American Association for Cancer Research
00085472can040452-sup-supplementary_figure_1.pdf (30.24 kB)

Supplementary Figure 1 from Gene Expression Profiling of Gliomas Strongly Predicts Survival

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journal contribution
posted on 2023-03-30, 16:24 authored by William A. Freije, F. Edmundo Castro-Vargas, Zixing Fang, Steve Horvath, Timothy Cloughesy, Linda M. Liau, Paul S. Mischel, Stanley F. Nelson
Supplementary Figure 1 from Gene Expression Profiling of Gliomas Strongly Predicts Survival



In current clinical practice, histology-based grading of diffuse infiltrative gliomas is the best predictor of patient survival time. Yet histology provides little insight into the underlying biology of gliomas and is limited in its ability to identify and guide new molecularly targeted therapies. We have performed large-scale gene expression analysis using the Affymetrix HG U133 oligonucleotide arrays on 85 diffuse infiltrating gliomas of all histologic types to assess whether a gene expression-based, histology-independent classifier is predictive of survival and to determine whether gene expression signatures provide insight into the biology of gliomas. We found that gene expression-based grouping of tumors is a more powerful survival predictor than histologic grade or age. The poor prognosis samples could be grouped into three different poor prognosis groups, each with distinct molecular signatures. We further describe a list of 44 genes whose expression patterns reliably classify gliomas into previously unrecognized biological and prognostic groups: these genes are outstanding candidates for use in histology-independent classification of high-grade gliomas. The ability of the large scale and 44 gene set expression signatures to group tumors into strong survival groups was validated with an additional external and independent data set from another institution composed of 50 additional gliomas. This demonstrates that large-scale gene expression analysis and subset analysis of gliomas reveals unrecognized heterogeneity of tumors and is efficient at selecting prognosis-related gene expression differences which are able to be applied across institutions.

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