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Kyu-Chang Wang

Researcher at New Generation University College

Publications -  393
Citations -  11744

Kyu-Chang Wang is an academic researcher from New Generation University College. The author has contributed to research in topics: Moyamoya disease & Medicine. The author has an hindex of 48, co-authored 375 publications receiving 10129 citations. Previous affiliations of Kyu-Chang Wang include Seoul National University Hospital & Seoul National University.

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Journal ArticleDOI

Subgroup-specific structural variation across 1,000 medulloblastoma genomes

Paul A. Northcott, +139 more
- 02 Aug 2012 - 
TL;DR: Somatic copy number aberrations (SCNAs) in 1,087 unique medulloblastomas are reported, including recurrent events targeting TGF-β signalling in Group 3, and NF-κB signalling in Groups 4, which suggest future avenues for rational, targeted therapy.
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Intertumoral Heterogeneity within Medulloblastoma Subgroups

Florence M.G. Cavalli, +101 more
- 12 Jun 2017 - 
TL;DR: Similarity network fusion (SNF) applied to genome-wide DNA methylation and gene expression data across 763 primary samples identifies very homogeneous clusters of patients, supporting the presence of medulloblastoma subtypes.
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Subgroup-Specific Prognostic Implications of TP53 Mutation in Medulloblastoma

Nataliya Zhukova, +61 more
TL;DR: Subgroup-specific analysis reconciles prior conflicting publications and confirms that TP53 mutations are enriched among SHH medulloblastomas, in which they portend poor outcome and account for a large proportion of treatment failures in these patients.
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Divergent clonal selection dominates medulloblastoma at recurrence

A. Sorana Morrissy, +141 more
- 21 Jan 2016 - 
TL;DR: Targeted therapy is unlikely to be effective in the absence of the target, therefore the results offer a simple, proximal, and remediable explanation for the failure of prior clinical trials of targeted therapy.
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Cytogenetic Prognostication Within Medulloblastoma Subgroups

David Shih, +95 more
TL;DR: A small panel of cytogenetic biomarkers is identified that reliably identifies very high-risk and very low-risk groups of patients, making it an excellent tool for selecting patients for therapy intensification and therapy de-escalation in future clinical trials.