The NCI60 human tumour cell line anticancer drug screen

By R. Shoemaker. Nature Reviews. Cancer, 2006

2,509 citations recorded by the source corpus.

Subfields: Molecular Biology, Oncology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/56178af6fed40b059bafb5f355203ae8ad3a224a

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Citations in the corpus

CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set (2012, background): “Many thousands of compounds have been and continue to be applied to the NCI-60 (1, 2).”
Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles (2020, background): “CCLE/CTRP and GDSC have expanded their cell line panels by more than 16-fold compared to the pioneering NCI-60 panel [6], enabling the study of over 30 cancer types in detail, albeit restricting their screening effort to a smaller set of clinical and pre-clinical compounds.”
Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources (2020, methodology): “These repositories include The Cancer Genome Atlas [31], the Cancer Cell Line Encyclopedia (CCLE) [32], the Therapeutically Applicable Research to Generate Effective Treatments Data Matrix [33], the Clinical Proteomic Tumor Analysis Consortium [34], and the NCI-60 Human Tumor Cell Line Screen (NCI-6”
An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules (2013, background): “These efforts initially focused on relating sensitivity to CCL lineage (Shoemaker, 2006) but now increasingly relate sensitivity to genetic and epigenetic features (Barretina et al., 2012; Garnett et al., 2012; Heiser et al., 2012; Larsen et al., 2011; Sharma et al., 2010; Sun et al., 2007).”
The EMT-activator ZEB1 promotes tumorigenicity by repressing stemness-inhibiting microRNAs (2009, background): “For analysis of ZEB1 and miRNA expression in the NCI60 pane”
Next-Generation Machine Learning for Biological Networks. (2018, positive): “…et al., 2012; Basu et al., 2013; Garnett et al., 2012; Goodspeed et al., 2016; Musa et al., 2017; Rees et al., 2016; Seashore-Ludlow et al., 2015; Shoemaker, 2006; Yang et al., 2013), together with large amounts of genotypic data collected and stored in repositories such as dbGAP (Mailman et…”
CellMiner: a relational database and query tool for the NCI-60 cancer cell lines (2009, background): “National Cancer Institute to screen > 100,000 compounds plus natural products since 1990 [7-10].”
Artificial Intelligence in Cancer Research and Precision Medicine (2021, background): “This work has gained importance due to availability of large cancer drug efficacy datasets, obtained from experiments done in cell lines (87, 89, 111, 112).”