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Integrative Subtype Discovery in Glioblastoma Using iCluster

Author

Listed:
  • Ronglai Shen
  • Qianxing Mo
  • Nikolaus Schultz
  • Venkatraman E Seshan
  • Adam B Olshen
  • Jason Huse
  • Marc Ladanyi
  • Chris Sander

Abstract

Large-scale cancer genome projects, such as the Cancer Genome Atlas (TCGA) project, are comprehensive molecular characterization efforts to accelerate our understanding of cancer biology and the discovery of new therapeutic targets. The accumulating wealth of multidimensional data provides a new paradigm for important research problems including cancer subtype discovery. The current standard approach relies on separate clustering analyses followed by manual integration. Results can be highly data type dependent, restricting the ability to discover new insights from multidimensional data. In this study, we present an integrative subtype analysis of the TCGA glioblastoma (GBM) data set. Our analysis revealed new insights through integrated subtype characterization. We found three distinct integrated tumor subtypes. Subtype 1 lacks the classical GBM events of chr 7 gain and chr 10 loss. This subclass is enriched for the G-CIMP phenotype and shows hypermethylation of genes involved in brain development and neuronal differentiation. The tumors in this subclass display a Proneural expression profile. Subtype 2 is characterized by a near complete association with EGFR amplification, overrepresentation of promoter methylation of homeobox and G-protein signaling genes, and a Classical expression profile. Subtype 3 is characterized by NF1 and PTEN alterations and exhibits a Mesenchymal-like expression profile. The data analysis workflow we propose provides a unified and computationally scalable framework to harness the full potential of large-scale integrated cancer genomic data for integrative subtype discovery.

Suggested Citation

  • Ronglai Shen & Qianxing Mo & Nikolaus Schultz & Venkatraman E Seshan & Adam B Olshen & Jason Huse & Marc Ladanyi & Chris Sander, 2012. "Integrative Subtype Discovery in Glioblastoma Using iCluster," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-9, April.
    • Handle: RePEc:plo:pone00:0035236
    DOI: 10.1371/journal.pone.0035236
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    References listed on IDEAS

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    1. Evelina Gabasova & John Reid & Lorenz Wernisch, 2017. "Clusternomics: Integrative context-dependent clustering for heterogeneous datasets," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-29, October.
    2. Xin Liu & Yingxian Chang & Honglei Yao & Bing Su, 2023. "Secure Transmission Method of Power Quality Data in Power Internet of Things Based on the Encryption Algorithm," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 19(1), pages 1-19, January.
    3. Jain Yashita & Ding Shanshan & Qiu Jing, 2019. "Sliced inverse regression for integrative multi-omics data analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(1), pages 1-13, February.
    4. Samorodnitsky, Sarah & Wendt, Chris H. & Lock, Eric F., 2024. "Bayesian simultaneous factorization and prediction using multi-omic data," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).
    5. Zhiguang Huo & Li Zhu & Tianzhou Ma & Hongcheng Liu & Song Han & Daiqing Liao & Jinying Zhao & George Tseng, 2020. "Two-Way Horizontal and Vertical Omics Integration for Disease Subtype Discovery," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(1), pages 1-22, April.

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