Percent free prostate-specific antigen is effective to predict prostate biopsy outcome in Chinese men with prostate-specific antigen between 10.1 and 20.0 ng ml(-1)

Prevalence of tumour-infiltrating CD103+ cells identifies therapeutic-sensitive prostate cancer with poor clinical outcome

BACKGROUND

The clinical significance and immune correlation of CD103+ cells in prostate cancer (PCa) remain explored.

METHODS

In total, 1080 patients with PCa underwent radical prostatectomy from three cohorts were enrolled for retrospective analysis. Tumour microarrays were constructed and fresh tumour samples were analysed by flow cytometry.

RESULTS

High CD103+ cell infiltration correlated with reduced biochemical recurrence (BCR)-free survival in PCa. Adjuvant hormone therapy (HT) prolonged the BCR-free survival for high-risk node-negative diseases with CD103+ cell abundance. CD103+ cell infiltration correlated with less cytotoxic expression and increased infiltration of CD8+ and CD4+ T cells, M1 macrophages and mast cells in PCa. Intratumoral CD8+ T cell was the predominant source of CD103, and the CD103+ subset of CD8+ T cells was featured with high IL-10, PD-1 and CTLA-4 expression. Tumour-infiltrating CD103+ CD8+ T cells exerted anti-tumour function when treated with HT ex vivo.

DISCUSSION

CD103+ cell infiltration predicted BCR-free survival and response to adjuvant HT in PCa. CD103+ cell infiltration correlated with an enriched but immune-evasive immune landscape. The study supported a model that CD103 expression conferred negative prognostic impact and immunosuppressive function to tumour-infiltrating CD8+ T cells, while the CD103+ CD8+ T cells exhibited a powerful anti-tumour immunity with response to HT.

Combining clinical parameters and multiparametric magnetic resonance imaging to stratify biopsy-naïve men for an optimum diagnostic strategy with prostate-specific antigen 4 ng ml-1 to 10 ng ml-1

We attempted to perform risk categories based on the free/total prostate-specific antigen ratio (%fPSA), prostate-specific antigen (PSA) density (PSAD, in ng ml-2), and multiparametric magnetic resonance imaging (mpMRI) step by step, with the goal of determining the best clinical diagnostic strategy to avoid unnecessary tests and prostate biopsy (PBx) in biopsy-naïve men with PSA levels ranging from 4 ng ml-1 to 10 ng ml-1. We included 439 patients who had mpMRI and PBx between August 2018 and July 2021 (West China Hospital, Chengdu, China). To detect clinically significant prostate cancer (csPCa) on PBx, receiver-operating characteristic (ROC) curves and their respective area under the curve were calculated. Based on %fPSA, PSAD, and Prostate Imaging-Reporting and Data System (PI-RADS) scores, the negative predictive value (NPV) and positive predictive value (PPV) were calculated sequentially. The optimal %fPSA threshold was determined to be 0.16, and the optimal PSAD threshold was 0.12 for %fPSA ≥0.16 and 0.23 for %fPSA <0.16, respectively. When PSAD <0.12 was combined with patients with %fPSA ≥0.16, the NPV of csPCa increased from 0.832 (95% confidence interval [CI]: 0.766-0.887) to 0.931 (95% CI: 0.833-0.981); the detection rate of csPCa was similar when further stratified by PI-RADS scores (P = 0.552). Combining %fPSA <0.16 with PSAD ≥0.23 ng ml-2 predicted significantly more csPCa patients than those with PSAD <0.23 ng ml-2 (58.4% vs 26.7%, P < 0.001). Using PI-RADS scores 4 and 5, the PPV was 0.739 (95% CI: 0.634-0.827) when further stratified by mpMRI results. In biopsy-naïve patients with PSA level of 4-10 ng ml-1, stratification of %fPSA and PSAD combined with PI-RADS scores may be useful in the decision-making process prior to undergoing PBx.

Development and validation of a predictive model for diagnosing prostate cancer after transperineal prostate biopsy

Objective

This study aimed to develop and validate a nomogram to predict the probability of prostate cancer (PCa) after transperineal prostate biopsy by combining patient clinical information and biomarkers.

Methods

First, we retrospectively collected the clinicopathologic data from 475 patients who underwent prostate biopsy at our hospital between January 2019 to August 2021. Univariate and multivariate logistic regression analyses were used to select risk factors. Then, we established the nomogram prediction model based on the risk factors. The model performance was assessed by receiver operating characteristic (ROC) curves, calibration plots and the Hosmer–Lemeshow test. Decision curve analysis (DCA) was used to evaluate the net benefit of the model at different threshold probabilities. The model was validated in an independent cohort of 197 patients between September 2021 and June 2022.

Results

The univariate and multivariate logistic regression analyses based on the development cohort indicated that the model should include the following factors: age (OR = 1.056, p = 0.001), NEUT (OR = 0.787, p = 0.008), HPR (OR = 0.139, p &lt; 0.001), free/total (f/T) PSA (OR = 0.013, p = 0.015), and PI-RADS (OR = 3.356, p &lt; 0.001). The calibration curve revealed great agreement. The internal nomogram validation showed that the C-index was 0.851 (95% CI 0.809-0.894). Additionally, the AUC was 0.851 (95% CI 0.809-0.894), and the Hosmer–Lemeshow test result presented p = 0.143 &gt; 0.05. Finally, according to decision curve analysis, the model was clinically beneficial.

Conclusion

Herein, we provided a nomogram combining patients’ clinical data with biomarkers to help diagnose prostate cancers.

Clinical research analysis based on prostate cancer screening diagnosis

This study aimed to analyse the clinical characteristics and risk factors of patients with positive prostate biopsy at 4-20 ng/mL of prostate-specific antigen (PSA), construct a new parameter based on this characteristics and assess its diagnostic value for prostate cancer (PCa). Logistic regression analysis was used to clarify the risk factors of PCa, and a new parameter based on the results was constructed. Compare the diagnostic value of various diagnostic parameters for PCa. Logistic multivariate regression analysis revealed that age (OR, 5.269; 95%CI, 2.762-10.050), comorbid diabetes (OR, 2.437; 95%CI, 1.162-5.111), PSA (OR, 2.462; 95%CI, 1.198-5.059) and prostate volume (PV) (OR, 0.227; 95%CI, 0.100-0.516) are risk factors for PCa. The age, PSA and PV of patients were combined to construct a new parameter, that is A-PSAD = (age × total PSA [TPSA])/PV]. The area under the receiver-operating characteristic curve(AUC) of A-PSAD (0.728) for PCa diagnosis was higher than the AUCs of TPSA (0.581), free prostate-specific antigen (0.514), (F/T)PSA (0.535) and PSAD (0.696), with significant differences. Age, history of diabetes, TPSA and PV are risk factors for PCa(PSA:4-20ng/mL); in addition, A-PSAD has a moderate diagnostic value for PCa and may become a new indicator for PCa screening.

Clinical application of free/total PSA ratio in the diagnosis of prostate cancer in men over 50 years of age with total PSA levels of 2.0-25.0 ng ml-1 in Western China

The goal of this study was to investigate the clinical application of free/total prostate-specific antigen (F/T PSA) ratio, considering the new broad serum total PSA (T-PSA) “gray zone” of 2.0–25.0 ng ml⁻¹ in differential diagnosis of prostate cancer (PCa) and benign prostate diseases (BPD) in men over 50 years in Western China. A total of 1655 patients were included, 528 with PCa and 1127 with BPD. Serum T-PSA, free PSA (F-PSA), and F/T PSA ratio were analyzed. Receiver operating characteristic curves were used to assess the efficiency of PSA and F/T PSA ratio. There were 47.4% of cancer patients with T-PSA of 2.0–25.0 ng ml⁻¹. When T-PSA was 2.0–4.0 ng ml⁻¹, 4.0–10.0 ng ml⁻¹, and 10.0–25.0 ng ml⁻¹, the area under the curve (AUC) of F/T PSA ratio was 0.749, 0.769, and 0.761, respectively. The best AUC of F/T PSA ratio was 0.811 when T-PSA was 2.0–25.0 ng ml⁻¹, with a specificity of 0.732, a sensitivity of 0.788, and an optimal cutoff value of 15.5%. The AUC of F/T PSA ratio in different age groups (50–59 years, 60–69 years, 70–79 years, and ≥80 years) was 0.767, 0.806, 0.815, and 0.833, respectively, and the best sensitivity (0.857) and specificity (0.802) were observed in patients over 80 years. The T-PSA trend was in accordance with the Gleason score, tumor node metastasis (TNM) stage, and American Joint Committee on Cancer prognosis group. Therefore, the F/T PSA ratio can facilitate the differential diagnosis of PCa and BPD in the broad T-PSA “gray zone”. Serum T-PSA can be a Gleason score and prognostic indicator.

Prostate cancer risk prediction models in Eastern Asian populations: current status, racial difference, and future directions

Prostate cancer (PCa) risk calculators (RCs) with prostate-specific antigen (PSA) and other risk factors can greatly improve the accurate prediction of potential risk of PCa compared to PSA. The European Randomized Study of Screening for PCa Risk Calculator (ERSPC-RC) and the Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC) are developed on the Western population. However, the Western RCs showed limited diagnostic efficacy in the Eastern Asian population, mainly due to racial differences between the two populations. We aimed to review the application of Western RCs and Eastern Asian RCs in Eastern Asian cohorts and to identify the characteristics and efficacy of these RCs.

A novel screening strategy for clinically significant prostate cancer in elderly men over 75 years of age

A standard modality for prostate cancer detection in men 75 years and older has not been established. A simple screening method for elderly patients is needed to avoid unnecessary biopsies and to effectively diagnose prostate cancer. A retrospective study was conducted on elderly patients who had prostate biopsy at Kanazawa University Hospital (Kanazawa, Japan) between 2000 and 2017. Of the 2251 patients who underwent prostate biopsy, 254 had clinically significant prostate cancer (CSPC) with a Gleason score (GS) of≥7 and 273 had a GS of <7 or no malignancy. In this study, patients aged 75 years or older were classified as elderly patients. GS ≥ 7 was characterized by a prostate-specific antigen (PSA) of the maximum area under the curve of 12 ng ml^-1 with a sensitivity of 76.2% and a specificity of 73.2%. For PSA levels between 4 ng ml^-1 and 12 ng ml^-1, based on the maximum area under the curve, patients with three or four of the following factors may present a GS of ≥ 7: percent free PSA >24, PSA density≥ 0.24 ng ml^-2, positive findings on digital rectal examination, and transrectal with 90.0% sensitivity and 67.4% specificity. In this study, we found that raising the PSA cutoff to 12 ng ml^-1 for CSPC in elderly individuals can significantly reduce unnecessary prostate biopsies. Furthermore, CSPC could be efficiently discovered by combining the four supplementary markers in patients with a PSA level of 4-12 ng ml^-1. By performing this screening for elderly men over 75 years of age, unnecessary biopsies may be reduced and CSPC may be detected efficiently.

Development and external multicenter validation of Chinese Prostate Cancer Consortium prostate cancer risk calculator for initial prostate biopsy

OBJECTIVE

Substantial differences exist in the relationship of prostate cancer (PCa) detection rate and prostate-specific antigen (PSA) level between Western and Asian populations. Classic Western risk calculators, European Randomized Study for Screening of Prostate Cancer Risk Calculator, and Prostate Cancer Prevention Trial Risk Calculator, were shown to be not applicable in Asian populations. We aimed to develop and validate a risk calculator for predicting the probability of PCa and high-grade PCa (defined as Gleason Score sum 7 or higher) at initial prostate biopsy in Chinese men.

MATERIALS AND METHODS

Urology outpatients who underwent initial prostate biopsy according to the inclusion criteria were included. The multivariate logistic regression-based Chinese Prostate Cancer Consortium Risk Calculator (CPCC-RC) was constructed with cases from 2 hospitals in Shanghai. Discriminative ability, calibration and decision curve analysis were externally validated in 3 CPCC member hospitals.

RESULTS

Of the 1,835 patients involved, PCa was identified in 338/924 (36.6%) and 294/911 (32.3%) men in the development and validation cohort, respectively. Multivariate logistic regression analyses showed that 5 predictors (age, logPSA, logPV, free PSA ratio, and digital rectal examination) were associated with PCa (Model 1) or high-grade PCa (Model 2), respectively. The area under the curve of Model 1 and Model 2 was 0.801 (95% CI: 0.771-0.831) and 0.826 (95% CI: 0.796-0.857), respectively. Both models illustrated good calibration and substantial improvement in decision curve analyses than any single predictors at all threshold probabilities. Higher predicting accuracy, better calibration, and greater clinical benefit were achieved by CPCC-RC, compared with European Randomized Study for Screening of Prostate Cancer Risk Calculator and Prostate Cancer Prevention Trial Risk Calculator in predicting PCa.

CONCLUSIONS

CPCC-RC performed well in discrimination and calibration and decision curve analysis in external validation compared with Western risk calculators. CPCC-RC may aid in decision-making of prostate biopsy in Chinese or in other Asian populations with similar genetic and environmental backgrounds.