tool to detect and discard low quality spectra prior to data analysis. It proved to be a reliable method to measure the similarities among SELDI mass spectra and can be used for quality control to decrease noise in proteomic profiling data prior to data analysis.

     

Characterizing the reproducibility of a protein profiling method for the analysis of mouse bronchoalveolar lavage fluid

The detection of biomarkers in biological fluids has been advanced by the introduction of mass spectrometry screening methods such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS), which enables the detection of the presence and the molecular mass of proteins in unfractionated mixtures. The generation of reproducible mass spectra over the course of an experiment is vital in obtaining data in which differences in protein profiles between diseased and healthy states can be assessed correctly. We have developed a protocol to automate the collection of protein profiling data from a large number of samples using MALDI-TOFMS, and we used these samples to characterize the technical reproducibility of the method. This protocol has been used for the analysis of proteins found in bronchoalveolar lavage fluid samples from mice with the ultimate goal of enabling the discovery of differential expression patterns predictive of the development of chronic obstructive pulmonary disease. Samples were purified using magnetic bead-based technology and analyzed on an AnchorChip target plate. Our results demonstrate that the number of peaks detected reproducibly decreases significantly as sample size increases, which motivates the need for technical replicates to be explicitly included in the analysis of MALDI-TOF-based protein profiling studies.     

Discovery of pathway biomarkers from coupled proteomics and systems biology methods

Background

Breast cancer is worldwide the second most common type of cancer after lung cancer. Plasma proteome profiling may have a higher chance to identify protein changes between plasma samples such as normal and breast cancer tissues. Breast cancer cell lines have long been used by researches as model system for identifying protein biomarkers. A comparison of the set of proteins which change in plasma with previously published findings from proteomic analysis of human breast cancer cell lines may identify with a higher confidence a subset of candidate protein biomarker.

Results

In this study, we analyzed a liquid chromatography (LC) coupled tandem mass spectrometry (MS/MS) proteomics dataset from plasma samples of 40 healthy women and 40 women diagnosed with breast cancer. Using a two-sample t-statistics and permutation procedure, we identified 254 statistically significant, differentially expressed proteins, among which 208 are over-expressed and 46 are under-expressed in breast cancer plasma. We validated this result against previously published proteomic results of human breast cancer cell lines and signaling pathways to derive 25 candidate protein biomarkers in a panel. Using the pathway analysis, we observed that the 25 “activated” plasma proteins were present in several cancer pathways, including ‘Complement and coagulation cascades’, ‘Regulation of actin cytoskeleton’, and ‘Focal adhesion’, and match well with previously reported studies. Additional gene ontology analysis of the 25 proteins also showed that cellular metabolic process and response to external stimulus (especially proteolysis and acute inflammatory response) were enriched functional annotations of the proteins identified in the breast cancer plasma samples. By cross-validation using two additional proteomics studies, we obtained 86% and 83% similarities in pathway-protein matrix between the first study and the two testing studies, which is much better than the similarity we measured with proteins.

Conclusions

We presented a ‘systems biology’ method to identify, characterize, analyze and validate panel biomarkers in breast cancer proteomics data, which includes 1) t statistics and permutation process, 2) network, pathway and function annotation analysis, and 3) cross-validation of multiple studies. Our results showed that the systems biology approach is essential to the understanding molecular mechanisms of panel protein biomarkers.

     

Using the protein chip interface with quadrupole time‐of‐flight mass spectrometry to directly identify peaks in SELDI profiles – initial evaluation using low molecular weight serum peaks

Mass spectrometric profiling, particularly in the form of SELDI, has been used in many studies, particularly in attempts to generate diagnostic serum profiles. Several studies have generated promising results but one of the limitations is the inability to identify easily potential discriminatory peaks. This may enable specific assays to be developed and increased biological insight. We describe the first systematic technical evaluation of the ProteinChip interface coupled to a tandem mass spectrometer which allows direct sequencing of peptides <6000 Da, and describe the direct sequence identification of 21 peaks commonly observed in serum samples. Additionally we describe for the first time the use of on‐chip acetylation to assist in the validation of sequence identification.     

Mitochondrial proteomics analysis of tumorigenic and metastatic breast cancer markers

Mitochondria are key organelles in mammary cells responsible for several cellular functions including growth, division, and energy metabolism. In this study, mitochondrial proteins were enriched for proteomics analysis with the state-of-the-art two-dimensional differential gel electrophoresis and matrix-assistant laser desorption ionization-time-of-flight mass spectrometry strategy to compare and identify the mitochondrial protein profiling changes between three breast cell lines with different tumorigenicity and metastasis. The proteomics results demonstrate more than 1,500 protein features were resolved from the equal amount pooled from three purified mitochondrial proteins, and 125 differentially expressed spots were identified by their peptide finger print, in which, 33 identified proteins belonged to mitochondrial proteins. Eighteen out of these 33 identified mitochondrial proteins such as SCaMC-1 have not been reported in breast cancer research to our knowledge. Additionally, mitochondrial protein prohibitin has shown to be differentially distributed in mitochondria and in nucleus for normal breast cells and breast cancer cell lines, respectively. To sum up, our approach to identify the mitochondrial proteins in various stages of breast cancer progression and the identified proteins may be further evaluated as potential breast cancer markers in prognosis and therapy.     

Lectin capture strategies combined with mass spectrometry for the discovery of serum glycoprotein biomarkers

The application of mass spectrometry to identify disease biomarkers in clinical fluids like serum using high throughput protein expression profiling continues to evolve as technology development, clinical study design, and bioinformatics improve. Previous protein expression profiling studies have offered needed insight into issues of technical reproducibility, instrument calibration, sample preparation, study design, and supervised bioinformatic data analysis. In this overview, new strategies to increase the utility of protein expression profiling for clinical biomarker assay development are discussed with an emphasis on utilizing differential lectin-based glycoprotein capture and targeted immunoassays. The carbohydrate binding specificities of different lectins offer a biological affinity approach that complements existing mass spectrometer capabilities and retains automated throughput options. Specific examples using serum samples from prostate cancer and hepatocellular carcinoma subjects are provided along with suggested experimental strategies for integration of lectin-based methods into clinical fluid expression profiling strategies. Our example workflow incorporates the necessity of early validation in biomarker discovery using an immunoaffinity-based targeted analytical approach that integrates well with upstream discovery technologies.     

Two-dimensional electrophoretic proteome study of serum thermostable fraction from patients with various tumor conditions

One of the problems of plasma proteomics is a presence of large major components. In this work, we use the thermostable fraction as a way to deplete these major proteins. The thermostable fraction of serum samples from patients with ovarian, uterus, and breast cancers and benign ovarian tumor was analyzed using two-dimensional electrophoresis combined with MALDI-TOF(-TOF)-mass spectrometry. Of them, alpha-1-acid glycoprotein and clusterin are expressly down-regulated in breast cancer, whereas transthyretin is decreased specifically in ovarian cancer. Apolipoprotein A-I forms have decreased spot volumes, while haptoglobin alpha1, in contrast, is elevated in several tumors. These data are partly consistent with previous art studies on cancer proteomics, which involve mass-spectrometry-based serum profiling techniques. Serum thermostable fraction may be recommended as a good tool for medium and small protein proteome investigation, in particular, by 2D-electrophoresis.     

Searching for consistently reported up- and down-regulated biomarkers in colorectal cancer: a systematic review of proteomic studies

The cumulative lifetime risk for the development of colorectal cancer in the general population is 6 %. In many cases, early detection by fecal occult blood test is limited regarding sensitivity. Therefore, there is an urgent need for improved diagnostic tests in colorectal cancer. The recent development of high-throughput molecular analytic techniques should allow the rapid evaluation of new diagnostic markers. However, researchers are faced with an overwhelming number of potential markers form numerous colorectal cancer protein expression profiling studies. To address the challenge, we have carried out a comprehensive systematic review of colorectal cancer biomarkers from 13 published studies that compared the protein expression profiles of colorectal cancer and normal tissues. A protein ranking system that considers the number of comparisons in agreement, total sample sizes, average fold-change and direction of differential expression was devised. We observed that some proteins were consistently reported by multiple studies as differentially expressed with a statistically significant frequency (P < 0.05) in cancer versus normal tissues comparison. Our systematic review method identified proteins that were consistently reported as differentially expressed. A review of the top four candidates revealed proteins described previously as having diagnostic value as well as novel candidate biomarkers. These candidates should help to develop a panel of biomarkers with sufficient sensitivity and specificity for the diagnosis of colorectal cancer in a clinical setting.     

重组C8orf32蛋白的表达、纯化及抗体制备

C8orf32是一种功能未知基因,其mRNA含量在乳腺癌组织中显著高于正常乳腺组织。将其开放式阅读框插入pGEX-6P1原核表达载体T7启动子控制下的GST编码基因下游,构建了C8orf32蛋白表达质粒pGEX-6P-C8。表达质粒转化入大肠杆菌BL21(DE3)菌株,经IPTG诱导,成功表达了GST- C8orf32融合蛋白。经带有GST标签的位点特异性蛋白酶切割除去GST-C8orf32融合蛋白的GST标签,获得了N端带有8个多余氨基酸残基的C8orf32蛋白,蛋白纯度为95%左右。N端氨基酸序列分析表明该蛋白N端氨基酸序列正确,质谱鉴定进一步证明所表达C8orf32蛋白的正确性。用制备的C8orf32蛋白免疫新西兰白兔,获得了能够正确识别C8orf32蛋白的抗血清。该蛋白及其抗体的成功制备,为进一步研究C8orf32蛋白的结构功能和体内分布打下了基础。     

Alternative profiling platform based on MELDI and its applicability in clinical proteomics

The presence of numerous proteomics data and their results in literature reveal the importance and influence of proteins and peptides on human cell cycle. For instance, the proteomic profiling of biological samples, such as serum, plasma or cells, and their organelles, carried out by surface-enhanced laser desorption/ionization mass spectrometry, has led to the discovery of numerous key proteins involved in many biological disease processes. However, questions still remain regarding the reproducibility, bioinformatic artifacts and cross-validations of such experimental set-ups. The authors have developed a material-based approach, termed material-enhanced laser desorption/ionization mass spectrometry (MELDI-MS), to facilitate and improve the robustness of large-scale proteomic experiments. MELDI-MS includes a fully automated protein-profiling platform, from sample preparation and analysis to data processing involving state-of-the-art methods, which can be further improved. Multiplexed protein pattern analysis, based on material morphology, physical characteristics and chemical functionalities provides a multitude of protein patterns and allows prostate cancer samples to be distinguished from non-prostate cancer samples. Furthermore, MELDI-MS enables not only the analysis of protein signatures, but also the identification of potential discriminating peaks via capillary liquid chromatography mass spectrometry. The optimized MELDI approach offers a complete proteomics platform with improved sensitivity, selectivity and short sample preparation times.     

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.     

Alternative profiling platform based on MELDI and its applicability in clinical proteomics

The presence of numerous proteomics data and their results in literature reveal the importance and influence of proteins and peptides on human cell cycle. For instance, the proteomic profiling of biological samples, such as serum, plasma or cells, and their organelles, carried out by surface-enhanced laser desorption/ionization mass spectrometry, has led to the discovery of numerous key proteins involved in many biological disease processes. However, questions still remain regarding the reproducibility, bioinformatic artifacts and cross-validations of such experimental set-ups. The authors have developed a material-based approach, termed material-enhanced laser desorption/ionization mass spectrometry (MELDI-MS), to facilitate and improve the robustness of large-scale proteomic experiments. MELDI-MS includes a fully automated protein-profiling platform, from sample preparation and analysis to data processing involving state-of-the-art methods, which can be further improved. Multiplexed protein pattern analysis, based on material morphology, physical characteristics and chemical functionalities provides a multitude of protein patterns and allows prostate cancer samples to be distinguished from non-prostate cancer samples. Furthermore, MELDI-MS enables not only the analysis of protein signatures, but also the identification of potential discriminating peaks via capillary liquid chromatography mass spectrometry. The optimized MELDI approach offers a complete proteomics platform with improved sensitivity, selectivity and short sample preparation times.