Cimino PJ, Kim Y, Wu HJ, Alexander J, Wirsching HG, Szulzewsky F, Pitter K, Ozawa T, Wang J, Vazquez J, Arora S, Rabadan R, Levine R, Michor F, Holland EC. 2018. Increased expression provides a selective advantage for gain of whole chromosome 7 in IDH wild-type glioblastoma. Genes & development. 32(7-8):512-523. Pubmed: 29632085 DOI:10.1101/gad.312157.118


Glioblastoma is the most frequently occurring and invariably fatal primary brain tumor in adults. The vast majority of glioblastomas is characterized by chromosomal copy number alterations, including gain of whole chromosome 7 and loss of whole chromosome 10. Gain of whole chromosome 7 is an early event in gliomagenesis that occurs in proneural-like precursor cells, which give rise to all isocitrate dehydrogenase (IDH) wild-type glioblastoma transcriptional subtypes. () is one gene on chromosome 7 known to drive gliomagenesis, but, given its location near the end of 7p, there are likely several other genes located along chromosome 7 that select for its increased whole-chromosome copy number within glioblastoma cells. To identify other potential genes that could select for gain of whole chromosome 7, we developed an unbiased bioinformatics approach that identified () as a gene whose expression correlated with gain of chromosome 7 and a more aggressive phenotype of the resulting glioma. High expression of in glioblastoma was associated with a proneural gene expression pattern and decreased overall survival in both human proneural and PDGF-driven mouse glioblastoma. Furthermore, overexpression promoted cellular proliferation and potentiated radioresistance. We also found enrichment of expression in recurrent human and mouse glioblastoma at first recurrence after radiotherapy. Overall, this study implicates as a chromosome 7-associated gene-level locus that promotes selection for gain of whole chromosome 7 and an aggressive phenotype in glioblastoma.
© 2018 Cimino et al.; Published by Cold Spring Harbor Laboratory Press.

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Franziska Michor uses the tools of theoretical evolutionary biology, applied mathematics, statistics, and computational biology to address important questions in cancer research.

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