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.

mCOPA: analysis of heterogeneous features in cancer expression data

Background Cancer outlier profile analysis (COPA) has proven to be an effective approach to analyzing cancer expression data, leading to the discovery of the TMPRSS2 and ETS family gene fusion events in prostate cancer. However, the original COPA algorithm did not identify down-regulated outliers, and the currently available R package implementing the method is similarly restricted to the analysis of over-expressed outliers. Here we present a modified outlier detection method, mCOPA, which contains refinements to the outlier-detection algorithm, identifies both over- and under-expressed outliers, is freely available, and can be applied to any expression dataset. Results We compare our method to other feature-selection approaches, and demonstrate that mCOPA frequently selects more-informative features than do differential expression or variance-based feature selection approaches, and is able to recover observed clinical subtypes more consistently. We demonstrate the application of mCOPA to prostate cancer expression data, and explore the use of outliers in clustering, pathway analysis, and the identification of tumour suppressors. We analyse the under-expressed outliers to identify known and novel prostate cancer tumour suppressor genes, validating these against data in Oncomine and the Cancer Gene Index. We also demonstrate how a combination of outlier analysis and pathway analysis can identify molecular mechanisms disrupted in individual tumours. Conclusions We demonstrate that mCOPA offers advantages, compared to differential expression or variance, in selecting outlier features, and that the features so selected are better able to assign samples to clinically annotated subtypes. Further, we show that the biology explored by outlier analysis differs from that uncovered in differential expression or variance analysis. mCOPA is an important new tool for the exploration of cancer datasets and the discovery of new cancer subtypes, and can be combined with pathway and functional analysis approaches to discover mechanisms underpinning heterogeneity in cancers

Circulating tumour cells, their role in metastasis and their clinical utility in lung cancer

Circulating tumour cells (CTCs) have attracted much recent interest in cancer research as a potential biomarker and as a means of studying the process of metastasis. It has long been understood that metastasis is a hallmark of malignancy, and conceptual theories on the basis of metastasis from the nineteenth century foretold the existence of a tumour "seed" which is capable of establishing discrete tumours in the "soil" of distant organs. This prescient "seed and soil" hypothesis accurately predicted the existence of CTCs; microscopic tumour fragments in the blood, at least some of which are capable of forming metastases. However, it is only in recent years that reliable, reproducible methods of CTC detection and analysis have been developed. To date, the majority of studies have employed the CellSearch™ system (Veridex LLC), which is an immunomagnetic purification method. Other promising techniques include microfluidic filters, isolation of tumour cells by size using microporous polycarbonate filters and flow cytometry-based approaches. While many challenges still exist, the detection of CTCs in blood is becoming increasingly feasible, giving rise to some tantalizing questions about the use of CTCs as a potential biomarker. CTC enumeration has been used to guide prognosis in patients with metastatic disease, and to act as a surrogate marker for disease response during therapy. Other possible uses for CTC detection include prognostication in early stage patients, identifying patients requiring adjuvant therapy, or in surveillance, for the detection of relapsing disease. Another exciting possible use for CTC detection assays is the molecular and genetic characterization of CTCs to act as a "liquid biopsy" representative of the primary tumour. Indeed it has already been demonstrated that it is possible to detect HER2, KRAS and EGFR mutation status in breast, colon and lung cancer CTCs respectively. In the course of this review, we shall discuss the biology of CTCs and their role in metastagenesis, the most commonly used techniques for their detection and the evidence to date of their clinical utility, with particular reference to lung cancer.

Lynch syndrome-associated breast cancers: clinicopathologic characteristics of a case series from the colon cancer family registry

Purpose: The recognition of breast cancer as a spectrum tumor in Lynch syndrome remains controversial. The aim of this study was to explore features of breast cancers arising in Lynch syndrome families. Experimental Design: This observational study involved 107 cases of breast cancer identified from the Colorectal Cancer Family Registry (Colon CFR) from 90 families in which (a) both breast and colon cancer co-occurred, (b) families met either modified Amsterdam criteria, or had at least one early-onset (<50 years) colorectal cancer, and (c) breast tissue was available within the biospecimen repository for mismatch repair (MMR) testing. Eligibility criteria for enrollment in the Colon CFR are available online. Breast cancers were reviewed by one pathologist. Tumor sections were stained for MLH1, PMS2, MSH2, and MSH6, and underwent microsatellite instability testing. Results: Breast cancer arose in 35 mutation carriers, and of these, 18 (51%) showed immunohistochemical absence of MMR protein corresponding to the MMR gene mutation segregating the family. MMR-deficient breast cancers were more likely to be poorly differentiated (P = 0.005) with a high mitotic index (P = 0.002), steroid hormone receptor–negative (estrogen receptor, P = 0.031; progesterone receptor, P = 0.022), and to have peritumoral lymphocytes (P = 0.015), confluent necrosis (P = 0.002), and growth in solid sheets (P < 0.001) similar to their colorectal counterparts. No difference in age of onset was noted between the MMR-deficient and MMR-intact groups. Conclusions: MMR deficiency was identified in 51% of breast cancers arising in known mutation carriers. Breast cancer therefore may represent a valid tissue option for the detection of MMR deficiency in which spectrum tumors are lacking

Sensitivity of edge detection methods for quantifying cell migration assays

Quantitative imaging methods to analyze cell migration assays are not standardized. Here we present a suite of two–dimensional barrier assays describing the collective spreading of an initially–confined population of 3T3 fibroblast cells. To quantify the motility rate we apply two different automatic image detection methods to locate the position of the leading edge of the spreading population after 24, 48 and 72 hours. These results are compared with a manual edge detection method where we systematically vary the detection threshold. Our results indicate that the observed spreading rates are very sensitive to the choice of image analysis tools and we show that a standard measure of cell migration can vary by as much as 25% for the same experimental images depending on the details of the image analysis tools. Our results imply that it is very difficult, if not impossible, to meaningfully compare previously published measures of cell migration since previous results have been obtained using different image analysis techniques and the details of these techniques are not always reported. Using a mathematical model, we provide a physical interpretation of our edge detection results. The physical interpretation is important since edge detection algorithms alone do not specify any physical measure, or physical definition, of the leading edge of the spreading population. Our modeling indicates that variations in the image threshold parameter correspond to a consistent variation in the local cell density. This means that varying the threshold parameter is equivalent to varying the location of the leading edge in the range of approximately 1–5% of the maximum cell density.

Ultra-trace detection of diagnostically important biomarkers using functionalised-Surface Enhanced Raman Spectroscopy (SERS)

Here we report an ultrasensitive method for detecting bio-active compounds in biological samples by means of functionalised nanoparticles interrogated by surface enhanced Raman spectroscopy (SERS). This method is applicable to the recovery and detection of many diagnostically important peptidyl analytes such as insulin, human growth hormone, growth factors (IGFs) and erythropoietin (EPO), as well as many small molecule analytes and metabolites. Our method, developed to detect EPO, demonstrates its utility in a complex yet well defined biological system. Recombinant human EPO (rhEPO) and EPO analogues have successfully been used to treat anaemia in end-stage renal failure, chronic disorders and infections, cancer and AIDS. Current methods for EPO testing are lengthy, laborious and relatively insensitive to low concentrations. In our rapid screening methodology, gold nanoparticles were functionalised with anti-EPO antibodies to provide very high selectivity towards the EPO protein in urine. These “smart sensor” nanoparticles interact with and trap EPO. Subsequent SERS screening allows for the detection and quantisation of ultra trace amounts (<<10-15 M) of EPO in urine samples with minimal sample preparation. We present data showing that the SERS spectrum differentiates between human endogenous EPO and rhEPO in unpurified urine, and potentially distinguishes between purified EPO isoforms. The elimination of sample preparation and direct screening in biological fluids significantly reduces the time required by current methods. Antibody recognition against a variety of biological targets and the availability of portable commercial SERS analysers for rapid onsite testing suggest broad diagnostic applicability in a flexible analytical platform.

The HealthyTexts study : A randomized controlled trial to improve skin cancer prevention behaviors among young people

Several randomized trials have found behavior change programs delivered via text messaging to be efficacious to improve preventive health behaviors such as physical activity and stopping smoking; however few have assessed its value in skin cancer prevention or early detection. The HealthyTexts study enrolled 678 participants 18–42 years, and assigned them to receive 21 text messages about skin cancer prevention, skin self-examination or physical activity(attention control) over the course of one year. Baseline data have been collected and outcomes will be assessed at three months and twelve months post intervention. The trial aims to increase the mean overall sun protection habits index score from 2.3 to 2.7 with a standard deviation of 0.5 (effect size of 0.5) and the proportion of people who conduct a whole-body skin self-examination by an absolute 10%. This paper describes the study design and participants' baseline characteristics. In addition, participants' goals for their health, and strategies they apply to achieve those goals are summarized.

Factors influencing the impact of primary and secondary prevention strategies for cervical cancer among Queensland women

Since the introduction of the National Human Papillomavirus Vaccine Program (NHPVP) in 2007, few studies have assessed women's knowledge, beliefs and attitudes towards cervical screening and human papillomavirus (HPV) vaccination in Australia. It is imperative to ascertain this, as substantial changes are anticipated to the National Cervical Screening Program (NCSP) through a process called 'the Renewal', to ensure any changes that are introduced will be acceptable and well understood by women. The objectives of this study were to describe Queensland women's current knowledge of cervical cancer/screening and HPV, their beliefs and attitudes towards Pap smears and the HPV vaccine and seek their advice on effective methods for communicating changes to the NCSP in their communities. This research was a descriptive-exploratory study that incorporated a combination of qualitative and quantitative methods within the context of the Health Belief Model (HBM). A computer-assisted telephone interview (CATI) survey of 1002 Queensland women was conducted in Phase 1 of the study. During Phase 2 of the study, 23 focus groups were conducted throughout Queensland to gather in-depth information about women's knowledge, awareness and acceptance about cervical cancer prevention strategies. This study found high levels of awareness of HPV (over 60%) and the HPV vaccine (over 86%) amongst Queensland women. However, it also identified considerable uncertainty amongst participants about perceived susceptibility to cervical cancer, especially, the link between cervical cancer, HPV and sexual activity. Women also had limited understanding of the benefit of the Pap smear as a preventative strategy, with many women thinking the main purpose of the Pap smear was for the early detection of cancer. Despite high awareness of HPV, women participating in this study also had significant knowledge deficits about their susceptibility to HPV and the severity of HPV infection. Queensland women had high levels of awareness of the HPV vaccine, which was most commonly via the media. High acceptance of the HPV vaccine was found amongst participants although awareness of the full benefits of vaccination was not evident with little acknowledgement that the quadrivalent vaccine used in the NHPVP would also prevent genital warts. Extensive barriers to having Pap smears, including physical and psychological discomfort, were identified and the most common barriers to vaccination were concerns about side effects and a lack of information upon which to make a decision about consent. Women described enablers for screening participation, such as reminder systems and practitioner characteristics, and expressed positive views towards self collected testing as an enabler, particularly for women who did not attend screening. As this study was conducted with Queensland women it may therefore not be representative of women from other parts of Australia and as participants were more likely to report they were regular screeners than Queensland women overall, these results may not be representative of women least likely to participate in cervical screening. The use of self-reported cervical screening history may also have led to over-reporting of screening status and previous abnormalities by participants. This study reveals significant gaps in Queensland women's knowledge that require effective communication strategies to address. Recommendations from this study highlight the need for increased community education to raise awareness about primary and secondary cervical cancer prevention strategies, training of cervical screening providers in sensitive examination techniques, a reduction in costs associated with screening, the exploration of alternative service models and communication plans that incorporate methods women trust and recommend for disseminating information about changes to the NCSP. This study is the first large study to explore women's perceptions of the Pap smear and barriers to screening, their knowledge about HPV and their attitudes towards the HPV vaccine in Queensland, since the introduction of the NHPVP. It highlights considerable uncertainty about many aspects of cervical cancer and primary and secondary prevention strategies available in Australia and identified many barriers to cervical screening and concerns about HPV vaccination. These knowledge gaps and barriers need to be taken into account and addressed within the context of anticipated changes to the NCSP to ensure benefits are maximised for women in future primary and secondary cervical cancer prevention strategies in the Australian context.

Analysis of acute-phase proteins, AHSG, C3, CLI, HP and SAA, reveals distinctive expression patterns associated with breast, colorectal and lung cancer

Early detection, clinical management and disease recurrence monitoring are critical areas in cancer treatment in which specific biomarker panels are likely to be very important in each of these key areas. We have previously demonstrated that levels of alpha-2-heremans-schmid-glycoprotein (AHSG), complement component C3 (C3), clusterin (CLI), haptoglobin (HP) and serum amyloid A (SAA) are significantly altered in serum from patients with squamous cell carcinoma of the lung. Here, we report the abundance levels for these proteins in serum samples from patients with advanced breast cancer, colorectal cancer (CRC) and lung cancer compared to healthy controls (age and gender matched) using commercially available enzyme-linked immunosorbent assay kits. Logistic regression (LR) models were fitted to the resulting data, and the classification ability of the proteins was evaluated using receiver-operating characteristic curve and leave-one-out cross-validation (LOOCV). The most accurate individual candidate biomarkers were C3 for breast cancer [area under the curve (AUC) = 0.89, LOOCV = 73%], CLI for CRC (AUC = 0.98, LOOCV = 90%), HP for small cell lung carcinoma (AUC = 0.97, LOOCV = 88%), C3 for lung adenocarcinoma (AUC = 0.94, LOOCV = 89%) and HP for squamous cell carcinoma of the lung (AUC = 0.94, LOOCV = 87%). The best dual combination of biomarkers using LR analysis were found to be AHSG + C3 (AUC = 0.91, LOOCV = 83%) for breast cancer, CLI + HP (AUC = 0.98, LOOCV = 92%) for CRC, C3 + SAA (AUC = 0.97, LOOCV = 91%) for small cell lung carcinoma and HP + SAA for both adenocarcinoma (AUC = 0.98, LOOCV = 96%) and squamous cell carcinoma of the lung (AUC = 0.98, LOOCV = 84%). The high AUC values reported here indicated that these candidate biomarkers have the potential to discriminate accurately between control and cancer groups both individually and in combination with other proteins. Copyright © 2011 UICC.

Somatostatin receptor expression in lung cancer

Experimental evidence suggests that somatostatin analogues may have a role to play in the management of lung tumours. We evaluated membrane preparations of nine small cell lung cancer (SCLC) cell lines and of tumour samples from 3 patients with non-small cell lung cancer (NSCLC), 1 patient with an atypical carcinoid and another with a bronchial carcinoid for the presence of specific binding sites for RC-160, a potent growth inhibitory octapeptide analogue of somatostatin. Specific binding was noted on six of nine SCLC lines. Radio-receptor assay on the cell line NCI H 69 showed evidence of two specific binding sites for RC-160, one with high affinity and the other with low affinity. Binding sites were also found on all five tumour samples. Scatchard analysis indicated the presence of a single class of receptors with high affinity in each case. Histological assessment of the resected specimens before binding assay showed them to be comprised of tumour cells and necrotic tissue, stroma and/or inflammatory cells. Therefore, the specific binding of RC-160 may be to tissues other than the tumour cells. In 3 patients, from whom the tumour samples were obtained, radiolabelled somatostatin analogue scintigraphy using [111In] pentetreotide was performed prior to surgery. In all cases, the radiolabel localised the disease. This study demonstrates the presence of specific binding sites for RC-160 in SCLC. Furthermore, the detection of specific binding in vitro and in vivo in NSCLC and intrapulmonary carcinoids demonstrates that these tumours contain cells which express specific binding sites for somatostatin. These results suggest that RC-160 may have a role toplay as a therapeutic agent in lung cancer.

Angiogenesis as a biomarker and target in cancer chemoprevention

Angiogenesis, the formation of new blood vessels from existing vasculature, is essential to the late stages of carcinogenesis, allowing tumours to grow beyond 1-2 mm in diameter, invade surrounding tissue, and metastasise. However, more than two decades ago, angiogenesis that preceded neoplastic transformation was seen. Indeed, it can be detected in inflammatory and infectious diseases that increase the risk of developing cancer. Recent advances in fluorescence endoscopy and histological assessment suggest that, for certain cancers, the degree of new blood-vessel formation may differ between the early and late stages of carcinogenic progression. The association between angiogenesis and cancer occurrence, and ease of detection of this process in accessible tissues early in carcinogenesis, mean that angiogenesis fulfils the criteria for a biomarker of the effectiveness of chemopreventive intervention. There is also some evidence that biochemical assays of angiogenic growth factors may after similar potential as surrogate biomarkers. Many natural and synthetic chemopreventive agents in development or in clinical use inhibit new vessel formation in vivo. Validation of angiogenesis as a biomarker for the effectiveness of chemoprevention should further the advancement of some chemopreventive agents.

Prognostic value of hTERT mRNA expression in surgical samples of lung cancer patients : the European Early Lung Cancer Project

Lung cancer is the most important cause of cancer-related mortality. Resectability and eligibility for treatment with adjuvant chemotherapy is determined by staging according to the TNM classification. Other determinants of tumour behaviour that predict disease outcome, such as molecular markers, may improve decision-making. Activation of the gene encoding human telomerase reverse transcriptase (hTERT) is implicated in the pathogenesis of lung cancer, and consequently detection of hTERT mRNA might have prognostic value for patients with early stage lung cancer. A cohort of patients who underwent a complete resection for early stage lung cancer was recruited as part of the European Early Lung Cancer (EUELC) project. In 166 patients expression of hTERT mRNA was determined in tumour tissue by quantitative real-time RT-PCR and related to that of a house-keeping gene (PBGD). Of a subgroup of 130 patients tumour-distant normal tissue was additionally available for hTERT mRNA analysis. The correlation between hTERT levels of surgical samples and disease-free survival was determined using a Fine and Gray hazard model. Although hTERT mRNA positivity in tumour tissue was significantly associated with clinical stage (Fisher's exact test p=0.016), neither hTERT mRNA detectability nor hTERT mRNA levels in tumour tissue were associated with clinical outcome. Conversely, hTERT positivity in adjacent normal samples was associated with progressive disease, 28% of patients with progressive disease versus 7.5% of disease-free patients had detectable hTERT mRNA in normal tissue [adjusted HR: 3.60 (1.64-7.94), p=0.0015]. hTERT mRNA level in tumour tissue has no prognostic value for patients with early stage lung cancer. However, detection of hTERT mRNA expression in tumour-distant normal lung tissue may indicate an increased risk of progressive disease.

A multiscale road map of cancer spheroids : incorporating experimental and mathematical modelling to understand cancer progression

Computational models represent a highly suitable framework, not only for testing biological hypotheses and generating new ones but also for optimising experimental strategies. As one surveys the literature devoted to cancer modelling, it is obvious that immense progress has been made in applying simulation techniques to the study of cancer biology, although the full impact has yet to be realised. For example, there are excellent models to describe cancer incidence rates or factors for early disease detection, but these predictions are unable to explain the functional and molecular changes that are associated with tumour progression. In addition, it is crucial that interactions between mechanical effects, and intracellular and intercellular signalling are incorporated in order to understand cancer growth, its interaction with the extracellular microenvironment and invasion of secondary sites. There is a compelling need to tailor new, physiologically relevant in silico models that are specialised for particular types of cancer, such as ovarian cancer owing to its unique route of metastasis, which are capable of investigating anti-cancer therapies, and generating both qualitative and quantitative predictions. This Commentary will focus on how computational simulation approaches can advance our understanding of ovarian cancer progression and treatment, in particular, with the help of multicellular cancer spheroids, and thus, can inform biological hypothesis and experimental design.

Proteomic and other analyses to determine the functional consequences of deregulated kallikrein-related peptidase (KLK) expression in prostate and ovarian cancer

Rapidly developing proteomic tools are improving detection of deregulated kallikrein-related peptidase (KLK) expression, at the protein level, in prostate and ovarian cancer, as well as facilitating the determination of functional consequences downstream. Mass spectrometry (MS)-driven proteomics uniquely allows for the detection, identification and quantification of thousands of proteins in a complex protein pool, and this has served to identify certain KLKs as biomarkers for these diseases. In this review we describe applications of this technology in KLK biomarker discovery, and elucidate MS-based techniques which have been used for unbiased, global screening of KLK substrates within complex protein pools. Although MS-based KLK degradomic studies are limited to date, they helped to discover an array of novel KLK substrates. Substrates identified by MS-based degradomics are reported with improved confidence over those determined by incubating a purified or recombinant substrate and protease of interest, in vitro. We propose that these novel proteomic approaches represent the way forward for KLK research, in order to correlate proteolysis of biological substrates with tissue-related consequences, toward clinical targeting of KLK expression and function for cancer diagnosis, prognosis and therapies.

Classification of cancer-related death certificates using machine learning

Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.