Ph.D., University of Toronto
Postdoctoral Fellowship, Massachusetts General Hospital
The central goal of my lab is to understand how inflammatory microenvironments affect epithelial cell behaviors in the context of human diseases, specifically, the role of the stem cell niche in colorectal cancer. Aberrant "stem cell-like" behavior is now recognized to contribute to the complexity of human diseases, for example, to therapeutic resistance and metastasis after conventional cancer therapies, which accounts for most of cancer-related fatalities. Devising ways to control these aberrant behaviors may lead to effective therapeutic strategies to combat these complex diseases. Why can't we simply eliminate these aberrant cells? Recent research shows that these aberrant cell populations are plastic. That is, differentiated cells can dynamically convert into stem-like cells and vice versa depending on environmental cues. Thus, instead of targeting a static cell type, the approach to manage these aberrant stem-like cells may be to control the environment where these cells are maintained.
In live tissues, cells must integrate dynamic mixtures of environmental cues through their signaling networks to arrive at response decisions. To understand the multivariate problem of how the microenvironment interacts with cells, we use multiplex and high throughput experimental approaches to characterize the network states (numbers and types of cells, secreted protein factors, intracellular signaling, and expressed genes, over time) within in vivo tissue. Our model system is the intestine of the laboratory mouse, whose state is controlled by interactions between the epithelium, immune system, and microbiota very much like in human. We then use the collected datasets over different experimental conditions to build data-driven mathematical models to quantitatively describe environment-cell input/output relationships and how these relationships are integrated to derive cellular outcomes. Because cell populations in in vivo tissues are not homogeneous, we will focus on generating data from single cells using techniques derived from flow cytometry and microscopy.
I received my training at Massachusetts General Hospital and MIT under joint supervision of Dr. Kevin Haigis (a mouse geneticist) and Dr. Douglas Lauffenburger (a bioengineer). There, I performed pioneering work on combining system-level signaling analyses and mouse models of intestinal diseases. I then further expanded these studies into looking at immune-epithelial cell interaction, and global signaling changes in mice with oncogenic mutations in the Ras proteins. Overall, I found that multivariate analyses are necessary for predicting signal-response relationships, whereas exploring single pathways one at a time is not predictive in vivo. I aim to foster a highly interdisciplinary and collaborative atmosphere in my lab, with a key focus on learning principles that will be translatable to human patients.