Joel B. Nelson

UNLABELLED: Neuroendocrine prostate cancer (NEPC) is an aggressive subtype of prostate cancer that most commonly evolves from preexisting prostate adenocarcinoma (PCA). Using Next Generation RNA-sequencing and oligonucleotide arrays, we profiled 7 NEPC, 30 PCA, and 5 benign prostate tissue (BEN), and validated findings on tumors from a large cohort of patients (37 NEPC, 169 PCA, 22 BEN) using IHC and FISH. We discovered significant overexpression and gene amplification of AURKA and MYCN in 40% of NEPC and 5% of PCA, respectively, and evidence that that they cooperate to induce a neuroendocrine phenotype in prostate cells. There was dramatic and enhanced sensitivity of NEPC (and MYCN overexpressing PCA) to Aurora kinase inhibitor therapy both in vitro and in vivo, with complete suppression of neuroendocrine marker expression following treatment. We propose that alterations in Aurora kinase A and N-myc are involved in the development of NEPC, and future clinical trials will help determine from the efficacy of Aurora kinase inhibitor therapy. SIGNIFICANCE: We report on the largest in-depth molecular analysis of NEPC and provide new insight into molecular events involved in the progression of prostate cancer.

PURPOSE: This guideline is structured to provide a clinical framework stratified by cancer severity to facilitate care decisions and guide the specifics of implementing the selected management options. The summary presented represents Part I of the two-part series dedicated to Clinically Localized Prostate Cancer: AUA/ASTRO/SUO Guideline discussing risk stratification and care options by cancer severity. MATERIALS AND METHODS: The systematic review utilized in the creation of this guideline was completed by the Agency for Healthcare Research and Quality and through additional supplementation by ECRI Institute. This review included articles published between January 2007 and March 2014 with an update search conducted through August 2016. When sufficient evidence existed, the body of evidence for a particular treatment was assigned a strength rating of A (high), B (moderate), or C (low) for support of Strong, Moderate, or Conditional Recommendations. Additional information is provided as Clinical Principles and Expert Opinions (table 2 in supplementary unabridged guideline, http://jurology.com/). RESULTS: The AUA (American Urological Association), ASTRO, and SUO (Society of Urologic Oncology) formulated an evidence-based guideline based on a risk stratified clinical framework for the management of localized prostate cancer. CONCLUSIONS: This guideline attempts to improve a clinician's ability to treat patients diagnosed with localized prostate cancer, but higher quality evidence in future trials will be essential to improve the level of care for these patients. In all cases, patient preferences should be considered when choosing a management strategy.

PURPOSE: The incidence of prostate cancer is frequent, occurring in almost one-third of men older than 45 years. Only a fraction of the cases reach the stages displaying clinical significance. Despite the advances in our understanding of prostate carcinogenesis and disease progression, our knowledge of this disease is still fragmented. Identification of the genes and patterns of gene expression will provide a more cohesive picture of prostate cancer biology. PATIENTS AND METHODS: In this study, we performed a comprehensive gene expression analysis on 152 human samples including prostate cancer tissues, prostate tissues adjacent to tumor, and organ donor prostate tissues, obtained from men of various ages, using the Affymetrix (Santa Clara, CA) U95a, U95b, and U95c chip sets (37,777 genes and expression sequence tags). RESULTS: Our results confirm an alteration of gene expression in prostate cancer when comparing with nontumor adjacent prostate tissues. However, our study also indicates that the gene expression pattern in tissues adjacent to cancer is so substantially altered that it resembles a cancer field effect. CONCLUSION: We also found that gene expression patterns can be used to predict the aggressiveness of prostate cancer using a novel model.