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63. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

02. - 06.09.2018, Osnabrück

Network-based identification of gene copy number mutations driving oligodendroglioma development

Meeting Abstract

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  • Michael Seifert - Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus, TU Dresden, Dresden, Deutschland
  • Josef Gladitz - Institut für Medizinische Informatik und Biometrie, Medizinische Fakultät Carl Gustav Carus, TU Dresden, Dresden, Deutschland
  • Barbara Klink - Institut für Klinische Genetik, Medizinische Fakultät Carl Gustav Carus, TU Dresden, Dresden, Deutschland

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 63. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Osnabrück, 02.-06.09.2018. Düsseldorf: German Medical Science GMS Publishing House; 2018. DocAbstr. 140

doi: 10.3205/18gmds099, urn:nbn:de:0183-18gmds0995

Published: August 27, 2018

© 2018 Seifert et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.


Outline

Text

Introduction: Oligodendrogliomas are primary human brain tumors with a characteristic 1p/19q co-deletion of important prognostic relevance, but little is known about the pathology of this chromosomal mutation. The occurrence of nearly identical co-deletions in individual oligodendrogliomas does not allow to narrow down the exact location of putative driver genes with standard statistical or bioinformatics approaches.

Methods: We developed a network-based approach to identify novel cancer gene candidates in the region of the 1p/19q co-deletion and for other rarely mutated chromosomal arms. Gene regulatory networks were learned from gene expression and copy number data of 178 oligodendrogliomas from The Cancer Genome Atlas (TCGA) and further used to quantify putative impacts of differentially expressed genes of the 1p/19q region on cancer-relevant pathways. Network inference and network-based propagation of gene expression alterations was done using our algorithms [1], [2]. Gene expression and gene copy number data was preprocessed in [3]. Related network-based approaches have been proposed for the analysis of gene mutations utilizing gene or protein interaction networks [4], [5].

Results: We predicted 8 genes with strong impact on signaling pathways and 14 genes with strong impact on metabolic pathways widespread across the region of the 1p/19 co-deletion. Many of these candidates (e.g. ELTD1, SDHB, SEPW1, SLC17A7, SZRD1, THAP3, ZBTB17) are likely to push, whereas others (e.g. CAP1, HBXIP, KLK6, PARK7, PTAFR) might counteract oligodendroglioma development. For example, ELTD1, a functionally validated glioblastoma oncogene located on 1p, was overexpressed. Further, the known glioblastoma tumor suppressor SLC17A7 located on 19q was underexpressed. Moreover, known epigenetic alterations triggered by mutated SDHB in paragangliomas suggest that underexpressed SDHB in oligodendrogliomas may support and possibly enhance the epigenetic reprogramming induced by the IDH-mutation. We further analyzed rarely observed deletions and duplications of chromosomal arms within oligodendroglioma subcohorts identifying putative oncogenes and tumor suppressors that possibly influence the development of oligodendroglioma subgroups.

Discussion: Our in-depth computational study contributes to a better understanding of the pathology of the 1p/19q co-deletion and other chromosomal arm mutations. This might open opportunities for functional validations and new therapeutic strategies. Our computational approaches can be transferred to other types of cancer or other diseases. More details to our study can be found in [6].

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.


References

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Seifert M, Friedrich B, Beyer A. Importance of rare gene copy number alterations for personalized tumor characterization and survival analysis. Genome Biology. 2016;17:204.
2.
Seifert M, Beyer A. regNet: an R package for network-based propagation of gene expression alterations. Bioinformatics. 2018;34(2):308-11.
3.
Lauber C, Klink B, Seifert M. Comparative analysis of histologically classified oligodendrogliomas reveals characteristic molecular differences between subgroups. BMC Cancer. 2018;18:399.
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Hofree M, Shen JP, Carter H, Gross A, Ideker T. Network-based stratification of tumor mutations. Nat Methods. 2013;10(11):1108-15.
5.
Leiserson MDM, Vandin F, Wu HT, Dobson JR, Eldridge JV, Thomas JL, et al. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat Genet. 2015;47(2):106-14.
6.
Gladitz J, Klink B, Seifert M. Network-based analysis of oligodendrogliomas predicts novel cancer gene candidates within the region of the 1p/19q co-deletion. Acta Neuropathol Commun. 2018. DOI: 10.1186/s40478-018-0544-y External link