Abstract 3497: Network-based systematic inference of lncRNA activity in breast cancer

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Abstract The production of lncRNA is widespread across the human genome, yet the functions of lncRNAs and the phenotypes resulting from their activation have remained largely unstudied. In cancer, several lncRNAs have been found to associate with disease development and poor patient prognosis including HOTAIR and MALAT1. Recent studies have been performed using lncRNA microarrays or re-annotated probes from coding gene microarrays to systematically investigate their roles in cancer. However, datasets from lncRNA microarrays are typically not accompanied by comprehensive clinical information that can be used to draw associations between lncRNA expression and clinical phenotypes. Furthermore, lncRNA stability and cellular localization is highly variable suggesting that expression-based inference of lncRNA activity may not always be an accurate measure of its activity. To address this issue, we introduce an activity-based approach whereby we analyze the expression of lncRNA target genes to infer lncRNA activity. Specifically, we construct a breast cancer specific lncRNA-target gene network from RNA-seq data using a network reconstruction algorithm. We then apply this network to several breast cancer microarray datasets to systematically investigate lncRNA activity and their association with clinical phenotypes. Notably, we find that certain lncRNAs are active in specific molecular subtypes of breast cancer even though their expression levels do not reveal significant differences. Lastly, we performed survival analysis to systematically screen for novel lncRNAs whose activity associate with patient prognosis. Our results suggest that activity-based inference of lncRNAs can identify novel lncRNAs that may serve as therapeutic targets or biomarkers in breast cancer. Citation Format: Matthew Ung, Daniel Mattox, George Wang, Chao Cheng. Network-based systematic inference of lncRNA activity in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3497. doi:10.1158/1538-7445.AM2017-3497