Identification of driver copy number alterations in diverse cancer types and application in drug repositioning. Issue 10 (3rd August 2017) - Zhou, Wenbin; Zhao, Zhangxiang; Wang, Ruiping; Han, Yue; Wang, Chengyu; Yang, Fan; Han, Ya; Liang, Haihai; Qi, Lishuang; Wang, Chenguang; Guo, Zheng; Gu, Yunyan
Abstract : Results from numerous studies suggest an important role for somatic copy number alterations (SCNAs) in cancer progression. Our work aimed to identify the drivers (oncogenes or tumor suppressor genes) that reside in recurrently aberrant genomic regions, including a large number of genes or non‐coding genes, which remain a challenge for decoding the SCNAs involved in carcinogenesis. Here, we propose a new approach to comprehensively identify drivers, using 8740 cancer samples involving 18 cancer types from The Cancer Genome Atlas (TCGA). On average, 84 drivers were revealed for each cancer type, including protein‐coding genes, long non‐coding RNAs (lncRNA) and microRNAs (miRNAs). We demonstrated that the drivers showed significant attributes of cancer genes, and significantly overlapped with known cancer genes, including MYC, CCND1 and ERBB2 in breast cancer, and the lncRNA PVT1 in multiple cancer types. Pan‐cancer analyses of drivers revealed specificity and commonality across cancer types, and the non‐coding drivers showed a higher cancer‐type specificity than that of coding drivers. Some cancer types from different tissue origins were found to converge to a high similarity because of the significant overlap of drivers, such as head and neck squamous cell carcinoma (HNSC) and lung squamous cell carcinoma (LUSC). The lncRNA SOX2‐OT, a common driver of HNSC and LUSC, showed significant expression correlation with the oncogene SOX2 . In addition, because some drivers are common in multiple cancer types and have been targeted by known drugs, we found that some drugs could be successfully repositioned, as validated by the datasets of drug response assays in cell lines. Our work reported a new method to comprehensively identify drivers in SCNAs across diverse cancer types, providing a feasible strategy for cancer drug repositioning as well as novel findings regarding cancer‐associated non‐coding RNA discovery. Abstract : This study proposes an approach to identify drivers that reside in recurrently aberrant genomic regions. We identify not only many known cancer genes such as MYC, ERBB2 and RB1 but also many novel protein‐coding genes as well as non‐coding RNAs that may contribute to carcinogenesis. We reveal that some drugs targeting the drivers could be successfully repositioned.