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Computational Cancer Immunology

The human body is able to produce 10^16 types of different ⍺β T cells, with each T cell possessing a unique set of the hypervariable receptors. The collection of T cell clonotypes is referred to as the T cell repertoire. The extremely diverse T cell repertoire is responsible for the recognition and elimination of a vast pool of external and internal pathogenic antigens, such as virus, bacteria and cancer antigens. We are interested in learning the connection between individual's immune repertoire and the antigenic landscape. An ongoing research direction in my lab is to use bioinformatics and genomics approaches to study the interaction between tumor-infiltrating T cell repertoire and the malignant cells. We are developing a novel algorithm that predicts closely related CDR3 groups based on protein sequence structures, to study the immunogenomics data and understand the antigenic landscape of human perihperal T cell repertoire.

Cancer Genomics

The Darwinian process during cancer evolution produces a heterogeneous pool of malignant cells with a distinct landscape of somatic changes in the genome. Cancer cells are under constant selective pressures, including immune cell attack, drug cytotoxicity, lack of nutrition, etc. The “winning” clone almost invariably overcomes environmental disadvantages and grows into an end-stage tumor. We are interested in understanding the process of co-evolution of tumor and immune cells during cancer development, which can be tracked from clonal expansion events, together with components of the tumor microenvironment and infiltrating immune repertoire. One frequent consequence of this co-evolution is the avoidance of almost all immune surveillance, including cytotoxicity mediated by CD8+ T cells or auto-antibodies. We will continue to investigate somatic changes involved in different immune evasive pathways to identify novel targets for cancer immunotherapies.


Immune checkpoint blockage (ICB) therapies have achieved remarkable clinical success in the treatment of multiple end-stage tumors, including melanoma, bladder cancer, Hodgkin lymphoma, non-small cell lung carcinoma, and kidney, head and neck cancers. However, for cancer patients and their families, accepting ICB treatment remains a difficult decision, due to prohibitive costs, undesirable side effects and low response rates. Therefore, it is critical to develop predictive biomarkers for ICB therapies to better inform clinical decision making. We are currently collaborating with Dr. Yang-Xin Fu to study the immune contexture of cancer microenvironment using animal models, with a focus on the identification of novel prognostic biomarkers to guide ICB therapies.

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