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available at http://www.ncbi.nlm.nih.gov/pubmed/29844167 Editorial Comment: Mechanisms underlying androgen receptor splice variant 7 (AR-V7) oncogenic function at the genomic level remain poorly defined. Studies in this article found that AR-V7 cistromes are cell context dependent in castration resistant prostate cancer (CRPC) cells and tissues, resulting in tremendous diversity in AR-V7 regulated transcriptomes across patients with CRPC. Thus, few downstream targets of AR-V7 can universally account for CRPC progression, leaving us without adequate, common, viable therapeutic targets for this heterogeneous disease in which AR-V7 itself is not treatable with antiandrogens. The authors discovered that HoxB13 governs the diverse AR-V7 cistromes among CRPCs, thus shifting focus from the previously characterized role of HoxB13 in androgen dependent prostate cancer to a distinct role in CRPC. These findings will significantly impact therapeutic strategies for AR-V7 driven CRPC, for which there is no approved therapy. Anthony Atala, MD Suggested Reading Lu J, Lonergan PE, Nacusi LP et al: The cistrome and gene signature of androgen receptor splice variants in castration resistant prostate cancer cells. J Urol 2015; 193: 690. Re: PrESOgenesis: A Two-Layer Multi-Label Predictor for Identifying Fertility-Related Proteins Using Support Vector Machine and Pseudo Amino Acid Composition Approach M. R. Bakhtiarizadeh, M. Rahimi, A. Mohammadi-Sangcheshmeh, J. V. Shariati and S. A. Salami Department of Animal and Poultry Science, College of Aburaihan, University of Tehran and Genome Center, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran Sci Rep 2018; 8: 9025. doi: 10.1038/s41598-018-27338-9 Abstract available at http://www.ncbi.nlm.nih.gov/pubmed/29899414available at http://www.ncbi.nlm.nih.gov/pubmed/29899414 Editorial Comment: The cascade of molecular and intracellular processes that occur in germ cells during spermatogenesis, oogenesis and later embryogenesis are still far from being fully exploited. Identifying specific fertility related proteins and elucidating their function are essential for gaining fundamental biological insight into clinical practice. Machine learning methods, such as support vector machine, have been used in many fields and their applications in bioinformatics are increasing. However, these methods have not previously been applied in the study of fertility related proteins and their relevant classes. This study applied support vector machine based approaches in combination with a comprehensive physical chemical property set to construct a method that could be used to predict the probability of a sequence, referred to as fertility related protein, as well as its class. Anthony Atala, MD Suggested Reading Samanta L, Agarwal A, Swain N et al: Proteomic signatures of sperm mitochondria in varicocele: clinical use as biomarkers of varicocele associated infertility. J Urol 2018; 200: 414. Li R, Vannitamby A, Meijer J et al: Postnatal germ cell development during mini-puberty in the mouse does not require androgen receptor: implications for managing cryptorchidism. J Urol 2015; 193: 1361. 34 URO-SCIENCE Copyright © 2019 American Urological Association Education and Research, Inc. Unauthorized reproduction of this article is prohibited.