Understanding the heterogeneous response of cancer cells to chemotherapy treatment
Isogenic populations of cells often show very different responses to identical perturbations. For cancer cells this can manifest in a fractional response to chemotherapy treatment. We use quantitative fluorescence microscopy to study the chemotherapy response in single cancer cells. The goal is to understand at the single cell level why some cells live while others die in response to treatment. We hope to use this information to devise novel strategies to force cancer cells to enact terminal cell fate programs.
The dynamics of key signaling pathways in response to chemotherapy treatment
The cellular response to chemotherapy treatment is often enacted by signaling hubs. These are signaling proteins that respond to multiple upstream pathways and integrate this information in order to decide between different cell fates. Single-cell studies have shown that the dynamics of these proteins (how their abundance or location changes over time) can encode information and dictate cell fate. We follow the dynamics of signaling proteins in response to chemotherapy in order to determine what patterns are associated with terminal cell fates. Dynamic patterns can reveal the architecture of signaling networks and point to potential targets to control these patterns. We leverage this information to devise strategies to push cancer cells to terminal cell fates by manipulating the dynamics of signaling proteins.
How the state of cancer cells prior to chemotherapy treatment predisposes cells to a particular cell fate
Cancer cells that divide before chemotherapy treatment often have similar responses to treatment. This suggests that the state of these cells prior to drug treatment predisposes a cell to either death or survival in response to chemotherapy. We are exploring how the correlation in fate between related cells changes with different drugs and how this affects the overall drug response in populations of cells. In addition we are using microfluidics and single-cell RNA sequencing to determine what cellular factors predispose cancer cells to a particular cell fate.