Abstract
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.
Highlights
The overall recurrence rate of estrogen receptor positive (ER+) early breast cancer can be reduced by adjuvant treatment with tamoxifen
Breast cancer gene expression data and corresponding clinical information were downloaded from the Gene Expression Omnibus (GEO) database where n denotes the number of overlapping differentially expressed genes (DEGs) between tissue and cell line, and k denotes the number of those overlapping DEGs with the same dysregulation direction
We identified the DEGs between tamoxifen-resistant and tamoxifensensitive cell line samples within each of the nine datasets using the significance analysis of microarrays (SAM) method (FDR < 20%)
Summary
The overall recurrence rate of estrogen receptor positive (ER+) early breast cancer can be reduced by adjuvant treatment with tamoxifen. Whether cell line models could adequately reflect the characteristics of clinical tissue samples is controversial (American Type Culture Collection Standards Development Organization Workgroup ASN-0002, 2010; Liedtke et al, 2010; Bayer et al, 2013; Capes-Davis et al, 2019; Wass et al, 2019). Cross-contamination (International Cell Line Authentication Committee, 2014) and misidentification (American Type Culture Collection Standards Development Organization Workgroup ASN-0002, 2010) of cell lines exacerbates such issues. There is no unified gold standard for the identification of drugresistant cell lines, which results in some cell lines poorly reflecting the characteristics of clinical tissue samples (Liedtke et al, 2010). It is of great value to find resistant/sensitive cell line models that are more representative of clinical tissue samples
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