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

Ji Hyun Lee, Xing Ming Zhao, Ina Yoon, Jin Young Lee, Nam Hoon Kwon, Yin Ying Wang, Kyung Min Lee, Min Joo Lee, Jisun Kim, Hyeong Gon Moon, Yongho In, Jin Kao Hao, Kyung Mii Park, Dong Young Noh, Wonshik Han, Sunghoon Kim

Research output: Contribution to journalArticle

22 Citations (Scopus)

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.

Original languageEnglish
Article number16025
JournalCell Discovery
Volume2
DOIs
StatePublished - 30 Aug 2016

Fingerprint

Genes
Breast Neoplasms
Mutation
Neoplasm Metastasis
Explosions
Transcription Factors
Pipelines
Cells
RNA
Neoplasms
Experiments
Exome
RNA Sequence Analysis
Systems Biology
Cell Movement
Mortality

Keywords

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

Cite this

Lee, Ji Hyun ; Zhao, Xing Ming ; Yoon, Ina ; Lee, Jin Young ; Kwon, Nam Hoon ; Wang, Yin Ying ; Lee, Kyung Min ; Lee, Min Joo ; Kim, Jisun ; Moon, Hyeong Gon ; In, Yongho ; Hao, Jin Kao ; Park, Kyung Mii ; Noh, Dong Young ; Han, Wonshik ; Kim, Sunghoon. / Integrative analysis of mutational and transcriptional profiles reveals driver mutations of metastatic breast cancers. In: Cell Discovery. 2016 ; Vol. 2.
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Lee, JH, Zhao, XM, Yoon, I, Lee, JY, Kwon, NH, Wang, YY, Lee, KM, Lee, MJ, Kim, J, Moon, HG, In, Y, Hao, JK, Park, KM, Noh, DY, Han, W & Kim, S 2016, 'Integrative analysis of mutational and transcriptional profiles reveals driver mutations of metastatic breast cancers', Cell Discovery, vol. 2, 16025. https://doi.org/10.1038/celldisc.2016.25

Integrative analysis of mutational and transcriptional profiles reveals driver mutations of metastatic breast cancers. / Lee, Ji Hyun; Zhao, Xing Ming; Yoon, Ina; Lee, Jin Young; Kwon, Nam Hoon; Wang, Yin Ying; Lee, Kyung Min; Lee, Min Joo; Kim, Jisun; Moon, Hyeong Gon; In, Yongho; Hao, Jin Kao; Park, Kyung Mii; Noh, Dong Young; Han, Wonshik; Kim, Sunghoon.

In: Cell Discovery, Vol. 2, 16025, 30.08.2016.

Research output: Contribution to journalArticle

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AU - Lee, Ji Hyun

AU - Zhao, Xing Ming

AU - Yoon, Ina

AU - Lee, Jin Young

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AU - Wang, Yin Ying

AU - Lee, Kyung Min

AU - Lee, Min Joo

AU - Kim, Jisun

AU - Moon, Hyeong Gon

AU - In, Yongho

AU - Hao, Jin Kao

AU - Park, Kyung Mii

AU - Noh, Dong Young

AU - Han, Wonshik

AU - Kim, Sunghoon

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N2 - 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.

AB - 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.

KW - RNA sequencing

KW - driver mutations

KW - exome sequencing

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