Abstract PR10: Multimodality imaging of human lung squamous cell carcinoma reveals unique metabolic dependencies that are effectively targeted with metabolic-based therapies

Altered metabolism is known to commonly contribute to cancer growth, forming the conceptual basis for the development of metabolic therapies as cancer treatments. However, the specific metabolic characteristics of individual cancer types in vivo are still largely unknown, limiting the translatability of metabolic therapies in the clinic. In this study we used a multimodality imaging approach to identify metabolic dependencies in lung squamous cell carcinomas (SCC) in order to guide precise treatments with metabolic therapies. We performed in vivo metabolic profiling and molecular analysis of lung SCC using positron emission tomography (PET) imaging, liquid chromatography mass spectrometry (LC-MS), and immunohistochemistry (IHC). PET imaging lung SCC tumors with 18F-fluoro-2-deoxyglucose (18F-FDG) and 11C-Glutamine PET tracers identifed a conserved metabolic reliance on both glucose and glutamine that we confirmed with LC-MS based metabolomics and quantitative IHC. The mTOR kinase is a central regulator of growth and metabolism and is readily inhibited using allosteric and catalytic kinase inhibitors. Using the mTOR catalytic kinase inhibitor MLN128, we successfully inhibited glycolysis in lung SCC. However, lung SCC tumors were able to metabolically adapt to chronic mTOR inhibition through upregulation of glutamine metabolism. We discovered that chronic mTOR inhibition induced phosphorylation and inactivation of GSK3a/b resulting in upregulation and activation of cMYC and cJUN—both central regulators of the glutaminase (GLS) enzyme and glutaminolysis. Importantly, phospho-GSK3α/β and phospho-cJUN proteins serve as functional biomarkers that predict MLN128 resistance and upregulation of glutaminolysis. To overcome MLN128 resistance we treated lung SCC tumors with the glutaminase inhibitor CB-839. The combination of MLN128 and CB-839 effectively overcame therapy resistance in genetically engineered mouse models (GEMMs) and patient-derived xenografts (PDXs) of lung SCC. Lastly, data mining from The Lung Cancer Genome Atlas coupled to our PET imaging and IHC biomarkers analysis allowed us to construct a metabolic signature that is conserved in human lung SCC as well as a broad spectrum of hypermetabolic human tumors. These tumors include lung SCC, head and neck squamous cell carcinoma (HNSCC), osteosarcomas (OS), and triple-negative breast cancer (TNBC). Importantly, this metabolic signature is predictive of patient outcome and response to combined metabolic therapies targeting mTOR and glutaminase. We therefore propose a clinically translatable treatment strategy for lung SCC patients that is driven by PET imaging and IHC to first stratify patients by their metabolic signature, which is then used to guide the delivery of precise metabolic-based therapies that target key metabolic nodes such as mTOR and GLS. This abstract is also being presented as Poster B11. Citation Format: Milica Momcilovic, David B. Shackelford. Multimodality imaging of human lung squamous cell carcinoma reveals unique metabolic dependencies that are effectively targeted with metabolic-based therapies [abstract]. In: Proceedings of the Fifth AACR-IASLC International Joint Conference: Lung Cancer Translational Science from the Bench to the Clinic; Jan 8-11, 2018; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(17_Suppl):Abstract nr PR10.