Presenting Author Elon University Hull, Massachusetts
Acute Myeloid Leukemia (AML) is a malignant disease where there is a high rate of chemoresistance, or drug resistance, in patients which is a leading cause of treatment failure. Current clinical treatments of AML include Cabazitaxel and Gemcidibine, which affect DNA replication in two different ways. A potential strategy to overcome chemoresistance is to treat resistant-AML cells with combinations of drugs that have different therapeutic targets. Since BCL-2 proteins, which control apoptosis, are often upregulated in AML, we hypothesize that BCL-2 inhibitors such as Venetoclax are candidate drugs to be considered for use in treating Cabazitaxel-resistant and/or Gemcitdibine-resistant AML. Moreover, cancer cells are also known to have dependence on glucose metabolism and therefore glucose inhibitors, such as WZB117, are also candidate drugs to overcome chemoresistance in AML. Therefore, the overarching goal of this project is to investigate the potential synergetic effects of a WZB117 and Venetoclax on chemo-resistant AML cells Using the AML cell line HL60, baseline sensitivity to Cabazitaxel and Gemcidibine have been determined using GI50 assays. The GI50 values obtained were 1.35uM for Cabazitaxel and 0.94uM for Gemcidibine. Currently, resistant cells are being developed by treating HL60 cells with the GI10 values for each drug until their normal growth rate returns, at which time new GI50 values will be obtained and the resistant cells dosed again to perpetuate resistance. Baseline sensitivity of HL60 cells to WZB117 and Venetoclax is also ongoing. Once cells reach 5-fold resistance for each drug, the effectiveness of WZB117 alone, Venetoclax alone, and both drugs in combination will be assessed to determine if these drugs are a viable option for inducing apoptosis in Cabazitaxel-resistant and/or Gemcitabine-resistant AML cells.
to Elon University office of Undergraduate Research, Elon University Pre-Health Scholars Program, FRamp;amp;D (VDGM), and NSF Grant 1229562 for funding this research
