PROJECT SUMMARY
Quantitatively predicting drug-resistant mutations to improve precision oncology
My work builds towards a mechanistically informed approach to model and predict drug-resistant kinase
mutations that will enhance patient treatment regimens. Protein kinases are important signaling enzymes
often dysregulated in cancer; their pharmacological value as drug targets exemplified by the clinical use of over
75 FDA-approved inhibitors. Unfortunately, multiple clinically observed kinase mutant resist inhibitors and
drastically reduce patient survival rates. Precision oncology approaches, matching specific tumor profiles to
optimally therapies, have proven useful thanks to tumor sequencing and mutation profiling. However, it remains
challenging to identify drug-resistant mutants prior to treatment and develop regimens to circumvent them.
A lack of mechanistic information describing clinically observed kinase mutants makes it difficult to predict
whether a mutation will resist canonically used kinase inhibitors. Kinase mutations may decrease drug-binding
affinity, increase kinase activity, tune inhibitor sensitivity profiles, or any combination of these mechanisms.
Structure-based methods promise to help predict the impact of kinase mutations. I hypothesize that a kinase
inhibitor's utility against drug-resistant mutants is expressed using physical, quantitative properties like
structural state populations and binding affinities.
My work quantitatively assesses the impact of clinical kinase mutations on inhibitor resistance, sensitivity, and
susceptibility. Specifically, I will develop models that predict whether clinical kinase mutations perturb
inhibitor-binding, increase kinase activity by stabilizing active configurations, or sensitize kinases to
alternative inhibitors. In this proposal, I draw upon clinical mutation databases to study mutation-inhibitor pairs
of c-Met kinase, the target in Non-Small-Cell lung cancers (NSCLCs), building upon previous studies of
resistance mutations in Abl kinase. As a mentee (K99), I will use binding free-energy calculations to predict how
clinical mutations reduce c-Met inhibitor affinity (Aim 1). As I transition to independence (K99/R00), I will use
molecular simulations to biophysically evaluate whether clinical mutations increase kinase activity by shifting
kinase populations to catalytically active conformations (Aim 2). Upon independence (R00), I will study whether
clinical mutations sensitize kinases to rarely used alternative inhibitors (Aim 3). These computationally intensive
calculations can often take years to collect sufficient data on a normal computer. Instead, I will run calculations
on the planetary-scale Folding@home distributed computing platform in collaboration with high-throughput
biophysical experiments that measure kinase activity and inhibitor binding affinity. Overall, my proposal, and
future lab, will build towards a precision-oncology platform that helps clinicians plan treatment regimens.