Suppr超能文献

下一代癌症细胞系百科全书的特征描述。

Next-generation characterization of the Cancer Cell Line Encyclopedia.

机构信息

Broad Institute of Harvard and MIT, Cambridge, MA, USA.

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.

出版信息

Nature. 2019 May;569(7757):503-508. doi: 10.1038/s41586-019-1186-3. Epub 2019 May 8.

Abstract

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.

摘要

大型全面鉴定的人类癌症模型面板,包括癌症细胞系百科全书(CCLE),为研究遗传变异、候选靶点、小分子和生物治疗药物以及鉴定新的标记驱动的癌症依赖性提供了一个严谨的框架。为了提高我们对导致癌症表型(包括药物反应)的分子特征的理解,我们在这里将癌症细胞系的特征扩展到包括遗传、RNA 剪接、DNA 甲基化、组蛋白 H3 修饰、microRNA 表达和 1072 个人类个体的各种谱系和种族来源的细胞系的反相蛋白阵列数据。将这些数据与功能特征(如药物敏感性、短发夹 RNA 敲低和 CRISPR-Cas9 敲除数据)进行整合,揭示了癌症药物的潜在靶点和相关生物标志物。总的来说,这个数据集和一个配套的公共数据门户为使用模型癌症细胞系加速癌症研究提供了资源。

相似文献

1
Next-generation characterization of the Cancer Cell Line Encyclopedia.
Nature. 2019 May;569(7757):503-508. doi: 10.1038/s41586-019-1186-3. Epub 2019 May 8.
2
3
New tools for old drugs: Functional genetic screens to optimize current chemotherapy.
Drug Resist Updat. 2018 Jan;36:30-46. doi: 10.1016/j.drup.2018.01.001. Epub 2018 Jan 12.
5
Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens.
Nature. 2019 Apr;568(7753):511-516. doi: 10.1038/s41586-019-1103-9. Epub 2019 Apr 10.
6
A Road Map to Personalizing Targeted Cancer Therapies Using Synthetic Lethality.
Trends Cancer. 2019 Jan;5(1):11-29. doi: 10.1016/j.trecan.2018.11.001. Epub 2018 Dec 7.
8
Epigenetic Alterations of Heat Shock Proteins (HSPs) in Cancer.
Int J Mol Sci. 2019 Sep 25;20(19):4758. doi: 10.3390/ijms20194758.
9
Using drug response data to identify molecular effectors, and molecular "omic" data to identify candidate drugs in cancer.
Hum Genet. 2015 Jan;134(1):3-11. doi: 10.1007/s00439-014-1482-9. Epub 2014 Sep 12.

引用本文的文献

1
3
Genetic suppression features ABHD18 as a Barth syndrome therapeutic target.
Nature. 2025 Sep 3. doi: 10.1038/s41586-025-09373-5.
4
Combined inhibition of SHP2 overcomes adaptive resistance to type 1 BRAF inhibitors in BRAF V600E-driven high-grade glioma.
Neurooncol Adv. 2025 Aug 2;7(1):vdaf170. doi: 10.1093/noajnl/vdaf170. eCollection 2025 Jan-Dec.
5
TrueProbes: Quantitative Single-Molecule RNA-FISH Probe Design Improves RNA Detection.
bioRxiv. 2025 Aug 19:2025.08.14.670355. doi: 10.1101/2025.08.14.670355.
6
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.
IEEE Trans Autom Sci Eng. 2025;22:10008-10028. doi: 10.1109/tase.2024.3515839. Epub 2024 Dec 18.
10
Narrative Review of the Use of Genomic-Adjusted Radiation Dose (GARD) in Radiotherapy.
Cancers (Basel). 2025 Aug 14;17(16):2650. doi: 10.3390/cancers17162650.

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