Optimization of cell lines as tumour models by integrating multi-omics data - PubMed (original) (raw)
Review
. 2017 May 1;18(3):515-529.
doi: 10.1093/bib/bbw082.
- PMID: 27694350
- DOI: 10.1093/bib/bbw082
Review
Optimization of cell lines as tumour models by integrating multi-omics data
Ning Zhao et al. Brief Bioinform. 2017.
Erratum in
- Optimization of cell lines as tumour models by integrating multi-omics data.
Ning Z, Yongjing L, Yunzhen W, Zichuang Y, Qiang Z, Cheng W, Zhiqiang C, Yan X. Ning Z, et al. Brief Bioinform. 2017 May 1;18(3):545. doi: 10.1093/bib/bbw121. Brief Bioinform. 2017. PMID: 28013237 No abstract available.
Abstract
Cell lines are widely used as in vitro models of tumorigenesis. However, an increasing number of researchers have found that cell lines differ from their sourced tumour samples after long-term cell culture. The application of unsuitable cell lines in experiments will affect the experimental accuracy and the treatment of patients. Therefore, it is imperative to identify optimal cell lines for each cancer type. Here, we review the methods used to evaluate cell lines since 2005. Furthermore, gene expression, copy number and mutation profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia are used to calculate similarity between tumours and cell lines. Then, the ideal cell lines to use for experiments for eight types of cancers are found by combining the results with Gene Ontology functional similarity. After verification, the optimal cell lines have the same genomic characteristics as their homologous tumour samples. The contaminated cell lines identified in previous research are also determined to be unsuitable in vitro cancer models here. Moreover, our study suggests that some of the commonly used cell lines are not suitable cancer models. In summary, we provide a reference for ideal cell lines to use in in vitro experiments and contribute to improving the accuracy of future cancer research. Furthermore, this research provides a foundation for identifying more effective treatment strategies.
Keywords: cancer in vitro model; cell line; multi-omics; optimization.
© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Similar articles
- Multi-omics of 34 colorectal cancer cell lines - a resource for biomedical studies.
Berg KCG, Eide PW, Eilertsen IA, Johannessen B, Bruun J, Danielsen SA, Bjørnslett M, Meza-Zepeda LA, Eknæs M, Lind GE, Myklebost O, Skotheim RI, Sveen A, Lothe RA. Berg KCG, et al. Mol Cancer. 2017 Jul 6;16(1):116. doi: 10.1186/s12943-017-0691-y. Mol Cancer. 2017. PMID: 28683746 Free PMC article. - Deciphering the Correlation between Breast Tumor Samples and Cell Lines by Integrating Copy Number Changes and Gene Expression Profiles.
Sun Y, Liu Q. Sun Y, et al. Biomed Res Int. 2015;2015:901303. doi: 10.1155/2015/901303. Epub 2015 Jul 26. Biomed Res Int. 2015. PMID: 26273658 Free PMC article. - Integrative modeling of multi-omics data to identify cancer drivers and infer patient-specific gene activity.
Pavel AB, Sonkin D, Reddy A. Pavel AB, et al. BMC Syst Biol. 2016 Feb 11;10:16. doi: 10.1186/s12918-016-0260-9. BMC Syst Biol. 2016. PMID: 26864072 Free PMC article. - Cancer Cell Line Panels Empower Genomics-Based Discovery of Precision Cancer Medicine.
Kim HS, Sung YJ, Paik S. Kim HS, et al. Yonsei Med J. 2015 Sep;56(5):1186-98. doi: 10.3349/ymj.2015.56.5.1186. Yonsei Med J. 2015. PMID: 26256959 Free PMC article. Review. - Translational Metabolomics of Head Injury: Exploring Dysfunctional Cerebral Metabolism with Ex Vivo NMR Spectroscopy-Based Metabolite Quantification.
Wolahan SM, Hirt D, Glenn TC. Wolahan SM, et al. In: Kobeissy FH, editor. Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects. Boca Raton (FL): CRC Press/Taylor & Francis; 2015. Chapter 25. In: Kobeissy FH, editor. Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects. Boca Raton (FL): CRC Press/Taylor & Francis; 2015. Chapter 25. PMID: 26269925 Free Books & Documents. Review.
Cited by
- A pan-cancer atlas of cancer hallmark-associated candidate driver lncRNAs.
Deng Y, Luo S, Zhang X, Zou C, Yuan H, Liao G, Xu L, Deng C, Lan Y, Zhao T, Gao X, Xiao Y, Li X. Deng Y, et al. Mol Oncol. 2018 Nov;12(11):1980-2005. doi: 10.1002/1878-0261.12381. Epub 2018 Oct 2. Mol Oncol. 2018. PMID: 30216655 Free PMC article. - An Interactive Resource to Probe Genetic Diversity and Estimated Ancestry in Cancer Cell Lines.
Dutil J, Chen Z, Monteiro AN, Teer JK, Eschrich SA. Dutil J, et al. Cancer Res. 2019 Apr 1;79(7):1263-1273. doi: 10.1158/0008-5472.CAN-18-2747. Epub 2019 Mar 20. Cancer Res. 2019. PMID: 30894373 Free PMC article. Review. - Mouse Models to Examine Differentiated Thyroid Cancer Pathogenesis: Recent Updates.
Choi HR, Kim K. Choi HR, et al. Int J Mol Sci. 2023 Jul 6;24(13):11138. doi: 10.3390/ijms241311138. Int J Mol Sci. 2023. PMID: 37446316 Free PMC article. Review. - OGEE v2: an update of the online gene essentiality database with special focus on differentially essential genes in human cancer cell lines.
Chen WH, Lu G, Chen X, Zhao XM, Bork P. Chen WH, et al. Nucleic Acids Res. 2017 Jan 4;45(D1):D940-D944. doi: 10.1093/nar/gkw1013. Epub 2016 Oct 30. Nucleic Acids Res. 2017. PMID: 27799467 Free PMC article. - Comparison of Proteomics Profiles Between Xenografts Derived from Cell Lines and Primary Tumors of Thyroid Carcinoma.
Fang L, Liu YJ, Zhang YW, Pan ZF, Zhong LK, Jiang LH, Wang JF, Zheng XW, Chen LY, Huang P, Ge MH, Tan Z. Fang L, et al. J Cancer. 2021 Jan 31;12(7):1978-1989. doi: 10.7150/jca.50897. eCollection 2021. J Cancer. 2021. PMID: 33753996 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources