Tumor heterogeneity is the observation that cancer cells can show distinct differences from patient to patient, from primary to secondary tumors, or even between cells within the same tumor. This phenomenon is a major barrier to effective cancer interventions. A better understanding of tumor heterogeneity is critical for improving cancer treatment. Using cutting-edge technology in single-cell and spatial omics assays, my research program will develop novel approaches to understanding tumor heterogeneity in the context of tumor initiation and evolution. Our long-term goal is to improve early detection and therapeutics for liver cancer.

1) Tumor heterogeneity.  Tumor cell biodiversity is a common feature in liver cancer. However, what determines the observed different degrees of cellular diversity among tumors is not known. One possible mechanism is that distinct cells within each tumor serve as cells of origin in tumorigenesis among patients. Thus, we are interested in understanding the tumor cells of origin. In addition, the tumor microenvironment (TME) has been recognized as a key player in promoting tumor heterogeneity. Within a tumor ecosystem, tumor cells continuously communicate with the TME through cell adhesion, signaling molecules and small vesicles. Our ability to dissect the communication networks connecting the tumor with the TME would allow a better understanding of tumor heterogeneity. Moreover, cellular functions are closely related to the cell’s location within the tumor. Deciphering the spatial information of the tumor and stromal/immune cells would provide a critical framework in which to understand their communications.

2) Tumor evolution in response to treatment.  Tumor evolution is a key feature of tumorigenesis and plays a pivotal role in driving intratumor heterogeneity, treatment failure, escape from immune surveillance and ultimately, patient prognosis. We have demonstrated that a tumor’s evolutionary trajectory may change over time in response to immunotherapy based on single-cell profiling of core needle tumor biopsies from liver cancer patients before and after treatment. It is known that only ~20% of liver cancer patients respond to immunotherapy, and it is unclear why some patients respond to immunotherapy while others do not. To improve liver cancer treatment, we use longitudinal clinical tumor specimens and animal models to identify molecular features that are responsible for tumor evolution.