Inventory of prostate cancer predictive tools

Joint models for dynamic prediction in localised prostate cancer: a literature review

BACKGROUND

Prostate cancer is a very prevalent disease in men. Patients are monitored regularly during and after treatment with repeated assessment of prostate-specific antigen (PSA) levels. Prognosis of localised prostate cancer is generally good after treatment, and the risk of having a recurrence is usually estimated based on factors measured at diagnosis. Incorporating PSA measurements over time in a dynamic prediction joint model enables updates of patients' risk as new information becomes available. We review joint model strategies that have been applied to model time-dependent PSA trajectories to predict time-to-event outcomes in localised prostate cancer.

METHODS

We identify articles that developed joint models for prediction of localised prostate cancer recurrence over the last two decades. We report, compare, and summarise the methodological approaches and applications that use joint modelling accounting for two processes: the longitudinal model (PSA), and the time-to-event process (clinical failure). The methods explored differ in how they specify the association between these two processes.

RESULTS

Twelve relevant articles were identified. A range of methodological frameworks were found, and we describe in detail shared-parameter joint models (9 of 12, 75%) and joint latent class models (3 of 12, 25%). Within each framework, these articles presented model development, estimation of dynamic predictions and model validations.

CONCLUSIONS

Each framework has its unique principles with corresponding advantages and differing interpretations. Regardless of the framework used, dynamic prediction models enable real-time prediction of individual patient prognosis. They utilise all available longitudinal information, in addition to baseline prognostic risk factors, and are superior to traditional baseline-only prediction models.

Structured reporting to improve transparency of analyses in prognostic marker studies

BACKGROUND

Factors contributing to the lack of understanding of research studies include poor reporting practices, such as selective reporting of statistically significant findings or insufficient methodological details. Systematic reviews have shown that prognostic factor studies continue to be poorly reported, even for important aspects, such as the effective sample size. The REMARK reporting guidelines support researchers in reporting key aspects of tumor marker prognostic studies. The REMARK profile was proposed to augment these guidelines to aid in structured reporting with an emphasis on including all aspects of analyses conducted.

METHODS

A systematic search of prognostic factor studies was conducted, and fifteen studies published in 2015 were selected, three from each of five oncology journals. A paper was eligible for selection if it included survival outcomes and multivariable models were used in the statistical analyses. For each study, we summarized the key information in a REMARK profile consisting of details about the patient population with available variables and follow-up data, and a list of all analyses conducted.

RESULTS

Structured profiles allow an easy assessment if reporting of a study only has weaknesses or if it is poor because many relevant details are missing. Studies had incomplete reporting of exclusion of patients, missing information about the number of events, or lacked details about statistical analyses, e.g., subgroup analyses in small populations without any information about the number of events. Profiles exhibit severe weaknesses in the reporting of more than 50% of the studies. The quality of analyses was not assessed, but some profiles exhibit several deficits at a glance.

CONCLUSIONS

A substantial part of prognostic factor studies is poorly reported and analyzed, with severe consequences for related systematic reviews and meta-analyses. We consider inadequate reporting of single studies as one of the most important reasons that the clinical relevance of most markers is still unclear after years of research and dozens of publications. We conclude that structured reporting is an important step to improve the quality of prognostic marker research and discuss its role in the context of selective reporting, meta-analysis, study registration, predefined statistical analysis plans, and improvement of marker research.

Modeling-Based Decision Support System for Radical Prostatectomy Versus External Beam Radiotherapy for Prostate Cancer Incorporating an In Silico Clinical Trial and a Cost-Utility Study

Simple Summary

Low–intermediate prostate cancer has a number of viable treatment options, such as radical prostatectomy and radiotherapy, with similar survival outcomes but different treatment-related side effects. The aim of this study is to facilitate patient-specific treatment selection by developing a decision support system (DSS) that incorporates predictive models for cancer-free survival and treatment-related side effects. We challenged this DSS by validating it against randomized clinical trials and assessing the benefit through a cost–utility analysis. We aim to expand upon the applications of this DSS by using it as the basis for an in silico clinical trial for an underrepresented patient group. This modeling study shows that DSS-based treatment decisions will result in a clinically relevant increase in the patients’ quality of life and can be used for in silico trials.

Abstract

The aim of this study is to build a decision support system (DSS) to select radical prostatectomy (RP) or external beam radiotherapy (EBRT) for low- to intermediate-risk prostate cancer patients. We used an individual state-transition model based on predictive models for estimating tumor control and toxicity probabilities. We performed analyses on a synthetically generated dataset of 1000 patients with realistic clinical parameters, externally validated by comparison to randomized clinical trials, and set up an in silico clinical trial for elderly patients. We assessed the cost-effectiveness (CE) of the DSS for treatment selection by comparing it to randomized treatment allotment. Using the DSS, 47.8% of synthetic patients were selected for RP and 52.2% for EBRT. During validation, differences with the simulations of late toxicity and biochemical failure never exceeded 2%. The in silico trial showed that for elderly patients, toxicity has more influence on the decision than TCP, and the predicted QoL depends on the initial erectile function. The DSS is estimated to result in cost savings (EUR 323 (95% CI: EUR 213–433)) and more quality-adjusted life years (QALYs; 0.11 years, 95% CI: 0.00–0.22) than randomized treatment selection.

Prediction models for prostate cancer to be used in the primary care setting: a systematic review

OBJECTIVE

To identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings.

DESIGN

Systematic review.

DATA SOURCES

MEDLINE and Embase databases combined from inception and up to the end of January 2019.

ELIGIBILITY

Studies were included based on satisfying all the following criteria: (i) presenting an evaluation of PCa risk at initial biopsy in patients with no history of PCa, (ii) studies not incorporating an invasive clinical assessment or expensive biomarker/genetic tests, (iii) inclusion of at least two variables with prostate-specific antigen (PSA) being one of them, and (iv) studies reporting a measure of predictive performance. The quality of the studies and risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

DATA EXTRACTION AND SYNTHESIS

Relevant information extracted for each model included: the year of publication, source of data, type of model, number of patients, country, age, PSA range, mean/median PSA, other variables included in the model, number of biopsy cores to assess outcomes, study endpoint(s), cancer detection, model validation and model performance.

RESULTS

An initial search yielded 109 potential studies, of which five met the set criteria. Four studies were cohort-based and one was a case-control study. PCa detection rate was between 20.6% and 55.8%. Area under the curve (AUC) was reported in four studies and ranged from 0.65 to 0.75. All models showed significant improvement in predicting PCa compared with being based on PSA alone. The difference in AUC between extended models and PSA alone was between 0.06 and 0.21.

CONCLUSION

Only a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. Further studies are needed to investigate other potential variables that could be integrated into models to improve their clinical utility for PCa testing in a community setting.

Systematic review of prediction models for gestational hypertension and preeclampsia

INTRODUCTION

Prediction models for gestational hypertension and preeclampsia have been developed with data and assumptions from developed countries. Their suitability and application for low resource settings have not been tested. This review aimed to identify and assess the methodological quality of prediction models for gestational hypertension and pre-eclampsia with reference to their application in low resource settings.

METHODS

Using combinations of keywords for gestational hypertension, preeclampsia and prediction models seven databases were searched to identify prediction models developed with maternal data obtained before 20 weeks of pregnancy and including at least three predictors (Prospero registration CRD 42017078786). Prediction model characteristics and performance measures were extracted using the CHARMS, STROBE and TRIPOD checklists. The National Institute of Health quality assessment tools for observational cohort and cross-sectional studies were used for study quality appraisal.

RESULTS

We retrieved 8,309 articles out of which 40 articles were eligible for review. Seventy-seven percent of all the prediction models combined biomarkers with maternal clinical characteristics. Biomarkers used as predictors in most models were pregnancy associated plasma protein-A (PAPP-A) and placental growth factor (PlGF). Only five studies were conducted in a low-and middle income country.

CONCLUSIONS

Most of the studies evaluated did not completely follow the CHARMS, TRIPOD and STROBE guidelines in prediction model development and reporting. Adherence to these guidelines will improve prediction modelling studies and subsequent application of prediction models in clinical practice. Prediction models using maternal characteristics, with good discrimination and calibration, should be externally validated for use in low and middle income countries where biomarker assays are not routinely available.

Methodological standards for the development and evaluation of clinical prediction rules: a review of the literature

Clinical prediction rules (CPRs) that predict the absolute risk of a clinical condition or future outcome for individual patients are abundant in the medical literature; however, systematic reviews have demonstrated shortcomings in the methodological quality and reporting of prediction studies. To maximise the potential and clinical usefulness of CPRs, they must be rigorously developed and validated, and their impact on clinical practice and patient outcomes must be evaluated. This review aims to present a comprehensive overview of the stages involved in the development, validation and evaluation of CPRs, and to describe in detail the methodological standards required at each stage, illustrated with examples where appropriate. Important features of the study design, statistical analysis, modelling strategy, data collection, performance assessment, CPR presentation and reporting are discussed, in addition to other, often overlooked aspects such as the acceptability, cost-effectiveness and longer-term implementation of CPRs, and their comparison with clinical judgement. Although the development and evaluation of a robust, clinically useful CPR is anything but straightforward, adherence to the plethora of methodological standards, recommendations and frameworks at each stage will assist in the development of a rigorous CPR that has the potential to contribute usefully to clinical practice and decision-making and have a positive impact on patient care.

Investigation on core-fucosylated prostate-specific antigen as a refined biomarker for differentiation of benign prostate hyperplasia and prostate cancer of different aggressiveness

Prostate cancer represents a major cause of cancer death in men worldwide. Novel non-invasive methods are still required for differentiation of non-aggressive from aggressive tumors. Recently, changes in prostate-specific antigen glycosylation pattern, such as core-fucosylation, have been described in prostate cancer. The objective of this study was to evaluate whether the core-fucosylation determinant of serum prostate-specific antigen may serve as refined marker for differentiation between benign prostate hyperplasia and prostate cancer or identification of aggressive prostate cancer. A previously developed liquid chromatography-mass spectrometry/mass spectrometry-based strategy was used for multiplex analysis of core-fucosylated prostate-specific antigen (fuc-PSA) and total prostate-specific antigen levels in sera from 50 benign prostate hyperplasia and 100 prostate cancer patients of different aggressiveness (Gleason scores, 5-10) covering the critical gray area (2-10 ng/mL). For identification of aggressive prostate cancer, the ratio of fuc-PSA to total prostate-specific antigen (%-fuc-PSA) yielded a 5%-8% increase in the area under the curve (0.60) compared to the currently used total prostate-specific antigen (area under the curve = 0.52) and %-free prostate-specific antigen (area under the curve = 0.55) tests. However, our data showed that aggressive prostate cancer (Gleason score > 6) and non-aggressive prostate cancer (Gleason score ≤ 6) could not significantly (p-value = 0.08) be differentiated by usage of %-fuc-PSA. In addition, both non-standardized fuc-PSA and standardized %-fuc-PSA had no diagnostic value for differentiation of benign prostate hyperplasia from prostate cancer. The %-fuc-PSA serum levels could not improve the differentiation of non-aggressive and aggressive prostate cancer compared to conventional diagnostic prostate cancer markers. Still, it is unclear whether these limitations come from the biomarker, the used patient cohort, or the imprecision of the applied method itself. Therefore, %-fuc-PSA should be further investigated, especially by more precise methods whether it could be clinically used in prostate cancer diagnosis.

Lowering positive margin rates at radical prostatectomy by color coding of biopsy specimens to permit individualized preservation of the neurovascular bundles: is it feasible? a pilot investigation

Objective:

To evaluate whether color-coding of prostate core biopsy specimens aids in preservation of the neurovascular bundles from an oncological perspective.

Materials and Methods:

MRI guided transrectal ultrasound and biopsy of the prostate were performed in 51 consecutive patients suspected of being at high risk for harboring prostate cancer. Core specimens were labeled with blue dye at the deep aspect and red dye at the superficial peripheral aspect of the core. The distance from the tumor to the end of the dyed specimen was measured to determine if there was an area of normal tissue between the prostate capsule and tumor.

Results:

Of the 51 patients undergoing prostate biopsy, 30 (58.8%) were found to have cancer of the prostate: grade group 1 in 13.7%, 2 in 25.5%, 3 in 7.8%, 4 in 7.8% and 5 in 3.9% of the cohort. A total of 461 cores were analyzed in the cohort, of which 122 showed cancer. Five patients opted to undergo robotic assisted laparoscopic radical prostatectomy. No patients had a positive surgical margin (PSM) or extra prostatic extension (EPE) on radical prostatectomy if there was a margin of normal prostatic tissue seen between the dye and the tumor on prostate biopsy.

Conclusion:

Color-coding of prostate biopsy core specimens may assist in tailoring the approach for preservation of the neurovascular bundles without compromising early oncological efficacy. Further study is required to determine whether this simple modification of the prostate biopsy protocol is valuable in larger groups of patients.

Evaluation of the proliferation marker Ki-67 in a large prostatectomy cohort

The tumor proliferation index marker Ki-67 is strongly associated with tumor cell proliferation, growth and progression, and is widely used in routine clinicopathological investigation. Prostate cancer is a complex multifaceted and biologically heterogeneous disease, and overtreatment of localized, low volume indolent tumors, is evident. Here, we aimed to assess Ki-67 expression and related outcomes of 535 patients treated with radical prostatectomy. The percentage of tumor epithelial cells expressing Ki-67 was determined by immunohistochemical assay, both digital image analysis and visual scoring by light microscope were used for quantification. The association of Ki-67 and prostate cancer was evaluated, as well as its prognostic value. There was a positive correlation between high expression of Ki-67 and Gleason score > 7 (p < 0.001) as well as tumor size (≥ 20 mm, p = 0.03). In univariate analyses, a high expression of Ki-67 in tumor epithelium was significantly associated with biochemical failure (BF) (digital scoring, p = 0.014) and (visual scoring, p = 0.004). In the multivariate analyses, a high level of Ki-67 was an independent poor prognostic factor for biochemical failure-free survival (BFFS) (Visual scoring, Ki67, p = 0.012, HR:1.50, CI95% 1.10–2.06). In conclusion, high Ki-67 expression is an independent negative prognostic marker for biochemical failure. Our findings support the role of Ki-67 as a significant, poor prognostic factor for in prostate cancer outcome.

Prognostic significance of six clinicopathological features for biochemical recurrence after radical prostatectomy: a systematic review and meta-analysis

Identifying patients with high risk of biochemical recurrence after radical prostatectomy is of immense value in clinical practice. Assessment of prognostic significance of specific clinicopathological features plays an important role in surgical management after prostatectomy. The purpose of our meta-analysis was to investigate the association between the six pathological characteristics and the prognosis of prostate cancer. We carried out a systematic document retrieval in electronic databases to sort out appropriate studies. Outcomes of interest were gathered from studies comparing biochemical recurrence-free survival (BCFS) in patients with the six pathological traits. Studies results were pooled, and hazard ratios (HRs) combined with corresponding 95% confidence intervals (CIs) for survival were used to estimate the effect size. 29 studies (21,683 patients) were enrolled in our meta-analysis. All the six predictors were statistically significant for BCFS with regard to seminal vesicle invasion (HR = 1.97, 95% CI = 1.79–2.18, p < 0.00001), positive surgical margin (HR = 1.79, 95% CI = 1.56–2.06, p < 0.00001), extracapsular extension (HR = 2.03, 95% CI = 1.65–2.50, p < 0.0001), lymphovascular invasion (HR = 1.85, 95% CI = 1.54–2.22, p < 0.00001), lymph node involvement (HR = 1.88, 95% CI = 1.37–2.60, p = 0.0001) and perineural invasion (HR = 1.59, 95% CI = 1.33–1.91, p < 0.00001). Subgroup analysis showed that all the six predictors had significantly relationship with poor BCFS. The pooled results demonstrated that the six clinical findings indicated a worse prognosis in patients with prostate cancer. In conclusion, our results show several clinicopathological characteristics can predict the risk of biochemical recurrence after radical prostatectomy. Prospective studies are needed to further confirm the predictive value of these features for the prognosis of prostate cancer patients after radical prostatectomy.

Prostate Magnetic Resonance Imaging and Magnetic Resonance Imaging Targeted Biopsy in Patients with a Prior Negative Biopsy: A Consensus Statement by AUA and SAR

PURPOSE

After an initial negative biopsy there is an ongoing need for strategies to improve patient selection for repeat biopsy as well as the diagnostic yield from repeat biopsies.

MATERIALS AND METHODS

As a collaborative initiative of the AUA (American Urological Association) and SAR (Society of Abdominal Radiology) Prostate Cancer Disease Focused Panel, an expert panel of urologists and radiologists conducted a literature review and formed consensus statements regarding the role of prostate magnetic resonance imaging and magnetic resonance imaging targeted biopsy in patients with a negative biopsy, which are summarized in this review.

RESULTS

The panel recognizes that many options exist for men with a previously negative biopsy. If a biopsy is recommended, prostate magnetic resonance imaging and subsequent magnetic resonance imaging targeted cores appear to facilitate the detection of clinically significant disease over standardized repeat biopsy. Thus, when high quality prostate magnetic resonance imaging is available, it should be strongly considered for any patient with a prior negative biopsy who has persistent clinical suspicion for prostate cancer and who is under evaluation for a possible repeat biopsy. The decision of whether to perform magnetic resonance imaging in this setting must also take into account the results of any other biomarkers and the cost of the examination, as well as the availability of high quality prostate magnetic resonance imaging interpretation. If magnetic resonance imaging is done, it should be performed, interpreted and reported in accordance with PI-RADS version 2 (v2) guidelines. Experience of the reporting radiologist and biopsy operator are required to achieve optimal results and practices integrating prostate magnetic resonance imaging into patient care are advised to implement quality assurance programs to monitor targeted biopsy results.

CONCLUSIONS

Patients receiving a PI-RADS assessment category of 3 to 5 warrant repeat biopsy with image guided targeting. While transrectal ultrasound guided magnetic resonance imaging fusion or in-bore magnetic resonance imaging targeting may be valuable for more reliable targeting, especially for lesions that are small or in difficult locations, in the absence of such targeting technologies cognitive (visual) targeting remains a reasonable approach in skilled hands. At least 2 targeted cores should be obtained from each magnetic resonance imaging defined target. Given the number of studies showing a proportion of missed clinically significant cancers by magnetic resonance imaging targeted cores, a case specific decision must be made whether to also perform concurrent systematic sampling. However, performing solely targeted biopsy should only be considered once quality assurance efforts have validated the performance of prostate magnetic resonance imaging interpretations with results consistent with the published literature. In patients with negative or low suspicion magnetic resonance imaging (PI-RADS assessment category of 1 or 2, respectively), other ancillary markers (ie PSA, PSAD, PSAV, PCA3, PHI, 4K) may be of value in identifying patients warranting repeat systematic biopsy, although further data are needed on this topic. If a repeat biopsy is deferred on the basis of magnetic resonance imaging findings, then continued clinical and laboratory followup is advised and consideration should be given to incorporating repeat magnetic resonance imaging in this diagnostic surveillance regimen.

Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example

BACKGROUND

Prognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI) is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches.

METHODS AND FINDINGS

Reanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived.

CONCLUSIONS

The prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases.

Comparison of digital image analysis and visual scoring of KI-67 in prostate cancer prognosis after prostatectomy

Background

The tumor proliferative index marker Ki-67 was shown to be associated with clinically significant outcomes in prostate cancer, but its clinical application has limitations due to lack of uniformity and consistency in quantification. Our objective was to compare the measurements obtained with digital image analysis (DIA) versus virtual microscopy (visual scoring (VS)).

Methods

To do so, we compared the measurement distributions of each technique and their ability to predict clinically useful endpoints. A tissue microarray series from a cohort of 225 men who underwent radical prostatectomy was immunostained for Ki-67. The percentage of Ki-67 positive nuclei in malignant cells was assessed both by VS and DIA, and a H–score was calculated. The distribution and predictive ability of these scoring methods to predict biochemical recurrence (BCR) and death from prostate cancer (DPCa) were compared using Mann–Whitney test and C-index.

Results

The measurements obtained with VS were similar to the DIA measurements (p = 0.73) but dissimilar to the H-score (p < 0.001). Cox regression models showed that Ki-67 was associated with BCR (HR 1.46, 95 % CI 1.10-1.94) and DPCa (HR 1.26, 95 % CI 1.06-1.50). C-indexes revealed that Ki-67 was a better predictor of DPCa (0.803, 0.8059 and 0.789; VS, DIA and H-score, respectively) than of BCR (0.625, 0.632 and 0.604; VS, DIA and H-score, respectively).

Conclusion

The measurement distributions and the predictive abilities of VS and DIA were similar and presented the same predictive behaviour in our cohort, supporting the role of Ki-67 proliferative index as an important prognostic factor of BCR and DPCa in prostate cancer post RP.

Virtual Slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/6656878501536663

Electronic supplementary material

The online version of this article (doi:10.1186/s13000-015-0294-0) contains supplementary material, which is available to authorized users.

Do prostate cancer risk models improve the predictive accuracy of PSA screening? A meta-analysis

BACKGROUND

Despite the extensive development of risk prediction models to aid patient decision-making on prostate screening, it is unknown whether these models could improve predictive accuracy of PSA testing to detect prostate cancer (PCa). The objective of this study was to perform a systematic review to identify PCa risk models and to assess the model's performance to predict PCa by conducting a meta-analysis.

DESIGN

A systematic literature search of Medline was conducted to identify PCa predictive risk models that used at least two variables, of which one of the variables was prostate-specific antigen (PSA) level. Model performance (discrimination and calibration) was assessed. Prediction models validated in ≥5 study populations and reported area under the curve (AUC) for prediction of any or clinically significant PCa were eligible for meta-analysis. Summary AUC and 95% CIs were calculated using a random-effects model.

RESULTS

The systematic review identified 127 unique PCa prediction models; however, only six models met study criteria for meta-analysis for predicting any PCa: Prostataclass, Finne, Karakiewcz, Prostate Cancer Prevention Trial (PCPT), Chun, and the European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (ERSPC RC3). Summary AUC estimates show that PCPT does not differ from PSA testing (0.66) despite performing better in studies validating both PSA and PCPT. Predictive accuracy to discriminate PCa increases with Finne (AUC = 0.74), Karakiewcz (AUC = 0.74), Chun (AUC = 0.76) and ERSPC RC3 and Prostataclass have the highest discriminative value (AUC = 0.79), which is equivalent to doubling the sensitivity of PSA testing (44% versus 21%) without loss of specificity. The discriminative accuracy of PCPT to detect clinically significant PCa was AUC = 0.71. Calibration measures of the models were poorly reported.

CONCLUSIONS

Risk prediction models improve the predictive accuracy of PSA testing to detect PCa. Future developments in the use of PCa risk models should evaluate its clinical effectiveness in practice.

Improving prediction of prostate cancer recurrence using chemical imaging

Precise Outcome prediction is crucial to providing optimal cancer care across the spectrum of solid cancers. Clinically-useful tools to predict risk of adverse events (metastases, recurrence), however, remain deficient. Here, we report an approach to predict the risk of prostate cancer recurrence, at the time of initial diagnosis, using a combination of emerging chemical imaging, a diagnostic protocol that focuses simultaneously on the tumor and its microenvironment, and data analysis of frequent patterns in molecular expression. Fourier transform infrared (FT-IR) spectroscopic imaging was employed to record the structure and molecular content from tumors prostatectomy. We analyzed data from a patient cohort that is mid-grade dominant – which is the largest cohort of patients in the modern era and in whom prognostic methods are largely ineffective. Our approach outperforms the two widely used tools, Kattan nomogram and CAPRA-S score in a head-to-head comparison for predicting risk of recurrence. Importantly, the approach provides a histologic basis to the prediction that identifies chemical and morphologic features in the tumor microenvironment that is independent of conventional clinical information, opening the door to similar advances in other solid tumors.

Prognostic Utility of PET in Prostate Cancer

Many standard nonimaging-based prediction tools exist for prostate cancer. However, these tools may be limited in individual cases and need updating based on the improved understanding of the underlying complex biology of the disease and the emergence of the novel targeted molecular imaging methods. A new platform of automated predictive tools that combines the independent molecular, imaging, and clinical information can contribute significantly to patient care. Such a platform will also be of interest to regulatory agencies and payers as more emphasis is placed on supporting those interventions that have quantifiable and significant beneficial impact on patient outcome.

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.

Potential utility of cancer-specific biomarkers for assessing response to hormonal treatments in metastatic prostate cancer

Prostate cancer is the second leading cause of cancer death in men and there is an urgent clinical need to improve its detection and treatment. The introduction of prostate-specific antigen (PSA) as a biomarker for prostate cancer several decades ago represented an important step forward in our ability to diagnose this disease and offers the potential for earlier and more effective treatment. PSA measurements are now routinely conducted alongside digital rectal examination, with raised PSA levels leading to biopsy. PSA is also used to monitor disease and assess therapeutic response. However, there are some important limitations to its use, not least its lack of specificity for prostate cancer, and increased PSA screening may have resulted in overdiagnosis and overtreatment of early, low-risk prostate cancer. Therefore, there is a need for more specific and sensitive biomarkers for the diagnosis and monitoring of prostate cancer and treatment response; in particular, biomarkers of response to hormonal treatments in prostate cancer and predictive biomarkers to identify who is most likely to respond to these treatments. Here we review the current utilization of PSA and data on potentially more specific and sensitive biomarkers for the diagnosis and monitoring of prostate cancer: prostate cancer antigen 3 (PCA3) and the TMPRSS2-ERG fusion gene. A description of the design of an ongoing study of the 6-month extended release formulation of leuprorelin acetate (Eligard(®) 45 mg) will provide preliminary data on the potential utility of these new biomarkers for detecting therapeutic response after hormonal therapy.

A systematic literature review of life expectancy prediction tools for patients with localized prostate cancer

PURPOSE

We aimed to develop a clinical decision support tool for clinicians counseling patients with localized prostate cancer. The tool would provide estimates of patient life expectancy based on age, comorbidities and tumor characteristics. We reviewed the literature to find suitable prediction models.

MATERIALS AND METHODS

We searched the literature for prediction models for life expectancy. Models were evaluated in terms of whether they provided an estimate of risk, incorporated comorbidities, were clinically feasible and gave plausible estimates. Clinical feasibility was defined in terms of whether the model provided coefficients and could be used in the initial consultation for men across a wide age range without an undue burden of data gathering.

RESULTS

Models in the literature were characterized by the use of life years rather than a risk of death, questionable approaches to comorbidities, implausible estimates, questionable recommendations and poor clinical feasibility. We found tools that involved applying an unvalidated approach to assessing comorbidities to a clearly erroneous life expectancy table, or requiring that a treatment decision be made before life expectancy could be calculated, or giving highly implausible estimates such as a substantial risk of prostate cancer specific mortality even for a highly comorbid 80-year-old with Gleason 6 disease.

CONCLUSIONS

We found gross deficiencies in current tools that predict risk of death from other causes. No existing model was suitable for implementation in our clinical decision support system.

Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist

Carl Moons and colleagues provide a checklist and background explanation for critically appraising and extracting data from systematic reviews of prognostic and diagnostic prediction modelling studies. Please see later in the article for the Editors' Summary.

The critical role of the pathologist in determining eligibility for active surveillance as a management option in patients with prostate cancer: consensus statement with recommendations supported by the College of American Pathologists, International Society of Urological Pathology, Association of Directors of Anatomic and Surgical Pathology, the New Zealand Society of Pathologists, and the Prostate Cancer Foundation

CONTEXT

Prostate cancer remains a significant public health problem. Recent publications of randomized trials and the US Preventive Services Task Force recommendations have drawn attention to overtreatment of localized, low-risk prostate cancer. Active surveillance, in which patients undergo regular visits with serum prostate-specific antigen tests and repeat prostate biopsies, rather than aggressive treatment with curative intent, may address overtreatment of low-risk prostate cancer. It is apparent that a greater awareness of the critical role of pathologists in determining eligibility for active surveillance is needed.

OBJECTIVES

To review the state of current knowledge about the role of active surveillance in the management of prostate cancer and to provide a multidisciplinary report focusing on pathologic parameters important to the successful identification of patients likely to succeed with active surveillance, to determine the role of molecular tests in increasing the safety of active surveillance, and to provide future directions.

DESIGN

Systematic review of literature on active surveillance for low-risk prostate cancer, pathologic parameters important for appropriate stratification, and issues regarding interobserver reproducibility. Expert panels were created to delineate the fundamental questions confronting the clinical and pathologic aspects of management of men on active surveillance.

RESULTS

Expert panelists identified pathologic parameters important for management and the related diagnostic and reporting issues. Consensus recommendations were generated where appropriate.

CONCLUSIONS

Active surveillance is an important management option for men with low-risk prostate cancer. Vital to this process is the critical role pathologic parameters have in identifying appropriate candidates for active surveillance. These findings need to be reproducible and consistently reported by surgical pathologists with accurate pathology reporting.

The development of nomograms for stratification of men at risk of prostate cancer prior to prostate biopsy

A main limitation of early prostate cancer (PCa) detection due to elevated PSA levels is caused by the low specificity of PSA, which is associated with a high proportion of men detected with nonmalignant findings at first or subsequent prostate biopsy (PBX). Multivariate prediction models, such as nomograms, have been developed, providing a more accurate method to prospectively determine the risk of a positive PBX. Combining established clinical risk factors with novel diagnostic markers of PCa appears promising to further improve predictive accuracy estimates. Ideally, these nomograms should be capable of identifying PCa at PBX without missing men with high-grade PCa, and preventing a significant proportion of men without, or with insignificant, PCa from undergoing PBX. The intention is to reduce disease morbidity and mortality by detecting significant PCa at an early stage, and at the same time to avoid overdiagnosis as well as overintervention.

The role of biomarkers in the assessment of prostate cancer risk prior to prostate biopsy: which markers matter and how should they be used?

Prostate cancer (PCa) screening has been substantially influenced by the clinical implementation of serum prostate-specific antigen (PSA). In this context, improvement of early PCa detection and stage migration as well as reduced PCa mortality were achieved, and up-to-date PSA represents the gold standard biomarker of PCa diagnosis together with clinical findings. Nonetheless, PSA shows weakness in discriminating between malign and benign prostatic disease or indolent and aggressive cancers. As a result, the expansion of PSA screening is extensively debated with regard to overdetection and ultimately overtreatment, keeping in mind that PCa is still the third leading cause of cancer-specific mortality in the Western male population. Consequently, today's task is to increase the accuracy of PCa detection and furthermore to allow stratification for indolent PCa that might permit active surveillance and to filter out aggressive cancers necessitating treatment. Thus, novel biomarkers, especially in combination with approved clinical risk factors (e.g., age or family history of PCa), within multivariable prediction models carry the potential to improve many aspects of PCa diagnosis and to enable risk classification in clinical practice. Multivariable models lead to superior accuracy for PCa prediction instead of the use of a single risk factor. The aim of this article was to present an overview of known risk factors for PCa together with new promising blood- and urine-based biomarkers and their application within risk models that may allow risk stratification for PCa prior to prostate biopsy. Risk models may optimize PCa detection and classification with regard to improved PCa risk assessment and avoidance of unnecessary prostate biopsies.

External validation of multivariable prediction models: a systematic review of methodological conduct and reporting

BACKGROUND

Before considering whether to use a multivariable (diagnostic or prognostic) prediction model, it is essential that its performance be evaluated in data that were not used to develop the model (referred to as external validation). We critically appraised the methodological conduct and reporting of external validation studies of multivariable prediction models.

METHODS

We conducted a systematic review of articles describing some form of external validation of one or more multivariable prediction models indexed in PubMed core clinical journals published in 2010. Study data were extracted in duplicate on design, sample size, handling of missing data, reference to the original study developing the prediction models and predictive performance measures.

RESULTS

11,826 articles were identified and 78 were included for full review, which described the evaluation of 120 prediction models. in participant data that were not used to develop the model. Thirty-three articles described both the development of a prediction model and an evaluation of its performance on a separate dataset, and 45 articles described only the evaluation of an existing published prediction model on another dataset. Fifty-seven percent of the prediction models were presented and evaluated as simplified scoring systems. Sixteen percent of articles failed to report the number of outcome events in the validation datasets. Fifty-four percent of studies made no explicit mention of missing data. Sixty-seven percent did not report evaluating model calibration whilst most studies evaluated model discrimination. It was often unclear whether the reported performance measures were for the full regression model or for the simplified models.

CONCLUSIONS

The vast majority of studies describing some form of external validation of a multivariable prediction model were poorly reported with key details frequently not presented. The validation studies were characterised by poor design, inappropriate handling and acknowledgement of missing data and one of the most key performance measures of prediction models i.e. calibration often omitted from the publication. It may therefore not be surprising that an overwhelming majority of developed prediction models are not used in practice, when there is a dearth of well-conducted and clearly reported (external validation) studies describing their performance on independent participant data.

Prognostic utility of the cell cycle progression score generated from biopsy in men treated with prostatectomy

PURPOSE

The cell cycle progression score is associated with prostate cancer outcomes in various clinical settings. However, previous studies of men treated with radical prostatectomy evaluated cell cycle progression scores generated from resected tumor tissue. We evaluated the prognostic usefulness of the score derived from biopsy specimens in men treated with radical prostatectomy.

MATERIALS AND METHODS

We evaluated the cell cycle progression score in cohorts of patients from the Martini Clinic (283), Durham Veterans Affairs Medical Center (176) and Intermountain Healthcare (123). The score was derived from simulated biopsy (Martini Clinic) or diagnostic biopsy (Durham Veterans Affairs Medical Center and Intermountain Healthcare) and evaluated for an association with biochemical recurrence and metastatic disease.

RESULTS

In all 3 cohorts the cell cycle progression score was associated with biochemical recurrence and metastatic disease. The association with biochemical recurrence remained significant after adjusting for other prognostic clinical variables. On combined analysis of all cohorts (total 582 patients) the score was a strong predictor of biochemical recurrence on univariate analysis (HR per score unit 1.60, 95% CI 1.35-1.90, p=2.4×10(-7)) and multivariate analysis (HR per score unit 1.47, 95% CI 1.23-1.76, p=4.7×10(-5)). Although there were few events (12), the cell cycle progression score was the strongest predictor of metastatic disease on univariate analysis (HR per score unit 5.35, 95% CI 2.89-9.92, p=2.1×10(-8)) and after adjusting for clinical variables (HR per score unit 4.19, 95% CI 2.08-8.45, p=8.2×10(-6)).

CONCLUSIONS

The cell cycle progression score derived from a biopsy sample was associated with adverse outcomes after surgery. These results indicate that the score can be used at disease diagnosis to better define patient prognosis and enable more appropriate clinical care.

Assays for prostate cancer : changing the screening paradigm?

Prostate cancer (PCa) screening and detection have changed dramatically since the introduction of serum prostate-specific antigen (PSA) testing. Despite the resulting improvement in early PCa detection and stage migration, in clinical practice the use of PSA testing may cause overdetection and ultimately overtreatment. As a consequence, novel biomarkers are needed to increase the specificity of PCa detection. The aim of this article is to present an overview of novel blood- and urine-based biomarkers that may optimize PCa detection, with improved identification of patients with significant PCa and avoidance of unnecessary prostate biopsies. A systematic and comprehensive PubMed search was performed using the MeSH search terms 'prostate cancer', 'biomarker', 'marker', and 'detection'. Results were restricted to the English language. Several blood- and urine-based biomarkers have the potential to improve prediction of the presence and/or significance of PCa. Ideally, biomarkers should be used in combination within multivariate models, leading to superior accuracy for prediction of any PCa or clinically significant PCa, compared with the use of a single marker.

Artificial neural networks and prostate cancer--tools for diagnosis and management

Artificial neural networks (ANNs) are mathematical models that are based on biological neural networks and are composed of interconnected groups of artificial neurons. ANNs are used to map and predict outcomes in complex relationships between given 'inputs' and sought-after 'outputs' and can also be used find patterns in datasets. In medicine, ANN applications have been used in cancer diagnosis, staging and recurrence prediction since the mid-1990s, when an enormous effort was initiated, especially in prostate cancer detection. Modern ANNs can incorporate new biomarkers and imaging data to improve their predictive power and can offer a number of advantages as clinical decision making tools, such as easy handling of distribution-free input parameters. Most importantly, ANNs consider nonlinear relationships among input data that cannot always be recognized by conventional analyses. In the future, complex medical diagnostic and treatment decisions will be increasingly based on ANNs and other multivariate models.

Ask the Experts: Validating predictive models for colorectal cancer

Gary Collins is a senior medical statistician at the Centre for Statistics in Medicine, University of Oxford, UK. His main research interests are in the validation of clinical prediction models, and the methodological and design issues in conducting validation studies funded by the UK Medical Research Council. He is also interested in reporting of these studies and is leading a group of prediction modeling experts, clinicians and journal editors to produce a consensus-based guideline to improve the quality of studies reporting the development and validation of prediction models.

Nuclear morphometry, nucleomics and prostate cancer progression

Prostate cancer (PCa) results from a multistep process. This process includes initiation, which occurs through various aging events and multiple insults (such as chronic infection, inflammation and genetic instability through reactive oxygen species causing DNA double-strand breaks), followed by a multistep process of progression. These steps include several genetic and epigenetic alterations, as well as alterations to the chromatin structure, which occur in response to the carcinogenic stress-related events that sustain proliferative signaling. Events such as evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis are readily observed. In addition, in conjunction with these critical drivers of carcinogenesis, other factors related to the etiopathogenesis of PCa, involving energy metabolism and evasion of the immune surveillance system, appear to be involved. In addition, when cancer spread and metastasis occur, the 'tumor microenvironment' in the bone of PCa patients may provide a way to sustain dormancy or senescence and eventually establish a 'seed and soil' site where PCa proliferation and growth may occur over time. When PCa is initiated and progression ensues, significant alterations in nuclear size, shape and heterochromatin (DNA transcription) organization are found, and key nuclear transcriptional and structural proteins, as well as multiple nuclear bodies can lead to precancerous and malignant changes. These series of cellular and tissue-related malignancy-associated events can be quantified to assess disease progression and management.

Prostate cancer risk variants are not associated with disease progression

BACKGROUND

Currently used prognostic markers are limited in their ability to accurately predict disease progression among patients with localized prostate cancer. We examined 23 reported prostate cancer susceptibility variants for association with disease progression.

METHODS

Disease progression was explored among 4,673 Swedish patients treated for clinically localized prostate cancer between 1997 and 2002. Prostate cancer progression was defined according to primary treatment as a composed event reflecting termination of deferred treatment, biochemical recurrence, local progression, or presence of distant metastasis. Association between single variants, and all variants combined, were performed in Cox regression analysis assuming both log-additive and co-dominant genetic models.

RESULTS

Three of the 23 genetic variants explored were nominally associated with prostate cancer progression; rs9364554 (P = 0.041) on chromosome 6q25 and rs10896449 (P = 0.029) on chromosome 11q13 among patients treated with curative intent; and rs4054823 (P = 0.008) on chromosome 17p12 among patients on surveillance. However, none of these associations remained statistically significant after correction for multiple testing. The combined effect of all susceptibility variants was not associated with prostate cancer progression neither among patients receiving treatment with curative intent (P = 0.14) nor among patients on surveillance (P = 0.92).

CONCLUSIONS

We observed no evidence for an association between any of 23 established prostate cancer genetic risk variants and disease progression. Accumulating evidence suggests separate genetic components for initiation and progression of prostate cancer. Future studies systematically searching for genetic risk variants associated with prostate cancer progression and prognosis are warranted.

Use of nomograms for predictions of outcome in patients with advanced bladder cancer

INTRODUCTION

Accurate estimates of risk are essential for physicians if they are to recommend a specific management to patients with bladder cancer. In this review, we discuss the criteria for the evaluation of nomograms and review current available nomograms for advanced bladder cancer.

METHODS

A retrospective review of the Pubmed database between 2002 and 2008 was performed using the keywords 'nomogram' and 'bladder'. We limited the articles to advanced bladder cancer. We recorded input variables, prediction form, number of patients used to develop the prediction tools, the outcome being predicted, prediction tool-specific features, predictive accuracy, and whether validation was performed.

RESULTS

We discuss the characteristics needed to evaluate nomograms such as predictive accuracy, calibration, generalizability, level of complexity, effect of competing risks, conditional probabilities, and head-to-head comparison with other prediction methods. The predictive accuracies of the pre-cystectomy tools (n = 2) range from ∼65-75% and that of the post-cystectomy tools (n = 5) range from ∼75-80%. While some of these nomograms are well-calibrated and outperform AJCC staging, none has been externally validated. To date, four studies demonstrated a statistically significant improvement in predictive accuracy of nomograms by including biomarkers.

CONCLUSIONS

Nomograms provide accurate individualized estimates of outcomes. They currently represent the most accurate and discriminatory decision-making aids tools for predicting outcomes in patients with bladder cancer. Use of current nomograms could improve current selection of patients for standard therapy and investigational trial design by ensuring homogeneous groups. The addition of biological markers to the currently available nomograms using clinical and pathologic data holds the promise of improving prediction and refining management of patients with bladder cancer.

Tumour markers in prostate cancer II: diagnostic and prognostic cellular biomarkers

The main goal of prostate cancer tissue biomarkers is to improve diagnostic and prognostic accuracy. A particularly important question is whether the cancer needs immediate treatment or if treatment can be deferred. It is highly unlikely that a single biomarker that provides comprehensive prognostic information about a newly diagnosed prostate cancer will be forthcoming. Despite extensive research efforts, very few biomarkers of prostate cancer have been successfully implemented into clinical practice today. This can be partly explained by a lack of standardised methods for performance and interpretation of immunohistochemistry, but also by poor study design with insufficient biomaterial or inappropriate statistical analysis. Also appropriate cohorts to test prostate cancer biomarkers do not exist. It must be kept in mind that unsuccessful integration of new biomarkers in nomograms can also be explained by the good performance of the clinical and pathological base model with serum PSA as the only independent biomarker. A new biomarker must be powerful enough to improve this prediction model and not merely replace.

MATERIAL AND METHODS

In this report, we focus on diagnostic and prognostic cellular biomarkers in prostate cancer, recent advances and future aspects by reviewing currently available literature.

RESULTS

Similar to other malignancies, the proliferation marker Ki-67 seems to be a prognostic tissue biomarker and a strong candidate for integration in prediction models. Circulating tumour cells are promising markers of response to treatments in patients with metastatic disease.

CONCLUSION

Important technical advances together with histological techniques of antibody or probes conjugated with different fluorophores will certainly improve standardisation and make immunohistochemical biomarker research more reliable and precise in the future. Cellular biomarker studies are also expected to change in the future towards a complexed individualised profiling of human tumours with integrative analysis using different technologies, genome-wide scanning and expression profiling.

Tumor markers in prostate cancer I: blood-based markers

The introduction of total prostate specific antigen (total PSA) testing in blood has revolutionized the detection and management of men with prostate cancer (PCa). The objective of this review was to discuss the challenges of PCa biomarker research, definition of the type of PCa biomarkers, the statistical considerations for biomarker discovery and validation, and to review the literature regarding total PSA velocity and novel blood-based biomarkers.

METHODS

An English-language literature review of the Medline database (1990 to August 2010) of published data on blood-based biomarkers and PCa was undertaken.

RESULTS

The inherent biological variability of total PSA levels affects the interpretation of any single result. Men who will eventually develop PCa have increased total PSA levels years or decades before the cancer is diagnosed. Total PSA velocity improves predictiveness of total PSA only marginally, limiting its value for PCa screening and prognostication. The combination of PSA molecular forms and other biomarkers improve PCa detection substantially. Several novel blood-based biomarkers such as human glandular kallikrein 2 (hK2), urokinase plasminogen activator (uPA) and its receptor (uPAR), transforming growth factor-beta 1 (TGF-β1); interleukin-6 (IL-6) and its receptor (IL-6R) may help PCa diagnosis, staging, prognostication, and monitoring. Panels of biomarkers that capture the biologic potential of PCa are in the process of being validated for PCa prognostication.

CONCLUSIONS

PSA is a strong prognostic marker for long-term risk of clinically relevant cancer. However, there is a need for novel biomarkers that aid clinical decision making about biopsy and initial treatment. There is no doubt that progress will continue based on the integrated collaboration of researchers, clinicians and biomedical firms.

A tri-marker proliferation index predicts biochemical recurrence after surgery for prostate cancer

Prostate cancer exhibits tremendous variability in clinical behavior, ranging from indolent to lethal disease. Better prognostic markers are needed to stratify patients for appropriately aggressive therapy. By expression profiling, we can identify a proliferation signature variably expressed in prostate cancers. Here, we asked whether one or more tissue biomarkers might capture that information, and provide prognostic utility. We assayed three proliferation signature genes: MKI67 (Ki-67; also a classic proliferation biomarker), TOP2A (DNA topoisomerase II, alpha), and E2F1 (E2F transcription factor 1). Immunohistochemical staining was evaluable on 139 radical prostatectomy cases (in tissue microarray format), with a median clinical follow-up of eight years. Each of the three proliferation markers was by itself prognostic. Notably, combining the three markers together as a “proliferation index” (0 or 1, vs. 2 or 3 positive markers) provided superior prognostic performance (hazard ratio = 2.6 (95% CI: 1.4–4.9); P = 0.001). In a multivariate analysis that included preoperative serum prostate specific antigen (PSA) levels, Gleason grade and pathologic tumor stage, the composite proliferation index remained a significant predictor (P = 0.005). Analysis of receiver-operating characteristic (ROC) curves confirmed the improved prognostication afforded by incorporating the proliferation index (compared to the clinicopathologic data alone). Our findings highlight the potential value of a multi-gene signature-based diagnostic, and define a tri-marker proliferation index with possible utility for improved prognostication and treatment stratification in prostate cancer.

Endothelin-1: a predictor of extracapsular extension in clinically localized prostate cancer?

OBJECTIVE

To assess the value of endothelin-1 (ET-1) expression in predicting extracapsular extension (ECE) in clinically localized prostate cancer (PCa).

PATIENTS AND METHODS

ET-1 expression was determined by immunohistochemistry on archival needle biopsies (NBs) from 94 patients (49 pT2 and 45 pT3a) who underwent radical prostatectomy (RP) for clinical T1-T2 PCa. Each sample was analysed independently by two pathologists blinded to the clinical data.

RESULTS

In univariate analysis, high ET-1 expression in NBs, pre-operative prostate-specific antigen (PSA) level >10 ng/ml, percentage of positive biopsy cores and NB Gleason score ≥7 were significantly associated with ECE as determined on subsequent RP. No significant association was found between clinical stage and ECE. In multivariate analysis, there was a significant association with high ET-1 expression in NBs (p = 0.006), pre-operative PSA level >10 ng/ml (p = 0.049), and NB Gleason score ≥7 (p = 0.002). These three pre-operative factors combined provided the best model for predicting ECE with 93.3% sensitivity, 49% specificity, 62.5% positive predictive value, 88.9% negative predictive value. The combination yielded a higher concordance index (0.760 vs 0.720) and offered a higher log partial likelihood than the same model without ET1 (112.8 vs 105.7, p = 0.01).

CONCLUSIONS

ET-1 expression was strongly associated with ECE and, when combined with pre-operative PSA level and Gleason score, improved the predictive accuracy of pre-operative NBs. Its assessment in patients with localized PCa might be useful when making treatment decisions. Further studies with standardisation of immunohistochemical staining and multi-institutional validation are now needed to establish the appropriate use of ET-1 staining in PCa staging and to evaluate inter-observer reproducibility.

External validation of the cancer of the prostate risk assessment (CAPRA) score in a single-surgeon radical prostatectomy series

OBJECTIVES

Prostate cancer clinical staging has significant limitations in the ability to accurately risk-stratify patients for prompt treatment or expectant management. The University of California San Francisco Cancer of the Prostate Risk Assessment (UCSF CAPRA) was recently described as a straightforward staging system that uses clinical variables to generate a score ranging from 0 to 10. Our objective was to perform an external validation of the CAPRA score as a predictor of 5-year progression-free survival (PFS) in a single-surgeon radical retropubic prostatectomy (RRP) series.

MATERIALS AND METHODS

We examined the performance characteristics of the preoperative CAPRA score (0-10) to predict biochemical progression-free survival (PFS) in 990 men who underwent RRP by a single surgeon from 2003 to 2009.

RESULTS

CAPRA scores were significantly associated with the risk of early biochemical progression in our series. For example, 5-year PFS was markedly different for scores at the extremes of 0 to 1 vs. ≥7 (95% vs. 40%, respectively). The concordance index was 0.764 for the prediction of biochemical progression using CAPRA scores in this cohort, which compares favorably with the concordance index of 0.66 in the original CaPSURE dataset.

CONCLUSIONS

Our results validate the UCSF-CAPRA score as a significant predictor of 5-year PFS in a single surgeon series. The CAPRA score is a simple preoperative tool that can be readily applied in clinical practice to help risk-stratify prostate cancer patients.

Retrospective analysis of prostate cancer recurrence potential with tissue metabolomic profiles

BACKGROUND

In clinical care of prostate cancer patients, an improved method to assess the risk of recurrence after surgical treatment is urgently needed. We aim to retrospectively evaluate the ability of ex vivo tissue magnetic-resonance-spectroscopy-based metabolomic profiles to estimate the risk of recurrence.

METHODS

PCa recurrence is defined biochemically as the detection of serum PSA after radical prostatectomy. Sixteen consecutive PCa-recurrent cases, those with an initial PSA increase of 0.69 +/- 0.26 ng/ml monitored 47.7 +/- 2.6 months after prostatectomy were paired by age and Gleason score with cases without recurrence of the same pathological and clinical stages (n = 16/each). We analyzed ex vivo intact-tissue spectroscopy results from these 48 individuals at the time of prostatectomy at 14T. From these spectra, we identified the 27 most common and intense spectral metabolic regions for statistical analyses.

RESULTS

Principal component analysis (PCA) on these spectral regions from cases of clinical-stage-matched groups with and without recurrence identified four pathology-related principal components. Canonical analysis of these four and the first nine principal components for cases in the two groups defined metabolomic profiles as the canonical score that can differentiate the two groups with statistical significance. By applying the coefficients from PCA and canonical analysis to the pathological-stage-matched groups, recurrence was predicted with an accuracy of 78%.

CONCLUSIONS

Results indicate the potential of tissue metabolomic profiles measured with ex vivo spectroscopy to identify PCa aggressiveness in terms of cancer recurrence. With further study, this may greatly contribute to the future design of clinical strategy for personalized treatment of PCa patients.

Tumor angiogenesis as an early marker of long-term prostate cancer mortality

Evaluation of: Mucci LA, Powolny A, Giovannucci E et al.: Prospective study of prostate tumor angiogenesis and cancer-specific mortality in the health professionals follow-up study. J. Clin. Oncol. 27, 5627–5633 (2009). Most prostate cancer patients are diagnosed with localized disease. Long-term prostate cancer mortality is low, and many patients have been overtreated. Better prediction tools are needed to distinguish clinically insignificant, low-risk prostate cancers from lethal diseases. Tumor microvessel density, a marker of angiogenesis, has been shown to be related to the development of prostate cancer metastasis and even mortality. Mucci et al. found that morphologic characteristics of tumor microvessels, such as vessel size and irregularity, from prostatectomy specimen may be better than microvessel density as indicators of active angiogenesis, cancer aggressiveness and metastatic potential, and potentially prostate cancer-specific mortality. Prognostic tools combining angiogenesis biomarkers and clinical features led to marked improvement in the prediction of lethal prostate cancer. If these results are confirmed with studies from prostate biopsy samples, this novel prognosis tool will be of significant importance to guide clinical decision-making for localized prostate cancer.

Discovery of prostate cancer biomarkers by microarray gene expression profiling

Prostate cancer is the most commonly diagnosed malignancy in males in the Western world. This review focuses on advances in biomarker discovery for prostate cancer by microarray profiling of mRNA and microRNA expression. Novel biomarkers are strongly needed to enable more accurate detection of prostate cancer, improve prediction of tumor aggressiveness and facilitate discovery of new therapeutic targets for tailored medicine. Promising molecular markers identified from gene expression profiling studies include AMACR, EZH2, TMPRSS2-ERG, miR-221 and miR-141, which are described in more detail. In addition, a compilation of prognostic gene expression signatures for prediction of prostate cancer patient outcome is provided, and their possible clinical utility is discussed. Furthermore, limitations in the application of microarray-based expression profiling for identification of prostate cancer biomarkers are addressed.

Validation in a multiple urology practice cohort of the Prostate Cancer Prevention Trial calculator for predicting prostate cancer detection

PURPOSE

The Prostate Cancer Prevention Trial prostate cancer risk calculator was developed in a clinical trial cohort that does not represent men routinely referred for prostate biopsy. We assessed the generalizability of the Prostate Cancer Prevention Trial calculator in a cohort more representative of patients referred for consideration of prostate biopsy in American urology practice.

MATERIALS AND METHODS

Patients undergoing prostate biopsy by 12 urologists at 5 sites were enrolled in an Early Detection Research Network cohort. The Prostate Cancer Prevention Trial risk calculator was validated by examining area underneath the receiver operating characteristic curve, sensitivity, specificity and calibration comparing observed vs predicted risk of prostate cancer detection.

RESULTS

Cancer incidence was greater (43% vs 22%, p = 0.001) in the Early Detection Research Network validation cohort (645) compared to the Prostate Cancer Prevention Trial group (5,519). Early Detection Research Network participants were younger and more racially diverse, and had more abnormal digital rectal examinations and higher prostate specific antigen than Prostate Cancer Prevention Trial participants (all p <0.001). Cancer severity was worse in the Early Detection Research Network cohort than in the Prostate Cancer Prevention Trial (Gleason 7 or higher 60% vs 21%, p <0.001). Nevertheless, the Prostate Cancer Prevention Trial risk calculator was superior to prostate specific antigen alone for predicting cancer in the Early Detection Research Network (AUC 0.691 vs 0.655, p = 0.009) and calibration confirmed that the Prostate Cancer Prevention Trial risk score accurately predicted individual risks in the Early Detection Research Network cohort.

CONCLUSIONS

Differences between the Early Detection Research Network validation cohort and the Prostate Cancer Prevention Trial cohort underscore the importance of validating calculator performance in the multicenter urology practice setting. Our findings extend the applicability of the Prostate Cancer Prevention Trial calculator for measuring the risk of prostate cancer detection on biopsy to the routine American urology practice setting.

Development of a prostate cancer metagram: a solution to the dilemma of which prediction tool to use in patient counseling

Many treatment options are available to the human with clinically localized prostate cancer, including surgery, radiation, and even active surveillance. To the authors' knowledge, there is no consensus on the optimal management of this patient population, with most clinicians tending to recommend the treatment with which they are most familiar. Effective patient counseling allowing informed decision making can be best achieved with a formalized system that offers accurate predictions of outcomes for all available treatment approaches. The authors organized the currently available prostate cancer prediction tools toward the formation of a metagram that can be used to tailor management to the individual patient. A comprehensive review of the literature was performed to identify published prediction tools intended for use in prostate cancer. Tools were categorized by a combination of treatment modality and the outcome being predicted, and incorporated into a metagram constructed of 16 different treatment options and 10 outcomes related to cancer control, survival, and morbidity. A search of the literature revealed 44 prostate cancer prediction tools that assessed at least 1 of the 160 treatment/outcome combinations that comprise the metagram. Only 31 cells of the metagram were populated with currently available tools. Prediction tools offer the most accurate estimates of outcomes in prostate cancer, but their current role in patient counseling is complicated by the large number of existing tools, as well as a lack of comparative data. To address this, the authors incorporated the most relevant prediction tools currently available into a prostate cancer metagram that may offer evidence-based and individualized predictions for multiple endpoints after all available treatment options in clinically localized prostate cancer. The metagram also reveals areas of deficiency in the current catalog of prediction tools. Many more prediction tools are needed. Cancer 2009;115(13 suppl):3039-45. (c) 2009 American Cancer Society.