Among the various types of breast cancer, the luminal B subtype is the most common in young women, and ESR1-CCDC170 (E:C) fusion is the most frequent oncogenic fusion driver of the luminal B subtype. Nevertheless, treatments targeting E:C fusion has not been well established yet. Hence, the aim of this study is to investigate potential therapies targeting E:C fusion based on systematic bioinformatical analysis of the Cancer Genome Atlas (TCGA) data. One thousand related genes were extracted using transcriptome analysis, and major signaling pathways associated with breast cancer were identified with over-representation analysis. Then, we conducted drug-target network analysis based on the OncoKB and CIViC databases, and finally selected potentially applicable drug candidates. Six major cancer-related signaling pathways (p53, ATR/ATM, FOXM1, hedgehog, cell cycle, and Aurora B) were significantly altered in E:C fusion-positive cases of breast cancer. Further investigation revealed that nine genes (AURKB, HDAC2, PLK1, CENPA, CHEK1, CHEK2, RB1, CCNA2, and MDM2) in coordination with E:C fusion were found to be common denominators in three or more of these pathways, thereby making them promising gene biomarkers for target therapy. Among the 21 putative actionable drugs inferred by drug-target network analysis, palbociclib, alpelisib, ribociclib, dexamethasone, checkpoint kinase inhibitor AXD 7762, irinotecan, milademetan tosylate, R05045337, cisplatin, prexasertib, and olaparib were considered promising drug candidates targeting genes involved in at least two E:C fusion-related pathways.
Keywords: CCDC170; ESR1; TCGA; bioinformatics; breast cancer; drug repositioning; gene fusion.