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Cell Discov. 2016 Aug 30;2:16025. doi: 10.1038/celldisc.2016.25. eCollection 2016.

Integrative analysis of mutational and transcriptional profiles reveals driver mutations of metastatic breast cancers.

Author information

1
Medicinal Bioconvergence Research Center, College of Pharmacy, Seoul National University, Seoul, Republic of Korea; Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul, Republic of Korea.
2
Department of Computer Science and Technology, Tongji University , Shanghai, China.
3
Medicinal Bioconvergence Research Center, College of Pharmacy, Seoul National University , Seoul, Republic of Korea.
4
Department of Surgery, Seoul National University College of Medicine , Seoul, Republic of Korea.
5
LERIA, University of Angers , Angers, France.
6
Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University , Seoul, Republic of Korea.
7
Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea; Cancer Research Institute, Seoul National University, Seoul, Republic of Korea.
8
Medicinal Bioconvergence Research Center, College of Pharmacy, Seoul National University, Seoul, Republic of Korea; Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea.

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.

KEYWORDS:

RNA sequencing; driver mutations; exome sequencing; integrative analysis; metastatic breast cancer

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