Cells need nutrition and energy to growth and divide. Cancer cells have the same needs as normal cells, but use different metabolic programs to proliferate in an uncontrolled manner, which therefore provide vulnerabilities that can serve as therapeutic targets. However, a bottleneck for targeting cancer metabolism is the current lack of precisely kill cancer cells but avoid damage to normal cells.
Tumor is a complicated system which is consists of different cullular (e.g. diverse non-tumor cells) and non-cellular components (e.g. metabolic waste) in and around tumor. Various regulation of gene transcription, translation, and metabolism also occurs in different types of cells. Thus, studying the metabolic behavior of cells at the specific genetic and environmental conditions, and understanding how the metabolism is controlled can help to break through that bottleneck of precisely targeting cancer cells. To achieve these goals, we leverage high-throughput sequencing technologies and bioinformatic approaches to investigate many different aspects of cancer metabolism and the tumor microenvironment. Our long-term goal is to design more effective therapeutic strategies that can prevent cancer metastasis while strength antineoplasmic activity of immune cells infiltrating in tumors.
Our group include both computational and experimental platforms. The computational studies includes: (1) biomedical data mining (2) studying dynamic genetic and metabolic regulations at different levels and scales. Recently, we are focusing on applying single cell sequencing technologies to study TME and developping information entropy-based models to elucidate the dynamics of regulatory networks driving metabolic reprogramming and immune parameters in the tumor microenviroment. Our experimental work includes: single-cell RNA sequencing, spatial transcriptomics and metabolomics, cell culture, and tumor mouse modelling.