This DepMap release contains data from CRISPR knockout screens from project Achilles, as well as genomic characterization data from the CCLE project.For more information, please see README.txt.

Portal for identifying genetic and pharmacologic dependencies and biomarkers that predicts them by providing access to datasets, visualizations, and analysis tools that are being used by Cancer Dependency Map Project at Broad Institute. Project to systematically identify genes and small molecule dependencies and to determine markers that predict sensitivity. All data generated by DepMap Project are available to public under CC BY 4.0 license on quarterly basis and pre-publication.

RNAseq TPM gene expression data for protein coding genes in various cell lines.

This dataset contains the results of Avana library CRISPR-Cas9 genome-scale knockout (prefixed with Achilles) as well as mutation, copy number and gene expression data (prefixed with CCLE) for cancer cell lines as part of the Broad Institute’s Cancer Dependency Map project. We have repackaged our fileset to include all quarterly-updating datasets produced by DepMap.The Avana CRISPR-Cas9 genome-scale knockout data has expanded to include 689 cell lines, the RNAseq data includes 1249 cell lines, and the copy number data includes 1682 cell lines. Please see the README files for details regarding data processing pipeline procedures updates.As our screening efforts continue, we will be releasing additional cancer dependency data on a quarterly basis for unrestricted use. For the latest datasets available, further analyses, and to subscribe to our mailing list visit https://depmap.org.Descriptions of the experimental methods and the CERES algorithm are published in http://dx.doi.org/10.1038/ng.3984. Some cell lines were process using copy number data based on the Sanger Institute whole exome sequencing data (COSMIC: http://cancer.sanger.ac.uk.cell_lines, EGA accession number: EGAD00001001039) reprocessed using CCLE pipelines. A detailed description of the pipelines and tool versions for CCLE expression can be found here: https://github.com/broadinstitute/gtex-pipeline/blob/v9/TOPMed_RNAseq_pipeline.md.

Originally a reproduction of the EFO/Cellosaurus/DepMap/CCLE scenario posed in the Biomappings paper, this configuration imports several different cell and cell line resources and identifies mappings between them.

This DepMap Release contains new cell models and data from Whole Genome/Exome Sequencing (Copy Number and Mutation), RNA Sequencing (Expression and Fusions), Genome-wide CRISPR knockout screens. Also included are updated metadata and mapping files for information about cell models and data relationships, respectively. Each release may contain improvements to our pipelines that generate this data so you may notice changes from the last release. For more information, please see README.txt.

Information about the dataset files:

1) pancan_rnaseq_freeze.tsv.gz: Publicly available gene expression data for the TCGA Pan-cancer dataset. File: PanCanAtlas EBPlusPlusAdjustPANCAN_IlluminaHiSeq_RNASeqV2.geneExp.tsv was processed using script process_sample_freeze.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [http://api.gdc.cancer.gov/data/3586c0da-64d0-4b74-a449-5ff4d9136611] [https://doi.org/10.1016/j.celrep.2018.03.046]

2) pancan_mutation_freeze.tsv.gz: Publicly available Mutational information for TCGA Pan-cancer dataset. File: mc3.v0.2.8.PUBLIC.maf.gz was processed using script process_sample_freeze.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [http://api.gdc.cancer.gov/data/1c8cfe5f-e52d-41ba-94da-f15ea1337efc] [https://doi.org/10.1016/j.celrep.2018.03.046]

3) pancan_GISTIC_threshold.tsv.gz: Publicly available Gene- level copy number information of the TCGA Pan-cancer dataset. This file is processed using script process_copynumber.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. The files copy_number_loss_status.tsv.gz and copy_number_gain_status.tsv.gz generated from this data are used as inputs in our Galaxy pipeline. [https://xenabrowser.net/datapages/?cohort=TCGA%20Pan-Cancer%20(PANCAN)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443] [https://doi.org/10.1016/j.celrep.2018.03.046]

4) mutation_burden_freeze.tsv.gz: Publicly available Mutational information for TCGA Pan-cancer dataset mc3.v0.2.8.PUBLIC.maf.gz was processed using script process_sample_freeze.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [https://github.com/greenelab/pancancer/][http://api.gdc.cancer.gov/data/1c8cfe5f-e52d-41ba-94da-f15ea1337efc] [https://doi.org/10.1016/j.celrep.2018.03.046]

5) sample_freeze.tsv or sample_freeze_version4_modify.tsv: The file lists the frozen samples as determined by TCGA PanCancer Atlas consortium along with raw RNAseq and mutation data. These were previously determined and included for all downstream analysis All other datasets were processed and subset according to the frozen samples.[https://github.com/greenelab/pancancer/]

6) vogelstein_cancergenes.tsv: compendium of OG and TSG used for the analysis. [https://github.com/greenelab/pancancer/]

7) CCLE_DepMap_18Q1_maf_20180207.txt.gz Publicly available Mutational data for CCLE cell lines from Broad Institute Cancer Cell Line Encyclopedia (CCLE) / DepMap Portal. [https://depmap.org/portal/download/api/download/external?file_name=ccle%2FCCLE_DepMap_18Q1_maf_20180207.txt]

8) ccle_rnaseq_genes_rpkm_20180929.gct.gz: Publicly available Expression data for 1019 cell lines (RPKM) from Broad Institute Cancer Cell Line Encyclopedia (CCLE) / DepMap Portal. [https://depmap.org/portal/download/api/download/external?file_name=ccle%2Fccle_2019%2FCCLE_RNAseq_genes_rpkm_20180929.gct.gz]

9) CCLE_MUT_CNA_AMP_DEL_binary_Revealer.gct: Publicly available merged Mutational and copy number alterations that include gene amplifications and deletions for the CCLE cell lines. This data is represented in the binary format and provided by the Broad Institute Cancer Cell Line Encyclopedia (CCLE) / DepMap Portal. [https://data.broadinstitute.org/ccle_legacy_data/binary_calls_for_copy_number_and_mutation_data/CCLE_MUT_CNA_AMP_DEL_binary_Revealer.gct]

10) GDSC_cell_lines_EXP_CCLE_names.csv.gz Publicly available RMA normalized expression data for Genomics of Drug Sensitivity in Cancer(GDSC) cell-lines. File gdsc_cell_line_RMA_proc_basalExp.csv was downloaded. This data was subsetted to 389 cell lines that are common among CCLE and GDSC. All the GDSC cell line names were replaced with CCLE cell line names for further processing. [https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources//Data/preprocessed/Cell_line_RMA_proc_basalExp.txt.zip]

11) GDSC_CCLE_common_mut_cnv_binary.csv.gz: A subset of merged Mutational and copy number alterations that include gene amplifications and deletions for common cell lines between GDSC and CCLE. This file is generated using CCLE_MUT_CNA_AMP_DEL_binary_Revealer.gct and a list of common cell lines.

12) gdsc1_ccle_pharm_fitted_dose_data.txt.gz: Pharmacological data for GDSC1 cell lines. [ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/current_release/GDSC1_fitted_dose_response_15Oct19.xlsx]

13) gdsc2_ccle_pharm_fitted_dose_data.txt.gz: Pharmacological data for GDSC2 cell lines. [ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/current_release/GDSC2_fitted_dose_response_15Oct19.xlsx]

14) compounds.csv: list of pharmacological compounds tested for our analysis

15) tcga_dictonary.tsv: list of cancer types used in the analysis.

16) seg_based_scores.tsv: Measurement of total copy number burden, Percent of genome altered by copy number alterations. This file was used as part of the Pancancer analysis by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [https://github.com/greenelab/pancancer/]

GDSC2 PharmacoSet (PSet) generated by ORCESTRA. Metadata can be found on ORCESTRA at: http://orcestra.ca/10.5281/zenodo.3905481

Disclaimer

The GDSC2 data have been generated and shared by the Wellcome Trust Sanger Institute as part of the Genomics of Drug Sensitivity in Cancer (GDSC) Programme. The Haibe-Kains Lab has reprocessed and re-annotated the data to maximize overlap with other pharmacogenomic datasets.

Data Usage Policy

Users have a non-exclusive, non-transferable right to use data files for internal proprietary research and educational purposes, including target, biomarker and drug discovery. Excluded from this licence are use of the data (in whole or any significant part) for resale either alone or in combination with additional data/product offerings, or for provision of commercial services.

Please note: The data files are experimental and academic in nature and are not licensed or certified by any regulatory body. Genome Research Limited provides access to data files on an “as is” basis and excludes all warranties of any kind (express or implied). If you are interested in incorporating results or software into a product, or have questions, please contact depmap@sanger.ac.uk.

Source: https://depmap.sanger.ac.uk/documentation/data-usage-policy/

Sanger's terms and conditions: http://www.cancerrxgene.org/legal

Please cite the following when using these data

Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, Aben N, Gonçalves E, Barthorpe S, Lightfoot H, Cokelaer T, Greninger P, van Dyk E, Chang H, de Silva H, Heyn H, Deng X, Egan RK, Liu Q, Mironenko T, Mitropoulos X, Richardson L, Wang J, Zhang T, Moran S, Sayols S, Soleimani M, Tamborero D, Lopez-Bigas N, Ross-Macdonald P, Esteller M, Gray NS, Haber DA, Stratton MR, Benes CH, Wessels LFA, Saez-Rodriguez J, McDermott U, Garnett MJ. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. 2016 Jul 28;166(3):740-754. doi: 10.1016/j.cell.2016.06.017. Epub 2016 Jul 7. PMID: 27397505; PMCID: PMC4967469.

Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR, Ramaswamy S, Futreal PA, Haber DA, Stratton MR, Benes C, McDermott U, Garnett MJ. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013 Jan;41(Database issue):D955-61. doi: 10.1093/nar/gks1111. Epub 2012 Nov 23. PMID: 23180760; PMCID: PMC3531057.

Picco G, Chen ED, Alonso LG, Behan FM, Gonçalves E, Bignell G, Matchan A, Fu B, Banerjee R, Anderson E, Butler A, Benes CH, McDermott U, Dow D, Iorio F, Stronach E, Yang F, Yusa K, Saez-Rodriguez J, Garnett MJ. Functional linkage of gene fusions to cancer cell fitness assessed by pharmacological and CRISPR-Cas9 screening. Nat Commun. 2019 May 16;10(1):2198. doi: 10.1038/s41467-019-09940-1. PMID: 31097696; PMCID: PMC6522557.

The transcription factor TEAD, together with its coactivator YAP/TAZ, is a key transcriptional modulator of the Hippo pathway. Activation of TEAD transcription by YAP has been implicated in a number of malignancies, and this complex represents a promising target for drug discovery. Here, we employed covalent fragment screening approach followed by structure-based design to develop an irreversible TEAD inhibitor MYF-03-69. Using a range of in vitro and cell-based assays we demonstrated that through a covalent binding with TEAD palmitate pocket, MYF-03-69 disrupts YAP-TEAD association, suppresses TEAD transcriptional activity and inhibits cell growth of Hippo signaling defective malignant pleural mesothelioma (MPM). Further, a cell viability screening with a panel of 903 cancer cell lines indicated a high correlation between TEAD-YAP dependency and the sensitivity to MYF-03-69. To validate MYF-03-69 as potent and selective pan-TEAD inhibitor, we interrogated the proteome-wide selectivity profile of MYF-03-69 on cysteine labeling using a streamlined cysteine activity-based protein profiling (SLC-ABPP) approach and generated the spreadsheet "Supplementary_Dataset_1._Proteome-wide_selectivity_profile_of_MYF-03-69_on_cysteines_labeling_using_SLC-ABPP_approach". We employed the cysteine reactive desthiobiotin iodoacetamide (DBIA) probe which was reported to map more than 8,000 cysteines and performed a competition study on NCI-H226 cells pretreated with 0.5, 2, 10 or 25 µM of MYF-03-69 for 3 hours in triplicate. The cysteines that were conjugated >50% (competition ratio CR>2) compared to DMSO control were analyzed and assigned to the protein targets. In the DMSO control group, although DBIA mapped 12,498 cysteines in total, the TEAD PBP cysteines were not detected. This might be due to low TEAD1-4 protein abundance and/or inability of the PBP cysteines to be labeled given that they are mostly modified by palmitate under physiological conditions. Among 12,498 mapped cysteines, only 7 cysteines were significantly labeled (i.e. exhibited >50% conjugation or CR>2) by 25 µM of MYF-03-69, and all of these sites exhibited dose-dependent engagement. To study the whole transcriptome perturbation by TEAD inhibitor MYF-03-69, mRNA sequencing was performed in NCI-H226 cells that were treated with 0.1 μM, 0.5 μM, and 2 μM of MYF-03-69 and generated the spreadsheet "Supplementary_Dataset_2._List_of_differentially_expressed_genes_under_MYF-03-69_treatments". The genes that were differentially expressed with statistical significance (Fold change > 1.5 and adjusted p value < 0.05) are listed in this dataset. To investigate whether TEAD inhibition by MYF-03-69 was selectively lethal to YAP/TEAD-dependent cancers, PRISM screening across a broad panel of cell lineages were performed and generated the spreadsheet "Supplementary_Dataset_3". 903 cancer cells were treated with TEAD inhibitor MYF-03-69 for 5 days. The viability values were measured at 8-point dose manner (3-fold dilution from 10 μM) and fitted a dose-response curve for each cell line. Area under the curve (AUC) was calculated as a measurement of compound effect on cell viability. CERES score of YAP1 or TEADs from CRISPR (Avana) Public 21Q1 dataset (DepMap) were listed in the spreadsheet and used to estimate gene-dependency. The CERES Score of most dependent TEAD isoform was used to represent TEAD dependency. With PRISM screen dataset of TEAD inhibitor MYF-03-69, we investigated whether TEAD inhibition recapulates genetically knockout outcome of YAP or TEADs and generated the spreadsheet "Supplementary_Dataset_4". Correlation analysis between compound PRISM sensitivity (log2.AUC of each cell line) and dependency of certain gene (CRISPR knockout score for each cell line, from DepMap Public 20Q4 Achilles_gene_effect.csv dataset) across the PRISM cell line panel. The Pearson correlation coefficients and associated p-values were computed. Positive correlations correspond to dependency correlating with increased sensitivity. The q-values (a corrected significance value accounting for false discovery rate) are computed from p-values using the Benjamini Hochberg algorithm. Associations with q-values above 0.1 are filtered out. This correlation analysis reveals that the dependency scores of TEAD1 and YAP1 according to genomic knockout dataset (DepMap portal) provided the highest correlation with the compound PRISM sensitivity profile. This is followed by TP53BP2, a gene that is also involved in Hippo pathway as activator of TAZ. Methods For "Supplementary_Dataset_1._Proteome-wide_selectivity_profile_of_MYF-03-69_on_cysteines_labeling_using_SLC-ABPP_approach", the date was collected on NCI-H226 cells using the same methods reported in reference paper Reimagining high-throughput profiling of reactive cysteines for cell-based screening of large electrophile libraries | Nature Biotechnology. (Kuljanin, M.; Mitchell, D. C.; Schweppe, D. K.; Gikandi, A. S.; Nusinow, D. P.; Bulloch, N. J.; Vinogradova, E. V.; Wilson, D. L.; Kool, E. T.; Mancias, J. D.; Cravatt, B. F.; Gygi, S. P., Reimagining high-throughput profiling of reactive cysteines for cell-based screening of large electrophile libraries. Nature Biotechnology 2021, 39, 630-641) The competition ratio CR was calculated as descibed in the above reference paper. For "Supplementary_Dataset_2._List_of_differentially_expressed_genes_under_MYF-03-69_treatments", the date was collected on NCI-H226 cells treated with MYF-03-69 at indicated concentrations for 6 hours (n=3). The RNA was extracted using RNeasy plus mini kit (Qiagen, cat no.74134) according to the manufacturer instructions. Then libraries were prepared using Roche Kapa mRNA HyperPrep strand specific sample preparation kits from 200 ng of purified total RNA according to the manufacturer’s protocol on a Beckman Coulter Biomek i7. The finished dsDNA libraries were quantified by Qubit fluorometer and Agilent TapeStation 4200. Uniquely dual indexed libraries were pooled in an equimolar ratio and shallowly sequenced on an Illumina MiSeq to further evaluate library quality and pool balance. The final pool was sequenced on an Illumina NovaSeq 6000 targeting 40 million 100bp read pairs per library at the Dana-Farber Cancer Institute Molecular Biology Core Facilities. Sequenced reads were aligned to the UCSC hg19 reference genome assembly and gene counts were quantified using STAR (v2.7.3a). Differential gene expression testing was performed by DESeq2 (v1.22.1). RNAseq analysis was performed using the VIPER snakemake pipeline. KEGG pathway enrichment analysis was performed through metascape webportal. For "Supplementary_Dataset_3", the date was collected using the methods reported in reference paper Discovering the anticancer potential of non-oncology drugs by systematic viability profiling | Nature Cancer. Briefly, up to 931 barcoded cell lines in pools of 20-25 were thawed and plated into 384-well plates (1250 cells/well for adherent cell pools, 2000 cells/well for suspension or mixed suspension/adherent cell pools) containing compound (top concentration: 10 µM, 8-point, threefold dilution). All conditions were tested in triplicate. Cells were lysed after 5 days of treatment and mRNA based Luminex detection of barcode abundance from lysates was carried out as in the reference paper above. Luminex median fluorescence intensity (MFI) data was input to a standardized R pipeline (https://github.com/broadinstitute/prism_data_processing) to generate viability estimates relative to vehicle treatment for each cell line and treatment condition, and to fit dose-response curves from viability data. CERES score of YAP1 or TEADs from CRISPR (Avana) Public 21Q1 dataset (DepMap) were downloaded from DepMap portal (DepMap Data Downloads) and listed with the viability data. For "Supplementary_Dataset_4", the data was correlation analysis results of "Supplementary_Dataset_3", which was performed in the R pipeline mentioned above (https://github.com/broadinstitute/prism_data_processing).

GDSC1 PharmacoSet (PSet) generated by ORCESTRA. Metadata can be found on ORCESTRA at: http://orcestra.ca/10.5281/zenodo.4730670

Disclaimer

The GDSC1 data have been generated and shared by the Wellcome Trust Sanger Institute as part of the Genomics of Drug Sensitivity in Cancer (GDSC) Programme. The Haibe-Kains Lab has reprocessed and re-annotated the data to maximize overlap with other pharmacogenomic datasets.

Data Usage Policy

Users have a non-exclusive, non-transferable right to use data files for internal proprietary research and educational purposes, including target, biomarker and drug discovery. Excluded from this licence are use of the data (in whole or any significant part) for resale either alone or in combination with additional data/product offerings, or for provision of commercial services.

Please note: The data files are experimental and academic in nature and are not licensed or certified by any regulatory body. Genome Research Limited provides access to data files on an “as is” basis and excludes all warranties of any kind (express or implied). If you are interested in incorporating results or software into a product, or have questions, please contact depmap@sanger.ac.uk.

Source: https://depmap.sanger.ac.uk/documentation/data-usage-policy/

Sanger's terms and conditions: http://www.cancerrxgene.org/legal

Please cite the following when using these data

Iorio F, Knijnenburg TA, Vis DJ, Bignell GR, Menden MP, Schubert M, Aben N, Gonçalves E, Barthorpe S, Lightfoot H, Cokelaer T, Greninger P, van Dyk E, Chang H, de Silva H, Heyn H, Deng X, Egan RK, Liu Q, Mironenko T, Mitropoulos X, Richardson L, Wang J, Zhang T, Moran S, Sayols S, Soleimani M, Tamborero D, Lopez-Bigas N, Ross-Macdonald P, Esteller M, Gray NS, Haber DA, Stratton MR, Benes CH, Wessels LFA, Saez-Rodriguez J, McDermott U, Garnett MJ. A Landscape of Pharmacogenomic Interactions in Cancer. Cell. 2016 Jul 28;166(3):740-754. doi: 10.1016/j.cell.2016.06.017. Epub 2016 Jul 7. PMID: 27397505; PMCID: PMC4967469.

Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR, Ramaswamy S, Futreal PA, Haber DA, Stratton MR, Benes C, McDermott U, Garnett MJ. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013 Jan;41(Database issue):D955-61. doi: 10.1093/nar/gks1111. Epub 2012 Nov 23. PMID: 23180760; PMCID: PMC3531057.

Picco G, Chen ED, Alonso LG, Behan FM, Gonçalves E, Bignell G, Matchan A, Fu B, Banerjee R, Anderson E, Butler A, Benes CH, McDermott U, Dow D, Iorio F, Stronach E, Yang F, Yusa K, Saez-Rodriguez J, Garnett MJ. Functional linkage of gene fusions to cancer cell fitness assessed by pharmacological and CRISPR-Cas9 screening. Nat Commun. 2019 May 16;10(1):2198. doi: 10.1038/s41467-019-09940-1. PMID: 31097696; PMCID: PMC6522557.

Disclaimer

The CCLE data were generated and shared by the Broad Institute of Harvard and MIT as part of the Cancer Cell Line Encyclopedia project. The Haibe-Kains Lab has reprocessed and re-annotated the data to maximize overlap with other pharmacogenomic datasets.

Data Usage Policy

CCLE publishes its data under the Terms and Conditions linked here. The DepMap data, including the CCLE data, are provided under Creative Commons Attribution 4.0 license.

Contact depmap@broadinstitute.org for more information

Please cite the following when using these data

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 omics data:

Mahmoud Ghandi, Franklin W. Huang, Judit Jané-Valbuena, Gregory V. Kryukov, ... Todd R. Golub, Levi A. Garraway & William R. Sellers. 2019. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).

For metabolomics:

Haoxin Li, Shaoyang Ning, Mahmoud Ghandi, Gregory V. Kryukov, Shuba Gopal, ... Levi A. Garraway & William R. Sellers. The landscape of cancer cell line metabolism. Nature Medicine 25, 850-860 (2019).

CTRPv2 PharmacoSet (PSet) generated by ORCESTRA. Metadata can be found on ORCESTRA at: http://orcestra.ca/10.5281/zenodo.3905470

Disclaimer

The CTRPv2 data were generated by the Broad Institute CTD^2 Center and originally released via the Cancer Therapeutics Response Portal (CTRP). The Haibe-Kains Lab has reprocessed and re-annotated the data to maximize overlap with other pharmacogenomic datasets.

Data Usage Policy

The CTD^2 releases data in accordance with their data release policy. The DepMap data, including the CTRPv2 data, are provided under Creative Commons Attribution 4.0 license.

Contact depmap@broadinstitute.org for more information.

Please cite the following when using these data

Seashore-Ludlow B, Rees MG, Cheah JH, Cokol M, Price EV, Coletti ME, Jones V, Bodycombe NE, Soule CK, Gould J, Alexander B, Li A, Montgomery P, Wawer MJ, Kuru N, Kotz JD, Hon CS, Munoz B, Liefeld T, Dančík V, Bittker JA, Palmer M, Bradner JE, Shamji AF, Clemons PA, Schreiber SL. Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset. Cancer Discov. 2015 Nov;5(11):1210-23. doi: 10.1158/2159-8290.CD-15-0235. Epub 2015 Oct 19. PMID: 26482930; PMCID: PMC4631646.

Rees MG, Seashore-Ludlow B, Cheah JH, Adams DJ, Price EV, Gill S, Javaid S, Coletti ME, Jones VL, Bodycombe NE, Soule CK, Alexander B, Li A, Montgomery P, Kotz JD, Hon CS, Munoz B, Liefeld T, Dančík V, Haber DA, Clish CB, Bittker JA, Palmer M, Wagner BK, Clemons PA, Shamji AF, Schreiber SL. Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nat Chem Biol. 2016 Feb;12(2):109-16. doi: 10.1038/nchembio.1986. Epub 2015 Dec 14. PMID: 26656090; PMCID: PMC4718762.

(B) Analyzis of the depmap portal (www.depmap.org) reveals MYCN-amplified SK-N-DZ as hypersensitive cell lines to the GPX4 inhibitors RSL3 and ML210.. List of tagged entities: SK-N-DZ (cellosaurus:CVCL_1701), , cell viability assay (bao:BAO_0003009)

In this study, DDX23 regulates gene splicing, thereby promotes cell proliferation. Supplement table 1 The original data from multiple databases and Venn results，related to Figure1 Supplement table 2 The original data from pull down results，related to Figure2 Supplement table 3 The original data from Depmap database，related to Figure2

This dataset contains the results of screening 43 cancer cell lines with the GeCKO CRISPR-Cas9 genome-scale knockout library generated as part of the Broad Institute's Cancer Dependency Map project. It includes the results of 10 GeCKO screens not previously made public. Descriptions of the experimental methods and the CERES algorithm are published in http://dx.doi.org/10.1038/ng.3984.

Data used for Project: "BIT: Bayesian Identification of Transcriptional Regulators from Epigenomics-Based Query Region Sets"

BIT package is available on GitHub: GitHub

We also provide a online web portal: BIT Portal

Please consult the manual for instructions on loading the reference data.

Please note that the preprocessed reference database must be pre-loaded before running function in BIT!!

File Description:

hg38_200.tar.gz: Pre-processed TR ChIP-seq reference datasets for genome hg38 with bin width 200.

hg38_500.tar.gz: Pre-processed TR ChIP-seq reference datasets for genome hg38 with bin width 500.

hg38_1000.tar.gz: Pre-processed TR ChIP-seq reference datasets for genome hg38 with bin width 1000.

mm10_200.tar.gz: Pre-processed TR ChIP-seq reference datasets for genome mm10 with bin width 200.

mm10_500.tar.gz: Pre-processed TR ChIP-seq reference datasets for genome mm10 with bin width 500.

mm10_1000.tar.gz: Pre-processed TR ChIP-seq reference datasets for genome mm10 with bin width 1000.

Input_Data.tar.gz: contains the input data for the four application cases, including differentially accessible regions (DARs) from bulk and single-cell perturbation experiments, cancer-type-specific accessible regions, and cell-type-specific accessible regions.

Figure_Data_v2.tar.gz: is the updated figure data folder, which includes the data used to generate the manuscript’s plots, as well as the output from the benchmarking methods.

Figure.R: R code to replicate the figures, used together with Figure_Data_v2.tar.gz.

Depmap data can be accessed on DepMap Consortium: DepMap

Photoreactive fragment-like probes have been applied to discover target proteins that constitute novel cellular vulnerabilities and to identify viable chemical hits for drug discovery. Through forming covalent bonds, functionalized probes can achieve stronger target engagement and require less effort for on-target mechanism validation. However, the design of probe libraries, which directly affects the biological target space that is interrogated, and effective target prioritization remain critical challenges of such a chemical proteomic platform. In this study, we designed and synthesized a diverse panel of twenty fragment-based probes with privileged structural motifs containing both natural product and lead-like elements. These probes were fully functionalized with orthogonal diazirine and alkyne moieties and used for protein crosslinking in live lung cancer cells, target enrichment via “click chemistry,” and subsequent target identification through label-free quantitative LC-MS/MS analysis. Pair-wise comparison with a blunted negative control probe and stringent prioritization via individual cross-comparisons against the entire panel identified glutathione S-transferase zeta 1 (GSTZ1) as a specific and unique target candidate. DepMap database query, RNA interference-based gene silencing and proteome-wide tyrosine reactivity profiling suggested that GSTZ1 cooperated with different oncogenic alterations by supporting survival signaling in refractory NSCLC cells. This finding may form the basis for developing novel GSTZ1 inhibitors to improve the therapeutic efficacy of oncogene-directed targeted drugs. In summary, we designed a novel fragment-based probe panel and developed a target prioritization scheme with improved stringency, which allows for identification of unique target candidates, such as GSTZ1 in refractory lung cancer.

Development of MATLAB code to generate a context-specific metabolic model for the A2780 cell line, based on transcriptomic data retrieved from the CCLE_expression_full_22Q2 database (available at: https://depmap.org/portal/download/all/).
In parallel, implementation of Python code to construct a custom metabolic model tailored to specific experimental conditions derived from measurements obtained using Nuclear Magnetic Resonance (NMR) spectroscopy.

RBM39 is a crucial component of the spliceosome that is critical for the integrity of mature mRNA, while depletion of RBM39 by Indisulam significantly increases RNA splicing defects. The antitumor activity of Indisulam is partially attributed to its inhibitory effects on cell cycle progression. To identify the key effector proteins responsible for cell cycle arrest following RBM39 depletion, we employed a multi-omics approach utilizing two chemotypes of RBM39 degraders: Indisulam and CB039. Through proteomics analysis, RNA sequencing, and DepMap cancer cell line dependency analysis, we identified CEP192 as a key gene that exhibited dependency in 96% of 1,100 cancer cell lines. In a panel of eight cancer cell lines, we observed consistent phenotypes upon treatment with CB039 and Indisulam, including skipping of CEP192 exon 42 and downregulation of the CEP192 protein. Mechanistically, treatment with CB039 and Indisulam, as well as CEP192 knockdown via RNA interference, resulted in arrest of cell cycle progression at the G2/M phase. This treatment also induced a disorganized spindle phenotype, as well as condensed and undivided chromosomes. In summary, this work enhances our understanding of the anti-mitotic mechanism underlying RBM39 molecular glue degraders.

Mutational activation of the KRAS oncogene is a major genetic driver of pancreatic ductal adenocarcinoma (PDAC) growth. KRAS-dependent PDAC growth is mediated primarily through persistent activation of the RAF-MEK-ERK mitogen-activated protein kinase (MAPK) cascade, one of the most extensively studied cancer signaling networks. While substrates of RAF and MEK kinases are highly restricted, ERK1/2 has been attributed to over 1,000 substrates. In this study, we used the highly selective ERK1/2 inhibitor, SCH772984, and proteomic and phosphoproteomic analyses to extend the repertoire of ERK-dependent phosphosites and phosphoproteins in PDAC. We validated the specificity of SCH772984 in our cell lines using multiplexed inhibitor beads coupled with mass spectrometry (MIB/MS). We then performed phosphoproteomics and global proteomics in a panel of PDAC cell lines and identified 5,117 ERK-dependent phosphosites on 2,252 proteins, of which 88% and 67%, respectively, were not previously associated with ERK. We then utilized our recently annotated serine/threonine kinome motif database to dissect the phosphoproteome and reveal an expansive ERK-regulated kinase network. We found that ERK- and immediate downstream kinase RSK-substrate motifs predominated after one hour of ERK inhibition, whereas cell cycle regulatory cyclin-dependent kinase motifs predominated by 24 h, reflecting a highly dynamic ERK-dependent phosphoproteome. We find compensatory activation of HIPK, CLK, PKN, PAK, and DYRK family kinases. Finally, using the genome-wide CRISPR-Cas9 dataset in the Cancer Dependency Map portal (DepMap), we determined that approximately 18% of ERK dependent phosphoproteins are essential for pancreatic cancer growth, and these are enriched in nuclear proteins. Together, our findings provide a system-wide profile of the mechanistic basis for ERK-driven pancreatic cancer growth.