Characterization of drug mechanisms of lethality in vivo

PROJECT SUMMARY/ABSTRACT Understanding mechanisms of drug action is important for identifying settings in which a drug will be effective, and for interpreting – even predicting – potential mechanisms of drug resistance. For anti-cancer drugs, a key aspect of their mechanism of action is the mechanism by which these drugs promote cell death. The first

identified death pathway was apoptosis, a form of death that is now reasonably understood. In addition, it has long been known that cells can also die in non-apoptotic ways. The conventional wisdom had been that these necrotic forms of death are not regulated; that is, that they do not result from activation of defined signaling

cascades or use of specific effector molecules. It is now clear that this traditional thinking is incorrect. Several distinct forms of regulated necrosis have now been identified. As the definition of regulated cell death has expanded, so has our understanding that anti-cancer drugs generally have the capacity to activate multiple

types of cell death. The exact composition of death types, and how/why these vary across contexts remains challenging to study due to the lack of tools for simultaneously evaluating all mechanisms of cell death from a single sample. To address these issues, we propose to develop a targeted sgRNA library that can be used to

infer which type of death is activated in vivo. This proposal focuses on the development, optimization, and failure testing of this new tool, to establish the utility and resolution limits of this method. Our tool will be evaluated in the context of drugs that inhibit histone deacetylases (HDACs), which we have previously found to

induce varied forms of cell death across doses and environments. The methods developed in this proposal have the potential to be transformative by providing the first insights into the mechanisms of regulation for many understudied types of necrotic death and by enabling new studies that explore how these mechanisms

vary across dose, time, and disease contexts.