Abstract 3297: A tyrosine kinase interactome reveals network states that guide the use of targeted therapies in cancer
Authors: Kaushik Swati , Jang Gwendolyn , Hu Hsien-Ming , Shah Khyati , Zhao Xin , Jascur John , Dollen John Von , Verschueren Erik , Johnson Jeffrey , Krogan Nevan , Bandyopadhyay Sourav 
Published: August 2018

Characterization of the genomic landscapes of cancer patients has provided valuable insights into the key oncogenic drivers and revolutionized the concept of precision treatment of patients. However, a key limitation is that targetable alterations are only found in a small fraction of patients. This is due to the fact that the majority of cancer drugs are developed against specific oncogenes. However, oncogenes do not act in isolation but rather function as a part of complex protein interactions that can alter oncogene activity and dependence. We hypothesize that a systems approach to read the cellular activity of oncogenic proteins by mapping interaction network states of cancer cells can aid in patient stratification for targeted therapy. To identify interaction networks centered on the major class of cancer drug targets, we experimentally mapped protein-protein interaction (PPI) networks of all 90 human tyrosine kinases (TK) using proteomics approach of affinity purification and mass spectrometry. This analysis identified 1,458 high confidence interactors of TK in HEK293 cells. Detailed analyses of this interactome revealed the diverse cellular localizations and novel associations of TK with multiple protein complexes, suggesting a broader functional role in cellular signaling than previously appreciated. To map the cellular activity of TK in cancer patients, we developed a novel computational approach to integrate PPI networks with genomic data from cancer patients profiled in TCGA. Application to lung adenocarcinoma samples identified that activity of EGFR interactors could be used to define an EGFR network state that was highly predictive of the presence of EGFR mutation. Intriguingly, our analysis identified that 23% of EGFR wild-type samples were positive for the EGFR network state, suggesting a role for EGFR in lung cancer beyond EGFR mutant cases. Furthermore, this state was highly predictive of erlotinib sensitivity in EGFR wild-type lung PDX and cell lines. We identified that many KRAS and NF1 mutant NSCLC samples were EGFR network state positive and displayed evidence of EGFR activation identified by RPPA. These results indicate that a network state approach can precisely expand the pool of patients that may benefit from EGFR TK inhibitors (TKi). Finally, we tested whether components of TK networks were critical for their function by performing synthetic lethal RNAi screens in cell lines with mutation in key TK and identified many kinase interactors as drug sensitizers. Our results indicate that integration of high-throughput genomic datasets with the PPI networks provides an effective tool to understand complex oncogenic network states of cancer cells and provides a high-resolution readout of tumor cell dependence. This work provides the most complete interaction map for TK to date and is a valuable resource to probe mechanisms of oncogene addiction to improve the utility of TKi in cancer.

Citation Format: Swati Kaushik, Gwendolyn Jang, Hsien-Ming Hu, Khyati Shah, Xin Zhao, John Jascur, John Von Dollen, Erik Verschueren, Jeffrey Johnson, Nevan Krogan, Sourav Bandyopadhyay. A tyrosine kinase interactome reveals network states that guide the use of targeted therapies in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3297.