Defining a Cancer Dependency Map
Open accessAbstract
Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.
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The advent of the CRISPR/Cas9 system has enabled screens that cover whole genomes.
“In addition, the advent of the CRISPR/Cas9 system has enabled screens that cover whole genomes [17,96].”
From Functional unknomics: Systematic screening of conserved genes of unknown function (2023) · cites this paper as [96]
The Discussion describes CRISPR-Cas9 as a method for identifying essential genes and refers to CRISPR-Cas9 screens. The Introduction identifies the paper’s genome-scale screens as RNAi-based, however; our copy does not establish that CRISPR/Cas9 enabled whole-genome screens. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAnalysis of PTGES3 gene essentiality upon Cas9 knockout across 1,070 diverse cancer cell lines in the Cancer Dependency Map demonstrated PTGES3 is not classified as a commonly essential gene.
“Notably, analysis of PTGES3 gene essentiality upon Cas9 knockout across 1,070 diverse cancer cell lines present in the Cancer Dependency Map53 demonstrated that PTGES3 is not classified as a commonly essential gene (Extended Data Fig. 10c).”
From Genome-scale CRISPR screens identify PTGES3 as a direct modulator of androgen receptor function in advanced prostate cancer (2025) · cites this paper as [53]
The Discussion identifies this work as a cancer dependency map, supporting the citation as an identification of the resource. But the Abstract and Discussion describe RNAi screens in 501 cell lines, not Cas9 knockout screens in 1,070 lines. Our copy does not report the PTGES3 classification; the citing sentence presents that analysis as its own (Extended Data Fig. 10c). (LLM, conf=0.84)
Experimental — accuracy not yet measuredAlthough several large databases of perturbations with bulk readouts exist, single-cell perturbation technologies are newer and their data are not unified.
“Although several large databases of perturbations with bulk readouts exist, single-cell perturbation technologies are newer and the data are not unified31,32.”
From scPerturb: harmonized single-cell perturbation data (2024) · cites this paper as [32]
RNAi and CRISPR studies in hundreds of cancer cell lines show KRAS mutant pancreatic, lung, and colorectal adenocarcinoma cells are more sensitive to loss of the KRAS oncogene than KRAS WT cancer cell lines.
“RNAi mediated gene knockdown studies and CRISPR mediated gene knockout studies in hundreds of cancer cell lines have shown that KRAS mutant pancreatic, lung and colorectal adenocarcinoma cells are more sensitive to the loss of the KRAS oncogene than KRAS WT cancer cell lines (64, 65) (Figure 4).”
From KRAS mutation in pancreatic cancer (2021) · cites this paper as [65]
In silico analysis showed all KRAS-mutant-dependent cell lines exhibited growth modulation related to PTK2 expression, unlike any KRAS wild-type cancer cell lines.
“Analysis of data for shRNA modulated PTK2 knockdown showed that, unlike any of the KRAS wild‐type cancer cell lines, all of the KRAS‐mutant‐dependent cell lines exhibited growth modulation related to PTK2 expression (Figure S2B, Supporting Information).”
From Focal Adhesion Kinase (FAK) Inhibition Synergizes with KRAS G12C Inhibitors in Treating Cancer through the Regulation of the FAK–YAP Signaling (2021) · cites this paper as [22]
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Self-citationProteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma
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Artificial Intelligence in Cancer Research and Precision Medicine
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Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
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Patient-derived xenografts undergo mouse-specific tumor evolution
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Self-citationMutational processes shape the landscape of TP53 mutations in human cancer
Giacomelli, A. O., Yang, X., Lintner, R. E., McFarland, J. M., Duby, M., Kim, J., Howard, T. P., Takeda, D. Y., Ly, S. H., Kim, E., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0204-y · Cited by 556 · see citing works
Self-citationBAF complex vulnerabilities in cancer demonstrated via structure-based PROTAC design
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WRN helicase is a synthetic lethal target in microsatellite unstable cancers
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Self-citationGenome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis
Tian, R., Abarientos, A., Hong, J., Hashemi, S. H., Yan, R., Dräger, N., Leng, K., Nalls, M. A., Singleton, A. B., Xu, K., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00862-0 · Cited by 414 · see citing works
The BCL2 family: from apoptosis mechanisms to new advances in targeted therapy
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KRAS mutation in pancreatic cancer
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Functional precision cancer medicine—moving beyond pure genomics
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Targeting an RNA-Binding Protein Network in Acute Myeloid Leukemia
Wang, E., Lu, S. X., Pastore, A., Chen, X., Imig, J., Chun-Wei Lee, S., Hockemeyer, K., Ghebrechristos, Y. E., Yoshimi, A., Inoue, D., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.01.010 · Cited by 338 · see citing works
CRISPR screens in cancer spheroids identify 3D growth-specific vulnerabilities
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Mapping the Genetic Landscape of Human Cells
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A non-canonical tricarboxylic acid cycle underlies cellular identity
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Cas9 activates the p53 pathway and selects for p53-inactivating mutations
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Self-citationGlobal Genetic Networks and the Genotype-to-Phenotype Relationship
Costanzo, M., Kuzmin, E., van Leeuwen, J., Mair, B., Moffat, J., Boone, C., & Andrews, B. (2019) · Cell · doi:10.1016/j.cell.2019.01.033 · Cited by 241 · see citing works
PROTACs: great opportunities for academia and industry (an update from 2020 to 2021)
He, M., Cao, C., Ni, Z., Liu, Y., Song, P., Hao, S., He, Y., Sun, X., & Rao, Y. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-00999-9 · Cited by 283 · see citing works
Maintaining Iron Homeostasis Is the Key Role of Lysosomal Acidity for Cell Proliferation
Weber, R. A., Yen, F. S., Nicholson, S. P. V., Alwaseem, H., Bayraktar, E. C., Alam, M., Timson, R. C., La, K., Abu-Remaileh, M., Molina, H., et al. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.01.003 · Cited by 236 · see citing works
Big data in basic and translational cancer research
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TF-PROTACs Enable Targeted Degradation of Transcription Factors
Liu, J., Chen, H., Kaniskan, H. Ü., Xie, L., Chen, X., Jin, J., & Wei, W. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c03852 · Cited by 247 · see citing works
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Genetic Screen for Cell Fitness in High or Low Oxygen Highlights Mitochondrial and Lipid Metabolism
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The origins and genetic interactions of KRAS mutations are allele- and tissue-specific
Cook, J. H., Melloni, G. E. M., Gulhan, D. C., Park, P. J., & Haigis, K. M. (2021) · Nature Communications · doi:10.1038/s41467-021-22125-z · Cited by 208 · see citing works
A genome-wide atlas of co-essential modules assigns function to uncharacterized genes
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Self-citationscPerturb: harmonized single-cell perturbation data
Peidli, S., Green, T. D., Shen, C., Gross, T., Min, J., Garda, S., Yuan, B., Schumacher, L. J., Taylor-King, J. P., Marks, D. S., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02144-y · Cited by 194 · see citing works
Selective alanine transporter utilization creates a targetable metabolic niche in pancreatic cancer
Parker, S. J., Amendola, C. R., Hollinshead, K. E. R., Yu, Q., Yamamoto, K., Encarnación-Rosado, J., Rose, R. E., LaRue, M. M., Sohn, A. S. W., Biancur, D. E., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-0959 · Cited by 175 · see citing works
Targeting histone acetylation dynamics and oncogenic transcription by catalytic P300/CBP inhibition
Hogg, S. J., Motorna, O., Cluse, L. A., Johanson, T. M., Coughlan, H. D., Raviram, R., Myers, R. M., Costacurta, M., Todorovski, I., Pijpers, L., et al. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.04.015 · Cited by 170 · see citing works
A first-generation pediatric cancer dependency map
Dharia, N. V., Kugener, G., Guenther, L. M., Malone, C. F., Durbin, A. D., Hong, A. L., Howard, T. P., Bandopadhayay, P., Wechsler, C. S., Fung, I., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00819-w · Cited by 153 · see citing works
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“These correlates can serve as potential biomarkers, guiding patient stratification and helping clinicians tailor treatments to molecular profiles most likely to respond (C. Yu et al. 2016; Tsherniak et al. 2017).”
Exploration of databases and methods supporting drug repurposing: a comprehensive survey
Tanoli, Z., Seemab, U., Scherer, A., Wennerberg, K., Tang, J., & Vähä-Koskela, M. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa003 · Cited by 131 · see citing works
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MAP kinase and autophagy pathways cooperate to maintain RAS mutant cancer cell survival
Lee, C.-S., Lee, L. C., Yuan, T. L., Chakka, S., Fellmann, C., Lowe, S. W., Caplen, N. J., McCormick, F., & Luo, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1817494116 · Cited by 126 · see citing works
Functional genomics in vivo reveal metabolic dependencies of pancreatic cancer cells
Zhu, X. G., Chudnovskiy, A., Baudrier, L., Prizer, B., Liu, Y., Ostendorf, B. N., Yamaguchi, N., Arab, A., Tavora, B., Timson, R., et al. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2020.10.017 · Cited by 130 · see citing works
Selective USP7 inhibition elicits cancer cell killing through a p53-dependent mechanism.
Schauer, N. J., Liu, X., Magin, R. S., Doherty, L. M., Chan, W. C., Ficarro, S. B., Hu, W., Roberts, R. M., Iacob, R. E., Stolte, B., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-62076-x · Cited by 125 · see citing works
Self-citationHarnessing the predictive power of preclinical models for oncology drug development
Honkala, A., Malhotra, S. V., Kummar, S., & Junttila, M. R. (2022) · Nature Reviews Drug Discovery · doi:10.1038/s41573-021-00301-6 · Cited by 121 · see citing works
Predictomes, a classifier-curated database of AlphaFold-modeled protein-protein interactions
Schmid, E. W., & Walter, J. C. (2025) · Molecular Cell · doi:10.1016/j.molcel.2025.01.034 · Cited by 120 · see citing works
“We therefore included genome-wide features of each protein pair that were external to AlphaFold, including co-dependency data from DEPMAP 46 , coexpression data 47 , T5 protein language model embeddings 48 , subcellular colocalization predictions from DeepLoc 2.0 49 , hit profiles from the BioORCS CRISPR screen databa…”
InfluentialKnowledge graphs and their applications in drug discovery
MacLean, F. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1910673 · Cited by 105 · see citing works
Predicting cellular responses to perturbation across diverse contexts with State
Adduri, A. K., Gautam, D., Bevilacqua, B., Naghipourfar, M., Imran, A., Shah, R., Teyssier, N., Verma, R., Carpenter, C., Eraslan, B., et al. (2026) · Cell · doi:10.1016/j.cell.2026.07.052 · Cited by 100 · see citing works
Viswanatha, R., Li, Z., Hu, Y., & Perrimon, N. (2018) · eLife · doi:10.7554/elife.36333 · Cited by 79 · see citing works
Synthetic Lethal Vulnerabilities in <i>KRAS</i>-Mutant Cancers
Aguirre, A. J., & Hahn, W. C. (2018) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a031518 · Cited by 90 · see citing works
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Zhang, B., Zhang, Y., Zhang, J., Liu, P., Jiao, B., Wang, Z., & Ren, R. (2021) · Advanced Science · doi:10.1002/advs.202100250 · Cited by 83 · see citing works
Computational drug repurposing: approaches, evaluation of in silico resources and case studies
Tanoli, Z., Fernández‐Torras, A., Özcan, U. O., Kushnir, A., Nader, K. M., Gadiya, Y., Fiorenza, L., Ianevski, A., Vähä-Koskela, M. J. V., Miihkinen, M., et al. (2025) · Nature Reviews Drug Discovery · doi:10.1038/s41573-025-01164-x · Cited by 99 · see citing works
Predicting and characterizing a cancer dependency map of tumors with deep learning
Chiu, Y.-C., Zheng, S., Wang, L.-J., Iskra, B. S., Rao, M. K., Houghton, P. J., Huang, Y., & Chen, Y. (2021) · Science Advances · doi:10.1126/sciadv.abh1275 · Cited by 84 · see citing works
Functional unknomics: Systematic screening of conserved genes of unknown function
Rocha, J. J., Jayaram, S. A., Stevens, T. J., Muschalik, N., Shah, R. D., Emran, S., Robles, C., Freeman, M., & Munro, S. (2023) · PLOS Biology · doi:10.1371/journal.pbio.3002222 · Cited by 70 · see citing works
Multiple oestradiol functions inhibit ferroptosis and acute kidney injury
Tonnus, W., Maremonti, F., Gavali, S., Schlecht, M., Gembardt, F., Belavgeni, A., Leinung, N., Flade, K., Bethe, N., Traikov, S., et al. (2025) · Nature · doi:10.1038/s41586-025-09389-x · Cited by 79
“We compared expression of ACLS4 , FAR1 and AGPS , respectively, in ESR1 + and ESR1 − breast cancer cell lines and detected a strong inverse correlation of all three genes with ESR1 expression 37 (Fig.”
InfluentialPrognostic significance and potential association between ALDOA and ENO1 in gastric cancer
Wang, X., Lu, Y.-B., Cheng, Z., Yao, Y.-Z., & Shao, X. (2025) · Journal of Cancer · doi:10.7150/jca.114369 · Cited by 76
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Mapping the multiscale structure of biological systems
Schaffer, L. V., & Ideker, T. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.012 · Cited by 75 · see citing works
CDK4 selective inhibition improves preclinical anti-tumor efficacy and safety
Palmer, C. L., Boras, B. W., Pascual, B., Li, N., Li, D., Garza, S. J., Huser, N. H., Yuan, J. T., Cianfrogna, J. A., Sung, T., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.02.006 · Cited by 79 · see citing works
Hallmarks of artificial intelligence contributions to precision oncology
Chang, T., Park, S., Schäffer, A. A., Jiang, P., & Ruppin, E. (2025) · Nature Cancer · doi:10.1038/s43018-025-00917-2 · Cited by 80 · see citing works
“The need to better characterize cancer initiation and progression has driven the generation of large datasets through diverse high-throughput techniques 5,6 .”
Isolating salient variations of interest in single-cell data with contrastiveVI
Weinberger, E., Lin, C., & Lee, S.-I. (2023) · Nature Methods · doi:10.1038/s41592-023-01955-3 · Cited by 66 · see citing works
Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk
Reshef, Y. A., Finucane, H. K., Kelley, D. R., Gusev, A., Kotliar, D., Ulirsch, J. C., Hormozdiari, F., Nasser, J., O’Connor, L., van de Geijn, B., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0196-7 · Cited by 68 · see citing works
Targeting the Hippo pathway in cancer
Harvey, K. F., & Tang, T. T.-L. (2025) · Nature Reviews Drug Discovery · doi:10.1038/s41573-025-01234-0 · Cited by 64 · see citing works
“Thus far, the field has attempted to infer the relevance of the TEADs in cancer by examining their expression in cell lines and patient samples and via large-scale genetic screens performed as part of the Cancer Dependency Map 176 .”
Brubaker, D. K., Paulo, J. A., Sheth, S., Poulin, E. J., Popow, O., Joughin, B. A., Strasser, S. D., Starchenko, A., Gygi, S. P., Lauffenburger, D. A., et al. (2019) · Cell Systems · doi:10.1016/j.cels.2019.07.006 · Cited by 57 · see citing works
“Integration of Project Achilles data—We obtained gene dependency probability data for PDAC (n = 22) and CRC (n = 27) cell lines from Project Achilles to investigate the essentiality of genes in the humanized KiPNA networks and identify potential therapeutic targets (Aguirre et al., 2016; Cowley et al., 2014; Meyers et…”
BackgroundMethodHigh-content phenotypic and pathway profiling to advance drug discovery in diseases of unmet need
Hughes, R. E., Elliott, R. J. R., Dawson, J. C., & Carragher, N. O. (2021) · Cell Chemical Biology · doi:10.1016/j.chembiol.2021.02.015 · Cited by 62 · see citing works
VRK1 as a synthetic lethal target in VRK2 promoter-methylated cancers of the nervous system
So, J., Mabe, N. W., Englinger, B., Chow, K.-H., Moyer, S. M., Yerrum, S., Trissal, M. C., Marques, J. G., Kwon, J. J., Shim, B., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.158755 · Cited by 45 · see citing works
Self-citationResistance mechanisms and therapeutic strategies of CDK4 and CDK6 kinase targeting in cancer
Asciolla, J. J., Wu, X., Adamopoulos, C., Gavathiotis, E., & Poulikakos, P. I. (2025) · Nature Cancer · doi:10.1038/s43018-024-00893-z · Cited by 62 · see citing works
Sviderskiy, V. O., Blumenberg, L., Gorodetsky, E., Karakousi, T. R., Hirsh, N., Alvarez, S. W., Terzi, E. M., Kaparos, E., Whiten, G. C., Ssebyala, S., et al. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.10.016 · Cited by 56 · see citing works
Solute carriers: The gatekeepers of metabolism
Khan, A., Liu, Y., Gad, M., Kenny, T. C., & Birsoy, K. (2025) · Cell · doi:10.1016/j.cell.2025.01.015 · Cited by 63 · see citing works
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Molecular grammars of predicted intrinsically disordered regions that span the human proteome
Ruff, K. M., King, M. R., Ying, A. W., Liu, V., Pant, A., Lieberman, W. E., Shinn, M. K., Su, X., Kadoch, C., & Pappu, R. V. (2025) · Cell · doi:10.1016/j.cell.2025.10.019 · Cited by 57 · see citing works
New horizons at the interface of artificial intelligence and translational cancer research
Yates, J., & Allen, E. M. V. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.03.018 · Cited by 55 · see citing works
Converging mechanism of UM171 and KBTBD4 neomorphic cancer mutations
Xie, X., Zhang, O., Yeo, M. J. R., Lee, C., Tao, R., Harry, S. A., Payne, N. C., Nam, E., Paul, L., Li, Y., et al. (2025) · Nature · doi:10.1038/s41586-024-08533-3 · Cited by 50 · see citing works
“1c) 23 .”
Genome-wide CRISPR screens identify critical targets to enhance CAR-NK cell antitumor potency
Biederstädt, A., Başar, R., Park, J.-M., Uprety, N., Shrestha, R., Silva, F. R., Dede, M., Watts, J., Acharya, S., Xiong, D., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.07.021 · Cited by 55 · see citing works
High-resolution characterization of gene function using single-cell CRISPR tiling screen
Yang, L., Chan, A. K. N., Miyashita, K., Delaney, C. D., Wang, X., Li, H., Pokharel, S. P., Li, S., Li, M., Xu, X., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24324-0 · Cited by 45 · see citing works
Lambourne, L., Mattioli, K., Santoso, C., Sheynkman, G., Inukai, S., Kaundal, B., Berenson, A., Spirohn-Fitzgerald, K., Bhattacharjee, A., Rothman, E., et al. (2025) · Molecular Cell · doi:10.1016/j.molcel.2025.03.004 · Cited by 42 · see citing works
The role of wild-type RAS in oncogenic RAS transformation
Sheffels, E., & Kortum, R. L. (2021) · Genes · doi:10.3390/genes12050662 · Cited by 42 · see citing works
Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis
Tian, R., Abarientos, A., Hong, J., Hashemi, S. H., Yan, R., Dräger, N., Leng, K., Nalls, M. A., Singleton, A. B., Xu, K., et al. (2020) · doi:10.1101/2020.06.27.175679 · Cited by 414 · see citing works
Identification and Characterization of Oncogenic <i>SOS1</i> Mutations in Lung Adenocarcinoma
Cai, D., Choi, P. S., Gelbard, M., & Meyerson, M. (2019) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-18-0316 · Cited by 41 · see citing works
Identification of human gene research articles with wrongly identified nucleotide sequences
Park, Y., West, R. A., Pathmendra, P., Favier, B., Stoeger, T., Capes-Davis, A., Cabanac, G., Labbé, C., & Byrne, J. A. (2022) · Life Science Alliance · doi:10.26508/lsa.202101203 · Cited by 37 · see citing works
“Researchers can also compare the claims of gene-focussed articles with those from high-throughput experimental studies (65) and/or predictive algorithms (63, 66).”
Direct sensing of dietary ω-6 linoleic acid through FABP5-mTORC1 signaling.
Koundouros, N., Nagiec, M. J., Bullen, N., Noch, E., Burgos-Barragan, G., Li, Z.-C., He, L., Cho, S., Parang, B., Leone, D., et al. (2025) · Science · doi:10.1126/science.adm9805 · Cited by 39 · see citing works
Das, R., Sjöström, M., Shrestha, R., Yogodzinski, C., Egusa, E. A., Chesner, L. N., Chen, W. S., Chou, J., Dang, D. K., Swinderman, J. T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24919-7 · Cited by 38 · see citing works
Kim, D., Kesavan, R., Ryu, K., Dey, T., Marckx, A., Menezes, C. J., Praharaj, P. P., Morley, S. A., Ko, B., Soflaee, M. H., et al. (2025) · Nature Cell Biology · doi:10.1038/s41556-025-01655-4 · Cited by 38 · see citing works
Sparse dictionary learning recovers pleiotropy from human cell fitness screens
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“Drug response information was sourced from three databases: GDSC (https://www.cancerrxgene.org/), CTRP (https://portals.broadinstitute.org/ctrp/), 33 .”
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Perturbomics: CRISPR–Cas screening-based functional genomics approach for drug target discovery
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“Landmark projects such as the DepMap project 19 have generated extensive datasets of genome-wide CRISPR screening results across diverse cancer cell lines.”
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“The broad role that PRMT5 plays in regulating cellular homeostasis renders it an essential gene, which is supported by data from CRISPR-based cellular screens [7].”
- Authors
- Tsherniak, A., Vazquez, F., Montgomery, P. G., Weir, B. A., Kryukov, G., Cowley, G. S., Gill, S., Harrington, W. F., Pantel, S., Krill-Burger, J. M., Meyers, R. M., Ali, L., Goodale, A., Lee, Y., Jiang, G., Hsiao, J., Gerath, W. F. J., Howell, S., Merkel, E., Ghandi, M., Garraway, L. A., Root, D. E., Golub, T. R., Boehm, J. S., & Hahn, W. C.
- Affiliations
- Broad Institute (US)
- Corresponding author
- Hahn, William C.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 170
- Issue
- 3
- Pages
- 564-576.e16
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC5667678
- OpenAlex
- W2741508285
- Cited by
- 3,276
- References
- 57
- Citations by year
- 2026: 464 · 2025: 678 · 2024: 521 · 2023: 406 · 2022: 342 · 2021: 334 · 2020: 244 · 2019: 171 · 2018: 91 · 2017: 22
- FWCI
- 49.24
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (U01 CA176058)
- NIH ICBP (U54 CA112962)
- The Carlos Slim Foundation
- Slim Initiative for Genomic Medicine
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- cancer dependency, dependency map, loss-of-function screen, Demeter, gene essentiality, prediction model, gene expression biomarkers, DNA hypermethylation, UBB, UBC, drug target prioritization, epithelial tumors, cancer cell lines
- MeSH terms
- Humans, Neoplasms, Software, Ubiquitin, RNA Interference, Cell Line, Tumor
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