Mass Spectrometry: Uncovering the Cancer Proteome for Diagnostics

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

Label-free LC-MS/MS proteomics analyses reveal CLIC1 as a predictive biomarker for bladder cancer staging and prognosis

Introduction

Bladder cancer (BC) is a significant carcinoma of the urinary system that has a high incidence of morbidity and death owing to the challenges in accurately identifying people with early-stage BC and the lack of effective treatment options for those with advanced BC. Thus, there is a need to define new markers of prognosis and prediction.

Methods

In this study, we have performed a comprehensive proteomics experiment by label-free quantitative proteomics to compare the proteome changes in the serum of normal people and bladder cancer patients-the successful quantification of 2064 Quantifiable proteins in total. A quantitative analysis was conducted to determine the extent of changes in protein species' relative intensity and reproducibility. There were 43 upregulated proteins and 36 downregulated proteins discovered in non-muscle invasive bladder cancer and normal individuals. Sixty-four of these proteins were elevated, and 51 were downregulated in muscle-invasive and non-muscle-invasive bladder cancer, respectively. Functional roles of differentially expressed proteins were annotated using Gene Ontology (GO) and Clusters of Orthologous Groups of Proteins (COG). To analyze the functions and pathways enriched by differentially expressed proteins, GO enrichment analysis, protein domain analysis, and KEGG pathway analysis were performed. The proteome differences were examined and visualized using radar plots, heat maps, bubble plots, and Venn diagrams.

Results

As a result of combining the Venn diagram with protein-protein interactions (PPIs), Chloride intracellular channel 1 (CLIC1) was identified as the primary protein. Using the Gene Set Cancer Analysis (GSCA) website, the influence of CLIC1 on immune infiltration was analyzed. A negative correlation between CD8 naive and CLIC1 levels was found. For validation, immunohistochemical (IHC), qPCR, and western blotting (WB) were performed.Further, we found that CLIC1 was associated with a poor prognosis of bladder cancer in survival analysis.

Discussion

Our research screened CLIC1 as a tumor-promoting protein in bladder cancer for the first time using serum mass spectrometry. And CLIC1 associated with tumor stage, and immune infiltrate. The prognostic biomarker and therapeutic target CLIC1 may be new for bladder cancer patients.

Mass spectrometry applied to the identification of Mycobacterium tuberculosis and biomarker discovery

An adequate and effective tuberculosis (TB) diagnosis system has been identified by the World Health Organization as a priority in the fight against this disease. Over the years, several methods have been developed to identify the bacillus, but bacterial culture remains one of the most affordable methods for most countries. For rapid and accurate identification, however, it is more feasible to implement molecular techniques, taking advantage of the availability of public databases containing protein sequences. Mass spectrometry (MS) has become an interesting technique for the identification of TB. Here, we review some of the most widely employed methods for identifying Mycobacterium tuberculosis and present an update on MS applied for the identification of mycobacterial species.

Polarity switching mass spectrometry imaging of healthy and cancerous hen ovarian tissue sections by infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI)

Mass spectrometry imaging (MSI) is a rapidly evolving field for monitoring the spatial distribution and abundance of analytes in biological tissue sections. It allows for direct and simultaneous analysis of hundreds of different compounds in a label-free manner. In order to obtain a comprehensive metabolite and lipid data, a polarity switching MSI method using infrared matrix assisted laser desorption electrospray ionization (IR-MALDESI) was developed and optimized where the electrospray polarity was alternated from one voxel to the next. Healthy and cancerous ovarian hen tissue sections were analyzed using this method. Distribution and relative abundance of different metabolites and lipids within each tissue section were discerned, and differences between the two were revealed. Additionally, the utility of using mass spectrometry concepts such as spectral accuracy and sulfur counting for confident identification of analytes in an untargeted method are discussed.

MALDI imaging mass spectrometry - From bench to bedside

Today, pathologists face many challenges in defining the precise morphomolecular diagnosis and in guiding clinicians to the optimal patients' treatment. To achieve this goal, increasingly, classical histomorphological methods have to be supplemented by high throughput molecular assays. Since MALDI imaging mass spectrometry (IMS) enables the assessment of spatial molecular arrangements in tissue sections, it goes far beyond microscopy in providing hundreds of different molecular images from a single scan without the need of target-specific reagents. Thus, this technology has the potential to uncover new markers for diagnostic purposes or markers that correlate with disease severity as well as prognosis and therapeutic response. Additionally, in the future MALDI IMS based classifiers measured with this technology in real time in the diagnostic setting might be applicable in the routine diagnostic setting. In this review, recently published studies that show the usefulness, advantages, and applicability of MALDI IMS in different fields of pathology (diagnosis, prognosis and treatment response) are highlighted. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

CAFÉ-Map: Context Aware Feature Mapping for mining high dimensional biomedical data

Feature selection and ranking is of great importance in the analysis of biomedical data. In addition to reducing the number of features used in classification or other machine learning tasks, it allows us to extract meaningful biological and medical information from a machine learning model. Most existing approaches in this domain do not directly model the fact that the relative importance of features can be different in different regions of the feature space. In this work, we present a context aware feature ranking algorithm called CAFÉ-Map. CAFÉ-Map is a locally linear feature ranking framework that allows recognition of important features in any given region of the feature space or for any individual example. This allows for simultaneous classification and feature ranking in an interpretable manner. We have benchmarked CAFÉ-Map on a number of toy and real world biomedical data sets. Our comparative study with a number of published methods shows that CAFÉ-Map achieves better accuracies on these data sets. The top ranking features obtained through CAFÉ-Map in a gene profiling study correlate very well with the importance of different genes reported in the literature. Furthermore, CAFÉ-Map provides a more in-depth analysis of feature ranking at the level of individual examples.

AVAILABILITY

CAFÉ-Map Python code is available at: http://faculty.pieas.edu.pk/fayyaz/software.html#cafemap . The CAFÉ-Map package supports parallelization and sparse data and provides example scripts for classification. This code can be used to reconstruct the results given in this paper.

Antibody-based proteomics and biomarker research - current status and limitations

Antibody-based proteomics play a very important role in biomarker discovery and validation, facilitating the high-throughput evaluation of candidate markers. Most proteomics-driven discovery is nowadays based on the use of MS. MS has many advantages, including its suitability for hypothesis-free biomarker discovery, since information on protein content of a sample is not required prior to analysis. However, MS presents one main caveat which is the limited sensitivity in complex samples, especially for body fluids, where protein expression covers a huge dynamic range. Antibody-based technologies remain the main solution to address this challenge since they reach higher sensitivity. In this article, we review the benefits and limitations of antibody-based proteomics in preclinical and clinical biomarker research for discovery and validation in body fluids and tissue. The combination of antibodies and MS, utilizing the best of both worlds, opens new avenues in biomarker research.

Co-registration of multi-modality imaging allows for comprehensive analysis of tumor-induced bone disease

Bone metastases are a clinically significant problem that arises in approximately 70% of metastatic breast cancer patients. Once established in the bone, tumor cells induce changes in the bone microenvironment that lead to bone destruction, pain, and significant morbidity. While much is known about the later stages of bone disease, less is known about the earlier stages or the changes in protein expression in the tumor micro-environment. Due to promising results of combining magnetic resonance imaging (MRI) and Matrix-Assisted Laser Desorption/Ionization Imaging Mass Spectrometry (MALDI IMS) ion images in the brain, we developed methods for applying these modalities to models of tumor-induced bone disease in order to better understand the changes in protein expression that occur within the tumor-bone microenvironment. Specifically, we integrated 3-dimensional-volume reconstructions of spatially resolved MALDI IMS with high-resolution anatomical and diffusion weighted MRI data and histology in an intratibial model of breast tumor-induced bone disease. This approach enables us to analyze proteomic profiles from MALDI IMS data with corresponding in vivo imaging and ex vivo histology data. To the best of our knowledge, this is the first time that these three modalities have been rigorously registered in the bone. The MALDI mass-to-charge ratio peaks indicate differential expression of calcyclin, ubiquitin, and other proteins within the tumor cells, while peaks corresponding to hemoglobin A and calgranulin A provided molecular information that aided in the identification of areas rich in red and white blood cells, respectively. This multi-modality approach will allow us to comprehensively understand the bone-tumor microenvironment and thus may allow us to better develop and test approaches for inhibiting bone metastases.

Keratin 8 is involved in hepatitis B virus replication

Hepatitis B virus (HBV) infection can result in fatal liver diseases, including cirrhosis or liver failure, and its replication and pathogenesis depend on the critical interplay between viral and host factors. This study investigated HBV replication-related host proteins and the effect of candidate proteins on HBV replication. Isobaric tags for relative and absolute quantitation (iTRAQ) were used to measure HBV replication-related proteins in HepG2 cells and HepG2.2.15 cells. KRT8 was up-regulated in HepG2.2.15 cells but not in HepG2 cells, and KRT8 was overexpressed in an HBV-infected patient's liver tissue. This result suggested that KRT8 is involved in HBV replication. To further clarify the relationship between KRT8 and HBV replication, KRT8 gene expression was inhibited by siRNA. The silencing of KRT8 mildly suppressed HBV replication. Moreover, overexpressed KRT8 significantly increased HBV replication, and the inhibition of HBV DNA did not suppress KRT8 expression. Thus, the host protein KRT8 is involved in the replication of HBV DNA, and it dramatically enhances HBV replication.

Better cancer biomarker discovery through better study design

BACKGROUND

High-throughput laboratory technologies coupled with sophisticated bioinformatics algorithms have tremendous potential for discovering novel biomarkers, or profiles of biomarkers, that could serve as predictors of disease risk, response to treatment or prognosis. We discuss methodological issues in wedding high-throughput approaches for biomarker discovery with the case-control study designs typically used in biomarker discovery studies, especially focusing on nested case-control designs.

METHODS

We review principles for nested case-control study design in relation to biomarker discovery studies and describe how the efficiency of biomarker discovery can be effected by study design choices. We develop a simulated prostate cancer cohort data set and a series of biomarker discovery case-control studies nested within the cohort to illustrate how study design choices can influence biomarker discovery process.

RESULT

Common elements of nested case-control design, incidence density sampling and matching of controls to cases are not typically factored correctly into biomarker discovery analyses, inducing bias in the discovery process. We illustrate how incidence density sampling and matching of controls to cases reduce the apparent specificity of truly valid biomarkers 'discovered' in a nested case-control study. We also propose and demonstrate a new case-control matching protocol, we call 'antimatching', that improves the efficiency of biomarker discovery studies.

CONCLUSIONS

For a valid, but as yet undiscovered, biomarker(s) disjunctions between correctly designed epidemiologic studies and the practice of biomarker discovery reduce the likelihood that true biomarker(s) will be discovered and increases the false-positive discovery rate.

Imaging mass spectrometry in biomarker discovery and validation

Biomarker discovery and validation involves the consideration of many issues and challenges in order to be effectively used for translation from bench to bedside. Imaging mass spectrometry (IMS) is a new technology to assess spatial molecular arrangements in tissue sections, going far beyond microscopy in providing hundreds of different molecular images from a single scan without the need of target-specific reagents. The possibility to correlate distribution maps of multiple analytes with histological and clinical features makes it an ideal tool to discover diagnostic and prognostic markers of diseases. Some recently published studies that show the usefulness and advantages of this technology in the field of cancer research are highlighted.

Precision profiling and identification of human serum peptides using Fourier transform ion cyclotron resonance mass spectrometry

Many biomarker discovery studies are based on matrix-assisted laser desorption/ionisation (MALDI) peptide profiles. In this study, 96 human serum samples were analysed on a Bruker solariX(TM) MALDI Fourier transform ion cyclotron resonance (FTICR) system equipped with a 15 tesla magnet. Isotopically resolved peptides were observed in ultrahigh resolution FTICR profiles up to m/z 6500 with mass measurement errors (MMEs) of previously identified peptides at a sub-ppm level. For comparison with our previous platform for peptide profile mass analysis (i.e. Ultraflex II) the corresponding time-of-flight (TOF) spectra were obtained with isotopically resolved peptides up to m/z 3500. The FTICR and TOF systems performed rather similar with respect to the repeatability of the signal intensities. However, the mass measurement precision improved at least 10-fold in ultrahigh resolution data and thus simplified spectral alignment necessary for robust and quantitatively precise comparisons of profiles in large-scale clinical studies. From each single MALDI-FTICR spectrum an m/z-list was obtained with sub-ppm precision for all different species, which is beneficial for identification purposes and interlaboratory comparisons. Furthermore, the FTICR system allowed new peptide identifications from collision-induced dissociation (CID) spectra using direct infusion of reversed-phase (RP) C(18)-fractionated serum samples on an electrospray ionisation (ESI) source.

Identification of serum biomarkers for ovarian cancer using MALDI-TOF-MS combined with magnetic beads

BACKGROUND

The objective of this study was to search for potential protein biomarkers in serum for diagnosis of ovarian cancer, by use of proteomic fingerprint techniques.

METHOD

MALDI-TOF-MS was combined with magnetic beads to profile and compare serum protein spectra from 40 ovarian cancer patients and from 60 healthy controls.

RESULTS

The tree analysis model of potential cancer biomarkers was constructed with Biomarker Patterns software on the basis of three identified biomarkers (5486, 6440, and 13720 Da), resulting in excellent discrimination between the ovarian cancer and non-cancer in our tests. The sensitivity was 90% and the specificity was 86.7%. In a blind test the sensitivity was 88% and the specificity was 83.3%.

CONCLUSION

The results suggested that biomarkers for ovarian cancer diagnosis in serum could be identified by MALDI-TOF-MS combined with the use of magnetic beads. The use of combined biomarkers would further enable powerful and reliable diagnosis of ovarian cancer with high sensitivity and specificity.

Drug metabolite profiling and identification by high-resolution mass spectrometry

Mass spectrometry plays a key role in drug metabolite identification, an integral part of drug discovery and development. The development of high-resolution (HR) MS instrumentation with improved accuracy and stability, along with new data processing techniques, has improved the quality and productivity of metabolite identification processes. In this minireview, HR-MS-based targeted and non-targeted acquisition methods and data mining techniques (e.g. mass defect, product ion, and isotope pattern filters and background subtraction) that facilitate metabolite identification are examined. Methods are presented that enable multiple metabolite identification tasks with a single LC/HR-MS platform and/or analysis. Also, application of HR-MS-based strategies to key metabolite identification activities and future developments in the field are discussed.

Tissue and serum proteomic profiling for diagnostic and prognostic bladder cancer biomarkers

A panel of biomarkers for the early detection of bladder cancer has not yet been identified. Many different molecules, including DNA, RNA or proteins have been reported but none have provided adequate sensitivity for a single-tier screening test or a test to replace cystoscopy. Therefore, multimarker panels are discussed at present to give a more-precise answer to the biomarker quest. Mass spectrometry or 2D gel-electrophoresis have evolved greatly within recent years and are capable of analyzing multiple proteins or peptides in parallel with high sensitivity and specificity. However, transmission of screening results from one laboratory to another is still the main pitfall of those methods; a fact that emphasizes the need for consistent and standardized procedures as suggested by the Human Proteome Organization (HUPO). In this article, recent results in screening approaches and other proteomic techniques used for biomarker evaluation in bladder cancer are discussed with a focus on serum and tissue biomarkers.

Identification of novel molecular targets for endometrial cancer using a drill-down LC-MS/MS approach with iTRAQ

Background

The number of patients with endometrial carcinoma (EmCa) with advanced stage or high histological grade is increasing and prognosis has not improved for over the last decade. There is an urgent need for the discovery of novel molecular targets for diagnosis, prognosis and treatment of EmCa, which will have the potential to improve the clinical strategy and outcome of this disease.

Methodology and Results

We used a “drill-down” proteomics approach to facilitate the identification of novel molecular targets for diagnosis, prognosis and/or therapeutic intervention for EmCa. Based on peptide ions identified and their retention times in the first LC-MS/MS analysis, an exclusion list was generated for subsequent iterations. A total of 1529 proteins have been identified below the Proteinpilot® 5% error threshold from the seven sets of iTRAQ experiments performed. On average, the second iteration added 78% new peptides to those identified after the first run, while the third iteration added 36% additional peptides. Of the 1529 proteins identified, only 40 satisfied our criteria for significant differential expression in EmCa in comparison to normal proliferative tissues. These proteins included metabolic enzymes (pyruvate kinase M2 and lactate dehydrogenase A); calcium binding proteins (S100A6, calcyphosine and calumenin), and proteins involved in regulating inflammation, proliferation and invasion (annexin A1, interleukin enhancer-binding factor 3, alpha-1-antitrypsin, macrophage capping protein and cathepsin B). Network analyses revealed regulation of these molecular targets by c-myc, Her2/neu and TNF alpha, suggesting intervention with these pathways may be a promising strategy for the development of novel molecular targeted therapies for EmCa.

Conclusions

Our analyses revealed the significance of drill-down proteomics approach in combination with iTRAQ to overcome some of the limitations of current proteomics strategies. This study led to the identification of a number of novel molecular targets having therapeutic potential for targeted molecular therapies for endometrial carcinoma.

MALDI imaging mass spectrometry--painting molecular pictures

MALDI Imaging Mass Spectrometry is a molecular analytical technology capable of simultaneously measuring multiple analytes directly from intact tissue sections. Histological features within the sample can be correlated with molecular species without the need for target-specific reagents such as antibodies. Several studies have demonstrated the strength of the technology for uncovering new markers that correlate with disease severity as well as prognosis and therapeutic response. This review describes technological aspects of imaging mass spectrometry together with applications in cancer research.

Personalized medicine: marking a new epoch in cancer patient management

Personalized medicine (PM) is defined as "a form of medicine that uses information about a person's genes, proteins, and environment to prevent, diagnose, and treat disease." The promise of PM has been on us for years. The suite of clinical applications of PM in cancer is broad, encompassing screening, diagnosis, prognosis, prediction of treatment efficacy, patient follow-up after surgery for early detection of recurrence, and the stratification of patients into cancer subgroup categories, allowing for individualized therapy. PM aims to eliminate the "one size fits all" model of medicine, which has centered on reaction to disease based on average responses to care. By dividing patients into unique cancer subgroups, treatment and follow-up can be tailored for each individual according to disease aggressiveness and the ability to respond to a certain treatment. PM is also shifting the emphasis of patient management from primary patient care to prevention and early intervention for high-risk individuals. In addition to classic single molecular markers, high-throughput approaches can be used for PM including whole genome sequencing, single-nucleotide polymorphism analysis, microarray analysis, and mass spectrometry. A common trend among these tools is their ability to analyze many targets simultaneously, thus increasing the sensitivity, specificity, and accuracy of biomarker discovery. Certain challenges need to be addressed in our transition to PM including assessment of cost, test standardization, and ethical issues. It is clear that PM will gradually continue to be incorporated into cancer patient management and will have a significant impact on our health care in the future.

Applying mass spectrometry based proteomic technology to advance the understanding of multiple myeloma

Multiple myeloma (MM) is the second most common hematological malignancy in adults. It is characterized by clonal proliferation of terminally differentiated B lymphocytes and over-production of monoclonal immunoglobulins. Recurrent genomic aberrations have been identified to contribute to the aggressiveness of this cancer. Despite a wealth of knowledge describing the molecular biology of MM as well as significant advances in therapeutics, this disease remains fatal. The identification of biomarkers, especially through the use of mass spectrometry, however, holds great promise to increasing our understanding of this disease. In particular, novel biomarkers will help in the diagnosis, prognosis and therapeutic stratification of MM. To date, results from mass spectrometry studies of MM have provided valuable information with regards to MM diagnosis and response to therapy. In addition, mass spectrometry was employed to study relevant signaling pathways activated in MM. This review will focus on how mass spectrometry has been applied to increase our understanding of MM.

Enabling individualized therapy through nanotechnology

Individualized medicine is the healthcare strategy that rebukes the idiomatic dogma of 'losing sight of the forest for the trees'. We are entering a new era of healthcare where it is no longer acceptable to develop and market a drug that is effective for only 80% of the patient population. The emergence of "-omic" technologies (e.g. genomics, transcriptomics, proteomics, metabolomics) and advances in systems biology are magnifying the deficiencies of standardized therapy, which often provide little treatment latitude for accommodating patient physiologic idiosyncrasies. A personalized approach to medicine is not a novel concept. Ever since the scientific community began unraveling the mysteries of the genome, the promise of discarding generic treatment regimens in favor of patient-specific therapies became more feasible and realistic. One of the major scientific impediments of this movement towards personalized medicine has been the need for technological enablement. Nanotechnology is projected to play a critical role in patient-specific therapy; however, this transition will depend heavily upon the evolutionary development of a systems biology approach to clinical medicine based upon "-omic" technology analysis and integration. This manuscript provides a forward looking assessment of the promise of nanomedicine as it pertains to individualized medicine and establishes a technology "snapshot" of the current state of nano-based products over a vast array of clinical indications and range of patient specificity. Other issues such as market driven hurdles and regulatory compliance reform are anticipated to "self-correct" in accordance to scientific advancement and healthcare demand. These peripheral, non-scientific concerns are not addressed at length in this manuscript; however they do exist, and their impact to the paradigm shifting healthcare transformation towards individualized medicine will be critical for its success.

Serum protein signature may improve detection of ductal carcinoma in situ of the breast

Ductal carcinoma in situ (DCIS) of the breast is part of a spectrum of preinvasive lesions that originate within normal breast tissue and progress to invasive breast cancer. The detection of DCIS is important for the reduction of mortality from breast cancer, but the diagnosis of preinvasive breast tumors is hampered by the lack of an adequate detection method. To identify the changes in protein expression during the initial stage of tumorigenesis and to identify the presence of new DCIS markers, we analysed serum from 60 patients with breast cancer and 60 normal controls using mass spectrometry. A 23-protein index was generated that correctly distinguishes the DCIS and control groups with sensitivities and specificities in excess of 80% in two independent cohorts. Two candidate peptides were purified and identified as platelet factor 4 (PF-4) and complement C3a(desArg) anaphylatoxin (C3a(desArg)) using liquid chromatography-tandem mass spectrometry (LC-MS/MS). In an independent serum set of 165 patients, PF-4 and C3a(desArg) were significantly upregulated in DCIS compared with non-cancerous controls, as validated using western blot and enzyme-linked immunosorbent assay. We conclude that our serum protein-based test, used in conjunction with image-based screening practices, could improve the sensitivity and specificity of breast cancer detection.

Mass spectrometry based proteomics profiling as diagnostic tool in oncology: current status and future perspective

Proteomics analysis has been heralded as a novel tool for identifying new and specific biomarkers that may improve diagnosis and monitoring of various disease states. Recent years have brought a number of proteomics profiling technologies. Although proteomics profiling has resulted in the detection of disease-associated differences and modification of proteins, current proteomics technologies display certain limitations that are hampering the introduction of these new technologies into clinical laboratory diagnostics and routine applications. In this review, we summarize current advances in mass spectrometry based biomarker discovery. The promises and challenges of this new technology are discussed with particular emphasis on diagnostic perspectives of mass-spectrometry based proteomics profiling for malignant diseases.

Cytoplasmic proteome and secretome profiles of differently stimulated human dendritic cells

Dendritic cells (DCs), the most potent and specialized antigen-presenting cells, play a key role in the regulation of the adaptive immunity. Immature DCs were generated by in vitro culturing of peripheral blood monocytes and functionally activated with the classical pathogen-associated molecular pattern lipopolysaccharide (LPS). Alternative activation resulting in Th-2 polarization was induced with lipid oxidation products derived from 1-palmitoyl-2-arachidoyl-sn-glycerol-3-phosphorylcholin (OxPAPC). Tolerogenic cells were obtained by treating DCs with human rhinovirus (HRV). The aim of this study was the identification of proteome profiles related to the functionally different dendritic cell phenotypes. Cytoplasmic proteins were analyzed by shotgun proteomics resulting in the identification of 1690 proteins. While mature and alternatively activated DCs displayed highly distinct protein expression profiles, HRV-treated DCs showed minor proteome alterations. As DCs exert many specific functions via secretion, we investigated the secretomes by a combination of 2D-PAGE and shotgun proteomics. We successfully identified a broad variety of cytokines (e.g., GM-CSF, TNF-alpha, interleukin-1beta, 6, 12 beta, 28B and 29), chemokines (e.g., CCL3, 5, 8, 17, 18, 19, 24, CXCL1, 2, 9 and 10) and growth factors (growth/differentiation factor 8, C-type lectin domain family 11 member A). The relative composition of secretome profiles, although comprising much less proteins, was found to be much more affected by functional alteration of cells than the cytoplasmic protein composition. In conclusion, we demonstrate that functional distinct subsets of DCs display distinct proteome profiles which comprise biomarker candidates. These proteins may prove useful for the interpretation of complex clinical proteomics data.

Serum peptidome profiling revealed platelet factor 4 as a potential discriminating Peptide associated with pancreatic cancer

PURPOSE

Mass spectrometry-based serum peptidome profiling is a promising tool to identify novel disease-associated biomarkers, but is limited by preanalytic factors and the intricacies of complex data processing. Therefore, we investigated whether standardized sample protocols and new bioinformatic tools combined with external data validation improve the validity of peptidome profiling for the discovery of pancreatic cancer-associated serum markers.

EXPERIMENTAL DESIGN

For the discovery study, two sets of sera from patients with pancreatic cancer (n = 40) and healthy controls (n = 40) were obtained from two different clinical centers. For external data validation, we collected an independent set of samples from patients (n = 20) and healthy controls (n = 20). Magnetic beads with different surface functionalities were used for peptidome fractionation followed by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS). Data evaluation was carried out by comparing two different bioinformatic strategies. Following proteome database search, the matching candidate peptide was verified by MALDI-TOF MS after specific antibody-based immunoaffinity chromatography and independently confirmed by an ELISA assay.

RESULTS

Two significant peaks (m/z 3884; 5959) achieved a sensitivity of 86.3% and a specificity of 97.6% for the discrimination of patients and healthy controls in the external validation set. Adding peak m/z 3884 to conventional clinical tumor markers (CA 19-9 and CEA) improved sensitivity and specificity, as shown by receiver operator characteristics curve analysis (AUROC(combined) = 1.00). Mass spectrometry-based m/z 3884 peak identification and following immunologic quantitation revealed platelet factor 4 as the corresponding peptide.

CONCLUSIONS

MALDI-TOF MS-based serum peptidome profiling allowed the discovery and validation of platelet factor 4 as a new discriminating marker in pancreatic cancer.

Classification-based comparison of pre-processing methods for interpretation of mass spectrometry generated clinical datasets

Background

Mass spectrometry is increasingly being used to discover proteins or protein profiles associated with disease. Experimental design of mass-spectrometry studies has come under close scrutiny and the importance of strict protocols for sample collection is now understood. However, the question of how best to process the large quantities of data generated is still unanswered. Main challenges for the analysis are the choice of proper pre-processing and classification methods. While these two issues have been investigated in isolation, we propose to use the classification of patient samples as a clinically relevant benchmark for the evaluation of pre-processing methods.

Results

Two in-house generated clinical SELDI-TOF MS datasets are used in this study as an example of high throughput mass-spectrometry data. We perform a systematic comparison of two commonly used pre-processing methods as implemented in Ciphergen ProteinChip Software and in the Cromwell package. With respect to reproducibility, Ciphergen and Cromwell pre-processing are largely comparable. We find that the overlap between peaks detected by either Ciphergen ProteinChip Software or Cromwell is large. This is especially the case for the more stringent peak detection settings. Moreover, similarity of the estimated intensities between matched peaks is high.

We evaluate the pre-processing methods using five different classification methods. Classification is done in a double cross-validation protocol using repeated random sampling to obtain an unbiased estimate of classification accuracy. No pre-processing method significantly outperforms the other for all peak detection settings evaluated.

Conclusion

We use classification of patient samples as a clinically relevant benchmark for the evaluation of pre-processing methods. Both pre-processing methods lead to similar classification results on an ovarian cancer and a Gaucher disease dataset. However, the settings for pre-processing parameters lead to large differences in classification accuracy and are therefore of crucial importance. We advocate the evaluation over a range of parameter settings when comparing pre-processing methods. Our analysis also demonstrates that reliable classification results can be obtained with a combination of strict sample handling and a well-defined classification protocol on clinical samples.

Prediction of ovarian cancer prognosis and response to chemotherapy by a serum-based multiparametric biomarker panel

Currently, there are no effective biomarkers for ovarian cancer prognosis or prediction of therapeutic response. The objective of this study was to examine a panel of 10 serum biochemical parameters for their ability to predict response to chemotherapy, progression and survival of ovarian cancer patients. Sera from ovarian cancer patients were collected prior and during chemotherapy and were analysed by enzyme-linked immunosorbent assay for CA125, kallikreins 5, 6, 7, 8, 10 and 11, B7-H4, regenerating protein IV and Spondin-2. The odds ratio and hazard ratio and their 95% confidence interval (95% CI) were calculated. Time-dependent receiver-operating characteristic (ROC) curves were utilised to evaluate the prognostic performance of the biomarkers. The levels of several markers at baseline (c₀), or after the first chemotherapy cycle (rc₁), predicted chemotherapy response and overall or progression-free survival in univariate analysis. A multiparametric model (c₀ of CA125, KLK5, KLK7 and rc₁ of CA125) provided predictive accuracy with area under the ROC curve (AUC) of 0.82 (0.62 after correction for overfitting). Another marker combination (c₀ of KLK7, KLK10, B7-H4, Spondin-2) was useful in predicting short-term (1-year) survival with an AUC of 0.89 (0.74 after correction for overfitting). All markers examined, except KLK7 and regenerating protein IV, were powerful predictors of time to progression (TTP) among chemotherapy responders. Individual and panels of biomarkers from the kallikrein family (and other families) can predict response to chemotherapy, overall survival, short-term (1-year) survival, progression-free survival and TTP of ovarian cancer patients treated with chemotherapy.

Glycoproteomic analyses of ovarian cancer cell lines and sera from ovarian cancer patients show distinct glycosylation changes in individual proteins

Ovarian cancer is difficult to diagnose in women because symptoms of the disease are often not noticed until the disease has progressed to an advanced untreatable stage. Although a serum test, CA125, is currently available to assist with monitoring treatment of ovarian cancer, this test lacks the necessary specificity and sensitivity for early detection. Therefore, better biomarkers of ovarian cancer are needed. A glycoprotein analysis approach was undertaken using high resolution Fourier transform ion cyclotron resonance mass spectrometry to analyze glycosylated proteins present in the conditioned media of ovarian cancer cell lines and in sera obtained from ovarian cancer patients and normal controls. In this study, glycosylated proteins were separated by gel electrophoresis, and individual glycoproteins were selected for glycosylation analysis and protein identification. The attached glycans from each protein were released and profiled by mass spectrometry. Glycosylation of a mucin protein and a large glycosylated protein isolated from the ES2 ovarian cancer cell line was determined to consist of mostly O-linked glycans. Four prominent glycoproteins of approximate 517, 370, 250, 163 kDa from serum samples were identified as two forms of apolipoprotein B-100, fibronectin, and immunoglobulin A1, respectively. Mass spectrometric analysis of glycans isolated from apolipoprotein B-100 (517 kD) showed the presence of small, specific O-linked oligosaccharides. In contrast, analysis of fibronectin (250 kD) and immunoglobulin A1 (163 kD) produced N-linked glycan fragments in forms that were sufficiently different from the glycans obtained from the corresponding protein band present in the normal serum samples. This study shows that not only a single protein but several are aberrantly glycosylated, and those abnormal glycosylation changes can be detected and may ultimately serve as glycan biomarkers for ovarian cancer.

A mouse model repository for cancer biomarker discovery

Early detection of cancer using biomarkers obtained from blood or other easily accessible tissues would have a significant impact on reducing cancer mortality. However, identifying new blood-based biomarkers has been hindered by the dynamic complexity of the human plasma proteome, confounded by genetic and environmental variability, and the scarcity of high quality controlled samples. In this report, we discuss a new paradigm for biomarker discovery through the use of mouse models. Inbred mouse models of cancer recapitulate many critical features of human cancer, while eliminating sources of environmental and genetic variability. The ability to collect samples from highly matched cases and controls under identical conditions further reduces variability which is critical for successful biomarker discovery. We describe the establishment of a repository containing tumor, plasma, urine, and other tissues from 10 different mouse models of human cancer, including two breast, two lung, two prostate, two gastrointestinal, one ovarian, and one skin tumor model. We present the overall design of this resource and its potential use by the research community for biomarker discovery.

Identification of accessible human cancer biomarkers using ex vivo chemical proteomic strategies

One promising avenue towards the development of more selective, better anticancer drugs lies in the targeted delivery of bioactive compounds to the tumor environment by means of binding molecules specific for tumor-associated biomarkers. Eligibility of such markers for therapeutic ideally use three criteria: accessibility from the bloodstream; expression at sufficient level, and no (or much lower) expression in normal tissues. Most current discovery strategies (such as biomarker searching into body fluids) provide no clue as to whether proteins of interest are accessible, in human tissues, to suitable high-affinity ligands, such as systemically delivered monoclonal antibodies. To address this limitation, our group recently developed two methodologies based on chemical proteomic modifications, enabling the discovery of proteins accessible from the bloodstream and the extracellular space in human pathological tissues. In this review, we will discuss the potential benefits of these methodologies for the fast discovery of therapeutically valuable biomarkers.

Recent advances in cancer stem/progenitor cell research: therapeutic implications for overcoming resistance to the most aggressive cancers

Overcoming intrinsic and acquired resistance of cancer stem/progenitor cells to current clinical treatments represents a major challenge in treating and curing the most aggressive and metastatic cancers. This review summarizes recent advances in our understanding of the cellular origin and molecular mechanisms at the basis of cancer initiation and progression as well as the heterogeneity of cancers arising from the malignant transformation of adult stem/progenitor cells. We describe the critical functions provided by several growth factor cascades, including epidermal growth factor receptor (EGFR), platelet-derived growth factor receptor (PDGFR), stem cell factor (SCF) receptor (KIT), hedgehog and Wnt/beta-catenin signalling pathways that are frequently activated in cancer progenitor cells and are involved in their sustained growth, survival, invasion and drug resistance. Of therapeutic interest, we also discuss recent progress in the development of new drug combinations to treat the highly aggressive and metastatic cancers including refractory/relapsed leukaemias, melanoma and head and neck, brain, lung, breast, ovary, prostate, pancreas and gastrointestinal cancers which remain incurable in the clinics. The emphasis is on new therapeutic strategies consisting of molecular targeting of distinct oncogenic signalling elements activated in the cancer progenitor cells and their local microenvironment during cancer progression. These new targeted therapies should improve the efficacy of current therapeutic treatments against aggressive cancers, and thereby preventing disease relapse and enhancing patient survival.

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.

Biomarker method validation in anticancer drug development

Over recent years the role of biomarkers in anticancer drug development has expanded across a spectrum of applications ranging from research tool during early discovery to surrogate endpoint in the clinic. However, in Europe when biomarker measurements are performed on samples collected from subjects entered into clinical trials of new investigational agents, laboratories conducting these analyses become subject to the Clinical Trials Regulations. While these regulations are not specific in their requirements of research laboratories, quality assurance and in particular assay validation are essential. This review, therefore, focuses on a discussion of current thinking in biomarker assay validation. Five categories define the majority of biomarker assays from 'absolute quantitation' to 'categorical'. Validation must therefore take account of both the position of the biomarker in the spectrum towards clinical end point and the level of quantitation inherent in the methodology. Biomarker assay validation should be performed ideally in stages on 'a fit for purpose' basis avoiding unnecessarily dogmatic adherence to rigid guidelines but with careful monitoring of progress at the end of each stage. These principles are illustrated with two specific examples: (a) absolute quantitation of protein biomarkers by mass spectrometry and (b) the M30 and M65 ELISA assays as surrogate end points of cell death.

Recent advances on the molecular mechanisms involved in the drug resistance of cancer cells and novel targeting therapies

This review summarizes the recent knowledge obtained on the molecular mechanisms involved in the intrinsic and acquired resistance of cancer cells to current cancer therapies. We describe the cascades that are often altered in cancer cells during cancer progression that may contribute in a crucial manner to drug resistance and disease relapse. The emphasis is on the implication of ATP-binding cassette (ABC) multidrug efflux transporters in drug disposition and antiapoptotic factors, including epidermal growth factor receptor cascades and deregulated enzymes in ceramide metabolic pathways. The altered expression and activity of these signaling elements may have a critical role in the resistance of cancer cells to cytotoxic effects induced by diverse chemotherapeutic drugs and cancer recurrence. Of therapeutic interest, new strategies for reversing the multidrug resistance and developing more effective clinical treatments against the highly aggressive, metastatic, and recurrent cancers, based on the molecular targeting of the cancer progenitor cells and their further differentiated progeny, are also described.

Oncoproteomics: current trends and future perspectives

Oncoproteomics is the application of proteomics technologies in oncology. Functional proteomics is a promising technique for the rational identification of biomarkers and novel therapeutic targets for cancers. Recent progress in proteomics has opened new avenues for tumor-associated biomarker discovery. With the advent of new and improved proteomics technologies, such as the development of quantitative proteomic methods, high-resolution, -speed and -sensitivity mass spectrometry and protein arrays, as well as advanced bioinformatics for data handling and interpretation, it is now possible to discover biomarkers that can reliably and accurately predict outcomes during cancer management and treatment. However, there are several difficulties in the study of proteins/peptides that are not inherent in the study of nucleic acids. New challenges arise in large-scale proteomic profiling when dealing with complex biological mixtures. Nevertheless, oncoproteomics offers great promise for unveiling the complex molecular events of tumorigenesis, as well as those that control clinically important tumor behaviors, such as metastasis, invasion and resistance to therapy. In this review, the development and advancement of oncoproteomics technologies for cancer research in recent years are expounded.

Diagnostic potential for urinary proteomics

Urine represents a modified ultrafiltrate of plasma, with protein concentrations typically approximately 1000-fold lower than plasma. Urine’s low protein concentration might suggest it to be a less promising diagnostic specimen than plasma. However, urine can be obtained noninvasively and tests of many urinary proteins are well-established in clinical practice. Proteomic technologies expand opportunities to analyze urinary proteins, identifying more than 1000 proteins and peptides in urine. Urine offers a sampling of most plasma proteins, with increased proportions of low-molecular-weight protein and peptide components. Urine also offers enriched sampling of proteins released along the urinary tract. Although urine presents some challenges as a diagnostic specimen, its diverse range of potential markers offers great potential for diagnosis of both systemic and kidney diseases. Examples of clinical situations where this may be of value are for more sensitive detection of kidney transplant rejection or of renal toxicity of medications.