Abstract 5236: Novel network analysis framework identifies context-specific drug combinations in melanoma.

Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC Gene expression data can be used to identify the activation status of genes and pathways in tumor samples and to infer the structure of regulatory networks. Most tumor datasets such as The Cancer Genome Atlas focus on unperturbed samples, and subsequently, the statistical frameworks that analyze them. We hypothesized that perturbation of cells can uncover network structures that are undetectable in basal state, and reveal the complexity of cancer cells. To demonstrate this, we generated gene expression data from a panel of melanoma cell lines before and after inhibition of MEK or AKT. Here we present a new computational framework to analyze gene expression data before and after perturbations. The algorithm aims to define “differential networks1” - rewiring of network components in different tumors. The algorithm sheds light on the complex and context-specific nature of network structure, including pathways that are activated, or under the control of MAPK or AKT, only in a subset of samples. The perturbation data allowed us to assess pathway activation levels that are undetectable in unperturbed state. For example, AKT is activated in melanoma by PTEN loss, but the effect of NRAS mutation on AKT is unknown, and AKT activation is undetectable in unperturbed cells. Our algorithm identified an AKT activation signature in perturbed state, and helped define the effects of PTEN and NRAS mutations on the pathway. Additionally, the algorithm inferred the genetic status of p53 in each cell line by identifying pathway activation, changes that are, once again, detectable only after perturbations. By linking protein data with the algorithm's results, we identified cell lines with constitutive activation of the STAT3 pathway, and demonstrated the crosstalk between MAPK and STAT3 pathways. Moreover, combining the results from the algorithm with known signatures of drug-sensitivity accurately predicted which drug combinations provided a synergistic effect in each cell line. Associations between pathway activity level and drug sensitivity are known for some drugs. By using our algorithm's ability to infer pathway status in different tumors and to detect changes in those pathways after drug treatment, it correctly predicted when drug treatment sensitized cells to the effects of chemotherapy and other drugs. To conclude, we show that gene expression of perturbed cells greatly enhances the power of network structure analysis. Our results reveal context-specific changes in network structure, and predict which cell lines are sensitized to specific drugs by pathway perturbations. These results have direct applicability to the development of drug combinations in melanoma. 1 Pe'er and Hacohen, Cell 2011 Citation Format: Oren Litvin, Sarit Schwartz, Mark Rocco, Tanya Schild, Neal Rosen, Dana Pe'er. Novel network analysis framework identifies context-specific drug combinations in melanoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5236. doi:10.1158/1538-7445.AM2013-5236 Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.