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Your Environment. Your Health.

Big Data Helps Identify Better Ways to Research Breast Cancer

Bin Chen, Ph.D.
Michigan State University

By analyzing large volumes of data, NIEHS grantees found substantial differences between lab-created breast cancer cell lines and actual advanced, or metastatic, breast cancer tumor samples. By identifying key genomic differences and similarities between cell lines and tumor samples, the study also introduced a framework for selecting relevant cell lines for modeling metastatic breast cancer.

Current models used in the lab frequently involve culturing cells on flat dishes, or cell lines, to predict tumor growth. The differences between cell lines and tumor samples have raised questions about whether the cell lines accurately capture what is going on in a tumor. To answer this question, the researchers performed an integrative analysis of data taken from genomic databases to comprehensively compare multiple types of molecular features between breast cancer cell lines and metastatic breast cancer samples.

The researchers found substantial differences between lab-created breast cancer cell lines and 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. The organoid model, a new technology that uses 3D tissue cultures, was found to most closely mirror patient samples. According to the authors, identifying differences in the models used to assess cancer metastasis, or spreading within the body, can help scientists develop more sophisticated research models and better interpret model results.

Citation: Liu K, Newbury PA, Glicksberg BS, Zeng WZD, Paithankar S, Andrechek ER, Chen B. 2019. Evaluating cell lines as models for metastatic breast cancer through integrative analysis of genomic data. Nat Commun 10(1):2138.

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