Abstract A23: Multi-parametric genetic interactions map dynamic genetic network rewiring upon anti-proliferative treatment

• Published in: Molecular cancer therapeutics 2017-10, Vol.16 (10_Supplement), p.A23-A23
• Main Authors: Heigwer, Florian, Scheeder, Christian, Miersch, Thilo, Blass, Claudia, Boutros, Michael
• Format: Article
• Language: English
• ISSN: 1535-7163; 1538-8514
• DOI: 10.1158/1538-8514.SYNTHLETH-A23

Signaling pathways are often characterized as rather static networks whose outcome is the result of state changes of pathway components, e.g. by protein phosphorylation or other post-translational modifications. These state changes along signaling networks have been systematically studied using, for example proteomics methods. However it remained unknown how networks rewire under the influence of external stimuli, such as anti-cancer drug treatment. To analyze dynamic rewiring of a signaling network, we performed an arrayed high-throughput co-RNAi screen in a Drosophila melanogaster cell line (Dmel-2). Therin, we assessed statistic genetic interactions measured by 26 880 pairwise RNAi experiments under MEK1/2 inhibitor treatment over the time course of 96 hours. As phenotypic readout we conducted high-content imaging of DNA, cytoskeleton and microtubule markers and extracted >150 cellular features from 4 423 680 images each representing a specific condition, and marker. Together these features precisely characterize ~100 000 alleviating and 160 000 aggravating synergistic effects resulting from combinatorial perturbations. While only 2 % of all interactions are explained by cell viability the vast majority of dynamic differential interactions is explained by other features. Correlation of genetic interaction profiles across those features allows us to precisely how genes change pathway affiliation under different conditions. Our results show that, among others, key signaling nodes e.g. ERK1/2 or the Mediator complex build different connections within genetic networks depending on the environmental conditions and reveal yet unknown synthetic interactions. Using the confidence we gain from time resolved measurements of different cellular features we could identify numerous interactions which could resolve mechanisms of resistance to MEK inhibitor driven treatments and reveal potential new therapeutic targets. Citation Format: Florian Heigwer, Christian Scheeder, Thilo Miersch, Claudia Blass, Michael Boutros. Multi-parametric genetic interactions map dynamic genetic network rewiring upon anti-proliferative treatment [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr A23.