The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredIn the citing paper
“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]
In the citing paper
“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 the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data
Wang, K., Li, M., & Hakonarson, H. H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq603 · Cited by 14,724
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950
Self-citationThe GeneCards suite: from gene data mining to disease genome sequence analyses
Stelzer, G., Rosen, N., Plaschkes, I., Zimmerman, S., Twik, M., Fishilevich, S., Stein, T. I., Nudel, R., Lieder, I., Mazor, Y., et al. (2016) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.5 · Cited by 5,550
Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples
Cibulskis, K., Lawrence, M. S., Carter, S. L., Sivachenko, A., Jaffe, D. B., Sougnez, C. L., Gabriel, S., Meyerson, M. M., Lander, E. S., & Getz, G. A. (2013) · Nature Biotechnology · doi:10.1038/nbt.2514 · Cited by 4,965
Unbiased Recursive Partitioning: A Conditional Inference Framework
Hothorn, T., Hornik, K., & Zeileis, A. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x133933 · Cited by 4,021
Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1
Barbie, D. A., Tamayo, P., Boehm, J. S., Kim, S. Y., Moody, S. E., Dunn, I. F., Schinzel, A. C., Sandy, P., Meylan, E., Scholl, C., et al. (2009) · Nature · doi:10.1038/nature08460 · Cited by 3,922
Self-citationConditional variable importance for random forests
Strobl, C., Boulesteix, A., Kneib, T., Augustin, T., & Zeileis, A. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-307 · Cited by 3,467
Lentivirus-delivered stable gene silencing by RNAi in primary cells.
Stewart, S. A., Dykxhoorn, D. M., Palliser, D., Mizuno, H., Yu, E. Y., An, D., Sabatini, D., Chen, I. S. Y., Hahn, W., Sharp, P., et al. (2003) · RNA: A publication of the RNA Society · doi:10.1261/rna.2192803 · Cited by 1,619
Self-citationIdentification and characterization of essential genes in the human genome
Wang, T., Birsoy, K., Hughes, N. W., Krupczak, K. M., Post, Y., Wei, J. J., Lander, E. S., & Sabatini, D. M. (2015) · Science · doi:10.1126/science.aac7041 · Cited by 1,617
Flagged first, then by citations
Cited by
Sorted by citations
A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles
Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.049 · Cited by 3,515
Self-citationNext-generation characterization of the Cancer Cell Line Encyclopedia
Ghandi, M., Huang, F. W., Jané-Valbuena, J., Kryukov, G. V., Lo, C. C., McDonald, E. R., Barretina, J., Gelfand, E. T., Bielski, C. M., Li, H., et al. (2019) · Nature · doi:10.1038/s41586-019-1186-3 · Cited by 3,394
Self-citationApplications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228
A view on drug resistance in cancer
Vasan, N., Baselga, J., & Hyman, D. M. (2019) · Nature · doi:10.1038/s41586-019-1730-1 · Cited by 2,770
Meyers, R. M., Bryan, J. G., McFarland, J. M., Weir, B. A., Sizemore, A. E., Xu, H., Dharia, N. V., Montgomery, P. G., Cowley, G. S., Pantel, S., et al. (2017) · Nature Genetics · doi:10.1038/ng.3984 · Cited by 1,952
Self-citationGarrido-Castro, A. C., Lin, N. U., & Polyak, K. (2019) · Cancer Discovery · doi:10.1158/2159-8290.cd-18-1177 · Cited by 1,451
Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens
Behan, F. M., Iorio, F., Picco, G., Gonçalves, E., Beaver, C. M., Migliardi, G., Santos, R., Rao, Y., Sassi, F., Pinnelli, M., et al. (2019) · Nature · doi:10.1038/s41586-019-1103-9 · Cited by 1,338
Hallin, J., Engstrom, L. D., Hargis, L., Calinisan, A., Aranda, R., Briere, D. M., Sudhakar, N., Bowcut, V., Baer, B. R., Ballard, J. A., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-1167 · Cited by 1,304
Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma
Ji, A. L., Rubin, A. J., Thrane, K., Jiang, S., Reynolds, D. L., Meyers, R. M., Guo, M. G., George, B. M., Mollbrink, A., Bergenstråhle, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.039 · Cited by 1,117
Self-citationProteolysis-Targeting Chimeras as Therapeutics and Tools for Biological Discovery
Burslem, G. M., & Crews, C. M. (2020) · Cell · doi:10.1016/j.cell.2019.11.031 · Cited by 1,079
Dual proteome-scale networks reveal cell-specific remodeling of the human interactome
Huttlin, E. L., Bruckner, R. J., Navarrete-Perea, J., Cannon, J. R., Baltier, K., Gebreab, F., Gygi, M. P., Thornock, A., Zarraga, G., Tam, S., et al. (2020) · doi:10.1101/2020.01.19.905109 · Cited by 960
Discovering the anticancer potential of non-oncology drugs by systematic viability profiling
Corsello, S. M., Nagari, R. T., Spangler, R. D., Rossen, J., Kocak, M., Bryan, J. G., Humeidi, R., Peck, D., Wu, X., Tang, A. A., et al. (2020) · Nature Cancer · doi:10.1038/s43018-019-0018-6 · Cited by 878
Self-citationZou, Y., Palte, M. J., Deik, A. A., Li, H., Eaton, J. K., Wang, W., Tseng, Y.-Y., Deasy, R., Kost-Alimova, M., Dančík, V., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09277-9 · Cited by 887
Self-citationProteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma
Gillette, M. A., Satpathy, S., Cao, S., Dhanasekaran, S. M., Vasaikar, S. V., Krug, K., Petralia, F., Li, Y., Liang, W.-W., Reva, B., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.013 · Cited by 820
Artificial Intelligence in Cancer Research and Precision Medicine
Bhinder, B., Gilvary, C., Madhukar, N. S., & Elemento, O. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0090 · Cited by 781
Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq
Replogle, J. M., Saunders, R. A., Pogson, A. N., Hussmann, J. A., Lenail, A., Guna, A., Mascibroda, L., Wagner, E. J., Adelman, K., Lithwick-Yanai, G., et al. (2022) · Cell · doi:10.1016/j.cell.2022.05.013 · Cited by 795
Patient-derived xenografts undergo mouse-specific tumor evolution
Ben-David, U., Ha, G., Tseng, Y.-Y., Greenwald, N. F., Oh, C., Shih, J., McFarland, J. M., Wong, B., Boehm, J. S., Beroukhim, R., et al. (2017) · Nature Genetics · doi:10.1038/ng.3967 · Cited by 716
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
Self-citationBAF complex vulnerabilities in cancer demonstrated via structure-based PROTAC design
Farnaby, W., Koegl, M., Roy, M. J., Whitworth, C., Diers, E., Trainor, N., Zollman, D., Steurer, S., Karolyi-Oezguer, J., Riedmueller, C., et al. (2019) · Nature Chemical Biology · doi:10.1038/s41589-019-0294-6 · Cited by 562
Rational combinations of targeted cancer therapies: background, advances and challenges
Jin, H., Wang, L., & Bernards, R. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-022-00615-z · Cited by 503
WRN helicase is a synthetic lethal target in microsatellite unstable cancers
Chan, E. M., Shibue, T., McFarland, J. M., Gaeta, B., Ghandi, M., Dumont, N., Gonzalez, A., McPartlan, J. S., Li, T., Zhang, Y., et al. (2019) · Nature · doi:10.1038/s41586-019-1102-x · Cited by 458
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
The BCL2 family: from apoptosis mechanisms to new advances in targeted therapy
Vogler, M., Braun, Y., Smith, V. M., Westhoff, M.-A., Pereira, R. S., Pieper, N. M., Anders, M., Callens, M., Vervliet, T., Abbas, M., et al. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02176-0 · Cited by 462
KRAS mutation in pancreatic cancer
Luo, J. (2021) · Seminars in Oncology · doi:10.1053/j.seminoncol.2021.02.003 · Cited by 360
Functional precision cancer medicine—moving beyond pure genomics
Letai, A. (2017) · Nature Medicine · doi:10.1038/nm.4389 · Cited by 366
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
Mapping the Genetic Landscape of Human Cells
Horlbeck, M. A., Xu, A., Wang, M., Bennett, N. K., Park, C. Y., Bogdanoff, D., Adamson, B., Chow, E. D., Kampmann, M., Peterson, T. R., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.010 · Cited by 319
CRISPR screens in cancer spheroids identify 3D growth-specific vulnerabilities
Han, K., Pierce, S. E., Li, A., Spees, K., Anderson, G. R., Seoane, J. A., Lo, Y.-H., Dubreuil, M., Olivas, M., Kamber, R. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2099-x · Cited by 311
A non-canonical tricarboxylic acid cycle underlies cellular identity
Arnold, P. K., Jackson, B. T., Paras, K. I., Brunner, J. S., Hart, M. L., Newsom, O. J., Alibeckoff, S. P., Endress, J., Drill, E., Sullivan, L. B., et al. (2022) · Nature · doi:10.1038/s41586-022-04475-w · Cited by 304
Cas9 activates the p53 pathway and selects for p53-inactivating mutations
Enache, O. M., Rendo, V., Abdusamad, M., Lam, D., Davison, D., Pal, S., Currimjee, N., Hess, J., Pantel, S., Nag, A., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0623-4 · Cited by 285
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
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
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
Big data in basic and translational cancer research
Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C., & Ruppin, E. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00502-0 · Cited by 254
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
Feng, X., Arang, N., Rigiracciolo, D. C., Lee, J. S., Yeerna, H., Wang, Z., Lubrano, S., Kishore, A., Pachter, J. A., König, G. M., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.01.009 · Cited by 237
Genetic Screen for Cell Fitness in High or Low Oxygen Highlights Mitochondrial and Lipid Metabolism
Jain, I. H., Calvo, S. E., Markhard, A. L., Skinner, O. S., To, T.-L., Ast, T., & Mootha, V. K. (2020) · Cell · doi:10.1016/j.cell.2020.03.029 · Cited by 224
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
A genome-wide atlas of co-essential modules assigns function to uncharacterized genes
Wainberg, M., Kamber, R. A., Balsubramani, A., Meyers, R. M., Sinnott-Armstrong, N., Hornburg, D., Jiang, L., Chan, J., Jian, R., Gu, M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00840-z · Cited by 204
Self-citationChen, J. J., Nathaniel, D. L., Raghavan, P., Nelson, M., Tian, R., Tse, E., Hong, J. Y., See, S. K., Mok, S.-A., Hein, M. Y., et al. (2019) · Journal of Biological Chemistry · doi:10.1074/jbc.ra119.009432 · Cited by 170
McFarland, J. M., Paolella, B. R., Warren, A., Geiger-Schuller, K., Shibue, T., Rothberg, M., Kuksenko, O., Colgan, W. N., Jones, A., Chambers, E., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17440-w · Cited by 173
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
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
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
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
Self-citationZhang, J. M., Ubas, A. A., de Borja, R., Svensson, V., Thomas, N., Thakar, N., Lai, I. K., Winters, A. F., Khan, U., Jones, M. G., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.20.639398 · Cited by 146
“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
Liu, Z., Liu, G., Ha, D. P., Wang, J., Xiong, M., & Lee, A. S. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2303448120 · Cited by 137
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
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
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
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
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
“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
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
Viswanatha, R., Li, Z., Hu, Y., & Perrimon, N. (2018) · eLife · doi:10.7554/elife.36333 · Cited by 79
Papier, K., Atkins, J. R., Tong, T. Y. N., Gaitskell, K., Desai, T., Ogamba, C. F., Parsaeian, M., Reeves, G. K., Mills, I. G., Key, T. J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48017-6 · Cited by 97
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 104
Synthetic Lethal Vulnerabilities in KRAS-Mutant Cancers
Aguirre, A. J., & Hahn, W. C. (2018) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a031518 · Cited by 90
Self-citationPredicting 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
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
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
Multiple oestradiol functions inhibit ferroptosis and acute kidney injury
Tonnus, W., Maremonti, F., Gavali, S., Schlecht, M. N., 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 89
“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
Zognjani, B., Nixha, A. R., Duran, H. E., Arslan, M., Yıldıztekin, G., Ece, A., & Türkeş, C. (2025) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2025.108788 · Cited by 77
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
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
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 83
“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
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
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 68
“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
“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
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
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 66
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
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
Hoti, D., Nixha, A. R., Duran, H. E., Arslan, M., Yıldıztekin, G., Ece, A., & Türkeş, C. (2025) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2025.118416 · Cited by 52
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 59
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 58
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
“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 56
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
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
The role of wild-type RAS in oncogenic RAS transformation
Sheffels, E., & Kortum, R. L. (2021) · Genes · doi:10.3390/genes12050662 · Cited by 42
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
Identification and Characterization of Oncogenic SOS1 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
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
“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., He, L., Cho, S., Parang, B., Leone, D., et al. (2025) · Science · doi:10.1126/science.adm9805 · Cited by 46
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
Kim, D., Kesavan, R., Ryu, K., Dey, T., Marckx, A. T., 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 40
Sparse dictionary learning recovers pleiotropy from human cell fitness screens
Pan, J., Kwon, J. J., Talamas, J. A., Borah, A. A., Vazquez, F., Boehm, J. S., Tsherniak, A., Zitnik, M., McFarland, J. M., & Hahn, W. C. (2022) · Cell Systems · doi:10.1016/j.cels.2021.12.005 · Cited by 37
Self-citationLuan, F., Cui, Y., Huang, R., Yang, Z., & Qiao, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-85444-x · Cited by 37
“Drug response information was sourced from three databases: GDSC (https://www.cancerrxgene.org/), CTRP (https://portals.broadinstitute.org/ctrp/), 33 .”
Matuszek, Ż., Arbab, M., Kesavan, M., Hsu, A., Roy, J. C. L., Zhao, J., Yu, T., Weisburd, B., Newby, G. A., Doherty, N. J., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02172-8 · Cited by 35
“Essential genes were identified with the Cancer DepMap database 74,75 and the effect of amino acid substitutions on protein folding and function was determined with the AlphaMissense database 76,77 .”
InfluentialMutant KRAS Peptide Targeted CAR-T Cells Engineered for Cancer Therapy
Benton, A., Liu, J.-G., Poussin, M., Goldgewicht, A. L., Udawela, M., Bear, A. S., Wellhausen, N., Carreno, B. M., Smith, P., Beasley, M. D., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.05.006 · Cited by 32
A genome-wide atlas of human cell morphology
Ramezani, M., Weisbart, E., Bauman, J., Singh, A. K., Yong, J., Lozada, M., Way, G. P., Kavari, S. L., Diaz, C., Leardini, E., et al. (2025) · Nature Methods · doi:10.1038/s41592-024-02537-7 · Cited by 74
“We subsequently evaluated the extent to which image-based gene KO profiles were correlated with gene KO fitness effects using the Broad Institute’s Dependency Map (DepMap) database 36 .”
Goel, I., Bhaskar, Y., Kumar, N., Singh, S., Amanullah, M., Dhar, R., & Karmakar, S. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1550407 · Cited by 31
Computational estimation of quality and clinical relevance of cancer cell lines
Trastulla, L., Noorbakhsh, J., Vazquez, F., McFarland, J., & Iorio, F. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211017 · Cited by 31
Perturbomics: CRISPR–Cas screening-based functional genomics approach for drug target discovery
Park, B.-S., Lee, M., Kim, J. Y., & Kim, T. (2025) · Experimental & Molecular Medicine · doi:10.1038/s12276-025-01487-0 · Cited by 41
“Landmark projects such as the DepMap project 19 have generated extensive datasets of genome-wide CRISPR screening results across diverse cancer cell lines.”
House, N. C. M., Brown, V. E., Chen, M. M., Yuan, L., Moore, S. L., Guo, J., Choi, Y. J., Muthuswamy, L. B., Ribich, S. A., Ramsden, P., et al. (2025) · Cancer Research · doi:10.1158/0008-5472.can-24-2360 · Cited by 33
Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
