Few kinase mutations have been found in pancreatic cancer despite whole exome sequencing studies. Therefore rationally devising novel kinase inhibitor combination therapies requires knowledge of kinome activity, not simply measuring the effect of an inhibitor on one or a few kinases in a pathway. In addition, it is clear that targetin combinations of kinases will be more efficacious than focusing on any single kinase or pathway due to inevitable resistance development. Acquired or selected mutations can decrease affinity for therapeutic kinase inhibitors, but resistance also develops by alternate kinase activation, bypassing the action of a highly specific inhibitor. We have developed an innovative technology to assess the baseline activation state of the kinome and the dynamic changes in kinome activity following targeted inhibition of specific kinases. In this proposal we use this technology to better understand the adaptive kinome response to novel agents that target signaling pathways. As most clinical trials will move forward with a novel agent in combination with a cytotoxic agent, we will also determine the adaptive kinome in response to current cytotoxic agents. We will use a combination of preclinical models including genetically engineered models, patient-derived xenografts and a pilot clinical trial to accomplish this. Thus we will establish a vast knowledgebase of the adaptive kinome that will allow us to determine rational combinations of cytotoxic drugs with novel agents. We will also develop computational methods to handle this large data in order to predict effective combinations to move forward into clinical trial.
This proposal seeks to develop rational combinations of cytotoxic drugs with novel agents and develop and apply computational methods for predicting these combinations through a systems-based approach of understanding the kinome.
| Lipner, Matthew B; Marayati, Raoud; Deng, Yangmei et al. (2016) Metformin Treatment Does Not Inhibit Growth of Pancreatic Cancer Patient-Derived Xenografts. PLoS One 11:e0147113 |
