Abstract 4384: Quantitative assessment of drug combination effects in NSCLC cell lines through network-based analysis of functional protein activity
Authors: Chiara Antonini, George Rosenberger, Fortunato Bianconi, Lorenzo Tomassoni, Vienna Ludovini, Sara Baglivo, Sara Calandrini, Elisa Baldelli, Mariaelena Pierobon, Emanuel F. Petricoin, Andrea Califano
Journal: Cancer Research (2020)

Abstract Non-small cell lung cancer (NSCLC) is the most prevalent type of lung cancer, which is the leading cause of cancer death worldwide. The KRAS oncogene is one of the most common driver mutations in NSCLC patients but, despite that, there are no approved targeted therapies for tumors harboring this mutation. The MEK inhibitor Selumetinib (SE) turned out to be an ineffective drug against KRAS mutated lung cancer. Since combination therapy has shown to be beneficial in many tumors, we wanted to investigate the combination of MEK inhibition with other targeted therapies. Thus, we measured the proteomic response of KRAS mutant (MUT) and wild-type (WT) NSCLC cell lines (CLs) to the combination of SE with Everolimus (EV), an mTOR inhibitor, and Tamoxifen (TA), an Estrogen inhibitor. The expression level of 183 proteins was measured through Reverse Phase Protein Array (RPPA) in eight NSCLC CLs treated with SE, SE plus EV and SE plus TA at 6 time points: 5 minutes (min), 30 min, 1, 2, 6 and 24 hours (h). As control, measures were taken in baseline CLs and in CLs with only dimethyl sulfoxide. To study the drug combination effects, we applied a computational workflow centered on two algorithms initially developed for gene expression data. We analyzed the CLs with the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) to reconstruct a context specific signaling network of 2635 interactions between 73 kinases and 6 phosphatases and their substrates. Then, we analyzed the obtained network with the Virtual Inference of Protein activity by Enriched Regulon analysis (VIPER) algorithm to assess the mechanism of action of the drug compounds and how they dysregulate the activity of kinases and phosphatases over time. With respect to SE alone and apart from the drug targets, VIPER predicted the following proteins as highly inhibited over time across all CLs : 1) Aurora and LKB1 in SE+EV, 2) AMPK, c-RAF and ATR in SE+TA, 3) mTOR, p90, p70S6K, c-MET and GSK-3 in both combinations. On the other hand, Akt, ETK, Axl and FAK are all activated in SE+EV as well as PP2A, SGK1 and IGF1R in SE+TA, after 1h from the treatment. By running Gene Set Enrichment Analysis on the VIPER output against the primary drug target pathways, we found that mTOR signaling pathway exhibits a quite different evolution between KRAS MUT and WT CLs. On the other hand, other pathways revealed mechanisms of response that are CL specific. For instance, the main MEK pathways in H1734 CL (KRAS MUT) treated with SE+TA are all insensitive to the drug compounds. In conclusion, this computational approach successfully predicted protein-protein interactions and elucidated both proteomic mechanisms of drug combinations and CL specific dependencies. It allows to develop effective predictive and quantitative models reproducing each CL behavior, in order to realize the fullest potential of a targeted therapeutic approach. Citation Format: Chiara Antonini, George Rosenberger, Fortunato Bianconi, Lorenzo Tomassoni, Vienna Ludovini, Sara Baglivo, Sara Calandrini, Elisa Baldelli, Mariaelena Pierobon, Emanuel F. Petricoin, Andrea Califano. Quantitative assessment of drug combination effects in NSCLC cell lines through network-based analysis of functional protein activity [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4384.