Impact of Body Mass Index, Age, Prostate Volume, and Genetic Polymorphisms on Prostate-specific Antigen Levels in a Control Population

Endemic statistical paradoxes in epidemiologic studies distort knowledge on prostate cancer: mitigation and caution of fallacies in prostate cancer causal epidemiological studies

PURPOSE OF REVIEW

Many studies on epidemiology of prostate cancer (PCa) are based on a diagnosis of PCa using PSA (prostate-specific antigen) level. However, biases can distort the interpretation of the results, which in turn limits policy and decision making on public health prevention strategies or clinical guidelines. The main confusion is to interpret the posterior probability of the outcome following the exposure as a change in the prevalence of the disease outcome, whereas this change reflects only the predictive values of the PSA test induced by the exposure of interest.

RECENT FINDINGS

Many studies report potential causal factors involved in PCa risk. However, the lack of integration of how physiological changes in PSA values are associated with the exposures being investigated, they explain in part contradictory and controversial results on PCa risk factors in the literature.

SUMMARY

A strategy to perform case--control studies based on PSA stratification is suggested to avoid misinterpretation related to PSA misclassification. Real data are analysed, and we show that we can exploit the mechanism of selection biases using different modalities of controls recruitment based on biomarker stratification to distinguish real from false causal factors.

Let’s Go 3D! New Generation of Models for Evaluating Drug Response and Resistance in Prostate Cancer

Prostate cancer (PC) is the third most frequently diagnosed cancer worldwide and the second most frequent in men. Several risk factors can contribute to the development of PC, and those include age, family history, and specific genetic mutations. So far, drug testing in PC, as well as in cancer research in general, has been performed on 2D cell cultures. This is mainly because of the vast benefits these models provide, including simplicity and cost effectiveness. However, it is now known that these models are exposed to much higher stiffness; lose physiological extracellular matrix on artificial plastic surfaces; and show changes in differentiation, polarization, and cell–cell communication. This leads to the loss of crucial cellular signaling pathways and changes in cell responses to stimuli when compared to in vivo conditions. Here, we emphasize the importance of a diverse collection of 3D PC models and their benefits over 2D models in drug discovery and screening from the studies done so far, outlining their benefits and limitations. We highlight the differences between the diverse types of 3D models, with the focus on tumor–stroma interactions, cell populations, and extracellular matrix composition, and we summarize various standard and novel therapies tested on 3D models of PC for the purpose of raising awareness of the possibilities for a personalized approach in PC therapy.

Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study

This work combines Raman spectroscopy (RS) with supervised learning methods-group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)-to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D'Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring.

Targeted Mass Spectrometry of a Clinically Relevant PSA Variant from Post-DRE Urines for Quantitation and Genotype Determination

PURPOSE

The rs17632542 single nucleotide polymorphism (SNP) results in lower serum prostate specific antigen (PSA) levels which may further mitigate against its clinical utility as a prostate cancer biomarker. Post-digital rectal exam (post-DRE) urine is a minimally invasive fluid that is currently utilized in prostate cancer diagnosis. To detect and quantitate the variant protein in urine.

EXPERIMENTAL DESIGN

Fifty-three post-DRE urines from rs17632542 genotyped individuals processed and analyzed by liquid chromatography/mass spectrometry (LC-MS) in a double-blinded randomized study. The ability to distinguish between homozygous wild-type, heterozygous, or homozygous variant is examined before unblinding.

RESULTS

Stable-isotope labeled peptides are used in the detection and quantitation of three peptides of interest in each sample using parallel reaction monitoring (PRM). Using these data, groupings are predicted using hierarchical clustering in R. Accuracy of the predictions show 100% concordance across the 53 samples, including individuals homozygous and heterozygous for the SNP.

CONCLUSIONS AND CLINICAL RELEVANCE

The study demonstrates that MS based peptide variant quantitation in urine could be useful in determining patient genotype expression. This assay provides a tool to evaluate the utility of PSA variant (rs17632542) in parallel with current and forthcoming urine biomarker panels.

[Study of anogenital distance as a diagnostic tool for prostate cancer]

INTRODUCTION AND OBJECTIVE

The distance from the genitals to the anus (anogenital distance [AGD]) reflects androgen concentration during prenatal development in mammals. At the present time, there is only one study suggesting the relationship between AGD and risk of prostate cancer (CaP). The goal of this study was to assess the performance and clinical utility of AGD, as a biomarker of prenatal androgenic milieu, and risk of CaP in a larger population, in CaP diagnosis.

MATERIAL AND METHODS

A case-control study was conducted on 260 men seen in a hospital outpatient clinic where underwent a physical and andrological examination and completed a brief questionnaire. CaP patients were confirmed by biopsy of the tumor. Controls were men without CaP seen in the urology outpatient clinic for routine examinations. Two variants of AGD (from the anus to the posterior base of the scrotum [AGD_AS] and to the cephalad insertion of the penis [AGD_AP]) were measured. Parametric and non-parametric tests and receiver operating characteristic (COR) analyses were used to determine relationships between AGD and presence of CaP.

RESULTS

The highest area under the curve (0.69; 95% CI 0.60 to 0.78 and 0.69; 95% CI 0.61 to 0.77) was obtained for the Gleason=7 subgroup with the AGD_AS and AGD_AP measurement, with a sensitivity and specificity of 83% and 55%, and 91% and 41%, the predictive positive value of 39% and 35% and negative value of 90% and 93% respectively.

CONCLUSION

AGD may be a useful clinical tool for the CaP diagnosis.

Search for genetic factor association with cancer-free prostate-specific antigen level elevation on the basis of a genome-wide association study in the Korean population

We investigated the genetic markers associated with elevated serum prostate-specific antigen (sPSA) levels to improve the predictive power of sPSA in screening for prostate cancer. A genome-wide association study was carried out among 4124 healthy Korean male adults using the Affymetrix Axiom Customized Biobank Genotyping Arrays for sPSA levels. A subgroup analysis for increased sPSA levels who underwent a prostate biopsy (n=64) was also carried out. We detected 11 single nucleotide polymorphisms (SNPs) near the Solute carrier family 45member 3, AGAP7P, MSMB, LOC101929917, and KLK3 genes associated with sPSA levels. The top SNP associated with the log of the sPSA levels was rs72434280 in the Solute carrier family 45 member 3 gene (P value, discovery set=2.98×10, replication set=7.31×10). A case-control study utilizing available biopsy reports (49 patients with normal biopsies vs. 15 patients with biopsies indicating cancer) for the sPSA more than 3 ng/ml group was carried out for the respective SNPs after adjusting for age. Only the SNPs near the KLK3 gene were associated with prostate cancer. In the model of the predictive elevation of sPSA level, adding the genetic risk score [area under the curve (AUC)=0.697] to age and BMI (AUC=0.602) significantly improved the results of the AUC (P<0.0001). We found seven SNPs associated with elevated prostate-specific antigen levels in healthy Korean men. Four SNPs were a novel marker in the Korean population. In men with increased prostate-specific antigen levels, genotyping SNP related to cancer-free elevation of sPSA level could be informative to decide the indication of prostate biopsy.