The Yang Lab generally interested in investigating the fundamental principles of tumor evolution and identifying key regulators of tumor progression. Our research is at the interface of cancer biology, technology development, and computational analysis, which combines CRISPR-based molecular recording tools, genetically engineered mouse models (GEMMs), single cell genomics-related algorithm development, and in vivo functional assays.
The main goal of the Yang Lab is to investigate the intrinsic and extrinsic mechanisms governing cancer cell state transitions and ultimately develop a quantitative roadmap of tumor evolution. Towards this goal, we have recently developed an autochthonous “KP-Tracer” mouse model, which allows us to continuously monitor the processes by which a single cell harboring oncogenic mutations evolves into an aggressive tumor. In collaboration with Nir Yosef's lab, we have developed a new computational algorithm "Cassiopeia" for building high-resolution tumor phylogenies. This offers a significant advance in tumor evolution modeling by enabling quantitative inference of fitness landscapes, cellular plasticity, evolutionary paths of primary tumors and metastases, and the role of any gene of interest in altering all these facets of tumor development.
Building upon these cutting-edge technologies, our lab aims to develop a comprehensive understanding of tumor evolution by integrating multiple data modalities. We will dissect the cell-intrinsic and extrinsic mechanisms that govern cancer cell state transitions, and identify the regulatory gene networks that drive cancer evolution. Furthermore, our lab is committed to expanding the molecular recording toolkits, enabling more precise and detailed reconstruction of tumor life histories. Through these collaborative efforts, we strive to reconstruct the entire trajectory of tumor development, capturing the process from a single transformed cell to a complex and aggressive tumor population, with unprecedented scale and resolution. Ultimately, this holistic approach will pave the way for the development of predictive models of tumor evolution, advancing our ability to predict and potentially intervene in cancer progression.