SNPs have been used to predict an individual’s susceptibility to diseases, response to therapies, and risks associated with environmental exposures. Age-specific genetic scores can apply to PCa risk assessment, informing screening strategies and identifying men at higher risk for developing metastatic or fatal disease. For example, Pagadala et al. [9] developed a polygenic hazard score (PHS290) to predict the likelihood of developing aggressive cancer at younger ages. In a study involving 590,750 men (median age, 69 years), men with higher PHS290 scores had significantly elevated risks of any PCa (hazard ratio [HR], 5.20), metastatic PCa (HR, 4.89), and fatal PCa (HR, 4.42). Black men had higher average PHS290 scores and PCa incidence compared to non-Hispanic White men, but those with low PHS290 scores had similar risks as the average non-Hispanic White population. These findings underscore the potential clinical application of genetic risk stratification in early PCa decision-making. For example, integrating polygenic risk scores could enhance early detection and prevention efforts in higher-risk groups. Studies suggest that men with high genetic risk or a family history of cancer account for the majority of early PCa deaths, with lifestyle modifications potentially preventing up to one-third of these cases [10] (Table 1).

The Stockholm3 (STHLM3) test is an advanced biomarker panel combining plasma protein biomarkers, genetic polymorphisms, and clinical variables to improve the detection of csPCa. In a prospective study involving 59,159 men aged 50-69 years, Ström et al. [11] demonstrated that the STHLM3 model outperformed PSA as a screening tool by reducing unnecessary biopsies while maintaining high sensitivity for csPCa detection. An updated version of the STHLM3 test, incorporating the HOXB13 genetic marker, further enhanced predictive accuracy and is increasingly adopted as a reflex test for men with PSA levels ≥3 ng/mL.

Integrating family history with germline genetic risk has also become integrated in precision oncology approaches to PCa. Approximately half of PCa risk may be attributed to inherited factors, including pathogenic variants in highrisk genes such as BRCA1/2 and HOXB13. Men with these mutations face a two- to tenfold increase in lifetime PCa risk compared to the general population, particularly if a firstdegree relative was diagnosed with PCa before age 65.

BRCA2 mutations are associated with more aggressive disease and poorer outcomes, while HOXB13 mutations have been implicated in hereditary PCa. Germline testing allows clinicians to tailor screening strategies and treatment decisions, particularly for individuals with a strong family history or high genetic risk. Current recommendations are to initiate PCa screening at age 40 for men of African descent, those with germline pathogenic variants, or those with multiple family members diagnosed with metastatic PCa.

The limitations of PSA have led to the development of novel blood-based biomarkers aimed at improving the risk assessment for csPCa, reducing unnecessary biopsies, and guiding personalized treatment strategies. The Prostate Health Index (PHI) is a blood-based assay that uses a combination of proPSA, free PSA, and total PSA into a single score to predict the likelihood of PCa on biopsy. Studies have demonstrated that PHI outperforms total PSA alone in distinguishing between benign conditions and clinically significant PCa, thereby aiding in decision-making regarding the need for a biopsy [12].

The 4Kscore test measures 4 kallikrein protein levels in the blood, total PSA, free PSA, intact PSA, and human kallikrein 2, to estimate the risk of high-grade PCa. Clinical validation has shown that the 4Kscore can reduce unnecessary biopsies by providing a more accurate risk assessment of aggressive PCa [13].

Urine-based biomarkers have also emerged as valuable risk assessment tools. ExoDX Prostate Intelliscore (EPI) analyzes urinary exosomal RNA expressions of 3 genes (ERG, PCA3, and SPDEF) to predict the presence of high-grade PCa. EPI has been shown to distinguish between low-risk and highrisk PCa, supporting its use in guiding biopsy decisions [14]. As for SelectMDx, another urine biomarker that incorporates mRNA levels of the biomarkers HOXC6 and DLX1 in urine with clinical factors of age, PSA, prostate volume, and digital rectal examination findings to assess the risk of high-grade PCa. SelectMDx may help identify men at increased risk for aggressive PCa, thereby improving patient selection for biopsy and reducing unnecessary procedures [15].

Multiparametric MRI (mpMRI) has revolutionized PCa diagnosis, serving as a cornerstone in detecting, risk stratifying, and managing csPCa. Combining T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast-enhanced imaging, mpMRI offers detailed insights into prostate anatomy, cellular density, and vascularity, significantly improving diagnostic accuracy [16].

The introduction of standardized reporting systems such as Prostate Imaging Reporting and Data System (PI-RADS) has further advanced mpMRI’s utility. In particular, mpMRI/transrectal ultrasonography fusion-guided biopsy, where mpMRI images are fused with real-time ultrasound, has significantly improved the detection of csPCa compared to traditional systematic biopsies. The fusion biopsy approach is now recommended by most centers with availability of MRI in patients with clinical suspicion of PCa, even after a negative systematic biopsy. Despite its benefits, mpMRI is limited by cost, availability, reliance on contrast agents, and the need for specialized expertise in interpreting imaging results, which can restrict access for some patients. Addressing these barriers remains a critical focus for expanding access to precision diagnostic tools [17].

Genomic classifiers available in the United States market include Decipher, Oncotype DX, and Polaris have emerged as tools to redefine the management of localized PCa. These tests use RNA extracted from prostate biopsy samples to analyze the gene expression levels associated with key biological pathways, including cell differentiation, proliferation, structure, androgen signaling, and immune response [19]. By measuring the activity of these pathways, genomics classifiers algorithmically combine these expressions to calculate a risk score representing the tumor’s aggressiveness. For example, Decipher is reported as a continuous risk score ranging from 0 to 1, associated with the probability of adverse events including metastasis risk. This score can be further stratified into low-, intermediate-, and high-risk diseases, providing a more nuanced risk stratification. In a meta-analysis of 5 studies that assessed the performance of Decipher, the combined cohort showed that the incidence metastases rates were 5.5%, 15.0%, and 26.7% (p<0.001) for the low, intermediate, and high-risk groups, respectively [20]. In a multivariable analysis adjusting for clinicopathologic variables, Decipher remained a statistically significant predictor of metastasis (HR, 1.30; 95% confidence interval, 1.14-1.47; p<0.001).

The integration of genomics classifiers with traditional clinicopathologic factors may enhance the precision of decision-making in localized PCa. A deeper understanding of a cancer’s potential for metastasis or recurrence after treatment can lead to individualized treatment. Active surveillance, a period of close disease monitoring, is increasingly used for patients with low-risk PCa. Genomic classifiers may provide additional prognostic information to select individuals at higher or lower risks for disease progression. Currently, actionable thresholds for the selection of active surveillance versus immediate treatment among otherwise low-risk patients based on genomic results have not been validated [21]. However, their use has increased in clinical practice [22].

Awareness of germline genetic alterations that contribute to PCa pathogenesis have growing roles in clinical management. DNA damage repair genes including, BRCA1, BRCA2, ATM, CHEK2, and ATM, play a role in aggressive disease progression, and may be identified in up to 20% of patients with advanced or metastatic disease [23,24]. These genes are involved in single- and double-strand repair encoding proteins involved in homologous recombination, mismatch repair, and base excision repair. Inactivating mutation in DNA repair can result in genomic instability, resulting om copy number and structural variants that result in cancer progression. In addition to DNA repair genes, the androgen receptor (AR) gene has been identified in its role of fueling aggressive PCa and resisting hormonal therapies. Alterations of the AR gene through high-level copy number or gain-of-function mutations cause an overactivation of the AR pathway, leading to uncontrollable cancer growth [25]. While androgen deprivation therapy treatments can interfere with the AR pathway early in the disease course, this subtype of PCa often becomes resistant to this treatment and progresses to castration resistance prostate cancer (CRPC) [26,27]. While these genomic alterations contribute to tumor progression and therapy resistance, they also represent actionable targets for treatment. Molecular profiling enables the identification of these mutations, allowing clinicians to tailor adjunctive treatments targeting these mutations.

Molecular profiling has an increasing role in identifying adjunctive treatment strategies in advanced PCa. PCa with BRCA 1/2 mutations can be targeted with poly(ADPribose) polymerase (PARP) inhibitors [28-30]. For example, in a randomized, double-blind, phase 3 trial, patients with metastatic CRPC (mCRPC) were randomized in a 1:1 fashion, with the first group receiving talazoparib, a PARP inhibitor, plus enzalutamide, AR blocker, and the second group receiving a placebo plus enzalutamide as firstline therapy [31]. The primary analysis showed that the talazoparib and enzalutamide group did not reach median radiographic progression-free survival (rPFS). In contrast, the enzalutamide and placebo group reached median rPFS at 21.9 months (HR, 0.63; p<0.0001). In another randomized, double-blind, phase 3 trial, patients with mCRPC were randomized in a 1:1 fashion, with the first group receiving olaparib, a PARP inhibitor, plus abiraterone, and the second group receiving placebo plus abiraterone as first-line therapy. Median rPFS was significantly longer in the abiraterone and olaparib group compared to abitaterone and placebo group (24.8 months vs. 16.6 months; HR, 0.66; p<0.001) [32]. These findings highlight the expanding role of PARP inhibitors with AR pathway blockers in improving rPFS for mCRPC patients with BRCA 1/2 mutations.

Advances in imaging have become crucial factors in clinical decision-making and treatment planning for PCa. Conventional imaging methods such as computed tomography (CT) and bone scans possess only modest diagnostic sensitivity and specificity for the detection of metastatic PCa, particularly in earlier phases of the disease [33,34]. Novel imaging modalities, particularly those employing molecular imaging agents, significantly enhance conventional imaging methods. These agents have a high molecular affinity for prostatic tissue, enabling more precise detection of nonlocalized disease and improving the accuracy of staging and treatment planning. One of these agents is gallium-68 PSMA PET-CT, which utilizes radioactive molecules bound to PSMA to identify nonlocalized lesions throughout the body. In a randomized phase 3 trial comparing PSMA PET-CT to conventional CT, PSMA PET-CT had superior accuracy than conventional CT in detecting metastatic lesions (92% vs 65%, p<0.0001) [6]. Additionally, PSMA PET-CT demonstrates greater specificity and sensitivity than CT (38% vs. 85%) and specificity (91% vs. 98%). In addition to gallium-68 PSMA PET-CT, 18F-DCFPyLPET-CT has shown success in improving detection of nonlocalized prostatic tissue. In a prospective study, 18FDCFPyL-PET-CT correctly localized 84.8%-87.0% of lesions, resulting in a change of management in 63.9% of patients [35]. Due to the improved detection of prostatic lesions through these molecular imaging agents, the staging has become more accurate, leading to more precise treatment planning.

The potential clinical role of PSMA-targeted agents extends beyond initial diagnosis into guidance for surgical and radiation-based treatment. For surgical treatment, PSMA-targeted radiotracers, such as 99mTc-PSMA, enable intraoperative image guidance, providing a detailed roadmap to identify prostatic lesions for resection while preserving healthy tissue. In a systemic review of 29 studies, PSMAtargeting agents have a median specificity of 98.9% and sensitivity of 84.8%. Outcomes demonstrated a decline of PSA level of >90% in 22.0% to 100% of patients and biochemical recurrence rates ranging from 50.0% to 61.8% of patients [36]. These results show the potential viability of introducing PSMA-target agents into surgical planning; however, long-term oncological data needs to be further evaluated in surgeries utilizing PSMA-targeted radiotracers.

A direct therapeutic role has also been established with PSMA radioligands such as lutetium-177 (177Lu)-PSMA-617 target PSMA-expressing cells and beta particle radiation to them and the surrounding microenvironment. In a randomized, phase 3 trial, patients with mCRPC were randomized in a 2:1 fashion, with the first group receiving 177Lu-PSMA-617 with standard care and second group receiving standard care. Median rPFS was significantly longer in 177Lu-PSMA-617 with the standard care arm than the standard care arm (8.7 months vs. 3.4 months; HR, 0.40; p<0.001) [37].

The genomic diversity of primary PCa within primary PCa withing patients presents significant challenges in accurately predicting prostate-specific mortality rates and effective biomarker driven treatments. Løvf et al. [43] analyzed over 89 tumors in 41 different patients, noting genetic distinctly tumor foci within the prostate. Specifically, they found that 76% of pairwise compared foci had no common mutations at all. Boutros et al. [44] analyzed over 74 patients and found no shared copy number aberrations and very few shared single nucleotide variants between disease foci. This genomic variability within primary PCa poses challenges to comprehensively characterize the nature of a given patient’s PCa

Rigorous integration of patient clinical factors including age and comorbidity also offer improvement and personalization of treatment strategies. Briganti et al. [45] analyzed over 3,838 patients with PCa in a multi-institutional study, finding that older patients and patients with comorbidities were not associated with cancer-specific mortality. However, older age and patients with comorbidities were associated with othercause mortality. These results suggest that older patients or comorbid patients may not benefit from aggressive treatment including biomarker-guided treatment due to the high risk of dying to other cause. Conversely, younger and healthier patients may benefit biomarker-guided treatment with a curative intent as the cancer-specific mortality rate is higher than other-cause mortality rate for these patients. As a result, these factors should be considered as part of integrative risk stratification strategy and factored into individualized treatment strategies.