| Literature DB >> 27625789 |
Ji-Hyun Lee1, Xing-Ming Zhao2, Ina Yoon3, Jin Young Lee3, Nam Hoon Kwon3, Yin-Ying Wang2, Kyung-Min Lee4, Min-Joo Lee4, Jisun Kim4, Hyeong-Gon Moon4, Yongho In3, Jin-Kao Hao5, Kyung-Mii Park6, Dong-Young Noh4, Wonshik Han7, Sunghoon Kim8.
Abstract
Despite the explosion in the numbers of cancer genomic studies, metastasis is still the major cause of cancer mortality. In breast cancer, approximately one-fifth of metastatic patients survive 5 years. Therefore, detecting the patients at a high risk of developing distant metastasis at first diagnosis is critical for effective treatment strategy. We hereby present a novel systems biology approach to identify driver mutations escalating the risk of metastasis based on both exome and RNA sequencing of our collected 78 normal-paired breast cancers. Unlike driver mutations occurring commonly in cancers as reported in the literature, the mutations detected here are relatively rare mutations occurring in less than half metastatic samples. By supposing that the driver mutations should affect the metastasis gene signatures, we develop a novel computational pipeline to identify the driver mutations that affect transcription factors regulating metastasis gene signatures. We identify driver mutations in ADPGK, NUP93, PCGF6, PKP2 and SLC22A5, which are verified to enhance cancer cell migration and prompt metastasis with in vitro experiments. The discovered somatic mutations may be helpful for identifying patients who are likely to develop distant metastasis.Entities:
Keywords: RNA sequencing; driver mutations; exome sequencing; integrative analysis; metastatic breast cancer
Year: 2016 PMID: 27625789 PMCID: PMC5004232 DOI: 10.1038/celldisc.2016.25
Source DB: PubMed Journal: Cell Discov ISSN: 2056-5968 Impact factor: 10.849