Abstract LB-010: Modeling the impact of mutations in ubiquitin pathway genes across human cancers

Abstract

Abstract A number of ubiquitin pathway genes show a strikingly high frequency of somatic alterations across diverse human cancers. While several ubiquitin ligase genes with recurrent mutations or copy number alterations been studied in individual cancers, their role in many malignancies remains poorly understood. To better model the impact of ubiquitin pathway alterations, we developed a computational strategy for exploiting parallel phosphoproteomics and mRNA sequencing data for large tumor sets to link dysregulation of upstream signaling pathways with altered transcriptional response through the transcriptional circuitry. Our modeling allows us to interpret the impact of mutations and copy number events in terms of altered signaling and transcription factor (TF) activity. We used a multi-task learning strategy to jointly train phosphoprotein-TF interaction models across 10 human cancers for which large reverse-phase protein array and RNA-seq data sets are available through TCGA. We then applied a novel algorithmic approach to extract networks of signaling proteins and TFs whose inferred activities are correlated across tumors and whose dysregulation is associated with specific somatically altered ubiquitin pathway genes. Our analysis revealed both known and novel interactions of ubiquitin ligase genes with signaling pathways and transcriptional programs in a pan-cancer context. Citation Format: Hatice U. Osmanbeyoglu, Christina Leslie. Modeling the impact of mutations in ubiquitin pathway genes across human cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-010. doi:10.1158/1538-7445.AM2017-LB-010