Research

Research

Why do we study Renal Cell Carcinoma?

The major subtypes of Renal Cell Carcinomas (RCCs) include clear cell RCC (ccRCC), papillary RCC (pRCC) and chromophobe RCC (chRCC). RCCs, especially ccRCCs, are marked by elevated inflammation and tumor immunogenecity, making RCC an ideal disease to study their relationship with cancer cells, with the potential for results to translate to other cancer types.

Heterogeneity at the intra-tumor level

Heterogeneity at the intra-tumor level

My research is mainly focused on profiling the immune cell infiltrates and stromal pathway activations in tumor stroma and understanding their interactions with the tumor cells and implication for therapeutic responsiveness. My previous work creatively leveraged tumorgrafts (PDX models) as a reference of tumor cells, so that we were able to accurately dissect the molecular and cellular patterns in the tumor stroma using a Bayesian Hierarchical Model (DisHet). We discovered a pan-RCC subgroup of patients with worse prognosis, high inflammation, and elevated immune cell infiltration levels.

Heterogeneity at the inter-tumor level

Heterogeneity at the inter-tumor level

My research focuses on how heterogeneous cancers can be better categorized according to their molecular abnormalities. For example, using integrative genomics and pathological imaging analyses, I have shown that HLRCC, a newly defined and rare but extremely aggressive RCC subtype with germline FH deficiencies, can be grouped together with other RCCs with somatic FH mutations, as well as with some RCCs lacking FH mutations. This novel subtype of RCCs (FHD and FHD-like) has similar pathway alterations and may be treated in a similar way.

My other research interests

I created the LinkageAnalyzer software for statistical mapping of phenotype-genotype in mouse forward genetic screening data. LinkageAnalyzer is being run daily for the Mutagenetix consortium led by Dr. Bruce Beutler, which is a large-scale screening project focused on identifying immune-response genes from ENU-mutated mice. From the huge amount of data (more than 39,000 mutations X more than 40 phenotypes) collected in this project, I am furthering my study to characterize the differential damaging effects of various types of missense and loss of function mutations.

I successfully developed many machine learning models to solve real-life biomedical questions. For example, I co-led a team to win the highly competitive NIEHS-NCATSUNC DREAM Toxicogenetics Challenge, an international competition for the estimation of drug treatment effects using genomic and chemical data (published in Nat. Biotech). I also participated in and won several other DREAM challenges. Recently, I co-organized the Prostate Cancer DREAM Challenge that aimed at predicting the prognosis of prostate cancer patients using commonly available clinical variables (published in Lancet Oncology).