Chen, Emily

Abstract The extracellular matrix (ECM) is increasingly recognized as having a key role in cancer development. We have developed a biomaterial tumor mimic that recapitulates systematically the main characteristics and heterogeneities of glioblastoma ECM. With the use of these 3D in vitro platforms we seek to understand the key features that make this cancer so challenging to treat. We are using this device in three different ways: (1) to further understand how particularities of brain tumor ECM affect cancer cells and assist in the diagnostic of different glioblastoma (GBM) tumor types, (2) provide new models for drug screening to afford more personalized therapies and (3) study the mechanisms of resistance to current therapies in order to identify more effective combinatorial treatments and new therapeutic targets. Hyaluronic acid (HA), is the main component of the brain ECM and GBM is associated with aberrant HA secretion and overexpression of receptors associated with HA, such as CD44 and EGFR. We cultured GBM patient-derived xenograft (PDX) cells within gelatin based hydrogels that contained variable concentrations of HA. Cell proliferation and gene expression analyses demonstrate that biomimetic hydrogels support xenograft culture and cells upregulate matrix remodeling genes and others related to tumor growth in response to matrix biophysical properties. We also used these platforms to evaluate the mechanisms of cell resistance to tyrosine kinase inhibitors. For this purpose, we first focused on the epidermal growth factor receptor (EGFR), which has been identified as a molecular target and associated with worse clinical outcomes. We characterized the 3D in vitro behavior of 3 PDX that represent these EGFR variants: GBM10 (EGFR, wild type), GBM12 (EGFR+) and GBM6 (EGFRvIII). We studied the relationship between the HA contained in the surrounding matrix and response of GBM cells to a tyrosine kinase inhibitor (TKI), erlotinib. Results indicate that while EGFR+ cells are sensitive to TKI in HA hydrogels, HA seems to collaborate with EGFRvIII signaling to stir cell activity. Immunoblots demonstrate that this enhanced cell activity is related to a significant increase in PDGFR concentration. Blockade of the CD44 receptor, in combination with erlotinib treatment, does not affect phosphorylation rates of PI3K or ERK, however, it leads to a significant decrease of STAT3 phosphorylation. This is translated into a lower tumor cell metabolic activity in HA matrices. Finally, we show that, patient-derived xenograft cells resistant to erlotinib in vivo (GBM10), become sensitive when CD44 is blocked in HA-containing matrices, as demonstrated by a decrease of phosphorylated PI3K. In summary, we highlight the importance of extracellular HA in EGFR inhibition efficiency. We demonstrate that this biomaterial tumor model can be used as a valuable tool in the mechanistic studies of tumor development and prediction of tyrosine kinase inhibitors efficacy. Citation Format: Sara Pedron, Gabrielle L. Wolter, Emily Chen, Jann N. Sarkaria, Brendan A. Harley. Glioblastoma tumor model to analyze the mechanisms of resistance to tyrosine kinase inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5776. doi:10.1158/1538-7445.AM2017-5776

Abstract Pancreatic adenocarcinoma generally presents with a poor prognosis and an extremely low response rate to first line therapies. There is a critical unmet need to discover and implement effective diagnostic panels to stratify outcomes and tailor treatment strategies to improve survival. The BERG Interrogative Biology® platform utilizes Artificial Intelligence to analyze and integrate multi-omic profiles with clinical annotation to define novel biomarkers and improve treatment interventions. Herein, we analyzed the serum proteome, signaling lipidome, structural lipidome, and metabolome of 163patients at multiple timepoints (pancreatic cancer: 115; pancreatitis: 15; and age-matched healthy controls: 33). Utilizing the power of the Bayesian Network learner, bAIcis™ (BERG Artificial Intelligence Clinical Information System), multi-omic profiles were aligned to the longitudinal clinical information and subjected to AI-algorithms to infer probabilistic cause-and-effect relationships among molecular and clinical variables explicitly explaining pancreatic cancer status, cancer progression, and survival, and defining the interconnectivity of molecular features with clinical phenotype. Network features linking into clinical endpoints and key network pressure points were identified as molecular drivers. The drivers of clinical endpoints were analyzed to rank potential biomarkers. Novel biomarkers were discovered that demonstrate diagnostic potential for pancreatic cancer detection, disease prognosis including metastatic disease progression, patient stratification associated with survival, and response to standard chemotherapy agents like Gemcitibine. A prospective study is underway to validate the biomarkers and discover additional diagnostic and therapeutic molecules to improve the outcomes for patients affected by adenocarcinoma of the pancreas. Citation Format: Niven R. Narain, A James Moser, Ramesh Ramanathan, John Crowley, Amy Stoll-D’Astice, Yezhou Sun, Leonardo O. Rodrigues, Eric M. Grund, Emily Chen, Vivek K. Vishnudas, Michael A. Kiebish, Viatcheslav R. Akmaev, Rangaprasad Sarangarajan. Project Survival: Interrogative Biology® platform mediated discovery of molecular markers for detection, stratification and outcomes in pancreatic cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 786.