Citations

Abstract Radiation therapy (RT) has long been included in the treatment of glioblastoma (GBM). However, radioresistance in cancer cells as well as toxicity in normal tissues are major obstacles to clinical efficacy. Improved understanding of the mechanisms of tumor microenvironment-induced radioresistance during and after radiation therapy can provide fundamental insights to improve clinical outcomes in GBM. Here, using three-dimensional engineered hydrogel models in vitro, we report the influence of extracellular matrix, hypoxia, and adjacent neuronal cells in radiotherapeutic sensitivity. We find that mechanical cues and oxygen availability regulate cellular response to radiation, with softer matrices allowing for more DNA damage. Hyaluronan fragments from the extracellular matrix also modulate rapid metabolic response to radiation, especially in hypoxic environments. We show that neuronal networks influence tumor metabolic activity and the inflammatory response. Overall, we demonstrate here that alternative radiation strategies, such as low dose rate radiation therapy and microenvironmental regulation, have the potential to be more effective in a specific subset of radiosensitive GBM tumors.

Abstract Despite extensive advances in cancer research, glioblastoma (GBM) still remains a very locally invasive and thus challenging tumor to treat, with a poor median survival. Tumor cells remodel their microenvironment and utilize extracellular matrix to promote invasion and therapeutic resistance. It is aimed here to determine how GBM cells exploit hyaluronan (HA) to maintain proliferation using ligand‐receptor dependent and ligand‐receptor independent signaling. Tissue engineering approaches are used to recreate the 3D tumor microenvironment in vitro, then analyze shifts in metabolism, hyaluronan secretion, HA molecular weight distribution, as well as hyaluronan synthetic enzymes (HAS) and hyaluronidases (HYAL) activity in an array of patient‐derived xenograft GBM cells. It is revealed that endogenous HA plays a role in mitochondrial respiration and cell proliferation in a tumor subtype‐dependent manner. A tumor‐specific combination treatment of HYAL and HAS inhibitors is proposed to disrupt the HA stabilizing role in GBM cells. Taken together, these data shed light on the dual metabolic and ligand – dependent signaling roles of hyaluronan in glioblastoma.

Abstract Purpose: Endocrine based therapy is an effective strategy to manage hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+/HER2-) advanced breast cancer (ABC). However, nearly all patients exhibit/develop either de novo or acquired resistance. While prognostic biomarkers of endocrine responsiveness are well established for the adjuvant treatment in ER+ breast cancer, less is known regarding prognostic and predictive biomarkers of response in the first line ABC setting. We sought to develop a clinical calculator based on clinical criteria for predicting progression-free survival (PFS) and overall survival (OS) of women with HR+/HER2- ABC who will be receiving endocrine monotherapy as first-line treatment for ABC. Methods: The development of the clinical calculator will be based on data from modern clinical trials in women with HR+/HER2- ABC. The studies to be included in the final analyses are given in Table 1. The control arm data from trials1-6 will form the training dataset (N = 1,223) and be used to construct the clinical prediction models. Variables considered include age, race, ECOG status, disease measurability, body mass index, disease-free interval, number of metastatic sites, locations of metastatic sites, prior endocrine therapy, and prior chemotherapy. Missing values will be imputed using single imputation with all variables included in the imputation model. For continuous variables, restricted cubic splines will be used to determine if non-linear effects may be more appropriate. The Lasso regression will be used as a variable selection technique to reduce the dimensionality of covariates; initially all pairwise interactions will be included in the model. Following Lasso regression, the multivariable Cox proportional hazards models will be constructed for PFS and OS including only variables retained in Lasso. The final model will be internally validated for discrimination and calibration using 10-fold cross-validation. External validation will be performed using control arm data from EGF 30008 (N = 536). Results: To date, control arm data from four trials (trials 1-4) have been received. The preliminary results presented here are based on pooled data from C40503 and LEA, for which data elements have been harmonized. Models for predicting PFS and OS have good calibration and are associated with bias-corrected C-indices of 0.61 and 0.65, respectively. These models will be updated using pooled data from trials 1-6. Conclusions: Our preliminary data demonstrate that clinical calculators based on baseline clinical factors can provide accurate prediction of PFS and OS in patients with HR+/HER2- ABC treated with first-line ET. If validated, these tools may be used for risk stratification in future clinical trials and to identify patients who may require more or less aggressive therapy. Table 1:Studies to be includedTrial NumberTrial NameTrial PISample Size in Control Arm1C40503Maura Dickler152 (letrozole)2LEAMiguel Martin179 (letrozole)3FACTJonas Bergh188 (anastrozole)4FALCONJohn Robertson194 (anastrozole)5S0226Rita Mehta345 (anastrozole)6MONARCH 3Matthew Goetz165 (nonsteroidal AI)7EGF 30008Stephen Johnston536 (letrozole) Citation Format: Polley M-YC, Dickler MN, Johnston S, Goetz MP, de la Haba J, Loibl S, Mehta RS, Bergh J, Roberston J, Barlow W, Liu H, Tenner K, Martin M. A clinical calculator to predict disease outcomes in women with hormone receptor-positive advanced stage breast cancer treated with first-line endocrine therapy [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P2-07-05.

Abstract 3D biomaterial models have potential to explore the influence of the tumor microenvironment on aberrant signaling pathways and compensatory signals using patient‐derived cells. Glioblastoma (GBM) tumors are highly heterogeneous, with both cell composition and extracellular matrix biophysical factors seen as key regulators of malignant phenotype and treatment outcomes. Amplification, overexpression, and mutation of the epidermal growth factor receptor (EGFR) tyrosine kinase have been identified in 50% of GBM patients. Here, hyaluronic acid (HA) decorated methacrylamide‐functionalized gelatin (GelMA) hydrogels are used to examine the synergies between microenvironmental factors and a model EGFR tyrosine kinase inhibitor (TKI) using patient‐derived xenograft cells. The in vitro behavior of 3 patient‐derived xenografts that reflect a clinically relevant range of EGFR variants is characterized: GBM10 (EGFR, wild type), GBM12 (EGFR+), and GBM6 (EGFRvIII). GelMA hydrogels support xenograft culture; cells remain viable, active, respond to matrix‐immobilized HA, and upregulate genes associated with matrix remodeling and tumor growth. Interestingly, matrix‐immobilized HA alters the response of GBM cells to a model tyrosine kinase inhibitor, erlotinib. While constitutively activated EGFRvIII cells are sensitive to TKI in gelatin hydrogels, hyaluronic acid mediated adhesive signaling interacts with EGFRvIII signaling to increase cell metabolic activity, increase soluble hyaluronic acid synthesis, and modify response to erlotinib exposure.