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View All DatasetsLatest Release DepMap Public 18Q3 | August 2018
The DepMap Public 18Q3 release includes CRISPR screen and omics datasets expanded to include new cell lines. The Avana CRISPR screen dataset is expanded to include 485 cell lines, including 49 lines not previously made public. Mutation, indel, copy number and gene expression data are also expanded to include new lines in Avana dataset. As our screening efforts continue, we will be releasing additional cancer dependency data on a quarterly basis.
This quarter, we have altered the way we label cell lines. Previously we used CCLE names. However, these names are unstable due to changes in tissue annotation. We now use Broad IDs. The map from Broad IDs to CCLE names can be found in the DepMap-2018q3-celllines.csv file.
We have also changed QC for the Avana slightly. Previously, we dropped lines if they showed insufficient separation between positive and negative controls. We now additionally drop replicates if they show insufficient separation. This has caused the previously released cell line MONOMAC1 to fail QC. The previously released cell line A4FUK has also been identified as a fingerprint failure and removed.
The CRISPR screen uses the Avana CRISPR-Cas9 genome-scale knockout library, and descriptions of the experimental methods and the CERES algorithm are published in here. Copy number and mutation data for some cell lines were based on Sanger Institute whole exome sequencing data (COSMIC: http://cancer.sanger.ac.uk/cell_lines, EGA accession number: EGAD00001001039) reprocessed using CCLE pipelines.
Cellular Models
Expression
Public 18Q3CCLE RNAseq gene expression data (RPKM)
Cell Lines 1156
Primary Diseases 73
Lineages 32
Cellular Models
Mutation
Public 18Q3Merged mutation calls (coding region, germline filtered)
Cell Lines 1570
Primary Diseases 116
Lineages 33
Cellular Models
Copy Number
Public 18Q3DepMap WES CN Data
Cell Lines 1080
Primary Diseases 71
Lineages 30
Cellular Models
Cell Line Metadata
Metadata about cell lines in the 18Q3 release, including mapping between Broad ID and CCLE names
Cell Lines 1673
Primary Diseases 119
Lineages 33
Genetic Dependency
CRISPR
(Avana) Public 18Q3CERES
CERES inferred gene effect matrix
Cell Lines 485
Primary Diseases 44
Lineages 27
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Genetic Dependency
Combined RNAi
(Broad, Novartis, Marcotte)
Cellular Models
Protein levels
CCLE Reverse Phase Protein Array (RPPA) data
Cell Lines 899
Primary Diseases 59
Lineages 28
Please cite the following publications when using these datasets
For CRISPR datasets:Broad Institute Cancer Dependency Map; Cancer Data Science (2018): Cancer Dependency Map, CRISPR Avana dataset 18Q3 (Avana_public_18Q3). figshare. Fileset. doi:10.6084/m9.figshare.6931364.v1
Robin M. Meyers, Jordan G. Bryan, James M. McFarland, Barbara A. Weir, ... David E. Root, William C. Hahn, Aviad Tsherniak. Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nature Genetics 2017 October 49:1779–1784. doi:10.1038/ng.3984
For omics datasets:Cancer Cell Line Encyclopedia Consortium, and Genomics of Drug Sensitivity in Cancer Consortium. 2015. Pharmacogenomic Agreement between Two Cancer Cell Line Data Sets. Nature 528 (7580):84–87. https://doi.org/10.1038/nature15736.
Jordi Barretina, Giordano Caponigro, Nicolas Stransky, Kavitha Venkatesan, William R. Sellers, Robert Schlegel, Levi A. Garraway, et. al. 2012. The Cancer Cell Line Encyclopedia Enables Predictive Modelling of Anticancer Drug Sensitivity. Nature 483 (7391):603–7. https://doi.org/10.1038/nature11003.
For the RNAi dataset:Data Science, Cancer (2018): DEMETER2 data. figshare. Fileset. doi:10.6084/m9.figshare.6025238.v2
James M. McFarland, Zandra V. Ho, Guillaume Kugener, Joshua M. Dempster, Phillip G. Montgomery, Jordan G. Bryan, John M. Krill-Burger, Thomas M. Green, Francisca Vazquez, Jesse S. Boehm, Todd R. Golub, William C. Hahn, David E. Root, Aviad Tsherniak. Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration. bioRxiv 305656. DOI: https://doi.org/10.1101/305656