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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...

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Autores principales: Jiang, Guanglong, Zhang, Shijun, Yazdanparast, Aida, Li, Meng, Pawar, Aniruddha Vikram, Liu, Yunlong, Inavolu, Sai Mounika, Cheng, Lijun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
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.
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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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