Identification of Tamoxifen-Resistant Breast Cancer Cell Lines and Drug Response Signature - Guan, Qingzhou; Song, Xuekun; Zhang, Zhenzhen; Zhang, Yizhi; Chen, Yating; Li, Jing (Frontiers in Molecular Biosciences, 2020)
Breast cancer cell lines are usually been used for elucidating the molecular mechanism of disease. However, a larger proportion of cell lines exist some problems, such as mislabeled or cross-contamination, etc. Therefore, it is of great clinical significance to select the optimal breast cancer cell line mode. Based on the tamoxifen survival related genes from breast cancer tissues as gold standard, we selected the optimal cell line mode that could well present the characteristics of clinical tissue samples. Moreover, using the relative expression orderings of gene pairs, we also developed a gene pair signature that could predict tamoxifen therapy outcome. Based on 235 consistently identified survival related genes from GSE17705 and GSE6532, we found that only the differentially expressed genes (DEGs) from the cell line dataset GSE26459 could significantly reproducible in tissue samples (binomial test, p = 2.13E-07). Finally, using the consistent DEGs form cell line dataset GSE26459 and tissue, based on the transcriptional qualitative feature, we developed a two-gene-pair (TOP2A, SLC7A5; NMU, PDSS1) for predicting clinical tamoxifen resistance in the training data (logrank p = 1.98E-07) and it was also well been verified in independent dataset (logrank p = 0.009909). Our result denotes that the cell line mode from dataset GSE26459 could well represent the characteristics of clinical tissue sample, which provides a better choice for the selection of drug-resistant and -sensitive breast cancer cell lines in the future. Moreover, our signature could well predict tamoxifen treatment outcome for breast cancer patients.