Suppr超能文献

利用基因表达谱数据对NCI60细胞系和原发性肿瘤进行比较分析与综合分类。

Comparative analysis and integrative classification of NCI60 cell lines and primary tumors using gene expression profiling data.

作者信息

Wang Huixia, Huang Shuguang, Shou Jianyong, Su Eric W, Onyia Jude E, Liao Birong, Li Shuyu

机构信息

Integrative Biology, Lilly Research Laboratories, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.

出版信息

BMC Genomics. 2006 Jul 3;7:166. doi: 10.1186/1471-2164-7-166.

Abstract

BACKGROUND

NCI60 cell lines are derived from cancers of 9 tissue origins and have been invaluable in vitro models for cancer research and anti-cancer drug screen. Although extensive studies have been carried out to assess the molecular features of NCI60 cell lines related to cancer and their sensitivities to more than 100,000 chemical compounds, it remains unclear if and how well these cell lines represent or model their tumor tissues of origin. Identification and confirmation of correct origins of NCI60 cell lines are critical to their usage as model systems and to translate in vitro studies into clinical potentials. Here we report a direct comparison between NCI60 cell lines and primary tumors by analyzing global gene expression profiles.

RESULTS

Comparative analysis suggested that 51 of 59 cell lines we analyzed represent their presumed tumors of origin. Taking advantage of available clinical information of primary tumor samples used to generate gene expression profiling data, we further classified those cell lines with the correct origins into different subtypes of cancer or different stages in cancer development. For example, 6 of 7 non-small cell lung cancer cell lines were classified as lung adenocarcinomas and all of them were classified into late stages in tumor progression.

CONCLUSION

Taken together, we developed and applied a novel approach for systematic comparative analysis and integrative classification of NCI60 cell lines and primary tumors. Our results could provide guidance to the selection of appropriate cell lines for cancer research and pharmaceutical compound screenings. Moreover, this gene expression profile based approach can be generally applied to evaluate experimental model systems such as cell lines and animal models for human diseases.

摘要

背景

NCI60细胞系源自9种组织来源的癌症,是癌症研究和抗癌药物筛选中非常有价值的体外模型。尽管已经进行了广泛的研究来评估NCI60细胞系与癌症相关的分子特征及其对100,000多种化合物的敏感性,但这些细胞系是否以及在多大程度上代表或模拟其起源的肿瘤组织仍不清楚。确定和确认NCI60细胞系的正确起源对于将其用作模型系统以及将体外研究转化为临床应用至关重要。在此,我们通过分析全局基因表达谱报告了NCI60细胞系与原发性肿瘤之间的直接比较。

结果

比较分析表明,我们分析的59个细胞系中有51个代表其假定的起源肿瘤。利用用于生成基因表达谱数据的原发性肿瘤样本的可用临床信息,我们进一步将那些起源正确的细胞系分类为癌症的不同亚型或癌症发展的不同阶段。例如,7个非小细胞肺癌细胞系中有6个被分类为肺腺癌,并且它们全部被分类为肿瘤进展的晚期阶段。

结论

综上所述,我们开发并应用了一种新方法,用于对NCI60细胞系和原发性肿瘤进行系统的比较分析和综合分类。我们的结果可为癌症研究和药物化合物筛选中合适细胞系的选择提供指导。此外,这种基于基因表达谱的方法可普遍应用于评估诸如细胞系和人类疾病动物模型等实验模型系统。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6182/1525183/820bbc966061/1471-2164-7-166-1.jpg

相似文献

2
Cross-platform comparison and visualisation of gene expression data using co-inertia analysis.
BMC Bioinformatics. 2003 Nov 21;4:59. doi: 10.1186/1471-2105-4-59.
4
Cytogenetic characterization and gene expression profiling in the rat reflux-induced esophageal tumor model.
J Thorac Cardiovasc Surg. 2007 Mar;133(3):763-9. doi: 10.1016/j.jtcvs.2006.07.044.
6
Decreased PITX1 homeobox gene expression in human lung cancer.
Lung Cancer. 2007 Mar;55(3):287-94. doi: 10.1016/j.lungcan.2006.11.001. Epub 2006 Dec 8.

引用本文的文献

1
Sarcoma_CellminerCDB: A tool to interrogate the genomic and functional characteristics of a comprehensive collection of sarcoma cell lines.
iScience. 2024 Apr 18;27(6):109781. doi: 10.1016/j.isci.2024.109781. eCollection 2024 Jun 21.
2
NetCellMatch: Multiscale Network-Based Matching of Cancer Cell Lines to Patients Using Graphical Wavelets.
Chem Biodivers. 2022 Dec;19(12):e202200746. doi: 10.1002/cbdv.202200746. Epub 2022 Nov 28.
3
Comparison of Proteomics Profiles Between Xenografts Derived from Cell Lines and Primary Tumors of Thyroid Carcinoma.
J Cancer. 2021 Jan 31;12(7):1978-1989. doi: 10.7150/jca.50897. eCollection 2021.
4
A Clinical Genomics-Guided Prioritizing Strategy Enables Selecting Proper Cancer Cell Lines for Biomedical Research.
iScience. 2020 Oct 28;23(11):101748. doi: 10.1016/j.isci.2020.101748. eCollection 2020 Nov 20.
6
The LL-100 Cell Lines Panel: Tool for Molecular Leukemia-Lymphoma Research.
Int J Mol Sci. 2020 Aug 13;21(16):5800. doi: 10.3390/ijms21165800.
7
Fast and robust deconvolution of tumor infiltrating lymphocyte from expression profiles using least trimmed squares.
PLoS Comput Biol. 2019 May 6;15(5):e1006976. doi: 10.1371/journal.pcbi.1006976. eCollection 2019 May.

本文引用的文献

1
Genes that mediate breast cancer metastasis to lung.
Nature. 2005 Jul 28;436(7050):518-24. doi: 10.1038/nature03799.
2
Influence of in vivo growth on human glioma cell line gene expression: convergent profiles under orthotopic conditions.
Proc Natl Acad Sci U S A. 2005 Jun 7;102(23):8287-92. doi: 10.1073/pnas.0502887102. Epub 2005 May 31.
3
Assessment of tumor characteristic gene expression in cell lines using a tissue similarity index (TSI).
Proc Natl Acad Sci U S A. 2005 Feb 8;102(6):2052-7. doi: 10.1073/pnas.0408105102. Epub 2005 Jan 25.
4
Authentication of scientific human cell lines: easy-to-use DNA fingerprinting.
Methods Mol Biol. 2005;290:35-50. doi: 10.1385/1-59259-838-2:035.
5
Karyotypic complexity of the NCI-60 drug-screening panel.
Cancer Res. 2003 Dec 15;63(24):8634-47.
6
Proteomic profiling of the NCI-60 cancer cell lines using new high-density reverse-phase lysate microarrays.
Proc Natl Acad Sci U S A. 2003 Nov 25;100(24):14229-34. doi: 10.1073/pnas.2331323100. Epub 2003 Nov 17.
7
Classification of clear-cell sarcoma as a subtype of melanoma by genomic profiling.
J Clin Oncol. 2003 May 1;21(9):1775-81. doi: 10.1200/JCO.2003.10.108.
9
False leukemia-lymphoma cell lines: an update on over 500 cell lines.
Leukemia. 2003 Feb;17(2):416-26. doi: 10.1038/sj.leu.2402799.
10
A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.
Bioinformatics. 2003 Jan 22;19(2):185-93. doi: 10.1093/bioinformatics/19.2.185.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验