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Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer
BACKGROUND: 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 evalua...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5001206/ https://www.ncbi.nlm.nih.gov/pubmed/27556158 http://dx.doi.org/10.1186/s12864-016-2911-z |
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author | Jiang, Guanglong Zhang, Shijun Yazdanparast, Aida Li, Meng Pawar, Aniruddha Vikram Liu, Yunlong Inavolu, Sai Mounika Cheng, Lijun |
author_facet | Jiang, Guanglong Zhang, Shijun Yazdanparast, Aida Li, Meng Pawar, Aniruddha Vikram Liu, Yunlong Inavolu, Sai Mounika Cheng, Lijun |
author_sort | Jiang, Guanglong |
collection | PubMed |
description | BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-2911-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5001206 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-50012062016-09-06 Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer Jiang, Guanglong Zhang, Shijun Yazdanparast, Aida Li, Meng Pawar, Aniruddha Vikram Liu, Yunlong Inavolu, Sai Mounika Cheng, Lijun BMC Genomics Research BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-2911-z) contains supplementary material, which is available to authorized users. BioMed Central 2016-08-22 /pmc/articles/PMC5001206/ /pubmed/27556158 http://dx.doi.org/10.1186/s12864-016-2911-z Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Jiang, Guanglong Zhang, Shijun Yazdanparast, Aida Li, Meng Pawar, Aniruddha Vikram Liu, Yunlong Inavolu, Sai Mounika Cheng, Lijun Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer |
title | Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer |
title_full | Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer |
title_fullStr | Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer |
title_full_unstemmed | Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer |
title_short | Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer |
title_sort | comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5001206/ https://www.ncbi.nlm.nih.gov/pubmed/27556158 http://dx.doi.org/10.1186/s12864-016-2911-z |
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