Abstract 2990: Molecular subtyping of glioblastoma based on isoform level network modeling
Authors: Pal Sharmistha , Zhang ZhongFa , Gupta Ravi , Bi Yingtao , Fetting Heather Heather , O'Rourke Donald M. , Davuluri Ramana V. 
Published: August 2012

Abstract
Glioblastoma multiforme (GBM) is the most common primary central nervous system tumor, accounting for nearly 80% of the 22,000 malignant brain tumors diagnosed annually in the United States. Patients who receive a diagnosis of glioblastoma have a dismal prognosis and median survival of 12-14 months following the standard therapy. Therefore, there is a need for improved molecular classification of GBM to identify highly sensitive and specific biomarkers and therapeutic targets. Recent genome wide transcriptome studies, by us and others, have discovered that one gene gives rise to multiple protein isoforms, which could be involved in multiple functional pathways. Based on this model, we hypothesize that the isoform-level expression profiling will lead to significantly improved classification of molecular sub-types and identification of more specific isoform based regulatory modules that are disrupted in each GBM molecular sub-type. Using the GBM data sets from TCGA data portal, we identified 2903 genes, which differ significantly in expression between the normal brain and GBM. In contrast, similar analysis using transcript-level expression showed that the expression of 7639 transcripts (correspond to 4401 genes) was significantly altered in GBMs, suggesting that measuring isoform/mRNA variant expression is more sensitive to identify molecular changes in GBM. Furthermore, we identified 51 genes (e.g. RTN3, DCLK2, PKM2, ACTN1), mostly associated with cellular assembly and organization, whose transcript variants show opposite expression pattern, an information lost in a gene centric approach. Although GBMs have been grouped into four subtypes (Proneural, Neural, Classical, Mesenchymal) based on gene expression and mutation profiling, these sub-types do not show any survival differences. Since we observed higher expression differences at isoform-level, we investigated whether isoform-level expression estimates can improve the grouping in terms of disease prognosis. We observed that isoform level clustering always generated a better survival grouping and effectively grouped the 173 core samples into 4 sub-groups with significant (p=0.024) survival differences between the groups. Interestingly, we identified a subgroup, consisting of selected proneural samples, which exhibits higher survival and is characterized by isoform specific overexpression of oligodendroglial markers including Olig1, Olig2, Sox 4, Sox6, Sox8, and Sox10. Gene level expression fails to identify Olig1, Sox6 and Sox10 as significantly misregulated in this subgroup. Similarly, we identify an isoform specific network in another subgroup that could confer resistance to EGFR tyrosine kinase inhibitor, erlotinib. Our analyses suggests that the quest for genomics based personalized medicine requires molecular classification and systematic understanding of the gene regulatory networks at the isoform level.
Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2990. doi:1538-7445.AM2012-2990