Prognostic relevance of genetic alterations in diffuse lower-grade gliomas
Diffuse lower-grade gliomas (LGGs) are genetically classified into 3 distinct subtypes based on isocitrate dehydrogenase (IDH) mutation status and codeletion of chromosome 1p and 19q (1p/19q). However, the subtype-specific effects of additional genetic lesions on survival are largely unknown. Using...
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Published in | Neuro-oncology (Charlottesville, Va.) Vol. 20; no. 1; pp. 66 - 77 |
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Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
England
Oxford University Press
10.01.2018
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Abstract | Diffuse lower-grade gliomas (LGGs) are genetically classified into 3 distinct subtypes based on isocitrate dehydrogenase (IDH) mutation status and codeletion of chromosome 1p and 19q (1p/19q). However, the subtype-specific effects of additional genetic lesions on survival are largely unknown.
Using Cox proportional hazards regression modeling, we investigated the subtype-specific effects of genetic alterations and clinicopathological factors on survival in each LGG subtype, in a Japanese cohort of LGG cases fully genotyped for driver mutations and copy number variations associated with LGGs (n = 308). The results were validated using a dataset from 414 LGG cases available from The Cancer Genome Atlas (TCGA).
In Oligodendroglioma, IDH-mutant and 1p/19q codeleted, NOTCH1 mutations (P = 0.0041) and incomplete resection (P = 0.0019) were significantly associated with shorter survival. In Astrocytoma, IDH-mutant, PIK3R1 mutations (P = 0.0014) and altered retinoblastoma pathway genes (RB1, CDKN2A, and CDK4) (P = 0.013) were independent predictors of poor survival. In IDH-wildtype LGGs, co-occurrence of 7p gain, 10q loss, mutation in the TERT promoter (P = 0.024), and grade III histology (P < 0.0001) independently predicted poor survival. IDH-wildtype LGGs without any of these factors were diagnosed at a younger age (P = 0.042), and were less likely to have genetic lesions characteristic of glioblastoma, in comparison with other IDH-wildtype LGGs, suggesting that they likely represented biologically different subtypes. These results were largely confirmed in the cohort of TCGA.
Subtype-specific genetic lesions can be used to stratify patients within each LGG subtype. enabling better prognostication and management. |
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AbstractList | Diffuse lower-grade gliomas (LGGs) are genetically classified into 3 distinct subtypes based on isocitrate dehydrogenase (IDH) mutation status and codeletion of chromosome 1p and 19q (1p/19q). However, the subtype-specific effects of additional genetic lesions on survival are largely unknown.
Using Cox proportional hazards regression modeling, we investigated the subtype-specific effects of genetic alterations and clinicopathological factors on survival in each LGG subtype, in a Japanese cohort of LGG cases fully genotyped for driver mutations and copy number variations associated with LGGs (n = 308). The results were validated using a dataset from 414 LGG cases available from The Cancer Genome Atlas (TCGA).
In Oligodendroglioma, IDH-mutant and 1p/19q codeleted, NOTCH1 mutations (P = 0.0041) and incomplete resection (P = 0.0019) were significantly associated with shorter survival. In Astrocytoma, IDH-mutant, PIK3R1 mutations (P = 0.0014) and altered retinoblastoma pathway genes (RB1, CDKN2A, and CDK4) (P = 0.013) were independent predictors of poor survival. In IDH-wildtype LGGs, co-occurrence of 7p gain, 10q loss, mutation in the TERT promoter (P = 0.024), and grade III histology (P < 0.0001) independently predicted poor survival. IDH-wildtype LGGs without any of these factors were diagnosed at a younger age (P = 0.042), and were less likely to have genetic lesions characteristic of glioblastoma, in comparison with other IDH-wildtype LGGs, suggesting that they likely represented biologically different subtypes. These results were largely confirmed in the cohort of TCGA.
Subtype-specific genetic lesions can be used to stratify patients within each LGG subtype. enabling better prognostication and management. Diffuse lower-grade gliomas (LGGs) are genetically classified into 3 distinct subtypes based on isocitrate dehydrogenase (IDH) mutation status and codeletion of chromosome 1p and 19q (1p/19q). However, the subtype-specific effects of additional genetic lesions on survival are largely unknown.BackgroundDiffuse lower-grade gliomas (LGGs) are genetically classified into 3 distinct subtypes based on isocitrate dehydrogenase (IDH) mutation status and codeletion of chromosome 1p and 19q (1p/19q). However, the subtype-specific effects of additional genetic lesions on survival are largely unknown.Using Cox proportional hazards regression modeling, we investigated the subtype-specific effects of genetic alterations and clinicopathological factors on survival in each LGG subtype, in a Japanese cohort of LGG cases fully genotyped for driver mutations and copy number variations associated with LGGs (n = 308). The results were validated using a dataset from 414 LGG cases available from The Cancer Genome Atlas (TCGA).MethodsUsing Cox proportional hazards regression modeling, we investigated the subtype-specific effects of genetic alterations and clinicopathological factors on survival in each LGG subtype, in a Japanese cohort of LGG cases fully genotyped for driver mutations and copy number variations associated with LGGs (n = 308). The results were validated using a dataset from 414 LGG cases available from The Cancer Genome Atlas (TCGA).In Oligodendroglioma, IDH-mutant and 1p/19q codeleted, NOTCH1 mutations (P = 0.0041) and incomplete resection (P = 0.0019) were significantly associated with shorter survival. In Astrocytoma, IDH-mutant, PIK3R1 mutations (P = 0.0014) and altered retinoblastoma pathway genes (RB1, CDKN2A, and CDK4) (P = 0.013) were independent predictors of poor survival. In IDH-wildtype LGGs, co-occurrence of 7p gain, 10q loss, mutation in the TERT promoter (P = 0.024), and grade III histology (P < 0.0001) independently predicted poor survival. IDH-wildtype LGGs without any of these factors were diagnosed at a younger age (P = 0.042), and were less likely to have genetic lesions characteristic of glioblastoma, in comparison with other IDH-wildtype LGGs, suggesting that they likely represented biologically different subtypes. These results were largely confirmed in the cohort of TCGA.ResultsIn Oligodendroglioma, IDH-mutant and 1p/19q codeleted, NOTCH1 mutations (P = 0.0041) and incomplete resection (P = 0.0019) were significantly associated with shorter survival. In Astrocytoma, IDH-mutant, PIK3R1 mutations (P = 0.0014) and altered retinoblastoma pathway genes (RB1, CDKN2A, and CDK4) (P = 0.013) were independent predictors of poor survival. In IDH-wildtype LGGs, co-occurrence of 7p gain, 10q loss, mutation in the TERT promoter (P = 0.024), and grade III histology (P < 0.0001) independently predicted poor survival. IDH-wildtype LGGs without any of these factors were diagnosed at a younger age (P = 0.042), and were less likely to have genetic lesions characteristic of glioblastoma, in comparison with other IDH-wildtype LGGs, suggesting that they likely represented biologically different subtypes. These results were largely confirmed in the cohort of TCGA.Subtype-specific genetic lesions can be used to stratify patients within each LGG subtype. enabling better prognostication and management.ConclusionsSubtype-specific genetic lesions can be used to stratify patients within each LGG subtype. enabling better prognostication and management. |
Author | Sanada, Masashi Ogawa, Seishi Yajima, Hironori Natsume, Atsushi Abe, Tatsuya Morita, Naoya Ohka, Fumiharu Yoshizato, Tetsuichi Nakamura, Hideo Momii, Yasutomo Miyano, Satoru Yamamoto, Takashi Kataoka, Keisuke Nagata, Yasunobu Shimamura, Teppei Mizoguchi, Masahiro Motomura, Kazuya Ito, Ichiro Aoki, Kosuke Watanabe, Reiko Wakabayashi, Toshihiko Shiina, Satoshi Muragaki, Yoshihiro Takeuchi, Ichiro Matsuo, Keitaro Suzuki, Hiromichi |
AuthorAffiliation | Department of Neurosurgery, Tokyo Women’s Medical University, Tokyo, Japan Department of Scientific and Engineering Simulation, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, Japan Department of Neurosurgery, School of Medicine, Kumamoto University, Kumamoto, Japan Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan Department of Neurosurgery, School of Medicine, Oita University, Oita, Japan Department of Neurosurgery, Nagoya University School of Medicine, Nagoya, Japan Division of Diagnostic Pathology, Shizuoka Cancer Center, Shizuoka, Japan RIKEN Center for Advanced Intelligence Project, Tokyo, Japan Division of Molecular Medicine, Aichi Cancer Center Research Institute, Nagoya, Japan Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan Department of Neurosurgery, Faculty of Medicine, Saga University, Saga, Japan Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyot |
AuthorAffiliation_xml | – name: Division of Molecular Medicine, Aichi Cancer Center Research Institute, Nagoya, Japan – name: Center for Materials Research by Information Integration, National Institute for Materials Science, Tsukuba, Japan – name: Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan – name: Division of Systems Biology, Nagoya University School of Medicine, Nagoya, Japan – name: RIKEN Center for Advanced Intelligence Project, Tokyo, Japan – name: Department of Neurosurgery, Faculty of Medicine, Saga University, Saga, Japan – name: Department of Neurosurgery, School of Medicine, Oita University, Oita, Japan – name: Department of Neurosurgery, Nagoya University School of Medicine, Nagoya, Japan – name: Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan – name: Department of Scientific and Engineering Simulation, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, Japan – name: Department of Neurosurgery, Tokyo Women’s Medical University, Tokyo, Japan – name: Division of Pathology and Clinical Laboratories, National Cancer Center Hospital, Tokyo, Japan – name: Department of Computer Science/Research Institute for Information Science, Nagoya Institute of Technology, Nagoya, Japan – name: Human Genome Center, Institute of Medical Science, The University of Tokyo, Tokyo, Japan – name: Division of Diagnostic Pathology, Shizuoka Cancer Center, Shizuoka, Japan – name: Department of Neurosurgery, School of Medicine, Kumamoto University, Kumamoto, Japan |
Author_xml | – sequence: 1 givenname: Kosuke surname: Aoki fullname: Aoki, Kosuke – sequence: 2 givenname: Hideo surname: Nakamura fullname: Nakamura, Hideo – sequence: 3 givenname: Hiromichi surname: Suzuki fullname: Suzuki, Hiromichi – sequence: 4 givenname: Keitaro orcidid: 0000-0003-1761-6314 surname: Matsuo fullname: Matsuo, Keitaro – sequence: 5 givenname: Keisuke surname: Kataoka fullname: Kataoka, Keisuke – sequence: 6 givenname: Teppei surname: Shimamura fullname: Shimamura, Teppei – sequence: 7 givenname: Kazuya surname: Motomura fullname: Motomura, Kazuya – sequence: 8 givenname: Fumiharu surname: Ohka fullname: Ohka, Fumiharu – sequence: 9 givenname: Satoshi surname: Shiina fullname: Shiina, Satoshi – sequence: 10 givenname: Takashi surname: Yamamoto fullname: Yamamoto, Takashi – sequence: 11 givenname: Yasunobu surname: Nagata fullname: Nagata, Yasunobu – sequence: 12 givenname: Tetsuichi surname: Yoshizato fullname: Yoshizato, Tetsuichi – sequence: 13 givenname: Masahiro surname: Mizoguchi fullname: Mizoguchi, Masahiro – sequence: 14 givenname: Tatsuya surname: Abe fullname: Abe, Tatsuya – sequence: 15 givenname: Yasutomo surname: Momii fullname: Momii, Yasutomo – sequence: 16 givenname: Yoshihiro surname: Muragaki fullname: Muragaki, Yoshihiro – sequence: 17 givenname: Reiko surname: Watanabe fullname: Watanabe, Reiko – sequence: 18 givenname: Ichiro surname: Ito fullname: Ito, Ichiro – sequence: 19 givenname: Masashi surname: Sanada fullname: Sanada, Masashi – sequence: 20 givenname: Hironori surname: Yajima fullname: Yajima, Hironori – sequence: 21 givenname: Naoya surname: Morita fullname: Morita, Naoya – sequence: 22 givenname: Ichiro surname: Takeuchi fullname: Takeuchi, Ichiro – sequence: 23 givenname: Satoru surname: Miyano fullname: Miyano, Satoru – sequence: 24 givenname: Toshihiko surname: Wakabayashi fullname: Wakabayashi, Toshihiko – sequence: 25 givenname: Seishi surname: Ogawa fullname: Ogawa, Seishi – sequence: 26 givenname: Atsushi surname: Natsume fullname: Natsume, Atsushi |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29016839$$D View this record in MEDLINE/PubMed |
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Copyright | The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 2017 |
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