Abstract 3958: Multi-Reg: An integrative parallel approach to uncover drivers of cancer

Abstract

Abstract Tumor samples harbor a vast number of genomic alterations of various kinds, and it is difficult to distinguish driver alterations that contribute to oncogenesis from passenger alterations. Most computational methods identify driver genes by focusing on the most frequent copy number alterations. Previous work in our lab has led to the development of CONEXIC, a Bayesian algorithm that identifies candidate driver genes in cancer and links them to gene expression signatures they govern by integrating copy number and gene expression (Akavia et al, Cell, 2010). This algorithm was applied to data from melanoma cell lines, where it correctly identified known drivers, such as MITF and KLF6, and connected them to their known targets. In addition, it predicted novel tumor dependencies not previously implicated in melanoma, which were validated experimentally. Drivers may act concurrently, where not only the strongest one is important. For example, either PTEN deletion or AKT activation can lead to a similar expression signature and phenotype. Therefore, we developed a new algorithm, based on the same principles as CONEXIC, with multiple improvements. The new algorithm - Multi-Reg - is capable of detecting multiple candidate regulators that can all act in parallel to regulate an expression signature, and is also capable of integrating mutations in addition to copy number and gene expression data. We applied Multi-Reg to glioblastoma data from The Cancer Genome Atlas (TCGA). This data includes copy number, gene expression and mutations for hundreds of primary tumor samples. Multi-Reg has identified putative drivers that were missed by CONEXIC, such as the known genes PDGFRA and NF1, in addition to EGFR & ERB2 (identified by CONEXIC). Additionally, since Multi-Reg candidate drivers act in parallel, we can group them by shared targets. For example, EGFR & ERRB2 induce the same genes, which represent the Mesenchymal subtype of glibolastoma. These same genes are repressed by PDGFRA & NF1. This matches the known behavior of glioblastoma subtypes, where EGFR & PDGFRA characterize the Mesenchymal and Proneural subtypes, respectively. Thus, our results correctly identify known drivers of glioblastoma. Additionally, Multi-Reg results identified RHPN2 as a novel oncogenic factor controlling a gene expression signature related to adhesion. Validation has shown that while RHPN2 has no effect on cell proliferation it induces invasiveness in glioblastoma cell lines. This shows that Multi-Reg cannot only discover drivers, but can link them to the oncogenic phenomena they controls, and suggest the appropriate biological validation for it. 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 3958. doi:1538-7445.AM2012-3958