Cited by 3,276Cancer Genomics and DiagnosticsChromatin Remodeling and CancerBioinformatics and Genomic Networks

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Defining a Cancer Dependency Map

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Tsherniak, Aviad, Vazquez, Francisca, Montgomery, Phil G., Weir, Barbara A., Kryukov, Gregory, Cowley, Glenn S., Gill, Stanley, Harrington, William F., Pantel, Sasha, Krill-Burger, John M., Meyers, Robin M., Ali, Levi, Goodale, Amy, Lee, Yenarae, Jiang, Guozhi, Hsiao, Jessica, Gerath, William F.J., Howell, Sara, Merkel, Erin, Ghandi, Mahmoud, Garraway, Levi A., Root, David E., Golub, Todd R., Boehm, Jesse S., Hahn, William C.·2017·Cell

DOI 10.1016/j.cell.2017.06.010PMC PMC5667678

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Abstract

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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“DepMap, for instance, employs large-scale CRISPR screening to systematically map genetic dependencies in cancer, allowing researchers to identify which genes are critical for tumor survival and growth [17].”
Common gene mutations in 103 authenticated colorectal cancer cell lines
Kranjec, C., Eilertsen, I. A., Nunes, L., Moosavi, S. H., Berg, K. C. G., Eknæs, M., Hektoen, M., Niederdorfer, B., Lind, G. E., Skotheim, R. I., et al. (2026) · Oncogenesis · doi:10.1038/s41389-026-00599-0 · Cited by 1
Integrative transcriptomic profiling reveals subtype-specific therapeutic vulnerabilities and resistance mechanisms in prostate cancer
Liu, W., Kong, W.-Y., Jiang, S., Wei, Y., Yu, Z.-J., Cao, X., Yang, J., Wei, X.-Y., Shen, L., Qin, C., et al. (2026) · BMC Cancer · doi:10.1186/s12885-025-15357-5 · Cited by 1
The role of histone demethylase PHF2 as a tumour suppressor in hepatocellular carcinoma by regulating SRXN1
Thng, D. K. H., Hooi, L., Yong, W. K., Kappei, D., Toh, T. B., & Chow, E. K. (2026) · Oncogenesis · doi:10.1038/s41389-026-00597-2 · Cited by 1
Recognizing dUTPase as a mitotic factor essential for early embryonic development
Nagy, N., Tóth, O., Oláh, E., Henn, L., Rácz, G., Szabó, E., Várady, G., Vigh, F. B., Golács, Z. R., Urbán, M., et al. (2026) · bioRxiv · doi:10.64898/2026.01.16.699898 · Cited by 1
“The co-dependency between dUTPase and centromere/kinetochore components [47], as well as mitotic regulators, such as INCENP [42] and CDK5RAP3 [43], reported in large-scale, non–hypothesis-driven exploratory studies, independently supports our findings.”
The SOX10-ACAT2-Cholesterol Synthesis Axis Is Required for Melanoma Proliferation
Wang, L., Dai, C.-Y., Yang, J., Ma, Y.-W., Ding, X., & Fan, X. (2026) · International Journal on Biological Sciences · doi:10.7150/ijbs.114084 · Cited by 1
“While its expression is suppressed in differentiated melanocytes (26), SOX10 is overexpressed in melanoma (27), making it a highly sensitive diagnostic marker for both primary and metastatic lesions (28, 29).”
EGFR inhibitor-resistant lung cancers exhibit collateral sensitivity to a covalent, cysteine-independent KEAP1 oligomerizing molecular bridge
Bassil, C. F., Dillon, K. A., Anderson, G. R., Mayro, B., Askin, K. N., Winter, P. S., Harry, S. A., Gruber, S., Hall, T. M., Hoj, J. P., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68424-1 · Cited by 2
Suppressive Genetic Interactions Between Haploinsufficient Mitochondrial Genes Encoded in the 22q11.2 Microdeletion Locus Define Brain and Cardiac Phenotypes
Wynne, M., Zlatic, S. A., Park, A., Crocker, A., Mendez-Vazquez, H., Liporace, E., Gokhale, A., Tower-Gilchrist, C., Robinette, M. I., Purcell, R. H., et al. (2026) · bioRxiv · doi:10.64898/2026.01.09.698677 · Cited by 1
“2) 32, 33 .”
Systemic control of HNF1B-driven redox homeostasis by N 6-adenosine methylation
Park, M., Kim, H.-R., Park, J. H., Kim, Y. T., Shin, J.-H., Lee, J. Y., Ju, Y., Hwang, G.-S., Shin, D.-M., Kim, M. W., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1504 · Cited by 1
“S1 A and B) [ 40 ].”
Exploiting tumor lineage features for precision cancer therapy
Kelly, L. M., Fenouille, N., Wood, K. C., & Puissant, A. (2026) · Trends in cancer · doi:10.1016/j.trecan.2025.12.004 · Cited by 1
An expanded role for single-cell chemical genomics profiling in drug discovery
Wyatt, A., Hoffer-Hawlik, K., Giglio, R. M., Azizi, E., & McFaline‐Figueroa, J. L. (2026) · Biochemical Journal · doi:10.1042/bcj20253273 · Cited by 3
“The advent of genomic technologies has significantly enhanced our ability to catalog how perturbations influence cellular states [1–6].”
SIMD: Synergistic integration mutualistic platform based on single-cell and proteotranscriptomics for drug repositioning
Lee, S. Y., Song, M. Y., Lee, Y., & An, J. H. (2025) · npj Breast Cancer · doi:10.1038/s41523-025-00869-x · Cited by 2
Self-citation
Advances in Targeting Growth Factor Signalling in Neuroblastoma and Overcoming Drug Resistance
Ivanenko, K. A., Shaymardanov, R., Prassolov, V., & Lebedev, T. D. (2025) · Cells · doi:10.3390/cells15010004 · Cited by 1
“To compare the importance of individual RTKs for NB survival we used DepMap data on gene depletion effects on cell proliferation [21] by combining data for CRISPR and RNAi screens as described in our paper [20] (Figure 4).”
Fragment-based discovery enables direct targeting of the melanoma oncogene MITF
Castelletti, D., Hinrichs, J., Malojčić, G., Ji, F., Desplat, A., Reimer, B., Henry, C., Porter, K. A., Mermet-Meillon, F., Wartmann, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-67297-0 · Cited by 1
“Consistent with the role of lineage-survival oncogene, genetic depletion of MITF profoundly affects the growth of melanoma cell lines, as shown by large high-throughput screening campaigns 27,28 .”
CD26 as a potential therapeutic target for lung adenocarcinoma
Jungraithmayr, W., Ohm, B., Haberecker, M., Curioni-Fontecedro, A., Janker, F., Gil-Bazo, I., Soltermann, A., Kirschner, M. B., Weder, W., Opitz, I., et al. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1552587 · Cited by 1
Challenges and opportunities for oncology drug repurposing informed by synthetic lethality
Vermeulen, M., Craig, A. W., & Babak, T. (2025) · npj Systems Biology and Applications · doi:10.1038/s41540-025-00618-7 · Cited by 1
Navigating the winding road toward precision prostate cancer care
Rahman, S. N., Arun, A. S., Kim, I. Y., Oh, W. K., Kim, J. W., & Kim, W. J. (2025) · Gene · doi:10.1016/j.gene.2025.149966 · Cited by 2
Off-target effects as confounders of Cas13d-based lncRNA screens
Montero, J. J., Trozzo, R., & Rad, R. (2025) · bioRxiv · doi:10.1101/2025.11.27.690751 · Cited by 1
“Subsequently, we downloaded the list of common essential protein-coding genes from the DepMap portal (DepMap release 24Q2) 12 .”
Addressing Ancestral Underrepresentation in Oncobiology: The Need for Sub-Saharan African-Specific In Vitro Models
dos Santos, C. S., Magalhães, A. C., Pinto, R., Carrilho, C., Pereira, C., Miguel, F., Borges, P., Santos, L. L., & Pereira, L. (2025) · Genes · doi:10.3390/genes16121403 · Cited by 1
“…of genomic-scale gene-interfering methods, such as RNAi and CRISPR-Cas9, the Wellcome Sanger Institute and Broad Institute have conducted the knockdown/out of thousands of genes in these panels, aiming to directly identify which ones are essential for cancer cell proliferation/survival [75,76].”
CELF1 promotes aerobic glycolysis and an aggressive phenotype in ER-positive breast cancer via GLUT1 regulation
Li, J., Wang, N., Bi, J., Guo, M., Xu, B., & Huang, G. (2025) · Frontiers in Genetics · doi:10.3389/fgene.2025.1687066 · Cited by 1
Global Optimization on Graph-Structured Data via Gaussian Processes with Spectral Representations
Hong, S., Mei, Y.-S., Imani, M., & Lan, T. (2025) · arXiv.org · doi:10.48550/arxiv.2511.07734 · Cited by 1
Endothelial CLK2 as a therapeutic target for acute radiation syndromePreprint
Posey, R. R., Özkan, A., Man, Y.-C., Feitor, J. F., Jiang, A., Ji, J., LoGrande, N. T., Kyprianou, C., Howley, A. M., Budnik, B., et al. (2025) · bioRxiv · doi:10.1101/2025.11.05.686891 · Cited by 1
“In fact, when we re-analyzed publicly available data from Project Achilles, an atlas of forward genetic screens across the cancer cell line encyclopedia (CCLE) [40], we found that CRISPR knockouts of CLK2 using the same library we used here also resulted in tumor cell killing ( Supplementary Fig.”
Unbiased combination screening on repurposed drugs reveals synergistic potential of copanlisib and cerivastatin against chemoresistant high-grade serous ovarian cancer
Sun, Y., Wang, Y., Umbreen, S., Pepperrell, B., Buckley, N. E., Mullan, P., Ali, A., & Furlong, F. (2025) · Journal of Ovarian Research · doi:10.1186/s13048-025-01828-7 · Cited by 1
“The list of chemoresistance genes in OC was obtained from a systematic review [29], and their expression levels were extracted from DepMap [30].”
A functional map of CDK-drug interactions at single amino acid resolutionPreprint
Gould, S. I., Acosta, J., Bielo, L. B., Jaboldinov, A., Johnson, G. A., Contreras, M. E., Wuest, A. N., Brémont, E., Toure, M. A., Xiang, Y., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.02.685764 · Cited by 2
“Advances in large-scale RNA interference and CRISPR-based functional genomics over the last two decades have streamlined the identification of cancer drivers, dependencies, and determinants of therapeutic response across many types of human malignancies ( 8 – 10 ).”
The LMO2-LDB1-TAL1 complex regulates transcription networks in acute myeloid leukemia
Dunham, N. R., Wang, Z., Neelamraju, Y., Guo, Y., Paudel, B. B., Meydan, C., Gandara, J. A., Abdelmalak, F., Fan, H., Hardwick, J. A., et al. (2025) · Blood Neoplasia · doi:10.1016/j.bneo.2025.100186 · Cited by 1
“…for known hematopoietic TF that interact and share common targets (including TAL1 and LDB1 9 ), which have been suggested to be essential in AML, 9,31 and are members of the LTMC. 25 To assess the LTMC in AML, we performed coimmunoprecipitation followed by mass spectroscopy in AML cell lines.”
Structural basis of DNA-dependent coactivator recruitment by the tuft cell master regulator POU2F3
Alpsoy, A., Ipsaro, J. J., Skopelitis, D., Pal, S., Chung, F. S., Carpenter, S., Desmarais, J. J., Wu, X. S., Chang, K., DiMare, M. T., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116572 · Cited by 1
“3,4 7 Tuft cells are chemosensory epithelial cells found in mucosal tissues, where they contribute to microbial sensing and local immune activation.”
Developing therapeutic strategies to target MCL1 and BCLXL in lethal prostate cancer
Westaby, D., Jiménez-Vacas, J., Figueiredo, I., Welti, J., Gurel, B., Bogdan, D., Buroni, L., Neeb, A., Rekowski, J., Padilha, A., et al. (2025) · iScience · doi:10.1016/j.isci.2025.113985 · Cited by 1
“32 Furthermore, treatment of 22Rv1, LNCaP95, and VCaP PCa cells with an MCL1 specific inhibitor (1 μ M AZD5991) led to increased caspase 3/7 activation and reduced cell viability (Figure 2C).”
Non-apoptotic regulated cell death based prognostic risk model for colorectal cancer using machine learning guided two-step framework
Sengupta, A., Kar, S. S., & Kumar, R. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf639 · Cited by 1
“Using “DepMap” portal (v. DepMap Public 23Q4) [33, 34], we downloaded the CRISPR-Cas9 based knockout screens data and performed gene dependency analysis.”
C1orf50 Accelerates Epithelial-Mesenchymal Transition and the Cell Cycle of Hepatocellular Carcinoma
Tanaka, A., Otani, Y., Maekawa, M., Rogachevskaya, A., Peña, T., Chin, V. D., Toyooka, S., Roehrl, M. H. A., & Fujimura, A. (2025) · Cancer Genomics & Proteomics · doi:10.21873/cgp.20541 · Cited by 1
“In addition, based on the CRISPR perturbation score from the Cancer Dependency Map (DepMap) Project (28) of 21 hepatocellular cell lines, the inhibition of CTCF and ZBTB11 at the gene level impaired cancer cell survival, suggesting these genes may have important roles in C1orf50 ‑ high cancers (Figure 2B, right).”
PDIA5 and ARFIP1 as Immunogenetic Biomarkers and Therapeutic Targets in Pancreatic Neuroendocrine Neoplasms: A Multi-Omics Study Integrating MR, Gene Expression Microarray, and Single-Cell Transcriptomics
Guo, H., Zheng, G., Yao, S., Jia, Q., Tan, J., & Meng, Z. (2025) · Neuroendocrinology · doi:10.1159/000548070 · Cited by 1
“Gene dependency was quantified using CERES scores derived from CRISPR knockout screens, where lower scores indicated stronger dependency; a threshold of CERES < −0.5 was applied to define essential genes [55].”
Influential
Cell cycle regulator MYBL2 is a distinct vulnerability in acute myeloid leukemia
Küchler, S., Brilloff, S., Schäfer, S., Rahimian, E., Kufrin, V., Peri, S. S., Musa, J., Grünewald, T. G. P., Schewe, D. M., Ball, C. R., et al. (2025) · Cell Death Discovery · doi:10.1038/s41420-025-02810-4 · Cited by 1
“Lower scores indicate greater dependency, with scores below − 0.5 for individual genes typically considered indicative of complete essentiality [12, 13].”
Influential
Identification of PKN2 and MOB4 as Coordinators of Collective Cell Migration
Fokin, A. I., Lin, Y., Guschin, D. Y., Chen, H., James, J., Yan, J., Silberzan, P., & Gautreau, A. M. (2025) · Advanced Science · doi:10.1002/advs.202502907
HOXB13 interactome in prostate cancer cells: biochemical and functional interactions between the transcription factors HOXB13 and TBX3
Erokhin, M., Kozelchuk, N. Y., Ziganshin, R., Tatarskiy, V. V., & Chetverina, D. (2025) · Vavilov Journal of Genetics and Breeding · doi:10.18699/vjgb-25-82
“The project uses CRISPR or RNAi methods to investigate the dependence of cell proliferation on the suppression of individual genes (Tsherniak et al., 2017; Vazquez, Sellers, 2021).”
NCAPH as a potential prognostic signaling biomarker regulating low-grade glioma cell proliferation, migration, invasion, immune microenvironment, and drug sensitivity
Dai, L., Jiang, S., & Zhou, P. (2025) · Journal of Cancer · doi:10.7150/jca.115870 · Cited by 1
“Drug response data was obtained from BEST website based on the following databases: GDSC (https://www.cancerrxgene.org/) [27], CTRP (https://portals.broadinstitute.org/ctrp/) [28], and PRISM (https://depmap.org/portal/prism/) [29].”
Mutant RIT1 cooperates with YAP to drive an EMT-like lung cancer state
Rominger, M. C., O’Brien, S., Gupta, S., Moorthi, S., McSharry, M. V., Kamlapurkar, S., Lowe, A. R., Waldum, A., Lo, A., Duke, F. F., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116185 · Cited by 1
“Despite decades of concerted effort in the establishment of cancer cell line models from the Cancer Cell Line Factory, 55 Cancer Dependency Map, 56 and others, 57 the representation of rare molecular subtypes of cancer, such as RIT1-mutant lung cancer, is limited.”
A decentralized future for the open-science databases
Sharma, G., Munteanu, V., Ghiasi, N., Banerjee, J., Varma, S., Foschini, L., Ellrott, K., Mutlu, O., Ciorbă, D., Ophoff, R., et al. (2025) · arXiv.org · doi:10.48550/arxiv.2509.19206 · Cited by 1
“50–53 This multitude of repositories grew organically at various points in time in response to the need for organized data collection to answer specific questions.”
Multiomic assessments of LNCaP and derived cell strains reveal determinants of prostate cancer pathobiology
Bose, A., Bankhead, A., Coleman, I. M., Persse, T. W., Han, W., Galipeau, P. C., Hanratty, B. P., Chu, T. L., Lucas, J. M., Li, D., et al. (2025) · Journal of Clinical Investigation · doi:10.1172/jci194727 · Cited by 1
“1 did not score in the DepMap LNCaP_ FGC data (80), and though depleted in the CRISPRi screens of LNCaP_FGC and LNCaP_C4-2B, it did not reach significance.”
Integrating computational protein structure predictions and genetic dependencies yields an atlas of human multi-protein complexes (AHMPC)Preprint
Uckelmann, M., Salmoral, D. Á., Maiella, C., Bleijerveld, O. B., Zwart, C., Marcus, E., Brunekreef, J., Teuwen, J., Beijersbergen, R. L., & Anastassis Perrakis, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.09.675133 · Cited by 1
“As a proxy for functional similarity we use correlation of gene pairs across the DepMap CRISPR dataset 18 that measures gene knockout e(cid:431)ects on cancer cell survival and proliferation across hundreds of cell lines 19 .”
The tRNA Editing Complex ADAT2/3 Promotes Cancer Cell Growth and Codon-biased mRNA Translation
Ramírez-Moya, J., Antika, T. R., Liu, Q., Xiong, X., Ali, R. H., Gutierrez, A., & Gregory, R. I. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169414 · Cited by 1
Phenotypic screening converges on CDK9 inhibition as a therapeutic strategy in translocation renal cell carcinomaPreprint
Thapa, D. R., Deodhar, R., Skepner, A. P., Konda, P., Zhou, M., Bluck, J. P., Pichlo, C., Cui, Y., Stocker, P., Khanna, P., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.25.672235 · Cited by 1
“Notably, although the DepMap includes more than 30 kidney cancer cell lines, tRCC cell lines have not yet been proﬁled (41,42).”
CRISPR-Cas13d-Mediated Targeting of a Context-Specific Essential Gene Enables Selective Elimination of Uveal Melanoma
Stauber, D., Sosnick, L., Ma, Y.-T., Pimcharoen, S., Lawanprasert, A., Murthy, N., Myung, D., & Qi, L. S. (2025) · bioRxiv · doi:10.1101/2025.08.21.671629 · Cited by 1
“Due to our pipelines’ modularity— target identification, guide-RNA design, and LNP formulation — we expect it to be able to address a wide range of context-specific essential genes revealed by DepMap or comparable screens.”
Influential
Exploration of signature-related FAM genes and correlation between FAM50A expression and the pathogenesis and prognosis of hepatocellular carcinoma
Wu, S., Chen, S.-J., Sun, X.-F., & Chen, X.-J. (2025) · Translational Cancer Research · doi:10.21037/tcr-2025-171 · Cited by 1
“One gene is essential in the cell growth if the dependency score is less than −0.5 based on the instructions of this database (37).”
Influential
Microenvironmental signals combine to induce non-additive molecular and phenotypic responses in mammary epithelial cells
Mclean, I. C., Gross, S. M., Copperman, J., Derrick, D. S., Paul, I., Emili, A., & Heiser, L. M. (2025) · iScience · doi:10.1016/j.isci.2025.113407 · Cited by 1
“63,64 We compared VIP scores to Gene Effect scores calculated by DEPMAP from CRISPR screens and investigated the statistical significance of the relationship between VIP and Gene Effect scores using chi-squared analysis.”
Influential
Inositol (1,4,5)-trisphosphate 5-phosphatase promotes survival of uveal melanoma by regulating oncogenic G protein–driven calcium oscillations
Onken, M. D., Makepeace, C. M., Kaltenbronn, K. M., Demourelle-Washington, M., Piggott, K., Goldfarb, D., Kast, D. J. E., Jansen, S., & Blumer, K. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.110589 · Cited by 1
“We determined that UM cell lines require INPP5A by querying the DepMap portal (https://depmap.org/portal), which collates data from high-throughput, genome-wide loss-of-function RNA interference and CRISPR–guide RNA screens of cell lines derived from many cancers and diseases (26).”
NAPRT expression and epigenetic regulation in pediatric rhabdomyosarcoma as a potential biomarker for NAMPT inhibition
Kim, A., Bhardwaj, P., Heer, C. D., Zhao, S. J., Lucas, K. N., Noronha, K. J., Friedman, S., Morotti, R., Sundaram, R., Rehman, S., et al. (2025) · bioRxiv · doi:10.1101/2025.07.28.667284 · Cited by 1
“We probed data from the Cancer Dependency Map (DepMap) to assess for NAPRT promoter methylation and protein expression across different cancer types (40).”
Interventionally-guided representation learning for robust and interpretable AI models in cancer medicinePreprint
Kirkham, D., Masina, R., Sammut, S.-J., Mukherjee, S., & Rueda, O. (2025) · bioRxiv · doi:10.1101/2025.07.21.662350 · Cited by 1
“…hypotheses, we leveraged data from 1019 cell lines with genome-wide CRISPR-Cas9 loss-of-function screening information from the DepMap 23Q2 dataset[27] and 686 cell lines with extensive pharmacological screening information from the GDSC2 dataset[28], drawing relationships between gene effect and…”
SHLD2 loss is a synthetic vulnerability to Polθ inhibition combined with radiotherapy
Rodriguez-Berriguete, G., Thambiayah, P., Cicconi, A., Machado, N., Gotorbe, C., Nderitu, D., Cheng, W.-C., Fowler, G., Boursier, M. L., Cerutti, A., et al. (2025) · bioRxiv · doi:10.1101/2025.07.04.662969 · Cited by 1
“To quantify the frequency of exomic Polθ-generated scars –defined here as deletions of ≥5 bp flanked by microhomologies of ≥2 bp– we applied the computational pipeline described by Carvajal-Garcia et al. 7 to the DepMap 24Q4 whole exome sequencing (WES) dataset 39 .”
Informatics at the Frontier of Cancer Research
Noller, K., Botsis, T., Cámara, P. G., Ciotti, L., Cooper, L., Goecks, J., Griffith, M., Haas, B. J., Ideker, T., Karchin, R., et al. (2025) · Cancer Research · doi:10.1158/0008-5472.can-24-2829 · Cited by 2
“The cBioPortal for Cancer Genomics (283) and Xena (255) provide these capabilities for cancer genomics data from TCGA and other initiatives (284).”
Gene expression-based rationale for investigating CDK4/6 inhibitors in primitive myelofibrosis
Murati, A., Finetti, P., Duprez, E., Rey, J., Mamessier, E., & Bertucci, F. (2025) · Leukemia · doi:10.1038/s41375-025-02669-5 · Cited by 1
“Before analysis, we veri ﬁ ed the positive correlation between gene and protein expression for each gene in the 363 cancer cell lines in the DepMap database [7]: correlations were strong for CDK4 ( p = 3.21E − 30) and CDK6 ( p = 5.89E − 59).”
Multidimensional pan-cancer analysis reveals the impact of PPIA on tumor epigenetic modifications and immune regulation
Yao, Y.-Q., Gong, X., & Li, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-05997-9 · Cited by 1
PRC2‐mediated apoptosis evasion is a therapeutic target of MDS/AML harboring inv(3)/t(3;3) and monosomy 7
Kunimoto, H., Miura, A., Sagisaka, M., Ito, M., Maenosono, R., Yamauchi, H., Nishimura, K., Honma, D., Tsutsumi, S., Adachi, A., et al. (2025) · HemaSphere · doi:10.1002/hem3.70149 · Cited by 1
“66 Therefore, whether EZH2 low ‐ expressing human MDS/AML cells, including those with EZH2 haploinsufficiency by − 7, show a higher vulnerability to pharmacological EZH2 inhibition compared to EZH2 high ‐ expressors with diploid chromosome 7 should be further investigated based on a FIGURE 5…”
Droplet Digital PCR Improves Detection of BRCA1/2 Copy Number Variants in Advanced Prostate Cancer
Rungkamoltip, P., Khongcharoen, N., Nokchan, N., Ali, Z. B., Plikomol, M., Bejrananda, T., Boonchai, S., Chamnina, S., Srakhao, W., & Khongkow, P. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26146904 · Cited by 1
“…shown in Figure 4, primary dermal fibroblast (HDFa) and human hair follicle dermal papilla cells (HFDPCs) were used as normal copy number controls for the detection of BRCA1 and BRCA2 CNVs. Additionally, HS578T and MDA-MB-231 cell lines were used as deletion controls for BRCA2 CNVs detection [27].”

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: Tsherniak, Aviad
Kryukov, Gregory
Cowley, Glenn S.
Gill, Stanley
Krill-Burger, John M.
Meyers, Robin M.
Ali, Levi
Goodale, Amy
Lee, Yenarae
Jiang, Guozhi
Ghandi, Mahmoud
Garraway, Levi A.
Root, David E.
Golub, Todd R.
Boehm, Jesse S.
Hahn, William C.
Venue: Cell
Publisher: Elsevier BV
Published: 2017-07
Volume: 170
Issue: 3
Pages: 564-576.e16
ISSN: 0092-8674 (ISSN-L)
DOI: 10.1016/j.cell.2017.06.010
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: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 391 d)
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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Defining a Cancer Dependency Map

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