This week's MSU Today featured an article on Pediatrics and Human Development Assistant Professor Bin Chen, PhD. Dr. Chen uses big data to aid the discovery of new and improved cancer therapeutics. In a recent study published in Nature Communications, Dr. Chen and collaborators pored over mountains of genomic data to determine better breast cancer research models.
“The differences between cell lines and tumor samples have raised the critical question to what extent cell lines can capture the makeup of tumors,” said Bin Chen, senior author and assistant professor in the College of Human Medicine.
To answer this question, Chen and Ke Liu, first author of the study and a postdoctoral scholar, performed an integrative analysis of data taken from genomic databases including The Cancer Genome Atlas, Cancer Cell Line Encyclopedia, Gene Expression Omnibus and the database of Genotypes and Phenotypes.
“Leveraging open genomic data to discover new cancer therapies is our ultimate goal,” said Chen, who is part of MSU’s Global Impact Initiative. “But before we begin to pour a significant amount of money into expensive experiments, we need to evaluate early research models and choose the appropriate one for drug testing based on genomic features.”
By using this data, the researchers found substantial differences between lab-created breast cancer cell lines and actual advanced, or metastatic, breast cancer tumor samples. Surprisingly, MDA-MB-231, a cancer cell line used in nearly all metastatic breast cancer research, showed little genomic similarities to patient tumor samples.
“I couldn’t believe the result,” Chen said. “All evidence pointed to large differences between the two. But, on the flip side, we were able to identify other cell lines that closely resembled the tumors and could be considered, along with other criteria, as better options for this research.”
Read the complete article here.