Improved prostate cancer detection with a human kallikrein 11 and percentage free PSA-based artificial neural network

Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects

Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. We developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology

BACKGROUND

Testing a hypothesis for 'factors-outcome effect' is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified.

METHODS

The PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system.

RESULTS

The search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible.

CONCLUSION

ES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research.

Prostate-specific antigen and other serum and urine markers in prostate cancer

Prostate-specific antigen (PSA) is one of the most widely used tumor markers, and strongly correlates with the risk of harboring from prostate cancer (PCa). This risk is visible already several years in advance but PSA has severe limitations for PCa detection with its low specificity and low negative predictive value. There is an urgent need for new biomarkers especially to detect clinically significant and aggressive PCa. From all PSA-based markers, the FDA-approved Prostate Health Index (phi) shows improved specificity over percent free and total PSA. Other serum kallikreins or sarcosine in serum or urine show more diverging data. In urine, the FDA-approved prostate cancer gene 3 (PCA3) has also proven its utility in the detection and management of early PCa. However, some aspects on its correlation with aggressiveness and the low sensitivity at very high values have to be re-examined. The detection of a fusion of the androgen regulated TMPRSS2 gene with the ERG oncogene (from the ETS family), which acts as transcription factor gene, in tissue of ~50% of all PCa patients was one milestone in PCa research. When combining the urinary assays for TMPRSS2:ERG and PCA3, an improved accuracy for PCa detection is visible. PCA3 and phi as the best available PCa biomarkers show an equal performance in direct comparisons.

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.

Alternative Tests to Psa for Prostate Cancer Diagnosis

Prostate specific antigen (PSA) is still the most useful tool to select the population requiring prostate biopsy. The main downsides of PSA are an inadequate sensitivity to be used in screening and a low specificity for cancer detection.

So far, a limited value for PSA derivates (velocity, density, free, proisoforms and doubling time) has been recognised.

We present a short review of the literature describing a selection of the most promising alternatives to PSA being studied currently: PCA3, serum kallikreins, serum detectable prostate specific membrane antigen, the nuclear matrix protein EPCA, EPCA-2, prostatic acid phosphatase, urine detectable GSTP1, anti-AMACR antibodies, sarcosine, plasminogen activating urokinase, IGFBP, TGF beta 1, PSP94, IL6, plasmatic DNA, serum autoantibodies, neuroendocrine markers, proteomic analysis.

KLK15 is a prognostic marker for progression-free survival in patients with radical prostatectomy

In search of biomarkers for prostate cancer, we evaluated the expression of the human kallikrein-related peptidase KLK15 in samples of prostatic adenocarcinomas from radical prostatectomies. Twenty-five pairs of cancerous and adjacent normal prostatic tissue were selected by laser capture microdissection. The tissue was used for quantification of KLK15 mRNA by reverse-transcriptase polymerase chain reaction. Immunohistochemical expression of the KLK15 protein in 193 samples of prostatic adenocarcinoma was analysed in relation to clinicopathological parameters of the patients and disease progression. Expression of KLK15 correlated with the pathological tumour stage and Gleason score of the cases, both at mRNA and at protein level. While mRNA expression in the tumour was elevated, the protein level of KLK15 was reduced compared with adjacent normal tissue and to prostatic intraepithelial neoplasia. Univariate Kaplan-Meier analysis showed a significant association of dichotomised KLK15 levels with disease progression defined by prostate-specific antigen relapse (p = 0.001). Multivariate analysis according to the Cox proportional hazards regression model identified dichotomised KLK15 expression, corrected for the patient parameters age, preoperative prostate-specific antigen level, pathological tumour stage, Gleason score and surgical margin status, as an independent prognostic factor for poor outcome (inclusion model, hazard ratio 1.802, 95% confidence interval 1.037-3.132, p = 0.037). We suggest KLK15 as a new independent tumour marker for patients at risk for disease progression after radical prostatectomy.

Diagnostic accuracy of the RBANS in mild cognitive impairment: limitations on assessing milder impairments

The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) has demonstrated adequate sensitivity in detecting cognitive impairment in a number of neuropsychiatric conditions, including Alzheimer's disease. However, its ability to detect milder cognitive deficits in the elderly has not been examined. The current study examined the clinical utility of the RBANS by comparing two groups: Patients with Mild Cognitive Impairment (MCI; n = 72) and cognitively intact peers (n = 71). Significant differences were observed on the RBANS Total score, 3 of the 5 Indexes, and 6 of the 12 subtests, with individuals with MCI performing worse than the comparison participants. Specificity was very good, but sensitivity ranged from poor to moderate. Areas under the receiver operating characteristic curves for the RBANS Immediate and Delayed Memory Indexes and the Total Scale score were adequate. Although significant differences were observed between groups and the areas under the curves were adequate, the lower sensitivity values of the RBANS suggests that caution should be used when diagnosing conditions such as MCI.

Techniques and predictive models to improve prostate cancer detection

The use of prostate-specific antigen (PSA) as a screening test remains controversial. There have been several attempts to refine PSA measurements to improve its predictive value. These modifications, including PSA density, PSA kinetics, and the measurement of PSA isoforms, have met with limited success. Therefore, complex statistical and computational models have been created to assess an individual's risk of prostate cancer more accurately. In this review, the authors examined the methods used to modify PSA as well as various predictive models used in prostate cancer detection. They described the mathematical underpinnings of these techniques along with their intrinsic strengths and weaknesses, and they assessed the accuracy of these methods, which have been shown to be better than physicians' judgment at predicting a man's risk of cancer. Without understanding the design and limitations of these methods, they can be applied inappropriately, leading to incorrect conclusions. These models are important components in counseling patients on their risk of prostate cancer and also help in the design of clinical trials by stratifying patients into different risk categories. Thus, it is incumbent on both clinicians and researchers to become familiar with these tools. Cancer 2009;115(13 suppl):3085-99. (c) 2009 American Cancer Society.

A [-2]proPSA-based artificial neural network significantly improves differentiation between prostate cancer and benign prostatic diseases

BACKGROUND

The aim of this study was to combine the new automated Access [-2]proPSA (p2PSA) assay with a percent free PSA (%fPSA) based artificial neural network (ANN) or logistic regression (LR) model to enhance discrimination between patients with prostate cancer (PCa) and with no evidence of malignancy (NEM) and to detect aggressive PCa.

METHODS

Sera from 311 PCa patients and 275 NEM patients were measured with the p2PSA, total PSA (tPSA) and free PSA (fPSA) assays on Access immunoassay technology (Beckman Coulter, Fullerton, CA) within the 0-30 ng/ml tPSA range. Four hundred seventy-five patients (264 PCa, 211 NEM) had a tPSA of 2-10 ng/ml. LR models and leave-one-out (LOO) ANN models with Bayesian regularization by using tPSA, %fPSA, p2PSA/fPSA (%p2PSA), age and prostate volume were constructed and compared by receiver-operating characteristic (ROC) curve analysis.

RESULTS

The ANN and LR model each utilizing %p2PSA, %fPSA, tPSA and age, but without prostate volume, reached the highest AUCs (0.85 and 0.84) and best specificities (ANN: 62.1% and 45.5%; LR: 53.1% and 41.2%) compared with tPSA (22.7% and 11.4%) and %fPSA (45.5% and 26.1%) at 90% and 95% sensitivity. The %p2PSA furthermore distinguished better than tPSA and %fPSA between pT2 and pT3, and Gleason sum <7 and >or=7 PCa.

CONCLUSIONS

The automated p2PSA assay offers a new tool to improve PCa detection, and especially aggressive PCa detection. Incorporation of %p2PSA into an ANN and LR model further enhances the diagnostic accuracy to differentiate between malignant and non-malignant prostate diseases.

Emerging Biomarkers for the Diagnosis and Prognosis of Prostate Cancer

Background: Early detection of prostate cancer (CaP), the most prevalent cancer and the second-leading cause of death in men, has proved difficult, and current detection methods are inadequate. Prostate-specific antigen (PSA) testing is a significant advance for early diagnosis of patients with CaP.

Content: PSA is produced almost exclusively in the prostate, and abnormalities of this organ are frequently associated with increased serum concentrations. Because of PSA’s lack of specificity for CaP, however, many patients undergo unnecessary biopsies or treatments for benign or latent tumors, respectively. Thus, a more specific method of CaP detection is required to augment or replace screening with PSA. The focus recently has been on creating cost-effective assays for circulating protein biomarkers in the blood, but because of the heterogeneity of CaP, it has become clear that this effort will be a formidable challenge. Each marker will require proper validation to ensure clinical utility. Although much work has been done on variations of the PSA test (i.e., velocity, density, free vs bound, proisoforms) with limited usefulness, there are many emerging markers at various stages of development that show some promise for CaP diagnosis. These markers include kallikrein-related peptidase 2 (KLK2), early prostate cancer antigen (EPCA), PCA3, hepsin, prostate stem cell antigen, and α-methylacyl-CoA racemase (AMACR). We review biomarkers under investigation for the early diagnosis and management of prostate cancer.

Summary: It is hoped that the use of panels of markers can improve CaP diagnosis and prognosis and help predict the therapeutic response in CaP patients.

Screening for prostate cancer in 2008 II: the importance of molecular subforms of prostate-specific antigen and tissue kallikreins

CONTEXT

Over the past decades, prostate-specific antigen (PSA), its isoforms, and other members of the tissue kallikrein family have been of continuous interest with regard to early detection and screening for prostate cancer (PCa).

OBJECTIVE

This review strives to give an overview of the possible clinical utilities of these markers, focused on early diagnostics and PCa screening.

EVIDENCE ACQUISITION

Using the Medline database, a literature search was performed on the role of molecular subforms of PSA and other members of the tissue kallikrein family in PCa detection.

EVIDENCE SYNTHESIS

With respect to PSA isoforms, only the combination of the various truncated forms (pPSA) shows additional value over total PSA (tPSA) and free PSA (fPSA) in PCa detection within the range of 2-10 ng/ml tPSA. At a high sensitivity for PCa, the specificity of the ratio of pPSA to fPSA (%pPSA) is, in general, better than that of the ratio of fPSA to tPSA (%fPSA), with a gain of 5-11%. The (-2)pPSA, (-4)pPSA, (-5)pPSA, (-7)pPSA, and benign PSA (BPSA) isoforms generally show no additional value over either pPSA or the existing parameters of tPSA and fPSA. Of the other members of the tissue kallikrein family, most studies on human kallikrein 2 (hK2) show an additional value of the ratio of hK2 to fPSA (%hK2) over %fPSA alone in PCa prediction. Other tissue kallikreins cannot be recommended for diagnosing PCa, due to the lack of additional value over tPSA or fPSA or to insufficient research. Regarding a prognostic role, the value of PSA subforms as well as of other members of the tissue kallikrein family is limited with regard to existing parameters.

CONCLUSIONS

pPSA and hK2 are able to improve PCa diagnosis in the range of 4-10 ng/ml tPSA over the existing variables tPSA and fPSA.

PSA and other tissue kallikreins for prostate cancer detection

Prostate cancer is the most common neoplasia of middle-aged men. Prostate specific antigen (PSA) is the first FDA-approved tumour marker for early detection of cancer and it is now in widespread clinical use. The discovery of different PSA molecular forms in serum (free PSA, PSA complexed with various protease inhibitors) in the early 1990s renewed clinical research to enhance the specificity of PSA. Also, the use of a homologous prostate-localised antigen, human glandular kallikrein 2 (KLK2) may further reduce the number of unnecessary prostate biopsies. More recently, promising data is emerging regarding molecular forms of free PSA (proPSA, BPSA, 'intact' PSA) and other members of the expanded human kallikrein family. These new findings may add substantial clinical information for early detection of prostate cancer.

PSA and new biomarkers within multivariate models to improve early detection of prostate cancer

This review gives an overview of the use of prostate-specific antigen (PSA) and percent free-PSA (%fPSA)-based artificial neural networks (ANNs) and logistic regression models (LR) to reduce unnecessary prostate biopsies. There is a clear advantage in including clinical data such as age, digital rectal examination and transrectal ultrasound (TRUS) variables like prostate volume and PSA density as additional factors to tPSA and %fPSA within ANNs and LR models. There is also positive impact of tPSA and fPSA assays on the outcome of ANNs. New markers provide additional value within ANNs but to prove their clinical usefulness further testing is necessary.

Human tissue kallikreins: the cancer biomarker family

Human tissue kallikreins (KLKs) are attracting increased attention due to their role as biomarkers for the screening, diagnosis, prognosis, and monitoring of various cancers including those of the prostate, ovarian, breast, testicular, and lung. Human tissue kallikrein genes represent the largest contiguous group of proteases within the human genome. Originally thought to consist of three genes, the identification of the human kallikrein locus has expanded this number to fifteen. These genes, and their encoded proteins, share a high degree of homology and are expressed in different tissues. Prostate-specific antigen (PSA), the most commonly known kallikrein, is a useful biomarker for prostate cancer. Several other kallikreins, including kallikreins 2 (KLK2) and 11 (KLK11) are emerging as complementary prostate cancer biomarkers. Along with these kallikreins, several others have been implicated in the other cancers. For example, KLK5, 6, 7, 10, 11, and 14 are emerging biomarkers for ovarian cancer. The identification of kallikrein substrates and the development of proteolytic cascade models implicate kallikrein proteins in cancer progression. This review describes the current status of kallikreins as cancer biomarkers.