Li, Eric V.

ABSTRACT Background While guidelines state that patients with NCCN favorable intermediate risk (FIR) prostate cancer (PCa) may consider active surveillance (AS), there is a relative lack of longer term follow up data informing AS inclusion criteria of patients with Grade Group (GG) 2 prostate cancer in the MRI era. Here we evaluate patient outcomes from a MRI‐screened AS‐eligible cohort of FIR GG2 PCa receiving radical prostatectomy (RP). Methods Patients with prostate MRI, NCCN FIR GG2 PCa (PSA < 10 ng/mL, ≤ cT2a, < 50% core positivity), and no prior treatments who underwent RP from 2015 to 2023 were included from a prospectively maintained prostatectomy registry. Primary outcomes were adverse pathologic features (APF) at RP (GG3‐5, ≥ pT3a, or pN1) and biochemical recurrence (BCR). Logistic regression and Cox regression identified factors associated with APF at RP and time to BCR, respectively. Results Of 522 patients, 110 (21%) had APF at RP and 45 (9%) developed BCR with median follow up 2.4 years (IQR 1.3–2.8). On multivariable logistic regression, % Gleason pattern 4 (%GP4, %GP4 > 25% OR 2.14, 95% CI 1.28–3.58, p = 0.004) was independently associated with APF. On multivariable Cox regression for time to BCR, PSAD (HR 1.06, 95% CI 1.04–1.09, p < 0.001) and positive surgical margins (HR 1.06, 95% CI 1.09–3.78, p = 0.03) were independently associated with BCR. PIRADS, % core positivity, and specimen involvement were associated with neither APF nor BCR. Conclusions % Gleason pattern 4 > 25% and PSAD are the primary factors associated with adverse outcomes and should be considered for patients with FIR GG2 PCa considering active surveillance. PIRADS and measures of biopsy cancer volume were not associated with APF or BCR. These results may inform patient selection for active surveillance among patients with FIR GG2 PCa.

AbstractBackgroundProstate cancer (PCa) is a clinically heterogeneous disease. The creation of an expression‐based subtyping model based on prostate‐specific biological processes was sought.MethodsUnsupervised machine learning of gene expression profiles from prospectively collected primary prostate tumors (training, n = 32,000; evaluation, n = 68,547) was used to create a prostate subtyping classifier (PSC) based on basal versus luminal cell expression patterns and other gene signatures relevant to PCa biology. Subtype molecular pathways and clinical characteristics were explored in five other clinical cohorts.ResultsClustering derived four subtypes: luminal differentiated (LD), luminal proliferating (LP), basal immune (BI), and basal neuroendocrine (BN). LP and LD tumors both had higher androgen receptor activity. LP tumors also had a higher expression of cell proliferation genes, MYC activity, and characteristics of homologous recombination deficiency. BI tumors possessed significant interferon γactivity and immune infiltration on immunohistochemistry. BN tumors were characterized by lower androgen receptor activity expression, lower immune infiltration, and enrichment with neuroendocrine expression patterns. Patients with LD tumors had less aggressive tumor characteristics and the longest time to metastasis after surgery. Only patients with BI tumors derived benefit from radiotherapy after surgery in terms of time to metastasis (hazard ratio [HR], 0.09; 95% CI, 0.01–0.71; n = 855). In a phase 3 trial that randomized patients with metastatic PCa to androgen deprivation with or without docetaxel (n = 108), only patients with LP tumors derived survival benefit from docetaxel (HR, 0.21; 95% CI, 0.09–0.51).ConclusionsWith the use of expression profiles from over 100,000 tumors, a PSC was developed that identified four subtypes with distinct biological and clinical features.Plain language summary Prostate cancer can behave in an indolent or aggressive manner and vary in how it responds to certain treatments. To differentiate prostate cancer on the basis of biological features, we developed a novel RNA signature by using data from over 100,000 prostate tumors—the largest data set of its kind. This signature can inform patients and physicians on tumor aggressiveness and susceptibilities to treatments to help personalize cancer management.