TEST:

Prolaris®

This study is among the first to show that histopathology-based AI algorithms applied to small samples of tumor tissue can predict the risk of lethal PCa. These algorithms perform comparably to commonly used genomic classifiers, and their predictive performance is enhanced when combined with clinicopathologic variables.

Current evidence supports the multidisciplinary integration of these biomarkers to overcome the limitations of PSA, improve biopsy decision-making, better distinguish indolent from aggressive tumors, and optimize therapeutic strategies. Finally, future research directions aimed at validating and incorporating emerging biomarkers into clinical practice are outlined, with the goal of improving outcomes in patients with localized prostate cancer.

We show contemporary, real-world GC utilization trends, costs, and associations with treatment patterns. Prospective trials are ongoing to validate GC-informed treatment, but US uptake has expanded and management is associated with the use and type of GC.

P=N/A, N=900, Recruiting, University of Michigan Rogel Cancer Center | Trial completion date: Jul 2025 --> Dec 2025 | Trial primary completion date: Jul 2025 --> Dec 2025

Methionine deprivation through MEGL-targeted gene therapy may be a viable option for inducing cancer cell death compared to unrestricted levels of methionine.

There is no consistent association between MRI-visible cancer and Prolaris risk profile. When utilizing multigene molecular testing in prostate cancer, each individual patient must be evaluated to decide the appropriate level of care.

Correlation between 22-gene GC and either GPS-derived or CCP-derived signatures is minimal to moderate. These tests are not interchangeable, and their use should be guided by the specific evidence supporting each signature.

CCP test may be a useful tool to estimate the risk of progression in PCa patients and guide the decision between AS and active treatment. Triple hit phenotype or TMPRSS:ERG fusion status was not associated with progression.

Tissue-based genomic tests, such as Decipher, Oncotype DX (GPS), and Prolaris, have emerged as prognostic tools for assessing tumor aggressiveness and metastatic potential. Current evidence supports Decipher's prognostic capabilities with studies demonstrating risk stratification while further research is needed for Prolaris and GPS to solidify their role in PCa risk stratification. These assays are intended to guide therapeutic choices, reducing overtreatment in low-risk cases while identifying high-risk patients who may benefit from more aggressive or definitive intervention. Despite growing clinical adoption, challenges such as cost, disparities in access, and variability in physician utilization still remain. Further prospective studies and randomized trials are required to optimize clinical implementation and validate the long-term impact of genomic testing on PCa outcomes.

Genomic classifiers and AI-based digital histopathology models might have superior prognostic and predictive value compared to established clinical and pathological parameters in localized, recurrent, and metastatic PCa. Despite promising results, prospective validation of these biomarkers in randomized trials remains limited. This review underscores the need for further prospective trials to confirm the usefulness of these biomarkers in PCa.

By assessing genetic mutations (e.g. BRCA1, BRCA2 genes, single nucleotide polymorphism) or the presence of splice variants of the androgen receptor (ARV7), we are able to identify patients in whom the planned treatment may be expected to be ineffective and thus choose other treatment modalities. In the present review article, we offer a comprehensive overview of current diagnostic tests that find application in the diagnosis of early and advanced prostate cancer.

P=N/A, N=140, Active, not recruiting, British Columbia Cancer Agency | Recruiting --> Active, not recruiting