Researchers are analyzing large volumes of data, often referred to as big data, to determine better research models to fight the spread of breast cancer and test potential drugs. Current models used in the lab frequently involve culturing cells on flat dishes, or cell lines, to model tumor growth in patients.

In a new study, Michigan State University researchers are analyzing large volumes of data, often referred to as big data, to determine better research models to fight the spread of breast cancer and test potential drugs. Current models used in the lab frequently involve culturing cells on flat dishes, or cell lines, to model tumor growth in patients.

The study is published in Nature Communications.

This spreading, or metastasis, is the most common cause of cancer-related death, with around 90% of patients not surviving. To date, few drugs can treat cancer metastasis and knowing which step could go wrong in the drug discovery process can be a shot in the dark.

«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.

Story Source: Materials provided by Michigan State University. Note: Content may be edited for style and length.