Big Data and AI to compare in vitro and in vivo tumours
2021
Johns Hopkins University, Baltimore, USA
Failure to adequately characterize cell lines, and understand the differences between in vitro and in vivo biology, can have serious consequences on the translatability of in vitro scientific studies to human clinical trials. This project focuses on MCF-7 cells (Michigan Cancer Foundation-7), a human breast adenocarcinoma cell line that is commonly used for in vitro cancer research. In this study, the key similarities and differences in gene expression networks of MCF-7 cell lines compared to human breast cancer tissues are explored. Two MCF-7 data sets (ARCHS4 including 1032 samples and Gene Expression Omnibus GSE50705 with 88 estradiol-treated MCF-7 samples) and one human breast invasive ductal carcinoma (BRCA) data set (The Cancer Genome Atlas, including 1212 breast tissue samples) are used. Weighted Gene Correlation Network Analysis (WGCNA) and functional annotations of the data show that MCF-7 cells and human breast tissues have only minimal similarity in biological processes, although some fundamental functions, such as cell cycle, are conserved. Scaled connectivity—a network topology metric—also show drastic differences in the behaviour of genes between MCF-7 and BRCA data sets. Finally, canSAR is used to compute ligand-based druggability scores of genes in the data sets, and the results suggest that using MCF-7 to study breast cancer may lead to missing important gene targets.
Similarities and differences in gene expression networks between the breast cancer cell line Michigan Cancer Foundation-7 and invasive human breast cancer tissues
Alexandra Maertens
Added on: 06-30-2021
[1] https://www.frontiersin.org/articles/10.3389/frai.2021.674370/full
