Systematic use of computational methods allows stratification of treatment responders in glioblastoma multiforme

Abstract

Background: Cancers are complex diseases whose comprehensive characterization requires genome-scale molecular data at multiple levels from genetics to transcriptomics and clinical data. Using our recently published Anduril bioinformatics framework and novel computational approaches, such as dependency analysis, we identify key variables at miRNA, copy number variation, expression, methylation, and pathway levels in glioblastoma multiforme (GBM) progression and drug resistance. Furthermore, we identify characteristics of clinically relevant subgroups, such as patients treated with temozolomide and patients with an EGFRvIII mutation, which is a constitutively active variant of EGFR. Results: We identify several novel genomic regions and transcript profiles that may contribute to GBM progression and drug resistance. All results and Anduril scripts are available at http://csbi.ltdk.helsinki.fi/camda/. Conclusions: Our results highlight the need for approaches that define context at several levels in order to identify genomic regions or transcript profiles playing key roles in cancer progression and drug resistance.