Abstract
BACKGROUND: Genomic biomarkers offer opportunities to improve risk stratification for patients with prostate cancer. We performed a transcriptomic profile of active surveillance (AS)-eligible patients who underwent radical prostatectomy (RP) to understand genomic associations with adverse pathologic features (APF) at RP. METHODS: Patients considered AS-eligible (NCCN low-favorable intermediate risk) but proceeded to RP from February 2012 to September 2024 were identified from our prospective institutional database. Outcomes were classified by presence or absence of APF at RP, which was defined as grade group (GG) ≥ 3, pT3b, or pN1 disease. Previously established genomic signatures of interest were compared between the two groups. Scaled multivariable logistic regression was performed to evaluate associations between multiple genomic classification systems and the outcome of APF. RESULTS: There were 184 AS-eligible patients, of whom 153 (83.2%) had favorable intermediate risk disease and 31 (16.8%) had low risk disease. There were 41 patients (22.3%) who had APF at RP. The incidence of favorable intermediate risk disease did not differ between those with and without APF (80.5% vs. 83.9%, p = 0.64). Patients with APF had a higher baseline PSA (5.6 ng/mL vs. 4.9 ng/mL, p = 0.01) and Decipher score (0.55 vs. 0.41, p = 0.004) compared to those without APF. On scaled logistic regression with adjustment for log-transformed PSA, the Decipher score, PTEN loss, activated CD4, and ERG positive rate were significantly associated with APF (OR 1.61, 95% CI 1.11-2.32, p = 0.01). Of ten other previously published genomic classifiers, nine were significantly associated with APF. CONCLUSION: AS-eligible patients with APF at RP demonstrate differences in gene expression when compared to those without APF. We established that multiple existing genomic classifiers not previously studied in this context demonstrate the ability to predict APF in this patient population. Inclusion of genomics in the risk stratification of AS-eligible patients has the potential to better inform clinical decisions.