Abstract A26: 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 not easy to distinguish driver alterations that contribute to oncogenesis from passenger alterations. Most computational methods attempt to identify driver alternations by focusing on the most frequent alterations. Previous work in our lab has led to the development of CONEXIC, a Bayesian framework for integrating copy number and gene expression to identify candidate driver genes in cancer and to link them to gene expression signatures they regulate (Akavia et al, Cell, 2010). This framework was applied to data from melanoma cell lines, where it correctly identified known drivers (MITF) and connected them to their known targets. In addition, it predicted novel tumor dependencies not previously implicated in melanoma, which were confirmed experimentally. In general, current algorithms identify only one driver (the strongest) controlling a gene expression signature. However, drivers may act in parallel, 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 (called Multi-Reg) is capable of detecting multiple candidate regulators that can all act in parallel to regulate an expression signature. This algorithm can integrate mutations in addition to copy number and expression. Finally, we have designed Multi-Reg to be easier and quicker to run and more robust. 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. We found 84 candidate regulators that were missed by CONEXIC, but discovered by Multi-Reg. Because of Multi-Reg's ability to search for genes working in parallel, it identified FGFR3, PDGFRA and NF1, in addition to EGFR & MET (identified by CONEXIC), as important candidate drivers. Additionally, Multi-Reg results identified RHPN2 as a novel oncogenic factor controlling a gene expression signature related to invasion and migration. Validation has shown that RHPN2 has limited effect on cell proliferation but induces invasiveness in glioblastoma cell lines. Our results correctly identify known drivers of glioblastoma progression, including oncogenes such as EGFR, MET, CEBPB and tumor suppressors such as p16 and NF1. Multi-Reg also has the capability to identify more regulators than CONEXIC, and has correctly linked RHPN2 to the oncogenic phenomena it controls. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the Second AACR International Conference on Frontiers in Basic Cancer Research; 2011 Sep 14-18; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2011;71(18 Suppl):Abstract nr A26.