Development and external validation of a nomogram for predicting cancer probability at initial prostate biopsy using the life expectancy- and prostate volume-adjusted biopsy scheme

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.

Prostate cancer nomograms: a review of their use in cancer detection and treatment

As prostate cancer treatment discussions have grown more complex, increasing numbers of nomograms to guide decision-making have been found in the literature. Such nomograms can influence every step in the prostate cancer therapeutic process, from determining the need for biopsy to the need for adjuvant therapy. With a properly counseled patient who is aware of the limitations of nomograms, such tools assist in the shared decision-making that characterizes modern informed consent.

Androgen receptor coactivator p120 subtype β is highly expressed in prostate cancer

Purpose:

The β form of p120 is reported to be a strong coactivator of the androgen receptor. We investigated the gene expression profiles of the α and β forms of p120 in prostate cancer cell lines, benign prostatic hyperplasia (BPH), nontreated prostate cancer (NTPC), and prostate cancer after androgen deprivation therapy (PCA-ADT).

Methods:

We obtained 154 prostate needle biopsy specimens (81 in BPH, 51 in NTPC, and 22 in PCA-ADT). Levels of p120α and β expression were determined by multiplex real-time polymerase chain reaction.

Results:

Prostate cancer cell lines, LNCaP, PC-3, DU-145, and LNCaP-LA, which is a derivative of LNCaP under androgen deprivation, expressed both p120α and p120β. p120α expression levels were significantly higher than those of p120β in all cell lines examined. In human prostate tissues, p120α expression was significantly higher than that of p120β in BPH and NTPC. p120α expression in BPH was significantly higher than in other groups. In contrast, p120β expression was significantly higher in NTPC and PCA-ADT than in BPH. Expression of the two forms of p120 was not correlated with age, prostate-specific antigen, or Gleason score.

Conclusions:

The expression profiles of p120α and p120β significantly differ in cancerous and benign prostatic tissues.

Role of squalene synthase in prostate cancer risk and the biological aggressiveness of human prostate cancer

BACKGROUND

We previously conducted a genome-wide linkage analysis of Japanese nuclear families affected with prostate cancer and showed that the susceptibility to prostate cancer was closely linked to D8S550 at 8p23. The role of farnesyl diphosphate farnesyltransferase (FDFT1), which is located under the peak marker D8S550 at 8p23, and squalene synthase, the enzyme encoded by FDFT1, in prostate cancer was studied.

METHODS

The association among common variants of FDFT1 with prostate cancer risk, the promoter activities of FDFT1 with different genotypes and the effects of inhibition of squalene synthase were studied, and the FDFT1 transcript levels of human prostate samples were quantified.

RESULTS

The A allele of rs2645429 was significantly associated with prostate cancer risk in a Japanese familial prostate cancer population. Rs2645429 was located in the promoter region of FDFT1, and the AA genotype showed significantly increased promoter activity. The knockdown of FDFT1 mRNA expression or squalene synthase inhibition led to a significant decrease in prostate cancer cell proliferation. Additionally, human prostate cancer specimens expressed significantly higher levels of FDFT1 mRNA compared with noncancerous specimens. Finally, aggressive cancers showed higher transcript levels.

CONCLUSIONS

FDFT1 and its encoded enzyme, squalene synthase, may play an important role in prostate cancer development and its aggressive phenotypes.