Broad-Novartis Cancer Cell Line Encyclopedia (CCLE)

The Cancer Cell Line Encyclopedia (CCLE) project is a collaboration between the Broad Institute, and the Novartis Institutes for Biomedical Research and its Genomics Institute of the Novartis Research Foundation to conduct a detailed genetic and pharmacologic characterization of a large panel of human cancer models, to develop integrated computational analyses that link distinct pharmacologic vulnerabilities to genomic patterns and to translate cell line integrative genomics into cancer patient stratification. The CCLE provides public access to genomic data, analysis and visualization for about 1000 cell lines.

The CCLE is an ongoing project and some data are not complete yet. The CCLE website is subject to periodic changes and improvements. Please visit regularly!

This project is funded by Novartis.

News / Events

Jun 15, 2015: Currently there is a problem loading the tdf file into IGV. We are working on the problem and in the meantime please download the file from the BROWSE/DATA page as a workaround 

Jun 8, 2015: The CCLE will be unavailable Sunday June 14th 12 noon through Monday June 15th, 12 noon for maintenance. 

Mar 11, 2015: The CCLE portal will be unavailable on Tuesday 3/24/2015 for scheduled maintenance. 

Feb 24, 2015: An updated version of the pharmacological profiling data is now available in Download section. 

Jul 23, 2014: The browse cell lines, differential expression and gene set enrichment analyses are currently off-line. We are working to restore this functionality. 

Jun 6, 2014: The differential expression, GSEA and GeneNeighbors analyses are currently not working due to the recent maintenance.  

Jun 3, 2014: The CCLE will be unavailable from 10AM through 1PM EST Friday June 6th for maintenance. 

New User?

Please register for full access to the data and analyses tools CCLE provides.

Terms of Access   Register

Tutorials / Manuals

Tutorials, analysis descriptions and other documentation is available at:

Frequently Altered Genes

Tag clouds summarizing genes frequently altered in the datasets of this portal are available in the
Mutation tag cloud


Pharmacogenomic agreement between two cancer cell line data sets.,  The Cancer Cell Line Encyclopedia Consortium & The Genomics of Drug Sensitivity in Cancer Consortium, Nature doi:10.1038/nature15736, 2015

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity,  Barretina Caponigro Stransky et al., Nature doi:10.1038/nature11003, 2012

All Publications

Complementary Resources

Project Achilles is a systematic effort aimed at identifying and cataloging genetic vulnerabilities across hundreds of genomically characterized cancer cell lines.

The Cancer Therapeutic Response Portal provides profiles of the impact of a small-molecule collection of 185 compounds on a panel of 242 cancer cell lines

The Genotype-Tissue Expression project (GTEx) aims to create a comprehensive public atlas of gene expression and regulation across multiple human tissues

What you can do on this portal

Search for information

Enter a keyword to search for genes, news items and publications. Search results for a gene include links to annotations and analyses.

Data Sets

Browse, analyze and download studies and data sets.


Analysis Tools

The portal provides the following analysis tools:

Integrative Genomics Viewer (IGV)
Visualize a data set in the Integrative Genomics Viewer (IGV), a high-performance visualization tool for interactive exploration of large integrated data sets.

Differential Expression Analysis
Find genes that are significantly differentially expressed between two user-defined classes of samples from an expression data set available on this portal.

Gene co-expression
View the top 20 genes in a data set that are co-expressed with a gene of interest.

Gene Set Enrichment Analysis (GSEA)
Find pathway gene sets correlated with a gene of interest. Domain experts curate the pathway gene sets based on data from several online pathway databases.


Sample Sets

Create your own sample sets based on a large number of available criteria and use them in differential expression analysis.