Identification of Tamoxifen-Resistant Breast Cancer Cell Lines and Drug Response Signature

• Main Authors: Qingzhou Guan, Xuekun Song, Zhenzhen Zhang, Yizhi Zhang, Yating Chen, Jing Li
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2020-12-01
• Series: Frontiers in Molecular Biosciences
• Subjects: breast cancer; tamoxifen; cell line; resistant; sensitive
• Online Access: https://www.frontiersin.org/articles/10.3389/fmolb.2020.564005/full

Breast cancer cell lines are frequently used to elucidate the molecular mechanisms of the disease. However, a large proportion of cell lines are affected by problems such as mislabeling and cross-contamination. Therefore, it is of great clinical significance to select optimal breast cancer cell lines models. Using tamoxifen survival-related genes from breast cancer tissues as the gold standard, we selected the optimal cell line model to represent the characteristics of clinical tissue samples. Moreover, using relative expression orderings of gene pairs, we developed a gene pair signature that could predict tamoxifen therapy outcomes. Based on 235 consistently identified survival-related genes from datasets GSE17705 and GSE6532, we found that only the differentially expressed genes (DEGs) from the cell line dataset GSE26459 were significantly reproducible in tissue samples (binomial test, p = 2.13E-07). Finally, using the consistent DEGs from cell line dataset GSE26459 and tissue samples, we used the transcriptional qualitative feature to develop a two-gene pair (TOP2A, SLC7A5; NMU, PDSS1) for predicting clinical tamoxifen resistance in the training data (logrank p = 1.98E-07); this signature was verified using an independent dataset (logrank p = 0.009909). Our results indicate that the cell line model from dataset GSE26459 provides a good representation of the characteristics of clinical tissue samples; thus, it will be a good choice for the selection of drug-resistant and drug-sensitive breast cancer cell lines in the future. Moreover, our signature could predict tamoxifen treatment outcomes in breast cancer patients.