Author: Graduate School of Health Sciences


Androgen receptor (AR) signalling is essential to nearly all prostate cancers. Any alterations to AR-mediated transcription can have a profound effect on prostate carcinogenesis and tumour growth. Yet despite this importance, many critical aspects of AR activity are poorly understood. Specifically, there is a stark contrast between the number of AR binding sites on DNA (tens of thousands) and androgen-driven gene transcription (hundreds). Functional validation of these AR binding sites (ARBS) is therefore required to better understand PCa growth and development. 
To characterize how AR induces transcription, we systematically tested the enhancer activity at every clinical ARBS with a novel massively multi-parallel enhancer assay.  Interestingly, only 7% of ARBS were found to be AR-mediated enhancers. Demonstrating the importance of this functional approach, these enhancers could not be readily identified by descriptive techniques including epigenetic ChIPseq or DNA accessibility. Further, we observed that a large number of ARBS were constitutively active enhancers that were not affected by AR binding. In functional validation we show that the androgen-inducible enhancers drive gene transcription. Using this large dataset we then developed and validated a machine learning approach that can predict AR enhancers. Finally combining these results with clinical whole genome sequencing and chromosomal confirmation capture methodologies we identified and validated specific somatic mutations at enhancer sites. Overall this work provides the first “map” of AR enhancers and their target genes. Annotation of these non-coding regulatory elements is critical to guide selection of potential driver mutations. 
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