Research projects in progress + future anticipated research projects:
Multi-Scale Analysis of Drug Resistance
The genesis of drug resistance is ultimately a problem of evolution, and evolutionary dynamics models of drug resistance have shown that in many cases, when large populations precede therapy, most drug resistance mutations tend to pre-occur6,10–16. However, most evolutionary dynamics models begin and end with a single abstract “resistant” species and ignore the molecular complexity of resistance evolution. In parallel selections, the same drug can select for many different resistant residues in a target protein. For instance, in multiple diseases the most drug resistant variant is not uniformly identified in most drug resistant patients, Why? Why can parallel evolution repeatedly favor amino acid substitutions that provide a relatively low level of drug resistance1,2,17? How do epistatic interactions within a protein govern the residues that are accessible to the evolution of drug resistance? Drug selections are an exciting opportunity to ask very basic questions about when and why evolution arrives at an observed solution.
Dual-Switch Selection Gene Drives to Combat Drug Resistance
Despite our best efforts, drug resistance evolution–from prokaryotes to cancers– is one of the largest threats to public health. We propose that a novel paradigm to understand and fight drug resistance evolution is to use forward engineering design. Instead of simply describing the outcome of resistance evolution after it has occurred, we will use model-driven design to build dual-switch selection gene drives, and we will test their ability to control resistant populations which will enable a deeper understanding of biology and a shift in clinical paradigms.
Data Mining to Understand Intrinsic and Acquired Drug Resistance to Immunotherapy
Immunotherapy is changing the landscape of lung cancer treatment by achieving durable treatment-free remissions in some patients1–4. But, single agent checkpoint inhibitor therapy fails to achieve objective responses in the majority of unstratified frontline Non-Small-Cell-Lung Cancer (NSCLC) patients2. We need to rapidly discover how to expand responses to more patients and extend the responses that we achieve. Achieving these goals requires a deeper understanding of the biology underlying intrinsic and acquired resistance to checkpoint inhibitors.