Projects
Dr. Wang's pharmacogenomics (PGx) research projects are focused on several areas.
Biomarkers
Efforts to identify response biomarkers through the application of multiple omics including genomics, transcriptomics, epigenomics, metabolomics and increasingly proteomics, followed by data analysis and functional characterization of these biomarkers using cell lines and animal models are ongoing.
Model systems
Developing various model systems for pharmacogenomics studies, including cell lines and animal models continues to be a strong focus.
- PGx cell line system. Dr. Wang's lab has developed 300 human lymphoblastoid cell lines representing 100 individuals from three ethnic groups that can be used as an in vitro system to study common germline genetic variation and their role in drug response. This system is also critical to help determine functions and mechanisms underlying genetic variation in response to various anti-neoplastic agents.
- Patient-derived xenograft models. Dr. Wang's lab has developed a series of patient-derived xenografts from breast cancer and prostate patients, all of which have been characterized extensively and have been applied in various studies to help screen drugs and understand mechanisms underlying treatment resistance. These models were developed from prospective trials conducted at Mayo.
Mechanist studies: Signaling pathways
The basic mechanisms of resistance and variation in drug response with a focus on specific signaling pathways involved in cancer development, metastasis and cancer metabolism are studied. For examples, we have identified resistance mechanisms to standard chemotherapy or targeted therapies that are involved in regulation of PI3K-mTOR pathway and AMPK pathways. By understanding these mechanisms, we can develop additional therapeutic strategies to help overcome resistance to standard therapies.
Data analysis tools
Extended collaborations have been established with computer scientists and bioinformaticians internally and externally (University of Illinois Urbana-Champaign) to help develop tools for data analysis. These activities also help to analyze big data and derive meaningful biological hypotheses for further testing in the lab.