Advances in proteomic prostate cancer biomarker discovery

Prostate cancer is the most common non-cutaneous cancer in men in the United States. For reasons largely unknown, the incidence of prostate cancer has increased in the last two decades, in spite or perhaps because of a concomitant increase in serum prostate-specific antigen (PSA) screening. While PSA is acknowledged not to be an ideal biomarker for prostate cancer detection, it is however widely used by physicians due to lack of an alternative. Thus, the identification of a biomarker(s) that can complement or replace PSA represents a major goal for prostate cancer research. Screening complex biological specimens such as blood, urine, and tissue to identify protein biomarkers has become increasingly popular over the last decade thanks to advances in proteomic discovery methods. The completion of human genome sequence together with new development in mass spectrometry instrumentation and bioinformatics has been a major driving force in biomarker discovery research. Here we review the current state of proteomic applications as applied to various sample sources including blood, urine, tissue, and “secretome” for the purpose of prostate cancer biomarker discovery. Additionally, we review recent developments in validation of putative markers, efforts at systems biology approach, and current challenges of proteomics in biomarker discovery.     

Computational protein biomarker prediction: a case study for prostate cancer

Background  

Recent technological advances in mass spectrometry pose challenges in computational mathematics and statistics to process the mass spectral data into predictive models with clinical and biological significance. We discuss several classification-based approaches to finding protein biomarker candidates using protein profiles obtained via mass spectrometry, and we assess their statistical significance. Our overall goal is to implicate peaks that have a high likelihood of being biologically linked to a given disease state, and thus to narrow the search for biomarker candidates.     

Utility of d-serine monitoring in kidney disease

d-Serine, a long-term undetected enantiomer of serine, is now showing its potential as a biomarker for kidney diseases. The intra-body dynamics of d-serine, currently defined by blood levels and urinary excretion dynamics, are useful for a comprehensive assessment of kidney function and disease activity. Thus, widespread adoption of d-serine as a biomarker can resolve the long-standing clinical challenge of the early detection and prognostic prediction of kidney diseases. Accuracy and reliability of the measurements are particularly important because these measurements will affect treatment decisions and thus impact the patient's emotional state and quality of life. Accordingly, this review focuses on current clinical challenges in kidney diseases and the potential for monitoring of d-serine to overcome these issues, and discuss the requirements of accurate quantification.     

Proteomics of human cerebrospinal fluid: discovery and verification of biomarker candidates in neurodegenerative diseases using quantitative proteomics

There is an urgent need for novel biomarkers that can be used to improve the diagnosis, predict the disease progression, improve our understanding of the pathology or serve as therapeutic targets for neurodegenerative diseases. Cerebrospinal fluid (CSF) is in direct contact with the CNS and reflects the biochemical state of the CNS under different physiological and pathological settings. Because of this, CSF is regarded as an excellent source for identifying biomarkers for neurological diseases and other diseases affecting the CNS. Quantitative proteomics and sophisticated computational software applied to analyze the protein content of CSF has been fronted as an attractive approach to find novel biomarkers for neurological diseases. This review will focus on some of the potential pitfalls in biomarker studies using CSF, summarize the status of the field of CSF proteomics in general, and discuss some of the most promising proteomics biomarker study approaches. A brief status of the biomarker discovery efforts in multiple sclerosis, Alzheimer's disease, and Parkinson's disease is also given.     

Soluble polymer-based isotopic labeling (SoPIL): a new strategy to discover protein biomarkers?

Much attention has been given to protein biomarker discovery in the field of proteomics in the past few years. Proteomic strategies for biomarker discovery normally include the identification of proteins that alter during the progression of a particular disease state in high throughput. To perform these studies requires the ability to measure changes of low-abundance proteins in highly complex mixtures from different biological states. Soluble polymer-based isotope labeling (SoPIL) is a new proteomics strategy that targets specific classes of proteins for isotopic labeling, efficient isolation and accurate quantitation by mass spectrometry. The method exploits the features of homogenous solution-phase reaction, simple solid-phase extraction and characteristic cell-permeable nanoparticles. Recent applications demonstrate that the SoPIL reagents are ideal for quantitative proteomics and phosphoproteomics, and could have the potential to discover disease markers in the most physiologically relevant settings.     

Soluble polymer-based isotopic labeling (SoPIL): a new strategy to discover protein biomarkers?

Much attention has been given to protein biomarker discovery in the field of proteomics in the past few years. Proteomic strategies for biomarker discovery normally include the identification of proteins that alter during the progression of a particular disease state in high throughput. To perform these studies requires the ability to measure changes of low-abundance proteins in highly complex mixtures from different biological states. Soluble polymer-based isotope labeling (SoPIL) is a new proteomics strategy that targets specific classes of proteins for isotopic labeling, efficient isolation and accurate quantitation by mass spectrometry. The method exploits the features of homogenous solution-phase reaction, simple solid-phase extraction and characteristic cell-permeable nanoparticles. Recent applications demonstrate that the SoPIL reagents are ideal for quantitative proteomics and phosphoproteomics, and could have the potential to discover disease markers in the most physiologically relevant settings.     

Serum 27-nor-5β-cholestane-3,7,12,24,25 pentol glucuronide discovered by metabolomics as potential diagnostic biomarker for epithelium ovarian cancer

The aim of this study was to use a two steps strategy metabolomics to screen/identify and validate novel metabolic biomarker(s) for epithelial ovarian cancer (EOC). In the screening step, serum samples from 27 healthy women, 28 benign ovarian tumors, and 29 EOCs were analyzed by using LC-MS based nontargeted metabolomics. The three groups were separated with OSC filtered PLS-DA model, and six metabolites (27-nor-5β-cholestane-3,7,12,24,25 pentol glucuronide (CPG), phenylalanine, glycocholic acid, propionylcarnitine, Phe-Phe and Lyso PC (18:2)) were considered as potential biomarker candidates. In the validation step, the six metabolites were analyzed in targeted metabolomics by LC-selective ion monitoring mass spectrometry in another 685 serum samples with various clinical backgrounds. As a result, CPG was evaluated to be a potential biomarker and its content was elevated in EOC tissues compared with benign ovarian tumor tissues (p = 0.0005). Besides, CPG levels were found to be up-regulated in early stage EOC and in the three types of EOC histological types. Other variables such as nonovarian diseases, medicine consumption, gynecological inflammations, and menopausal state did not interfere in using CPG as diagnosis marker. CPG was found to be complementary to CA125. Our findings suggest that CPG can be considered a statistical relevant biomarker of EOC, ready for early stage detection.     

Monitoring of cellular senescence by DNA-methylation at specific CpG sites

Replicative senescence has fundamental implications on cell morphology, proliferation, and differentiation potential. Here, we describe a simple method to track long-term culture based on continuous DNA-methylation changes at six specific CpG sites. This epigenetic senescence signature can be used as biomarker for various cell types to predict the state of cellular senescence with regard to the number of passages, population doublings, or days of in vitro culture.     

MALDI mass spectrometry for direct tissue analysis: a new tool for biomarker discovery

The direct analysis of tissue sections by MALDI mass spectrometry holds tremendous potential for biomarker discovery. This technology routinely allows many hundreds of proteins to be detected over a mass range of approximately 2000-70 000 Da while maintaining the spatial localization of the proteins detected. This technology has been applied to a wide range of tissue samples, including human glioma tissue and human lung tumor tissue. In many cases, biostatistical analyses of the resulting protein profiles revealed patterns that correlated with disease state and/or clinical endpoints. This work serves as a review of recent applications and summarizes the current state of technology.     

Identification of Protein Biomarkers for Cervical Cancer Using Human Cervicovaginal Fluid

Objectives

Cervicovaginal fluid (CVF) can be considered as a potential source of biomarkers for diseases of the lower female reproductive tract. The fluid can easily be collected, thereby offering new opportunities such as the development of self tests. Our objective was to identify a CVF protein biomarker for cervical cancer or its precancerous state.

Methods

A differential proteomics study was set up using CVF samples from healthy and precancerous women. Label-free spectral counting was applied to quantify protein abundances.

Results

The proteome analysis revealed 16 candidate biomarkers of which alpha-actinin-4 (p = 0.001) and pyruvate kinase isozyme M1/M2 (p = 0.014) were most promising. Verification of alpha-actinin-4 by ELISA (n = 28) showed that this candidate biomarker discriminated between samples from healthy and both low-risk and high-risk HPV-infected women (p = 0.009). Additional analysis of longitudinal samples (n = 29) showed that alpha-actinin-4 levels correlated with virus persistence and clearing, with a discrimination of approximately 18 pg/ml.

Conclusions

Our results show that CVF is an excellent source of protein biomarkers for detection of lower female genital tract pathologies and that alpha-actinin-4 derived from CVF is a promising candidate biomarker for the precancerous state of cervical cancer. Further studies regarding sensitivity and specificity of this biomarker will demonstrate its utility for improving current screening programs and/or its use for a cervical cancer self-diagnosis test.     

Recent advances in proteomically subtyping pancreatic ductal adenocarcinomas and their potential clinical impact

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a median overall survival of 6 months. Late diagnosis due to the absence of specific symptoms during disease development, in addition to extensive metastatic potential and resistance to chemotherapy and radiotherapy, are the most important reasons for short survival. Research efforts have therefore been focused on the development of early disease detection. However, the only US FDA-approved clinical biomarker, CA19–9, is considered inapplicable for screening and/or early detection of PDAC. The following editorial provides the reader with a short introduction to the topic of PDAC and gives focus to the current state of proteomic research in the field of PDAC biomarker discovery. This editorial also highlights the efforts made to subdivide this tumor entity and the potential clinical impact of patient stratification. Finally, the author provides opinions on the impact of proteomics to PDAC subtype stratification over the next 5 years.     

Targeted proteomics in biomarker validation: detection and quantification of proteins using a multi-dimensional peptide separation strategy

Authentic biomarkers, distilling the essence of a complex, functionally significant process in a mammalian system into a precise, physicochemical measurement have been implicated as a tool of increasing importance for drug discovery and development. However, even in spite of recent technological advances, validating a new biomarker candidate, where generation of suitable antibodies is required, is still a long-lasting task. Methods to accelerate initial validation by MS approaches have been suggested, but all methods described so far are associated with serious drawbacks, finally leading to non-generic methods of detection and quantification. Moreover, when complex body fluids are used as samples, efficient debulking strategies are crucial to open a window of analytical sensitivity in the ng/mL range, where many diagnostically relevant analytes are present. Here we report the proof-of-principle of a multi-dimensional strategy for accelerated initial validation of biomarker candidates by MS, which promises to be generally applicable, sensitive and quantitative. The method presented employs a combination of electrophoretic and chromatographic steps on the peptide level, followed by MS quantification using isotopically labeled synthetic peptides as internal standards. Our proposed workflow includes up to four dimensions, finally resulting in a desired LOD sufficient to detect and quantify diagnostically relevant analytes from complex samples. Although the current state of the method only represents a starting point for further validation and development, it reveals great potential in biomarker validation.     

Profiling of lysine-acetylated proteins in human urine

A biomarker is a measurable indicator associated with changes in physiological state or disease. In contrast to the blood which is under homeostatic controls, urine reflects changes in the body earlier and more sensitively, and is therefore a better biomarker source. Lysine acetylation is an abundant and highly regulated post-translational modification. It plays a pivotal role in modulating diverse biological processes and is associated with various important diseases. Enrichment or visualization of proteins with specific post-translational modifications provides a method for sampling the urinary proteome and reducing sample complexity. In this study, we used anti-acetyllysine antibody-based immunoaffinity enrichment combined with high-resolution mass spectrometry to profile lysine-acetylated proteins in normal human urine. A total of 629 acetylation sites on 315 proteins were identified, including some very low-abundance proteins. This is the first proteome-wide characterization of lysine acetylation proteins in normal human urine. Our dataset provides a useful resource for the further discovery of lysine-acetylated proteins as biomarkers in urine.     

Cancer screening: a mathematical model relating secreted blood biomarker levels to tumor sizes

Background

Increasing efforts and financial resources are being invested in early cancer detection research. Blood assays detecting tumor biomarkers promise noninvasive and financially reasonable screening for early cancer with high potential of positive impact on patients'' survival and quality of life. For novel tumor biomarkers, the actual tumor detection limits are usually unknown and there have been no studies exploring the tumor burden detection limits of blood tumor biomarkers using mathematical models. Therefore, the purpose of this study was to develop a mathematical model relating blood biomarker levels to tumor burden.

Methods and Findings

Using a linear one-compartment model, the steady state between tumor biomarker secretion into and removal out of the intravascular space was calculated. Two conditions were assumed: (1) the compartment (plasma) is well-mixed and kinetically homogenous; (2) the tumor biomarker consists of a protein that is secreted by tumor cells into the extracellular fluid compartment, and a certain percentage of the secreted protein enters the intravascular space at a continuous rate. The model was applied to two pathophysiologic conditions: tumor biomarker is secreted (1) exclusively by the tumor cells or (2) by both tumor cells and healthy normal cells. To test the model, a sensitivity analysis was performed assuming variable conditions of the model parameters. The model parameters were primed on the basis of literature data for two established and well-studied tumor biomarkers (CA125 and prostate-specific antigen [PSA]). Assuming biomarker secretion by tumor cells only and 10% of the secreted tumor biomarker reaching the plasma, the calculated minimally detectable tumor sizes ranged between 0.11 mm³ and 3,610.14 mm³ for CA125 and between 0.21 mm³ and 131.51 mm³ for PSA. When biomarker secretion by healthy cells and tumor cells was assumed, the calculated tumor sizes leading to positive test results ranged between 116.7 mm³ and 1.52 × 10⁶ mm³ for CA125 and between 27 mm³ and 3.45 × 10⁵ mm³ for PSA. One of the limitations of the study is the absence of quantitative data available in the literature on the secreted tumor biomarker amount per cancer cell in intact whole body animal tumor models or in cancer patients. Additionally, the fraction of secreted tumor biomarkers actually reaching the plasma is unknown. Therefore, we used data from published cell culture experiments to estimate tumor cell biomarker secretion rates and assumed a wide range of secretion rates to account for their potential changes due to field effects of the tumor environment.

Conclusions

This study introduced a linear one-compartment mathematical model that allows estimation of minimal detectable tumor sizes based on blood tumor biomarker assays. Assuming physiological data on CA125 and PSA from the literature, the model predicted detection limits of tumors that were in qualitative agreement with the actual clinical performance of both biomarkers. The model may be helpful in future estimation of minimal detectable tumor sizes for novel proteomic biomarker assays if sufficient physiologic data for the biomarker are available. The model may address the potential and limitations of tumor biomarkers, help prioritize biomarkers, and guide investments into early cancer detection research efforts.     

1H, 15N and 13C assignments of yellow fluorescent protein (YFP) Venus

We present here the backbone and side-chain NMR assignments of YFP Venus, a 238-residue protein that emits yellow fluorescence in its native state. Venus is a variant of the green fluorescent protein (GFP), which has improved chromophore maturation and brightness, and the photochemistry and photophysics of which are insensitive to experimental conditions, such as the pH value and buffer content, making it a favourable biomarker.     

The Marker State Space (MSS) Method for Classifying Clinical Samples

The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines “marker states” based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications.     

Mean Recency Period for Estimation of HIV-1 Incidence with the BED-Capture EIA and Bio-Rad Avidity in Persons Diagnosed in the United States with Subtype B

HIV incidence estimates are used to monitor HIV-1 infection in the United States. Use of laboratory biomarkers that distinguish recent from longstanding infection to quantify HIV incidence rely on having accurate knowledge of the average time that individuals spend in a transient state of recent infection between seroconversion and reaching a specified biomarker cutoff value. This paper describes five estimation procedures from two general statistical approaches, a survival time approach and an approach that fits binomial models of the probability of being classified as recently infected, as a function of time since seroconversion. We compare these procedures for estimating the mean duration of recent infection (MDRI) for two biomarkers used by the U.S. National HIV Surveillance System for determination of HIV incidence, the Aware BED EIA HIV-1 incidence test (BED) and the avidity-based, modified Bio-Rad HIV-1/HIV-2 plus O ELISA (BRAI) assay. Collectively, 953 specimens from 220 HIV-1 subtype B seroconverters, taken from 5 cohorts, were tested with a biomarker assay. Estimates of MDRI using the non-parametric survival approach were 198.4 days (SD 13.0) for BED and 239.6 days (SD 13.9) for BRAI using cutoff values of 0.8 normalized optical density and 30%, respectively. The probability of remaining in the recent state as a function of time since seroconversion, based upon this revised statistical approach, can be applied in the calculation of annual incidence in the United States.     

Current progress in protein profiling of complex samples

Accurate cancer biomarkers are needed for early detection, disease classification, prediction of therapeutic response and monitoring treatment. While there appears to be no shortage of candidate biomarker proteins, a major bottleneck in the biomarker pipeline continues to be their verification by enzyme linked immunosorbent assays. Multiple reaction monitoring (MRM), also known as selected reaction monitoring, is a targeted mass spectrometry approach to protein quantitation and is emerging to bridge the gap between biomarker discovery and clinical validation. Highly multiplexed MRM assays are readily configured and enable simultaneous verification of large numbers of candidates facilitating the development of biomarker panels which can increase specificity. This review focuses on recent applications of MRM to the analysis of plasma and serum from cancer patients for biomarker verification. The current status of this approach is discussed along with future directions for targeted mass spectrometry in clinical biomarker validation.