Abstract 822: Predicting response to kinase inhibitors based on tumor genomics in NF1-related MPNSTs

Abstract

Abstract Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are aggressive, chemoresistant sarcomas that develop in patients with the genetic disorder Neurofibromatosis Type 1 (NF1). Loss of TRP53 and amplification of the receptor tyrosine kinase (RTK) MET is observed 25-50% of cases. To investigate the role of MET and p53 in MPNST progression, we developed three MPNST mouse models: an NF1 null and MET overexpression MPNST model (NF1-MET), an NF1 and TRP53 heterozygous knockout model (NF1-p53), and a NF1 null model (NF1). Using orthotopic tumorgrafts from these models, we tested the efficacy of MET (capmatinib) and MEK (trametinib) inhibition on MPNST growth. Trametinib moderately inhibited tumor growth in all of the models; however, combined inhibition of MEK and MET was highly effective in both the NF1-MET and NF1 models. NF1-p53 tumors were the least responsive to combined inhibition and upregulated AKT signaling in response to single agent therapy. These data suggest that alternate RTK signaling pathways downstream of MET may compensate for MEK inhibition, and that p53 loss promotes kinome reprogramming in response to targeted inhibition in MPNSTs. To determine the effects of MEK, MET, PI3K, and mTOR inhibition on kinome signaling, we generated MPNST cell lines from each of our MPNST models. Western blot analysis revealed that NF1-MET MPNST cells are highly sensitive to MET inhibition, NF1-p53 MPNST cells maintain ERK and AKT activity in the presence of MET inhibition, and NF1-p53 MPNST cells uniquely upregulate MEK signaling in response to PI3K/mTOR inhibition. These results suggest p53 plays a unique role in kinome reprograming and resistance to kinase inhibitors. Currently, we are determining the effects of combined kinase inhibition on proliferation of MPNST cells and utilizing reverse phase protein arrays to evaluate the effect of MET and MEK inhibition on the phospho-proteome in our MPNST models. By understanding these complex signaling networks we will identify combinations of targeted therapies that can be used to effectively treat MPNSTs. Citation Format: Jamie Grit, Matt Pridgeon, Elizabeth Tovar, Zachary Madaj, Jackie Peacock, Julia Wulfkuhle, Emanuel Petricoin, Carrie Graveel, Matt Steensma. Predicting response to kinase inhibitors based on tumor genomics in NF1-related MPNSTs [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 822.