Prostate cancer biomarkers and the emerging proteomic techniques used in biomarker research

Prostate cancer (CaP) is the second leading cause of cancer-related deaths in North American males and the most common newly diagnosed cancer in men world wide. Biomarkers are widely used for both early detection and prognostic tests for cancer. The current, commonly used biomarker for CaP is serum prostate specific antigen (PSA). However, the specificity of this biomarker is low as its serum level is not only increased in CaP but also in various other diseases, with age and even body mass index. Human body fluids provide an excellent resource for the discovery of biomarkers, with the advantage over tissue/biopsy samples of their ease of access, due to the less invasive nature of collection. However, their analysis presents challenges in terms of variability and validation. Blood and urine are two human body fluids commonly used for CaP research, but their proteomic analyses are limited both by the large dynamic range of protein abundance making detection of low abundance proteins difficult and in the case of urine, by the high salt concentration. To overcome these challenges, different techniques for removal of high abundance proteins and enrichment of low abundance proteins are used. Their applications and limitations are discussed in this review. A number of innovative proteomic techniques have improved detection of biomarkers. They include two dimensional differential gel electrophoresis (2D-DIGE), quantitative mass spectrometry (MS) and functional proteomic studies, i.e., investigating the association of post translational modifications (PTMs) such as phosphorylation, glycosylation and protein degradation. The recent development of quantitative MS techniques such as stable isotope labeling with amino acids in cell culture (SILAC), isobaric tags for relative and absolute quantitation (iTRAQ) and multiple reaction monitoring (MRM) have allowed proteomic researchers to quantitatively compare data from different samples. 2D-DIGE has greatly improved the statistical power of classical 2D gel analysis by introducing an internal control. This chapter aims to review novel CaP biomarkers as well as to discuss current trends in biomarker research from two angles: the source of biomarkers (particularly human body fluids such as blood and urine), and emerging proteomic approaches for biomarker research.

Novel breast cancer biomarkers identified by integrative proteomic and gene expression mapping

Proteomic and transcriptomic platforms both play important roles in cancer research, with differing strengths and limitations. Here, we describe a proteo-transcriptomic integrative strategy for discovering novel cancer biomarkers, combining the direct visualization of differentially expressed proteins with the high-throughput scale of gene expression profiling. Using breast cancer as a case example, we generated comprehensive two-dimensional electrophoresis (2DE)/mass spectrometry (MS) proteomic maps of cancer (MCF-7 and HCC-38) and control (CCD-1059Sk) cell lines, identifying 1724 expressed protein spots representing 484 different protein species. The differentially expressed cell-line proteins were then mapped to mRNA transcript databases of cancer cell lines and primary breast tumors to identify candidate biomarkers that were concordantly expressed at the gene expression level. Of the top nine selected biomarker candidates, we reidentified ANX1, a protein previously reported to be differentially expressed in breast cancers and normal tissues, and validated three other novel candidates, CRAB, 6PGL, and CAZ2, as differentially expressed proteins by immunohistochemistry on breast tissue microarrays. In total, close to half (4/9) of our protein biomarker candidates were successfully validated. Our study thus illustrates how the systematic integration of proteomic and transcriptomic data from both cell line and primary tissue samples can prove advantageous for accelerating cancer biomarker discovery.

Transforming Growth Factor-beta superfamily:evaluation as breast cancer biomarkers and preventive agents

The Transforming Growth Factor-beta (TGFbeta) superfamily of cytokines is comprised of a number of structurally-related, secreted polypeptides that regulate a multitude of cellular processes including proliferation, differentiation and neoplastic transformation. These growth regulatory molecules induce ligand-mediated hetero-oligomerization of distinct type II and type I serine/threonine kinase receptors that transmit signals predominantly through receptor-activated Smad proteins but also induce Smad-independent pathways. Ligands, receptors and intracellular mediators of signaling initiated by members of the TGFbeta family are expressed in the mammary gland and disruption of these pathways may contribute to the development and progression of human breast cancer. Since many facets of TGFbeta and breast cancer have been recently reviewed in several articles, except for discussion of recent developments on some aspects of TGFbeta, the major focus of this review will be on the role of activins, inhibins, BMPs, nodal and MIS-signaling in breast cancer with emphasis on their utility as potential diagnostic, prognostic and therapeutic targets.

STUDYING ABERRANT METHYLATION AND miRNA PROFILING FOR THE DETECTION OF LUNG CANCER BIOMARKERS

Lung cancer is a major chronic disease responsible for the highest mortality rate, among other types of cancer, and represents 29% of all deaths in Canada. The clinical diagnosis of lung carcinoma still requires a standard diagnostic approach, as there are no symptoms in its early stage. Therefore, it is usually diagnosed at a later stage, when the survival rate is low. With the recent advancement in molecular biology and biotechnology, a molecular biomarker approach for the diagnosis of early lung cancer seems to be a potential option. In this study, we aimed to investigate and standardize a promising Lung ,Cancer Biomarker by studying the aberrant methylation of two tumour suppressor genes, namely RASSFIA and RAR-B, and the miRNA profiling of four . commonly deregulated miRNA (miR-199a-3p, miR-182, miR-lOO and miR-221). Four lung cancer cell lines were used (two SCLC and two NSCLC), with comparisons being made with normal lung cell lines. Our results, we found that none of these genes were methylated. We then evaluated TP53, and found the promoter of this gene to be methylated in the cancer cell lines, as compared to the normal cell lines, indicating gene inactivation. We carried out miRNA profiling of the cancer cell lines and reported that 80 miRNAs are deregulated in lung cancer cell lines as compared to the normal cell lines. Our study was the first of its kind to indicate that hsa-mir-4301, hsa-mir-4707-5p and hsa-mir-4497 (newly discovered miRNAs) are deregulated in lung cancer cell lines. We also investigated miR-199a-3p, mir-lOO and miR-182, and found that miR-199a -3p and mir-l00 were down-regulated in cancer lines, whereas miR-182 was up-regulated in the cancer cell lines. In the final part of the study we observed that mir-221 could be a putative biomarker to distinguish between the two types of lung cancer because it was down-regulated in SCLC, and up-regulated in the NSCLC cell lines. In conclusion, we found four miRNA molecular biomarkers that possibly could be used in the early diagnosis of the lung cancer. More studies are still required with larger numbers of samples to effectively establish these as molecular biomarkers for the diagnosis of lung cancer

Investigation of urinary volatile organic metabolites as potential cancer biomarkers by solid-phase microextraction in combination with gas chromatography-mass spectrometry

BACKGROUND: Non-invasive diagnostic strategies aimed at identifying biomarkers of cancer are of great interest for early cancer detection. Urine is potentially a rich source of volatile organic metabolites (VOMs) that can be used as potential cancer biomarkers. Our aim was to develop a generally reliable, rapid, sensitive, and robust analytical method for screening large numbers of urine samples, resulting in a broad spectrum of native VOMs, as a tool to evaluate the potential of these metabolites in the early diagnosis of cancer. METHODS: To investigate urinary volatile metabolites as potential cancer biomarkers, urine samples from 33 cancer patients (oncological group: 14 leukaemia, 12 colorectal and 7 lymphoma) and 21 healthy (control group, cancer-free) individuals were qualitatively and quantitatively analysed. Dynamic solid-phase microextraction in headspace mode (dHS-SPME) using a carboxenpolydimethylsiloxane (CAR/PDMS) sorbent in combination with GC-qMS-based metabolomics was applied to isolate and identify the volatile metabolites. This method provides a potential non-invasive method for early cancer diagnosis as a first approach. To fulfil this objective, three important dHS-SPME experimental parameters that influence extraction efficiency (fibre coating, extraction time and temperature of sampling) were optimised using a univariate optimisation design. The highest extraction efficiency was obtained when sampling was performed at 501C for 60min using samples with high ionic strengths (17% sodium chloride, wv 1) and under agitation. RESULTS: A total of 82 volatile metabolites belonging to distinct chemical classes were identified in the control and oncological groups. Benzene derivatives, terpenoids and phenols were the most common classes for the oncological group, whereas ketones and sulphur compounds were the main classes that were isolated from the urine headspace of healthy subjects. The results demonstrate that compound concentrations were dramatically different between cancer patients and healthy volunteers. The positive rates of 16 patients among the 82 identified were found to be statistically different (Po0.05). A significant increase in the peak area of 2-methyl3-phenyl-2-propenal, p-cymene, anisole, 4-methyl-phenol and 1,2-dihydro-1,1,6-trimethyl-naphthalene in cancer patients was observed. On average, statistically significant lower abundances of dimethyl disulphide were found in cancer patients. CONCLUSIONS: Gas chromatographic peak areas were submitted to multivariate analysis (principal component analysis and supervised linear discriminant analysis) to visualise clusters within cases and to detect the volatile metabolites that are able to differentiate cancer patients from healthy individuals. Very good discrimination within cancer groups and between cancer and control groups was achieved.

Solid phase microextraction, mass spectrometry and metabolomic approaches for detection of potential urinary cancer biomarkers: a powerful strategy for breast cancer diagnosis

A sensitive assay to identify volatile organic metabolites (VOMs) as biomarkers that can accurately diagnose the onset of breast cancer using non-invasively collected clinical specimens is ideal for early detection. Therefore the aim of this study was to establish the urinary metabolomic profile of breast cancer patients and healthy individuals (control group) and to explore the VOMs as potential biomarkers in breast cancer diagnosis at early stage. Solid-phase microextraction (SPME) using CAR/PDMS sorbent combined with gas chromatography–mass spectrometry was applied to obtain metabolomic information patterns of 26 breast cancer patients and 21 healthy individuals (controls). A total of seventy-nine VOMs, belonging to distinct chemical classes, were detected and identified in control and breast cancer groups. Ketones and sulfur compounds were the chemical classes with highest contribution for both groups. Results showed that excretion values of 6 VOMs among the total of 79 detected were found to be statistically different (p < 0.05). A significant increase in the peak area of (−)-4-carene, 3-heptanone, 1,2,4-trimethylbenzene, 2-methoxythiophene and phenol, in VOMs of cancer patients relatively to controls was observed. Statiscally significant lower abundances of dimethyl disulfide were found in cancer patients. Bioanalytical data were submitted to multivariate statistics [principal component analysis (PCA)], in order to visualize clusters of cases and to detect the VOMs that are able to differentiate cancer patients from healthy individuals. Very good discrimination within breast cancer and control groups was achieved. Nevertheless, a deep study using a larger number of patients must be carried out to confirm the results.

Proteomics-driven approach for the detection of breast cancer biomarkers

Breast cancer (BC) is the most often diagnosed cancer entity of women worldwide. No molecular biomarkers are usable in the clinical routine for the early detection of BC. Proteomics is one of the dynamic tools for the successful examination of changes on the protein level. In this thesis different proteomics-based investigations were performed for the detection of protein and autoantibody biomarkers in serum samples of BC and healthy (CTRL) subjects. First, protein levels of candidates from previous profiling studies were investigated via antibody-microarray platform. Three proteins were found in distinct levels in both groups: secretoglobin family 1D member 1, alpha-2 macroglobulin and inter-alpha-trypsin inhibitor heavy chain family member 4. The second part was dedicated to the de novo exploration of potentially immunogenic tumor antigens (TA’s) with immunoprecipitation and Western immunoblotting followed by identification over mass spectrometry. Autoantibody levels were verified in individual serum profiling via the protein microarray platform. Two autoantibody’ cohorts (anti-Histone 2B and anti-Recoverin) were found in different levels in both groups. The findings of this PhD thesis underline deregulated serum protein and autoantibody levels in the presence of BC. Further investigations are needed to confirm the results in an independent study population.

Metabolomic and proteomic analysis of breast cancer patient samples suggests that glutamate and 12-HETE in combination with CA15-3 may be useful biomarkers reflecting tumour burden

Evaluation of protein and metabolite expression patterns in blood using mass spectrometry and high-throughput antibody-based screening platforms has potential for the discovery of new biomarkers for managing breast cancer patient treatment. Previously identified blood-based breast cancer biomarkers, including cancer antigen 15.3 (CA15-3) are useful in combination with imaging (computed tomography scans, magnetic resonance imaging, X-rays) and physical examination for monitoring tumour burden in advanced breast cancer patients. However, these biomarkers suffer from insufficient levels of accuracy and with new therapies available for the treatment of breast cancer, there is an urgent need for reliable, non-invasive biomarkers that measure tumour burden with high sensitivity and specificity so as to provide early warning of the need to switch to an alternative treatment. The aim of this study was to identify a biomarker signature of tumour burden using cancer and non-cancer (healthy controls/non-malignant breast disease) patient samples. Results demonstrate that combinations of three candidate biomarkers from Glutamate, 12-Hydroxyeicosatetraenoic acid, Beta-hydroxybutyrate, Factor V and Matrix metalloproteinase-1 with CA15-3, an established biomarker for breast cancer, were found to mirror tumour burden, with AUC values ranging from 0.71 to 0.98 when comparing non-malignant breast disease to the different stages of breast cancer. Further validation of these biomarker panels could potentially facilitate the management of breast cancer patients, especially to assess changes in tumour burden in combination with imaging and physical examination.

Effects of synbiotics on cancer risk biomarkers

Colorectal cancer (CRC) is the fourth most common cause of death from cancer in the world and second most common (behind lung cancer) in developed countries. In recent years there has been much interest in the potential use of prebiotics, probiotics and synbiotics in the prevention and treatment of CRC. We have previously shown that synbiotic consumption in Azoxymethane treated rats modulates the immune system, influences the genotoxic potential of caecal contents and reduces the number of colonic tumours compared to control rats who did not receive the synbiotic. The aim of the current study was to identify biomarkers suitable for use as cancer risk markers and as intervention markers. A second aim was to determine the influence of synbiotic consumption on cancer risk biomarkers such as in vivo colonic mucosal proliferation and genotoxic damage along with examining the genotoxic, cytotoxic and tumour promoting potential of faecal water (FW). Synbiotic consumption altered the composition of the gastrointestinal flora and reduced in vivo genotoxic damage and the genotoxic potential of FW in cancer and polyp subjects. Synbiotic consumption also reduced the proliferative activity in the colonic mucosa in polyp subjects. In both cancer and polyp subjects gene expression in the colonic mucosa was modulated in synbiotic consuming subjects. In this and other studies the activity of natural killer cells, the level of PGE2 in FW, IL-12 production by PBMCs, genotoxic damage in the colonic mucosa and the tumour promoting activities of FW have been identified as possible biomarkers of cancer risk. Future large scale studies investigating these parameters in healthy and diseased individuals are needed to confirm the suitability of these markers in assessing cancer risk and the role of synbiotics in modulating them.

Peptides generated ex vivo from serum proteins by tumor-specific exopeptidases are not useful biomarkers in ovarian cancer

BACKGROUND: The serum peptidome may be a valuable source of diagnostic cancer biomarkers. Previous mass spectrometry (MS) studies have suggested that groups of related peptides discriminatory for different cancer types are generated ex vivo from abundant serum proteins by tumor-specific exopeptidases. We tested 2 complementary serum profiling strategies to see if similar peptides could be found that discriminate ovarian cancer from benign cases and healthy controls. METHODS: We subjected identically collected and processed serum samples from healthy volunteers and patients to automated polypeptide extraction on octadecylsilane-coated magnetic beads and separately on ZipTips before MALDI-TOF MS profiling at 2 centers. The 2 platforms were compared and case control profiling data analyzed to find altered MS peak intensities. We tested models built from training datasets for both methods for their ability to classify a blinded test set. RESULTS: Both profiling platforms had CVs of approximately 15% and could be applied for high-throughput analysis of clinical samples. The 2 methods generated overlapping peptide profiles, with some differences in peak intensity in different mass regions. In cross-validation, models from training data gave diagnostic accuracies up to 87% for discriminating malignant ovarian cancer from healthy controls and up to 81% for discriminating malignant from benign samples. Diagnostic accuracies up to 71% (malignant vs healthy) and up to 65% (malignant vs benign) were obtained when the models were validated on the blinded test set. CONCLUSIONS: For ovarian cancer, altered MALDI-TOF MS peptide profiles alone cannot be used for accurate diagnoses.

Eight-plex iTRAQ analysis of variant metastatic human prostate cancer cells identifies candidate biomarkers of progression: an exploratory study.

BACKGROUND: Due to the heterogeneity in the biological behavior of prostate cancer, biomarkers that can reliably distinguish indolent from aggressive disease are urgently needed to inform treatment choices. METHODS: We employed 8-plex isobaric Tags for Relative and Absolute Quantitation (iTRAQ), to profile the proteomes of two distinct panels of isogenic prostate cancer cells with varying growth and metastatic potentials, in order to identify novel biomarkers associated with progression. The LNCaP, LNCaP-Pro5, and LNCaP-LN3 panel of cells represent a model of androgen-responsive prostate cancer, while the PC-3, PC-3M, and PC-3M-LN4 panel represent a model of androgen-insensitive disease. RESULTS: Of the 245 unique proteins identified and quantified (>or=95% confidence; >or=2 peptides/protein), 17 showed significant differential expression (>or=+/-1.5), in at least one of the variant LNCaP cells relative to parental cells. Similarly, comparisons within the PC-3 panel identified 45 proteins to show significant differential expression in at least one of the variant PC-3 cells compared with parental cells. Differential expression of selected candidates was verified by Western blotting or immunocytochemistry, and corresponding mRNA expression was determined by quantitative real-time PCR (qRT-PCR). Immunostaining of prostate tissue microarrays for ERp5, one of the candidates identified, showed a significant higher immunoexpression in pre-malignant lesions compared with non-malignant epithelium (P < 0.0001, Mann-Whitney U-test), and in high Gleason grade (4-5) versus low grade (2-3) cancers (P < 0.05). CONCLUSIONS: Our study provides proof of principle for the application of an 8-plex iTRAQ approach to uncover clinically relevant candidate biomarkers for prostate cancer progression.

PTRF/cavin-1 neutralizes non-caveolar caveolin-1 microdomains in prostate cancer

Caveolin-1 has a complex role in prostate cancer and has been suggested to be a potential biomarker and therapeutic target. As mature caveolin-1 resides in caveolae, invaginated lipid raft domains at the plasma membrane, caveolae have been suggested as a tumor-promoting signaling platform in prostate cancer. However, caveola formation requires both caveolin-1 and cavin-1 (also known as PTRF; polymerase I and transcript release factor). Here, we examined the expression of cavin-1 in prostate epithelia and stroma using tissue microarray including normal, non-malignant and malignant prostate tissues. We found that caveolin-1 was induced without the presence of cavin-1 in advanced prostate carcinoma, an expression pattern mirrored in the PC-3 cell line. In contrast, normal prostate epithelia expressed neither caveolin-1 nor cavin-1, while prostate stroma highly expressed both caveolin-1 and cavin-1. Utilizing PC-3 cells as a suitable model for caveolin-1-positive advanced prostate cancer, we found that cavin-1 expression in PC-3 cells inhibits anchorage-independent growth, and reduces in vivo tumor growth and metastasis in an orthotopic prostate cancer xenograft mouse model. The expression of α-smooth muscle actin in stroma along with interleukin-6 (IL-6) in cancer cells was also decreased in tumors of mice bearing PC-3-cavin-1 tumor cells. To determine whether cavin-1 acts by neutralizing caveolin-1, we expressed cavin-1 in caveolin-1-negative prostate cancer LNCaP and 22Rv1 cells. Caveolin-1 but not cavin-1 expression increased anchorage-independent growth in LNCaP and 22Rv1 cells. Cavin-1 co-expression reversed caveolin-1 effects in caveolin-1-positive LNCaP cells. Taken together, these results suggest that caveolin-1 in advanced prostate cancer is present outside of caveolae, because of the lack of cavin-1 expression. Cavin-1 expression attenuates the effects of non-caveolar caveolin-1 microdomains partly via reduced IL-6 microenvironmental function. With circulating caveolin-1 as a potential biomarker for advanced prostate cancer, identification of the molecular pathways affected by cavin-1 could provide novel therapeutic targets.