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Prostate cancer (PCa) is the most common cancer in men. The androgen receptor (AR) has a pivotal role in the pathogenesis and progression of PCa. Many therapies targeting AR signaling have been developed over the years. AR signaling inhibitors (ARSIs), including androgen synthesis inhibitors and AR antagonists, have proven to be effective in castration-sensitive PCa (CSPC) and improve survival, but men with castration-resistant PCa (CRPC) continue to have a poor prognosis. Despite a good initial response, drug resistance develops in almost all patients with metastatic CRPC, and ARSIs are no longer effective. Several mechanisms confer resistance to ARSI and include AR mutations but also hyperactivation of other pathways, such as PI3K/AKT/mTOR. This pathway controls key cellular processes, including proliferation and tumor progression, and it is the most frequently deregulated pathway in human cancers. A significant interaction between AR and the PI3K/AKT/mTOR signaling pathway has been shown in PCa. This review centers on the current scene of different AR and PI3K signaling pathway inhibitors, either as monotherapy or in combination treatments in PCa, and the treatment outcomes involved in both preclinical and clinical trials. A PubMed-based literature search was conducted up to November 2022. The most relevant and recent articles were selected to provide essential information and current evidence on the crosstalk between AR and the PI3K signaling pathways. The ClinicalTrials.gov registry was used to report information about clinical studies and their results using the Advanced research tool, filtering for disease and target.

Herein, we analyze answers achieved, open questions, and future perspectives regarding the analysis of the pathogenetic variants (PV) of DNA damage response (and repair) (DDR) genes in prostate cancer (PC) patients. The incidence of PVs in homologous recombination repair (HRR) genes among men with metastatic PC varied between 11% and 33%, which was significantly higher than that in non-metastatic PC, and BRCA2 mutations were more frequent when compared to other DDR genes. The determination of the somatic or germline PVs of BRCA2 was able to define a tailored therapy using PARP inhibitors in metastatic castration-resistant prostate cancer (mCRPC) progression after first-line therapy, with significant improvements in the radiologic progression-free survival (rPFS) and overall survival (OS) rates. We propose testing all metastatic PC patients for somatic and germline HRR mutations. Somatic determination on the primary site or on historic paraffin preparations with a temporal distance of no longer than 5 years should be preferred over metastatic site biopsies. The prognostic use of DDR PVs will also be used in selected high-risk cases with non-metastatic stages to better arrange controls and therapeutic primary options. We anticipate that the use of poly-ADP-ribose polymerase (PARP) inhibitors in hormone-sensitive prostate cancer (HSPC) and in combination with androgen receptor signaling inhibitors (ARSI) will be new strategies.

The aim of our meta-analysis is to analyze data available in the literature regarding a possible prognostic value of the albumin to globulin ratio (AGR) in prostate cancer (PC) patients. We distinguished our analysis in terms of PC staging, histologic aggressiveness, and risk of progression after treatments. A literature search process was performed (“prostatic cancer”, “albumin”, “globulin”, “albumin to globulin ratio”) following the PRISMA guidelines. In our meta-analysis, the pooled Event Rate (ER) estimate for each group of interest was calculated using a random effect model. Cases were distinguished in Low and High AGR groups based on an optimal cut-off value defined at ROC analysis. Four clinical trials were enclosed (sample size range from 214 to 6041 cases). The pooled Risk Difference for a non-organ confined PC between High AGR and Low AGR cases was −0.05 (95%CI: −0.12–0.01) with a very low rate of heterogeneity (I2 < 0.15%; p = 0.43) among studies (test of group differences p = 0.21). In non-metastatic PC cases, the pooled Risk Difference for biochemical progression (BCP) between High AGR and Low AGR cases was −0.05 (95%CI: −0.12–0.01) (I2 = 0.01%; p = 0.69) (test of group differences p = 0.12). In metastatic PC cases, AGR showed an independent significant (p < 0.01) predictive value either in terms of progression free survival (PFS) (Odds Ratio (OR): 0.642 (0.430–0.957)) or cancer specific survival (CSS) (OR: 0.412 (0.259–0.654)). Our meta-analysis showed homogeneous results supporting no significant predictive values for AGR in terms of staging, grading and biochemical progression in non-metastatic PC.

Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.

Early detection of prostate cancer (PC) is largely carried out using assessment of prostate-specific antigen (PSA) level; yet it cannot reliably discriminate between benign pathologies and clinically significant forms of PC. To overcome the current limitations of PSA, new urinary and serum biomarkers have been developed in recent years. Although several biomarkers have been explored in various scenarios and patient settings, to date, specific guidelines with a high level of evidence on the use of these markers are lacking. Recent advances in metabolomic, genomics, and proteomics have made new potential biomarkers available. A number of studies focused on the characterization of the specific PC metabolic phenotype using different experimental approaches has been recently reported; yet, to date, research on metabolomic application for PC has focused on a small group of metabolites that have been known to be related to the prostate gland. Exosomes are extracellular vesicles that are secreted from all mammalian cells and virtually detected in all bio-fluids, thus allowing their use as tumor biomarkers. Thanks to a general improvement of the technical equipment to analyze exosomes, we are able to obtain reliable quantitative and qualitative information useful for clinical application. Although some pilot clinical investigations have proposed potential PC biomarkers, data are still preliminary and non-conclusive.