Abstract 4905: Patient-specific pathway analysis using PARADIGM identifies key activities in multiple cancers

Cancer is a disease of genomic perturbations that lead to dysregulation of multiple pathways within the cellular system. While common pathways are believed to be shared within specific cancer types, the mechanisms of why particular patients respond differently to treatment is not fully understood. Current -omics studies such as The Cancer Genome Atlas (TCGA) and Stand Up To Cancer (SU2C) have attempted to address this issue by using large-scale whole-genome measurements of mRNA expression, DNA copy number, and epigenetic features. Typical analysis of these measurements relies on integrating data from multiple samples to distinguish signal from noise. However, few analytical methods allow for sample-specific differences to identify features and pathways that are significant for prognosis and clinical treatment classifications. We developed a pathway inference method called PARADIGM (PAthway Recognition Algorithm using Data Integration on Genomic Models) (Bioinformatics (2010) vol. 26 (12) pp. i237) to identify patient- and sample-specific pathway activities. Previously we have shown that PARADIGM is capable of stratifying patients into clinically relevant subgroups in both TCGA ovarian serous carcinoma and glioblastoma multiforme using gene expression and copy number alteration values. We have since enhanced PARADIGM in multiple ways allowing more in-depth analysis and discovery across multiple cancer types. We have expanded the underlying pathway database previously consisting of NCI9s Pathway Interaction Database to include pathways from BioCarta and Reactome, increasing the number of pathways by ten-fold and increasing the total number of features to approximately 22,000. We have refined the central dogma framework underlying all features in each pathway to support a more accurate regulatory model, allowing us to efficiently learn the strength of each interaction. Finally, PARADIGM now supports additional input data sources in the form of methylation and mutational interventions. By adding gene-level mutational information on the new regulatory model we show it is possible to distinguish between gain-of-function and loss-of-function mutations in each patient. In addition, through these enhancements we show increased ability to identify key pathway activities that effectively stratify patient cohorts. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4905. doi:10.1158/1538-7445.AM2011-4905