Background: The treatment paradigm for metastatic prostate cancer has significantly improved in the past decade. Nevertheless, there remains an unmet need for novel biomarker-driven risk stratification. Studies have shown that genetic alterations in any of the PTEN, RB1, and TP53 genes influence the prognosis, potential for androgen independence and overall survival of individuals diagnosed with advanced prostate cancer. Based on these findings, we compared survival rates by contrasting patients with any of these mutations to those lacking them, using an extensive real-world sample from community oncology centers across the Guardian Research network (GRN). Methods: All patients diagnosed with metastatic prostate cancer who had undergone Next-Generation Sequencing (NGS) were included across an oncology-focused hospital consortium network database (GRN). Study participants were classified into two cohorts based on the presence or absence of biomarkers. Patients harboring at least one of the following genomic alterations—TP53, Rb, or PTEN—were categorized as "biomarker-positive." Those without these mutations were designated as "biomarker negative." Overall survival (OS) was compared between the two cohorts by Kaplan-Meier Curve analysis. Results: We identified 350 patients with metastatic prostate cancer, of which 140 were biomarker positive and 210 were biomarker negative. The biomarker-positive cohort had a lower overall survival (p=0.002) when compared to the biomarker-negative cohort. For the sub-group with a minimum follow up of four years, median survival was 35.8 months vs 67.2 months for biomarker marker positive and negative cohorts, respectively. Furthermore, the biomarker-positive cohort was associated with significantly higher number of patients with bone and liver metastases. Conclusions: Our study indicates poor overall survival in patients with TP53, RB1, and PTEN mutated prostate cancer, underscoring the need for novel treatment strategies for this aggressive disease genotype. Future studies based on genomic risk stratification are needed to customize therapies through appropriate intensification or de-intensification based on individual genomic profiles.