Li, Eric V

ABSTRACT Background Prior studies in the pre‐MRI era suggest that patients with NCCN Intermediate and High Risk Grade Group 1 prostate cancer (GG1 PCa) on biopsy have increased risk for adverse oncologic outcomes. However, prostate MRI has altered patient selection for biopsy and improved prostate sampling. Herein, we examined factors associated with adverse pathologic features (APF) at radical prostatectomy (RP) and biochemical recurrence (BCR) among patients with GG1 in the MRI era. Methods A prospectively maintained institutional prostate cancer registry identified patients with GG1 PCa on biopsy who underwent RP from 2000 to 2023, stratified by MRI use. Multivariable logistic regression and Cox regression identified factors associated with APF at RP (GG3‐5, ≥ pT3a, or pN1) and BCR free survival (two consecutive PSA ≥ 0.2 ng/mL), respectively. Results Among patients without MRI ( n = 6732), NCCN risk group was associated with APF, BCR, and distant metastasis. Among 302 patients with GG1 and pre‐operative MRI, NCCN risk group was associated with neither APF nor BCR. Age (OR 1.07, 95% CI 1.01–1.14, p = 0.01), PIRADS ( p = 0.01), and % core positivity (OR 1.02, 95% CI 1.00–1.03, p = 0.04) were associated with APF. Age (HR 1.07, 95% CI 1.01–1.13, p = 0.03), APF (HR 4.36, 95% CI 2.14–8.86, p < 0.001), and positive margins (HR 4.88, 95% CI 2.39–9.98, p < 0.001) were associated with BCR. Conclusions Prior to prostate MRI, NCCN risk groups offered meaningful prognostication among patients with GG1 PCa. However, the utility of NCCN risk groups for GG1 in the MRI era appears limited. Contemporary risk stratification for GG1 should focus on biopsy and MRI characteristics rather than NCCN criteria.

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.