Posted on March 09, 2018
In March 2018, Wenyi Wang, Ph.D., Associate Professor in the Department of Bioinformatics and Computational Biology at the University of Texas MD Anderson Cancer Center, visited DCEG to present a seminar and meet with staff. Dr. Wang, an expert in statistical bioinformatics, develops methods and software for measuring high-throughput genomic data and for personalized cancer risk prediction.
Dr. Wang’s seminar, titled, “Statistical methods for the deconvolution of high-throughput sequencing data from heterogeneous tumor samples,” outlined her team’s efforts to tackle a particularly thorny problem in tumor sample data analysis. When a sample of tissue is taken from a tumor, there are typically multiple types of cells present; for example, a breast cancer sample may contain fat cells and healthy breast cells in addition to the cancerous cells. These extra cell types can mask signals in data about the tumor, such as the gene expression profile. In order to get a clear picture of the gene expression of a particular tumor, it is important to understand the proportion of these various cell types present in the sample. Currently, this can be done using a technique called laser-capture microdissection, but it is expensive and time-consuming. Dr. Wang is developing time- and cost-saving statistical tools that allow scientists to estimate the proportions of different cells—or RNA and DNA from those cells—present in a tumor sample.
Validation with a variety of data sets is important to ensure the utility of such tools. Dr. Wang has worked with data from several studies and cancer types, ranging from manufactured cell lines with known proportions—the simplest validation method—to messier and more complex data sets from tumor samples. Her research has already shown the software can be used to improve signals when investigating associations between gene expressions and survival outcomes. Dr. Wang emphasized the need to develop tools that handle different cancer types individually, due to wide variation in the number and types of mutations involved.
Over the course of her visit, Dr. Wang met with investigators to discuss collaborations and research tools. She also discussed her research and gave career advice to fellows during a brown bag lunch.
Sharon Savage, M.D., Chief of the Clinical Genetics Branch and a longtime collaborator of Dr. Wang’s, hosted the visit. The two have worked together on projects related to predicting mutations and cancers in Li-Fraumeni syndrome, a rare inherited cancer syndrome.