Projects
Dr. Wang's pharmacogenomics research projects focus on several areas.
Biomarkers
Efforts are ongoing to identify response biomarkers by applying multiple omics. This includes genomics, transcriptomics, epigenomics, metabolomics and increasingly proteomics, followed by data analysis and functional characterization of these biomarkers using cell lines and animal models.
Model systems
Developing various model systems for pharmacogenomics studies, including cell lines and animal models, continues to be a strong focus.
- Pharmacogenomics cell line system. Dr. Wang's lab has developed 300 human lymphoblastoid cell lines representing 100 people from three ethnic groups. These cell lines can be used as an in vitro system to study common germline genetic variation and their role in drug response. This cell line system also is critical to help determine functions and mechanisms underlying genetic variation in response to various antineoplastic agents. We also have collected the most publicly available breast and prostate cancer cell lines for in vitro studies.
- Patient-derived xenograft models. Dr. Wang's lab has developed a series of patient-derived xenografts from patients with breast cancer and patients with prostate cancer. These xenografts have been characterized extensively, and they have been applied in various studies to help screen drugs and understand mechanisms underlying treatment resistance. These xenografts were developed from prospective trials conducted at Mayo Clinic.
Mechanist studies: Signaling pathways
The basic mechanisms of resistance and variation in drug response, with a focus on signaling pathways involved in cancer development, metastasis and cancer metabolism, are studied. For example, we have identified resistance mechanisms to standard chemotherapy or targeted therapies that are involved in regulating cell proliferation pathways and transcription regulation. We also identified a new function of the PD-L1 molecule to design therapies that might enhance the efficacy of immunotherapy. 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 to help develop tools for data analysis. These activities also help analyze big data and derive meaningful biological hypotheses for further testing in the lab.