Research Interest

Our laboratory utilizes experimental and computational approaches to systematically advance our understanding of biology and diseases. We are interested in

1. Molecular mechanisms of childhood cancers

Cancer remains one of the major causes of death in children. Many significant advance in adult cancer drug development cannot be directly applied to pediatric tumors due to their distinct molecular mechanisms. We would like to further understand disease cell of origin and heterogeneity of childhood cancers. We are also interested in identifying new drug targets using functional genomics in relevant childhood cancer models.

2. Mechanisms of cancer drug response and resistance

Immunotherapy and targeted therapy have significantly changed many patients’ lives. However, only one third cancer patients benefit from PD-1 blockade. Resistance to cancer targeted therapy frequently happens. We would like to use molecular and computational biology approaches to further understand cancer drug response and resistance. We are interested in elucidating tumor microenvironment and metabolic changes during drug resistance development.

3. Drug target identification

Drug target identification and indication selection remain challenging in drug discovery. We are interested in identifying new drug target and its predictive biomarkers using functional genomics and computational approaches.

4. Technology development

We are interested in developing genomics technologies and bioinformatics tools in

· Functional genomics: library design, combinatorial screens, computational tools

· Single cell multi-omics: spatial transcriptomics, integrative analysis tools

本实验室运用基因组学和计算生物学的方法系统性地研究生命过程和疾病的分子机制。我们感兴趣：

1. 儿童肿瘤的分子机制

癌症仍然是儿童死亡的主要原因之一。由于其独特的分子机制，成人癌症药物开发中的许多重大进展无法直接应用于儿童肿瘤。我们想进一步了解儿童癌症的细胞起源和异质性。我们会建立相关的儿童癌症模型，并运用功能基因组学和计算生物学寻找新的药物靶点。

2. 癌症药物作用机理和抗性

免疫疗法和靶向疗法显着改变了许多患者的生活。但只有三分之一的癌症患者受益于PD-1抑制剂。对癌症靶向治疗的抗性经常发生。我们希望运用生物信息学和分子生物学的手段进一步从肿瘤微环境和代谢变化的角度理解癌症药物的作用机理和抗性的产生，从而更好的控制癌症药物的抗性和副作用。

3. 寻找新药靶点

在药物发现中，药物靶标的识别和适应症的选择是非常具有挑战性的。我们运用功能基因组学和计算生物学的方法寻找新药靶点及其预测生物标志物。

4. 新技术开发

我们希望开发创新型基因组学技术和生物信息学分析方法，包括但不限于

· 功能基因组学：新型建库设计，组合筛选，分析计算工具

· 单细胞多组学：空间转录组学，综合分析工具