A Novel Risk Score (P-score) Based on a Three-Gene Signature, for Estimating the Risk of Prostate Cancer-Specific Mortality

Prognostic Testing for Prostate Cancer-A Cost-Effectiveness Analysis Comparing a Prostatype P-Score Biomarker Approach to Standard Clinical Practice

BACKGROUND

The Prostatype score (P-score) is a prognostic biomarker that integrates a three-gene (IGFBP3, F3, and VGLL3) signature derived from prostate biopsy samples, with key clinical parameters, including prostate-specific antigen (PSA) levels, Gleason grade, and tumor stage at diagnosis. The test has demonstrated superior predictive accuracy for prostate cancer outcomes compared with traditional risk categorization systems such as D'Amico. Notably, it reclassifies a higher proportion of patients into the low-risk category, making them eligible for active surveillance. This study assessed the cost-effectiveness of the P-score in comparison with D'Amico and the Swedish National Prostate Cancer Register (NPCR) risk categorization systems.

METHODS

A two-step decision analytic model was developed. The model consisted of a decision tree-informed Markov structure estimating the lifetime outcomes of 60-year-old men with diagnosed prostate cancer. Prostate cancer was classified as low-risk, intermediate-risk, or high-risk using either the P-score or D'Amico. Initial therapy was based on observed treatment patterns from the Swedish NPCR. Costs (SEK, year 2022) and quality-adjusted life years (QALYs) were estimated from a healthcare perspective and discounted at 3% per year; incremental cost-effectiveness ratio (ICER) was the primary outcome.

RESULTS

The P-score led to cost savings and generated an additional 0.19 QALYs compared with D'Amico. The added costs of the genetic test and higher costs of active surveillance and radiotherapy were counterbalanced by savings from reduced costs of surgery, treatment-related side-effects, and metastatic disease. The gain in QALYs was primarily due to the avoidance of metastatic disease and a reduction in treatment-related side-effects.

CONCLUSIONS

The results of this study suggest that the P-score is likely to be a cost-effective alternative to D'Amico for prognostic evaluation of newly diagnosed prostate cancer in Sweden and compared with NPCR when health-related quality of life was included.

Predicting prostate cancer recurrence: Introducing PCRPS, an advanced online web server

Background

Prostate cancer (PCa) is one of the leading causes of cancer death in men. About 30% of PCa will develop a biochemical recurrence (BCR) following initial treatment, which significantly contributes to prostate cancer-related deaths. In clinical practice, accurate prediction of PCa recurrence is crucial for making informed treatment decisions. However, the development of reliable models and biomarkers for predicting PCa recurrence remains a challenge. In this study, the aim is to establish an effective and reliable tool for predicting the recurrence of PCa.

Methods

We systematically screened and analyzed potential datasets to predict PCa recurrence. Through quality control analysis, low-quality datasets were removed. Using meta-analysis, differential expression analysis, and feature selection, we identified key genes associated with recurrence. We also evaluated 22 previously published signatures for PCa recurrence prediction. To assess prediction performance, we employed nine machine learning algorithms. We compared the predictive capabilities of models constructed using clinical variables, expression data, and their combinations. Subsequently, we implemented these machine learning models into a user-friendly web server freely accessible to all researchers.

Results

Based on transcriptomic data derived from eight multicenter studies consisting of 733 PCa patients, we screened 23 highly influential genes for predicting prostate cancer recurrence. These genes were used to construct the Prostate Cancer Recurrence Prediction Signature (PCRPS). By comparing with 22 published signatures and four important clinicopathological features, the PCRPS exhibited a robust and significantly improved predictive capability. Among the tested algorithms, Random Forest demonstrated the highest AUC value of 0.72 in predicting PCa recurrence in the testing dataset. To facilitate access and usage of these machine learning models by all researchers and clinicians, we also developed an online web server (https://urology1926.shinyapps.io/PCRPS/) where the PCRPS model can be freely utilized. The tool can also be used to (1) predict the PCa recurrence by clinical information or expression data with high accuracy. (2) provide the possibility of PCa recurrence by nine machine learning algorithms. Furthermore, using the PCRPS scores, we predicted the sensitivity of 22 drugs from GDSC2 and 95 drugs from CTRP2 to the samples. These predictions provide valuable insights into potential drug sensitivities related to the PCRPS score groups.

Conclusion

Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment for PCa.

Correlation Between Long-Term Acetylsalicylic Acid Use and Prostate Cancer Screening with PSA. Should We Reduce the PSA Cut-off for Patients in Chronic Therapy? A Multicenter Study

Purpose

To evaluate the prostate cancer (PCa) detection rate in men with chronic use of Aspirin and to compare it with the detection rate of non-users.

Patients and Methods

Prospectively maintained database regarding patients undergoing prostate biopsy over the last 10 years in five institutions. Patients were divided into two groups according to their exposure to Aspirin. We relied on multivariable linear and logistic regression models to test whether Aspirin administration was associated with lower PSA values at prostate biopsy, higher PCa diagnosis, and higher Gleason Grade Grouping (GGG) at biopsy.

Results

Were identified 1059 patients, of whom 803 (76%) did not take Aspirin vs 256 (24%) were taking it. In multivariable log-linear regression analysis, Aspirin administration was associated with lower PSA levels (OR 0.83, 95% CI 0.71–0.97, p = 0.01), after controlling for age, prostate volume, smoking history, associated inflammation at prostate biopsy, presence of PCa at biopsy, and GGG. In multivariable logistic regression analysis, Aspirin administration was not found to be a predictor of PCa at prostate biopsy (OR 1.40, 95% CI 0.82–2.40, p = 0.21) after controlling for age, PSA, smoking history, prostate volume, findings at digital rectal examination and the number of biopsy cores. In patients with PCa at prostate biopsy (n = 516), Aspirin administration was found to predict higher GGG (OR 2.24, 95% CI 1.01–4.87, p = 0.04).

Conclusion

Aspirin administration was found to be a predictor of more aggressive GGG. These findings suggest that a lower PSA threshold should be considered in patients taking Aspirin, as, despite low PSA levels, they might harbour aggressive PCa.