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 625 cell lines, the RNAseq data includes 1,210 cell lines, and the copy number data includes 1,657 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. Additional Achilles processing information is published here https://www.biorxiv.org/content/10.1101/720243v1.full. 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.version 2: uploaded a new version of CCLE_gene_cn.csv to correctly reflect released cell lines.version 3: uploaded a new version of CCLE_gene_cn.csv that has the log2 transform correctly applied to it and to removed duplicate cell lines.

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/]

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).

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.

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).

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.

Cancer cell line genetic dependencies estimated using the DEMETER2 model. DEMETER2 is applied to three large-scale RNAi screening datasets: the Broad Institute Project Achilles, Novartis Project DRIVE, and the Marcotte et al. breast cell line dataset. The model is also applied to generate a combined dataset of gene dependencies covering a total of 712 unique cancer cell lines. For more information visit https://depmap.org/R2-D2/. Visit the Cancer Dependency Map portal at https://depmap.org to explore related datasets. Email questions to depmap@broadinstitute.org This dataset includes gene dependencies estimated using the DEMETER2 model, the raw input datasets used to fit the models, as well as associated metadata. See Readme file for more details about the dataset contents and version history.-------------------------------------------------------------------Version history: (see README for more details)-------------------------------------------------------------------v1: Initial data releasev2: - Removed small number of non-human genes (e.g. GFP, RFP) from shRNA-to-gene mapping - Updated cell line names to be consistent with DepMap names, according to the following map (old -> new):v3: Added estimated seed effect matricesv4: Added RNAseq and mutation data files used in analysis for manuscriptv5: Fixed minor bug with Marcotte LFC data that caused hairpins targeting multiple genes to appear multiple times in the LFC matrix. This created bias in the seed effect estimates for those hairpins, causing very minor differences to the resulting model parameters.v6: Added tables with shRNA quality metrics for Achilles and DRIVE data

Additional file 3: S3. The relationship between the drug sensitivity and the expression of LARS and DNAJC17 based on the data from the GDSC.

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

Files contained in here come from data files used and are related to analysis and figure generation. Code notebooks within the code folder will point to these specific data files. Not all data files used are uploaded to this specific repository to avoid redistribution of other published work (specifically HumanNet files, CCLE/DepMap CERES, clinical files - TCGA/OHSU/TARGET data, and the Cancer Gene Census from COSMIC).Descriptions of data files contained in folder:AML_age.txt - curated AML cell line data and age of derived patient.Avana_Corrected_FC_2020_Q4.txt - Crispr cleanR corrected fold-change data of the 2020q4 Avana release.Avana_NORM_MIXEM_FC_2020_Q4.txt - mu and sigma calculations Mixed model (k=2) for each screen's null distribution from Avana 2020q4.avana_output_update_2020_Q4 - Primary data file used to complete figure analysis. Data file contains, depmap cell line id, entrez id, gene name, mean log2FC, CCLE expression, binary classification of mutation status, mixed z-score of gene, binary classification of cosmic TSG status, binary classification of non essential gene status, mean log2FC ranking, and hit_mix which represents PSG classification for each gene-cell line pair from of the Avana 2020q4 distribution.bf_avana_2020q4_CRISPRcleanR_corrected.noNA - Crispr cleanR corrected bagel scores for the Avana 2020q4 distribution.data_not_redistributed.xlsx - description and sources of data not uploaded to figshare to avoid redistribution of other published data. dPCC-AML-qualFilt-varFilt.txt - filtered dPCC correlations related to figure 3.fisher_edges_mix_hits_tsg.txt - Text file of all PSG gene pairs, and fishers test pvalue, and total count of gene observations as a hit (count not used for analysis).fisher_net_mix_Z_fdr_0.001.txt - FDR < 0.001 filtered network of all PSG gene pairs, and fishers test pvalue, and total count of gene observations as a hit (count not used for analysis). Main network used for analyses.genes-significant-dPCC-with-chp1-cluster-zSTD-filter.txt - Genes filtered and selected for dPCC heatmap analysis of figure 3e.Human_net_cutoff_results_updated.txt - Human net comparisons and cutoffs used for supplemental figure 4b.Hunet_comparison_update.Rdata - Human net comparisons and cutoffs used for supplemental figure 4a.JACKS_result_gene_JACKS_results.txt - Crispr cleanR corrected JACKS scores for Avana 2020q4 distribution. log_normalmixEM.txt - log file of mixture model iterations of avana2020q4.matrix-GMMZ-qualFilter-varFilter-9055genes-659cells-17aml.txt - Selecting appropriate AML cells for dpcc analysis in figure 3e.metabolite_error.txt - Metabolite variance measurements used in determining viable metabolites for analysis. Metabolites that had measurements below error were not used.Mix_Z_pr_values_updated.txt - precision recall measurements and associated mixed z-scores of pr cutoffs. used to determine FDR cutoff measurements. NEGv1.txt - Non essential genes from bagel.PTEN_CN.txt - PTEN copy number values from CCLE.Sanger_Corrected_FC.txt - Crispr cleanR corrected fold-change data of the Sanger 2019 release.

E. Dependency plot depicting the mean dependency of the 3 MCC cell lines PeTa, MKL-1, MKL-2 of the genes targeted by the compound library in Fig 1A. A score of 0 indicates that a gene is not essential; correspondingly -1 is comparable to the median of all pan-essential genes. Data obtained from DepMap; dependencies for the individual cell lines are displayed in Fig EV1F.. List of tagged entities: multiple components, BRD4 (ncbigene:23476), KDM1A (ncbigene:23028), PRMT5 (ncbigene:10419), TAF1 (ncbigene:6872), WDR5 (ncbigene:11091), , Dependency plot, Merkel cell carcinoma (doid:DOID:3965)

• Table 1: Pearson Correlation Analysis of ssGSEA EMT Enrichment score vs CRISPR gene dependency score for all solid tumor cell lines in DepMap, excluding sarcomas - Table 2: Pearson Correlation Analysis of ssGSEA EMT Enrichment score vs CRISPR gene dependency score and ssGSEA EMT Enrichment score vs mRNA Expression for all solid tumor cell lines in DepMap, excluding sarcomas - Table 3: Results of the Quantitative Phosphoproteomics experiment of Kinase Active (KA) vs Kinase Dead (KD) PKN2 expressing 293FT cells - Table 4: Candidate list of direct PKN2 substrates -Table 5: Correlation Analysis of ssGSEA YAP/TAZ Enrichment score vs Protein Abundance for all patient tumors in CPTAC - Table 6: Correlation Analysis of ssGSEA YAP/TAZ Enrichment score vs Phosphoprotein Abundance for all patient tumors in CPTAC - Table 7: Substrate Signatures used for CPTAC Kinase Enrichment Analysis

Additional File 3: Table S2. Positive and negative control genes and their membership to training sets and DepMap datasets.

Raw data related to 1) Drug Sensitivity Screening; 2) Metabolic Profiling; 3) Transcript levels of NAD; 4) DepMap Depletion Score for NAD; 5) NP ratio in mutated vs WT GCB-DLBCL samples; 6) NP mRNA ratio in DLBCL primary cases

Dynamins are defined as a group of molecules with GTPase activity. Among them, DNM3 has gained recognition in oncology for its tumor suppressor role. Based on this, the aim of this study is to investigate the effects of the DNM3 gene in patients diagnosed with pancreatic cancer using bioinformatics databases. For differential gene expression analysis, TCGA TARGET GTEx study on the UCSC Xena and GEO datasets were utilized; for the analysis of changes in gene expression according to clinical and pathological characteristics, UALCAN was employed; for Overall Survival (OS) analysis, Kaplan-Meier Plotter was used; for gene alteration analysis, cBioPortal was utilized; for immune cell infiltration analysis, Tumor Immune Estimation Resource (TIMER) and TIMER2.0 were employed; for enrichment analyses Enrichr was used; for Gene Set Correlation Enrichment Analysis Gscore was used on GSE15471; for essentiality of DNM3 gene in pancratic cancer cell lines DepMap was used; and for the detection of miRNAs, miRDB was utilized; ENCORI was used for gene-miRNA correlation and miRNA prognosis analyses. In the pancreatic adenocarcinoma (PAAD) cohort, DNM3 gene expression was higher in tumor samples, and there was no significant difference in expression among cancer stages. High levels of DNM3 gene expression were associated with longer OS in PAAD. A weak positive correlation was observed between DNM3 gene expression and B-Cell and CD4+ T Cell infiltrations, while a moderate positive correlation was found with CD8+ T Cell, Macrophage, Neutrophil, and Dendritic Cell infiltrations in TIMER. NK cell by QUANTISEQ, CD 4+ T Cell by TIMER, T cell regulatory (Tregs) by CIBERSORT-ABS infiltrations were positively associated with DNM3 gene expression and decreased risk in prognosis. Common lymphoid progenitor by XCELL and MDSC by TIDE infiltrations were negatively associated with DNM3 gene expression and increased risk of prognosis. Macrophage M1 by QUANTISEQ was positively associated with DNM3 gene expression and increased risk in prognosis. DNM3 gene appears to be associated with various pathways related to inflammation and the immune system. Amplification of the DNM3 gene was detected in 5 out of 175 patients. Enrichment was observed in pathways such as bacterial invasion of epithelial cells, endocytosis, endocrine and other factor-regulated calcium reabsorption, synaptic vesicle cycle, and phospholipase D signaling pathway. According to Gscore, DNM3 gene was associated with Fc epsilon RI signaling pathway, HALLMARK MTORC1 SIGNALING, HALLMARK EPITHELIAL MESENCHYMAL TRANSITION gene sets. According to ENCORI, DNM3 gene was negatively correlated with hsa-miR-203a-3p and increased expression of this miRNA was associated with adverse prognosis in PAAD. The DNM3 gene may play a tumor suppressor role in pancreatic cancer, similar to its role in other malignancies. The contribution of immune cells may also be significant in this effect. However, in vitro studies are needed to elucidate the mechanisms triggered in pancreatic cancer.

Supplementary Table 1: PRISM data integrated with omics data from depmap Supplementary Table 2:Top 20 sensitive cell lines (PRISM) Supplementary Table 3:Frequency of KRAS WT AMP in AACR Genie v 15.1 (MSK cohort) Supplementary Table 4: Mututal exclusivity for KRAS WT AMP (results downloaded from AACR Genie v15.1) Supplementary Table 5 IC50 values from CTG assays for KRAS wt amplified cell lines

Bladder cancer (BCa) is the 10th most commonly diagnosed cancer worldwide, and cellular senescence is defined as a state of permanent cell cycle arrest and considered to play important roles in the development and progression of tumor. However, the comprehensive effect of senescence in BCa has not ever been systematically evaluated. Using the genome-wide CRISPR screening data acquired from DepMap (Cancer Dependency Map), senescence genes from the CellAge database, and gene expression data from The Cancer Genome Atlas (TCGA), we screened out 12 senescence genes which might play critical roles in BCa. A four-cell-senescence-regulator-gene prognostic index was constructed using the least absolute shrinkage and selection operator (LASSO) and multivariate COX regression model. The transcriptomic data and clinical information of BCa patients were downloaded from TCGA and Gene Expression Omnibus (GEO). We randomly divided the patients in TCGA cohort into training and testing cohorts and calculated the risk score according to the expression of the four senescence genes. The validity of this risk score was validated in the testing cohort (TCGA) and validation cohort (GSE13507). The Kaplan–Meier curves revealed a significant difference in the survival outcome between the high- and low-risk score groups. A nomogram including the risk score and other clinical factors (age, gender, stage, and grade) was established with better predictive capacity of OS in 1, 3, and 5 years. Besides, we found that patients in the high-risk group had higher tumor mutation burden (TMB); lower immune, stroma, and ESTIMATE scores; higher tumor purity; aberrant immune functions; and lower expression of immune checkpoints. We also performed gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) to investigate the interaction between risk score and hallmark pathways and found that a high risk score was connected with activation of senescence-related pathways. Furthermore, we found that a high risk score was related to better response to immunotherapy and chemotherapy. In conclusion, we identified a four-cell-senescence-regulator-gene prognostic index in BCa and investigated its relationship with TMB, the immune landscape of tumor microenvironment (TME), and response to immunotherapy and chemotherapy, and we also established a nomogram to predict the prognosis of patients with BCa.