Introduction: MicroRNAs play an important role in regulation of gene expression and are known biomarkers for breast cancer as well as other malignancies. PARADIGM is a pathway based algorithm that… Click to show full abstract
Introduction: MicroRNAs play an important role in regulation of gene expression and are known biomarkers for breast cancer as well as other malignancies. PARADIGM is a pathway based algorithm that allows for integration of multiple genomic data types with a curated pathway database to make pathway activity predictions. We added a model of gene silencing due to miRNA to the PARADIGM algorithm in order to study miRNA expression in a pathway context. Results: We curated a set of 7751 miRNA-mRNA interactions from the intersection of 3 target prediction algorithms. These interactions involved 66 miRNA and 2814 mRNA transcripts. We ran this model on global DNA copy number, RNAseq and miRNAseq data from 697 patients in the TCGA breast cancer cohort, and studied changes in the interactions between miRNAs and their targets between different tumor subtypes. The median activity of the RNA-induced silencing complex (RISC) predicted by our model is significantly higher in Basal tumors than other subtypes. In addition, RISC activity is significantly associated with overall survival of patients with Luminal A tumors. The miRNA-target pairs with the largest correlation changes between Basal and Luminal A subtypes were enriched for putative oncogenes and oncomirs. The mRNA targets are involved in a number of important signaling pathways including PI3K-AKT, JAK-STAT, and Ras. Many of these highly differential links involved the miR-16 family of miRNAs which are known tumor suppressors. miR-16 shows significantly lower activity in basal tumors than other subtypes. Conclusions: By looking at changes in miRNA-target links between tumor subtypes, our algorithm was able to identify both miRNAs and target genes involved in pathways relevant to breast cancer. Our predictions of overall RNA-induced silencing activity show prognostic value in both determining subtype and predicting overall survival within subtypes. Citation Format: Andrew J. Sedgewick, Panayiotis V. Benos, Shahrooz Rabizadeh, Patrick Soon-Shiong, Charles J. Vaske. Modeling miRNA induced silencing in breast cancer with PARADIGM [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 479. doi:10.1158/1538-7445.AM2017-479
               
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