Michigan State University (MSU) investigators are putting computing power to good use by analyzing large volumes of genomic information, often referred to as big data, to determine better research models to fight the spread of breast cancer and test potential drugs. 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,” explained senior study investigator Bin Chen, PhD, assistant professor in the College of Human Medicine at MSU.

Findings from this new study were published today in Nature Communications through an article titled “Evaluating cell lines as models for metastatic breast cancer through integrative analysis of genomic data.”

To determine which cell lines work as the best models, the research team 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 also 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.”

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 noted. “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.”

Organoid models were found to most likely mirror patient samples. This relatively new technology uses 3D tissue cultures and can capture more of the complexities of how tumors form and grow.

“Studies have shown that organoids can preserve the structural and genetic makeup of the original tumor,” stated Chen. “We found at the gene expression level, it was able to do this, more so than cancer cell lines.”

Interestingly, however, the researchers noted that both the organoids and cell lines couldn’t adequately model the immediate molecular landscape surrounding a tumor found at different sites in the body. They said knowing all these factors will help scientists interpret results, especially unexpected ones, and urge the scientific community to develop more sophisticated research models.

“Our study demonstrates the power of leveraging open data to gain insights on cancer,” concluded Chen. “Any advances we can make in early research will help us facilitate the discovery of better therapies for people with breast cancer down the road.”

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