Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer

• Published in: BMC genomics 2016-08, Vol.17 Suppl 7 (Suppl 7), p.525-525, Article 525
• Main Authors: Jiang, Guanglong, Zhang, Shijun, Yazdanparast, Aida, Li, Meng, Pawar, Aniruddha Vikram, Liu, Yunlong, Inavolu, Sai Mounika, Cheng, Lijun
• Format: Article
• Language: English
• Subjects: Breast cancer; Breast Neoplasms - classification; Breast Neoplasms - genetics; Breast Neoplasms - metabolism; Breast Neoplasms - pathology; Cell Line, Tumor - metabolism; Cell Line, Tumor - pathology; Cell lines; Cluster Analysis; DNA Copy Number Variations - genetics; Female; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic - genetics; Health aspects; Humans; Neoplasm Proteins - genetics; Neoplasm Proteins - metabolism; Tumors
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-016-2911-z

Proper cell models for breast cancer primary tumors have long been the focal point in the cancer's research. The genomic comparison between cell lines and tumors can investigate the similarity and dissimilarity and help to select right cell model to mimic tumor tissues to properly evaluate the drug reaction in vitro. In this paper, a comprehensive comparison in copy number variation (CNV), mutation, mRNA expression and protein expression between 68 breast cancer cell lines and 1375 primary breast tumors is conducted and presented. Using whole genome expression arrays, strong correlations were observed between cells and tumors. PAM50 gene expression differentiated them into four major breast cancer subtypes: Luminal A and B, HER2amp, and Basal-like in both cells and tumors partially. Genomic CNVs patterns were observed between tumors and cells across chromosomes in general. High C > T and C > G trans-version rates were observed in both cells and tumors, while the cells had slightly higher somatic mutation rates than tumors. Clustering analysis on protein expression data can reasonably recover the breast cancer subtypes in cell lines and tumors. Although the drug-targeted proteins ER/PR and interesting mTOR/GSK3/TS2/PDK1/ER_P118 cluster had shown the consistent patterns between cells and tumor, low protein-based correlations were observed between cells and tumors. The expression consistency of mRNA verse protein between cell line and tumors reaches 0.7076. These important drug targets in breast cancer, ESR1, PGR, HER2, EGFR and AR have a high similarity in mRNA and protein variation in both tumors and cell lines. GATA3 and RP56KB1 are two promising drug targets for breast cancer. A total score developed from the four correlations among four molecular profiles suggests that cell lines, BT483, T47D and MDAMB453 have the highest similarity with tumors. The integrated data from across these multiple platforms demonstrates the existence of the similarity and dissimilarity of molecular features between breast cancer tumors and cell lines. The cell lines only mirror some but not all of the molecular properties of primary tumors. The study results add more evidence in selecting cell line models for breast cancer research.