Prostate cancer: progression of prostate-specific antigen after external beam irradiation

This paper is concerned with the development of a stochastic path of prostate-specific antigen (PSA) level after radiation treatment for prostate cancer. PSA is a biomarker for prostate cancer, higher levels of which indicate the seriousness of the cancer progression. Following the deterministic modeling of the data by the previous authors, Cox et al., this paper is concerned with the theoretical knowledge that could be gained by the stochastic modeling in discrete form of the PSA path over time. The expected value of the PSA level is computed and compared with the deterministic model and it is found that they are the same for about the first year after radiation therapy. The American Society for Therapeutic Radiology has set a consensus panel definition of biochemical failure following radiation therapy: the rise in three consecutive levels of PSA is considered to be a failure of the radiation therapy. Knowledge of the path of PSA presented in this paper would be useful in the management of the radiation treatment and in particular assessing quantitatively any clinically based policy for defining recurrence after radiation therapy. Application of the model is illustrated by fitting it to clinical data available in the University of Michigan cancer center.     

Proteomic analysis of formalin-fixed prostate cancer tissue

Proteomic analysis of formalin-fixed paraffin-embedded (FFPE) tissue would enable retrospective biomarker investigations of this vast archive of pathologically characterized clinical samples that exist worldwide. These FFPE tissues are, however, refractory to proteomic investigations utilizing many state of the art methodologies largely due to the high level of covalently cross-linked proteins arising from formalin fixation. A novel tissue microdissection technique has been developed and combined with a method to extract soluble peptides directly from FFPE tissue for mass spectral analysis of prostate cancer (PCa) and benign prostate hyperplasia (BPH). Hundreds of proteins from PCa and BPH tissue were identified, including several known PCa markers such as prostate-specific antigen, prostatic acid phosphatase, and macrophage inhibitory cytokine-1. Quantitative proteomic profiling utilizing stable isotope labeling confirmed similar expression levels of prostate-specific antigen and prostatic acid phosphatase in BPH and PCa cells, whereas the expression of macrophage inhibitory cytokine-1 was found to be greater in PCa as compared with BPH cells.     

Using spectral moments of spiral networks based on PSA/mass spectra outcomes to derive quantitative proteome-disease relationships (QPDRs) and predicting prostate cancer

In prostate cancer (PCa), prognostic (predictive) factors are particularly important given the marked heterogeneity of this disease at clinical, morphologic, and biomolecular levels. Blood contains a treasure of previously unstudied biomarkers that could reflect the ongoing physiological state of all tissue. The serum prostate-specific antigen (PSA) measurement is a very good biomarker for PCa, but the percentage of bad classification is somewhat high. The blood proteome mass spectra (MS) represent a potential tool for detection of diseases; however the identification of a single biomarker from the complex output from MS is often difficult. In this paper, we propose a general strategy, based on computational chemistry techniques, which should improve the predictive power of PSA. Our group adapted the square-spiral graph to represent human serum-plasma-proteome MS for healthy and PCa patients. These graphs were previously applied to DNA and/or protein sequences. In this work, we calculated different classes of connectivity indices (CIs), and created various models based on the spectral moments. The best QPDRs model found showed accuracy values ranging from 71.7% to 97.2%, and 70.4% to 99.2% of specificity. This methodology might be useful for several applications in computational chemistry.     

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.     

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.     

Intracellular cargo delivery by an octaarginine transporter adapted to target prostate cancer cells through cell surface protease activation

Delivery of therapeutics and imaging agents to target tissues requires localization and activation strategies with molecular specificity. Cell-associated proteases can be used for these purposes in a number of pathologic conditions, and their enzymatic activities can be exploited for activation strategies. Here, molecules based on the d-arginine octamer (r8) protein-transduction domain (PTD, also referred to as molecular transporters) have been adapted for selective uptake into cells only after proteolytic cleavage of a PTD-attenuating sequence by the prostate-specific antigen (PSA), an extracellular protease associated with the surface and microenvironment of certain prostate cancer cells. Convergent syntheses of these activatable PTDs (APTDs) are described, and the most effective r8 PTD-attenuating sequence is identified. The conjugates are shown to be stable in serum, cleaved by PSA, and taken up into Jurkat (human T cells) and PC3M prostate cancer cell lines only after cleavage by PSA. These APTD peptide-based molecules may facilitate targeted delivery of therapeutics or imaging agents to PSA-expressing prostate cancers.     

Nanostructured optical microchips for cancer biomarker detection

Herein we report the label-free detection of a cancer biomarker using newly developed arrayed nanostructured Fabry-Perot interferometer (FPI) microchips. Specifically, the prostate cancer biomarker free prostate-specific antigen (f-PSA) has been detected with a mouse anti-human PSA monoclonal antibody (mAb) as the receptor. Experiments found that the limit-of-detection of current nanostructured FPI microchip for f-PSA is about 10pg/mL and the upper detection range for f-PSA can be dynamically changed by varying the amount of the PSA mAb immobilized on the sensing surface. The control experiments have also demonstrated that the immunoassay protocol used in the experiments shows excellent specificity and selectivity, suggesting the great potential to detect the cancer biomarkers at trace levels in complex biofluids. In addition, given its nature of low cost, simple-to-operation and batch fabrication capability, the arrayed nanostructured FPI microchip-based platform could provide an ideal technical tool for point-of-care diagnostics application and anticancer drug screen and discovery.     

A stochastic model for prostate-specific antigen levels

We introduce a continuous stochastic model for the prostate-specific antigen (PSA) levels following radiotherapy and derive solutions for the associated partial differential (Kolmogorov-Chapman) equation. The solutions describe the evolution of the time-dependent density for PSA levels which take into account an absorbing condition along the boundary and various initial conditions. We include implications for single-dose and multi-dose radiation treatment regimens and discuss parameter estimation and sensitivity issues.     

Cancer de la prostate : les marqueurs biologiques

Normal prostate cells and prostate cancer cells produce prostate-specific antigen (PSA): thus, it is frequently increased in non-malignant conditions such as prostatitis and benign prostatic hyperplasia. Indeed, PSA is an excellent biomarker to monitor disease progression. The low diagnostic specificity of PSA leads to many false-positive and a large number of biopsies. These well-recognized limitations of PSA suggest that new prostate cancer biomarkers could play a useful role in reducing the number of unnecessary biopsies.     

A stochastic model for PSA levels: behavior of solutions and population statistics

This paper investigates the partial differential equation for the evolving distribution of prostate-specific antigen (PSA) levels following radiotherapy. We also present results on the behavior of moments for the evolving distribution of PSA levels and estimate the probability of long-term treatment success and failure related to values of treatment and disease parameters. Results apply to a much wider range of parameter values than was considered in earlier studies, including parameter combinations that are patient specific.     

In vivo detection of aflatoxin-induced lipid free radicals in rat bile

Human kallikrein hK3 (prostate-specific antigen) is a chymotrypsin-like serine protease which is widely used in the diagnosis of prostate cancer. Assays of the enzymatic activity of hK3 in extracellular fluids have been limited by a lack of sensitive synthetic substrates. This report describes the design of a series of internally quenched fluorescent peptides containing an amino acid sequence based on preferential hK3 cleavage sites in semenogelins. Those were identified by 2-D gel electrophoresis analysis and N-terminal sequencing of semenogelin fragments generated by ex vivo proteolysis in freshly ejaculated semen. These peptides were cleaved by hK3 at the C-terminal of certain tyrosyl or glutaminyl residues with k(cat)/K(m) values of 15000-60000 M(-1) s(-1). The substrate Abz-SSIYSQTEEQ-EDDnp was cleaved at the Tyr-Ser bond with a specificity constant k(cat)/K(m) of 60000 M(-1) s(-1), making it the best substrate for hK3 described to date.     

Dépistage du cancer de la prostate : les arguments « contre »

The mass screening for prostate cancer is often a subject of passionate debates since the availability of prostate-specific antigen testing, twenty years ago. The supporters of the mass screening emphasize on the fact that early detection of a tumour offers a better quality and less invasive treatment. In reality, this reasoning relies on some long-term studies on certain groups of subjects in spite of the fact that comparison with control groups has never been done. To examine the relationship between the mass screening and the decrease of population mortality (principal criteria for judgment), it is necessary to have recourse to randomized studies. At present, there are two such studies whose results will be available within three or four years. In the meantime, there is no answer to the question of the place for mass screening. Meanwhile, analysis of different parameters that would support the use of mass screening for prostate cancer to reduce the number of deaths has shown that the probability that it would is very low. Considering these conditions as well as the recommendations by the authorities, the mass screening has not yet found its place. The early detection requested by a patient accompanied by the discussion about its advantages and inconveniences are for now the right way between negligence and excessive management.     

A Bayesian method to estimate the optimal threshold of a longitudinal biomarker

The objective of this study was to develop methods to estimate the optimal threshold of a longitudinal biomarker and its credible interval when the diagnostic test is based on a criterion that reflects a dynamic progression of that biomarker. Two methods are proposed: one parametric and one non‐parametric. In both the cases, the Bayesian inference was used to derive the posterior distribution of the optimal threshold from which an estimate and a credible interval could be obtained. A numerical study shows that the bias of the parametric method is low and the coverage probability of the credible interval close to the nominal value, with a small coverage asymmetry in some cases. This is also true for the non‐parametric method in case of large sample sizes. Both the methods were applied to estimate the optimal prostate‐specific antigen nadir value to diagnose prostate cancer recurrence after a high‐intensity focused ultrasound treatment. The parametric method can also be applied to non‐longitudinal biomarkers.     

Prostate cancer diagnostics: Clinical challenges and the ongoing need for disruptive and effective diagnostic tools

The increased incidence and the significant health burden associated with carcinoma of the prostate have led to substantial changes in its diagnosis over the past century. Despite technological advancements, the management of prostate cancer has become progressively more complex and controversial for both early and late-stage disease. The limitations and potential harms associated with the use of prostate-specific antigen (PSA) as a diagnostic marker have stimulated significant investigation of numerous novel biomarkers that demonstrate varying capacities to detect prostate cancer and can decrease unnecessary biopsies. However, only a few of these markers have been approved for specific clinical settings while the others have not been adequately validated for use. This review systematically and critically assesses ongoing issues and emerging challenges in the current state of prostate cancer diagnostic tools and the need for disruptive next generation tools based on analysis of combinations of these biomarkers to enhance predictive accuracy which will benefit clinical diagnostics and patient welfare.     

Prostate cancer vaccines: the long road to clinical application

Cancer vaccines as a modality of immune-based cancer treatment offer the promise of a non-toxic and efficacious therapeutic alternative for patients. Emerging data suggest that response to vaccination largely depends on the magnitude of the type I immune response generated, epitope spreading and immunogenic modulation of the tumor. Moreover, accumulating evidence suggests that cancer vaccines will likely induce better results in patients with low tumor burden and less aggressive disease. To induce long-lasting clinical responses, vaccines will need to be combined with immunoregulatory agents to overcome tumor-related immune suppression. Immunotherapy, as a treatment modality for prostate cancer, has received significant attention in the past few years. The most intriguing characteristics that make prostate cancer a preferred target for immune-based treatments are (1) its relative indolence which allows sufficient time for the immune system to develop meaningful antitumor responses; (2) prostate tumor-associated antigens are mainly tissue-lineage antigens, and thus, antitumor responses will preferentially target prostate cancer cells. But, also in the event of eradication of normal prostate epithelium as a result of immune attack, this will have no clinical consequences because the prostate gland is not a vital organ; (3) the use of prostate-specific antigen for early detection of recurrent disease allows for the initiation of vaccine immunotherapy while tumor burden is still minimal. Finally, for improving clinical outcome further to increasing vaccine potency, it is imperative to recognize prognostic and predictive biomarkers of clinical benefit that may guide to select the therapeutic strategies for patients most likely to gain benefit.     

Biochemical (Prostate-Specific Antigen) Relapse: An Oncologist's Perspective

Consensus has not been reached on the exact definition of biochemical relapse after prostatectomy; individual institution definitions of relapse after prostatectomy range from consecutively rising prostate-specific antigen (PSA) values of > 0.2 to > 0.6 ng/mL. PSA measurements after radiation are even less predictable. PSA level is a sensitive marker of occult prostate-cancer relapse and provides early notification of recurrence, but a PSA relapse does not equal a clinical relapse or death from prostate cancer. Data are reviewed from retrospective, single-institution trials that have clarified features of PSA relapse after both prostatectomy and radiation, such as the PSA doubling time and the time to the first PSA elevation, which are associated with clinical progression. Various options for treatment of biochemical relapse are also reviewed; these include hormone therapy, combined chemohormonal therapy, alternative medicine and dietary tactics, new agents, and future strategies, such as vaccination. Currently, there is no standard treatment for biochemical failure with proven benefit in terms of quality of life, time to metastases, or survival. Current options include observation for patients with long PSA doubling times or comorbid medical issues and standard or nontraditional hormone therapy or a clinical trial for men who desire early therapy or who have rapid PSA doubling times (< 10-12 months). Trials combining the early use of chemotherapy with hormone therapy are promising. Patients should be encouraged to enroll in clinical trials to help establish standards of care.     

Semi-parametric ROC regression analysis with placement values

Advances in technology provide new diagnostic tests for early detection of disease. Frequently, these tests have continuous outcomes. One popular method to summarize the accuracy of such a test is the Receiver Operating Characteristic (ROC) curve. Methods for estimating ROC curves have long been available. To examine covariate effects, Pepe (1997, 2000) and Alonzo and Pepe (2002) proposed distribution-free approaches based on a parametric regression model for the ROC curve. Cai and Pepe (2002) extended the parametric ROC regression model by allowing an arbitrary non-parametric baseline function. In this paper, while we follow the same semi-parametric setting as in that paper, we highlight a new estimator that offers several improvements over the earlier work: superior efficiency, the ability to estimate the covariate effects without estimating the non-parametric baseline function and easy implementation with standard software. The methodology is applied to a case control dataset where we evaluate the accuracy of the prostate-specific antigen as a biomarker for early detection of prostate cancer. Simulation studies suggest that the new estimator under the semi-parametric model, while always being more robust, has efficiency that is comparable to or better than the Alonzo and Pepe (2002) estimator from the parametric model.     

Benign prostatic hyperplasia-associated prostate-specific antigen (BPSA) shows unique immunoreactivity with anti-PSA monoclonal antibodies.

We previously identified a modified molecular form of prostate-specific antigen that is significantly elevated in the nodular transition zone tissue of prostates with benign prostatic hyperplasia. This prostate-specific antigen form, designated BPSA, is inactive and contains clipped polypeptide bonds at amino-acid residues Lys145-146 and Lys182-183. BPSA is not elevated in prostate cancer tissues and may therefore be a prostate-specific antigen marker to better discriminate benign prostatic hyperplasia from early prostate cancer. In this work we characterize the immunoreactivity of BPSA in competition assays with prostate-specific antigen using anti-prostate-specific antigen mAb recognizing six different epitopes on the prostate-specific antigen molecule. One mAb showed > 50% loss of immunoreactivtiy with BPSA compared with prostate-specific antigen, while the binding of two mAbs was largely unaffected and three mAbs had intermediate reactivity. BPSA purified from prostate tissue and seminal plasma, as well as BPSA generated in vitro by mild trypsin-treatment were found to have a similar pattern of reactivity to the six mAbs. However, other forms of inactive seminal plasma prostate-specific antigen, either intact or clipped at Lys145 only, had immunoreactivity similar to total prostate-specific antigen. These results demonstrate that BPSA has unique immunological properties from other forms of prostate-specific antigen, which should allow the development of BPSA-specific mAbs for the study of benign prostatic hyperplasia. Measurement of BPSA levels in the serum may help discriminate benign prostatic hyperplasia from early prostate cancer.     

The basis for monitoring strategies in clinical guidelines: a case study of prostate-specific antigen for monitoring in prostate cancer

Background:

The volume of published literature on the evaluation and use of tests for monitoring purposes is sparse. Our aim was to determine the extent to which recommendations for monitoring prostate-specific antigen to detect recurrent prostate cancer consider key factors that should inform rule-based strategies for monitoring.

Methods:

We reviewed the recommendations made in clinical guidelines for the repeated measurement of prostate-specific antigen in men who have received primary treatment for localized prostate cancer. We assessed the guidelines using the Appraisal of Guidelines for Research and Evaluation Framework.

Results:

We identified guidelines and statements of best practice from nine organizations. We saw considerable inconsistency in recommendations for testing for prostate-specific antigen as a form of monitoring. Recommendations on when to test appeared to be almost exclusively determined using standard follow-up schedules rather than any scientific basis. Recommendations on when to take action were primarily based on consensus statements or retrospective case series. Eight of the nine guidelines acknowledged the potential presence of measurement variability, but they did not attempt to account for the effect of such variability on the interpretation of the results of tests for prostate-specific antigen. Many recommendations were made with few or no supporting references; however, a variety of papers were cited across guidelines. Of 48 papers cited, 29.1% (14/48) were reviews; the remaining 70.8% (34/48) of papers cited were primary studies.

Interpretation:

A systematic approach to the development of monitoring schedules using prostate-specific antigen in guidelines for prostate cancer is lacking, due to inadequacies in the available evidence and its use.Monitoring involves the scheduled, repeated use of a test or tests in an individual over time to make decisions about the management of a disease or condition. It is a central activity in the management of care, taking up a considerable part of the clinical workload and associated cost.¹ In contrast, the volume of published literature on the evaluation and use of tests for monitoring purposes is relatively sparse.Mant and others have provided a framework for developing and evaluating a monitoring strategy with four main steps:^1–3 deciding whether to monitor, choosing a test, specifying and assessing the monitoring strategy to be used, and implementing the strategy. Underlying this framework is the key concept that the “signal” from the test, reflecting the status of the underlying condition, should be greater than the surrounding “noise,” or measurement variability, that may affect the interpretation of the results.^2,3 If the noise is too high in relation to the signal, one’s certainty in a given test result will be considerably reduced.The repeated measurement of prostate-specific antigen among men who have received primary treatment for prostate cancer is an apparently successful example of a rule-based monitoring strategy. The levels of prostate-specific antigen following radical treatment vary. Recurrence of disease following radical prostatectomy is associated with the presence of prostate-specific antigen; following radical radiotherapy, it is associated with a rise in the level of prostate-specific antigen.⁴ When a predefined level is reached, biochemical failure is said to have occurred. The usefulness of testing for prostate-specific antigen as a form of monitoring is based on the assumption that biochemical failure predates clinical failure within some clinically meaningful time frame. The decision to initiate treatment for recurrence, however, will depend on multiple factors rather than on a single value.⁵ We reviewed clinical guidelines for recommendations for monitoring prostate-specific antigen for the detection of recurrent prostate cancer to determine the extent to which the guidelines consider key factors that should inform rule-based strategies for monitoring. In particular, we assessed the degree of consistency between guidelines, the explicit consideration of factors important for specifying a strategy for monitoring, and the use of supporting evidence to justify any recommendations.     

The Effect of an Upper Limit to Population Size on Persistence Time

For a population with density-independent vital rates in a randomly varying environment, previous authors have calculated the probability that population size will first drop to some specified (arbitrary) low level at a given time (the first passage time distribution (FPTD), which may be interpreted as a distribution of extinction times). In this paper, we study the FPTD For a stochastic model of density-independent population growth which includes a hard upper limit to population size. We discuss the conditions under which this distribution may be approximated by the FPTD of a Wiener process with a reflecting boundary condition, for which an exact calculation is presented in an appendix. We compare the FPTD of the new model with its counterpart in the model without an upper limit. The most important effects of introducing the upper limit are: (a) ultimate extinction becomes certain; (b) if the long run growth rate in the absence of the upper boundary was small but positive, extinction within ecologically significant times is likely; (c) for larger values of the long run growth rate, persistence over ecologically significant times is almost certain. We discuss the implications of result (b) for conservation. Result (c) establishes that "density-vague" regulation can produce persistent, but bounded, populations.