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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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“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]
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Items 901–1,000 of 1,076
Marchi, F. D., & Gottardi, M. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1837940
An atlas of the human metabolomePreprint
Chan, J. K., Ly, N. S., Taverniti, O., Gwynne, W. D., Lieng, B. Y., Affe, V., Urquhart-Cox, V. T., Alonzi, S. M., Muhundan, M., Denhart, A. J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.21.726638
“Resources like the Human Protein Atlas (HPA) and the Cancer Dependency Map (DepMap) illustrate how large-scale compendia of human biology across cells and tissues can be used as powerful tools to understand cellular function and processes ( 9 – 11 ).”
The essential chaperone DNAJC17 activates HSP70 to coordinate RNA splicing and G2-M progression
Allegakoen, D., Sahu, P., Kwong, K., Morales, J., Bivona, T., & Sabnis, A. J. (2026) · Scientific Reports · doi:10.1038/s41598-026-53526-z
Liang, C.-W., Lin, H.-Y., & Chu, P.-Y. (2026) · ONCOLOGIE · doi:10.1515/oncologie-2026-0003
“The correlation between UBE2C expression and Ruxolitinib drug sensitivity area under the curve (AUC) was determined based on datasets from DepMap [24] and GDSC [25], respectively.”
Wang, S., Zhang, D.-C., Liu, W., Zhu, M., Li, R., Liu, Z.-Y., Wu, P., & Liu, T.-Y. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1789743
CYCLON is a nucleolar protein that regulates pan-cancer cell fitness through ribosome biogenesisPreprint
Garcìa-Sandoval, A., Durand, S., Robertson, N., Hamaidia, S., Mikołajczyk, J., Bourdelais, F., Montaut, E., López, V., Turowski, T., Tollervey, D., et al. (2026) · bioRxiv · doi:10.64898/2026.05.13.724782
Xiao, W.-T., He, J.-Y., Yang, D., & Xun, Y. (2026) · BMC Cancer · doi:10.1186/s12885-026-16167-z
“One of the most influential initiatives in this field is the Cancer Dependency Map (DepMap) project, which integrates CRISPR-based loss-of-function screening data from hundreds of cancer cell lines [18].”
Kumar, V., Singh, V., Singh, R., Kumar, P., & Ghosh, T. (2026) · bioRxiv · doi:10.64898/2026.05.07.723511
“This is consistent with large-scale cancer dependency projects showing that genome-wide CRISPR and RNAi screens can identify lineage-specific and pan-cancer vulnerabilities [58, 59].”
Generalized Boundary FDR Control under Arbitrary Dependence: An Approach on Closure Principle
Zhang, Y.-F., Zhang, W., Zou, C.-L., & Ren, H.-J. (2026) · doi:10.48550/arxiv.2605.09953
“Since scientific discovery often hinges on validating these novel, borderline candidates rather than reconfirming the well-established strong signals, this lack of boundary con-trol undermines the statistical validity and reproducibility screening dataset (Tsherniak et al., 2017).”
Shukla, K., Castro, J., Cheng, D., Holley, L., & Brunk, E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.05.723010
“…we assembled a matched population-scale multi-omics resource that spans ~1,700 cancer cell lines 10–12 and integrates large-scale genomic variation 10 , bulk transcriptomic profiles 10–12 , pharmacologic response data 13 , AlphaFold structures 14,15 and single-cell perturbation data 16–18 ( Fig.”
InfluentialPapadopoli, D., Palia, R., Jovanovic, P., Tabariès, S., Ciccolini, E., Sabourin, V., Igelmann, S., Mclaughlan, S., Zhan, L., Kim, H., et al. (2026) · eLife · doi:10.7554/elife.106587
“Mining DepMap also revealed that ETFDH is essential in only 1 out of 1150 cancer cell lines ( Tsherniak et al., 2017 ).”
Agent-Driven Validation of Oncology Therapeutic TargetsPreprint
Huang, K.-L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.29.721634
“…such claims: the Cancer Dependency Map (DepMap) now aggregates genome-scale CRISPR/RNAi screens and multi-omic profiles for over 1,000 cell lines 6-8 while The Cancer Genome Atlas (TCGA) Pan-Cancer Atlas provides harmonized DNA, RNA, copy-number, epigenetic, and clinical outcome data for over…”
How DNA methylation predicts response to cancer therapies.
Davalos, V., Ferrer, G., & Esteller, M. (2026) · Trends in Cancer · doi:10.1016/j.trecan.2026.04.002
Li, Q., Wang, D., Zhang, Z., Perera, D., Chen, Z., Wen, W., MacDonald, M. E., Cai, W., Yan, J., Shu, X., et al. (2026) · PLoS Genetics · doi:10.1371/journal.pgen.1012145
“First, we evaluated their biological relevance by examining gene essentiality using data from the Cancer Dependency Map (Dep-Map) [43] which provides CERES scores derived from CRISPR-Cas9 knockout screens (DepMap 23Q4).”
Liao, C.-Q., Dou, R., Sun, D.-Y., Hu, C.-X., Hu, H., Cheng, L., Xu, Y., & Wang, X. (2026) · Bioinform. · doi:10.1093/bioinformatics/btag240
“Gene dependency was retrieved from DepMap (Arafeh et al. 2025) (release 25 Q3) and computed via the CERES model that normalizes for copy-number-associated effects in CRISPR-Cas9 knockout screens (Meyers et al. 2017, Tsherniak et al. 2017).”
The role of angiotensin II in cardiovascular disease-induced cancer growth
van Berlo, B., Esposito, P., Greco, A., van Assche, C. X. L., Bruyns, T., Asbroeck, B. V., Civati, C., Mohren, R., Cillero-Pastor, B., Guns, P., et al. (2026) · Cardio-Oncology · doi:10.1186/s40959-026-00495-x
“AT1 copy number and candesartan sensitivity for human cancer cell lines were obtained from the Depmap Portal database (https://depmap.org/portal) [18].”
Xie, Y., Li, H., Zhang, W., Sun, S., Zhang, J., & Wang, L. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1807701
“Baseline gene expression profiles for cancer cell lines were obtained from the DepMap portal (Tsherniak et al., 2017; Ghandi et al., 2019).”
Tong, S.-Y., Zhang, W., & Ji, S.-L. (2026) · bioRxiv · doi:10.64898/2026.04.22.720196
“To rigorously validate the in silico AI predictions us-ing real-world empirical data, two orthogonal clinical databases were leveraged: the Cancer Dependency Map (DepMap) [51] for genetic validation, and the Genomics of Drug Sensitivity in Cancer (GDSC) [52] for pharmacological evaluation.”
Dai, J., Posta, M., Marczyk, M., Qing, T., Győrffy, B., & Pusztai, L. (2026) · Genome Medicine · doi:10.1186/s13073-026-01644-8
“Next, we assign functional importance to a gene by calculating the average cancer type-specific CRISPR dependency scores from the Cancer Dependency Map (DepMap, h t t p s : / / d e p m a p . o r g / p o r t a l / , version Public 23Q2) using data from the relevant cancer cell lines [12].”
Differentiation state affects PD-L1 expression in cutaneous melanoma: a systematic review
Sævarsson, T., Vu, H. N., Steingrı́msson, E., & Einarsdottir, B. O. (2026) · Cellular & Molecular Biology Letters · doi:10.1186/s11658-026-00934-8
“Publicly accessible resources, such as the Cancer Cell Line Ency-clopedia, the DepMap project (https://depmap.org/portal) [ 101] and the Cellosaurus database [102] hold information on the mutational status of many melanoma cell lines that are commonly used in the included studies.”
Siwak, J. F., Connelly, J. P., & Pruett‐Miller, S. M. (2026) · Cells · doi:10.3390/cells15090768
“A comprehensive list of pan-essential genes has been identified in both cancer cell lines and stem cells [37–39], which served as a basis for identifying candidate essential selection sites in our work.”
DCN1 inhibitor induces fetal hemoglobin through self-limited regulation of CUL3 neddylationPreprint
Miller, S. A., Monti, A., Zhou, E. H., Lin, C. H. S., Lynch, O., Mukherjee, S., Hei, C., Nicewonger, R., Diner, C., Macias-Garcia, A., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.22.720177
“…we integrated a curated neddylation protein-protein interaction network with gene dependency profiles from the DepMap CRISPR viability dataset, reasoning that pathway components whose loss broadly impairs cell viability across diverse cell lines may be less tractable therapeutic targets ( 30 ).”
Single cell analysis reveals molecular traits of pediatric lymphoma resistant subclones
Yong, T., D’Aulerio, R., Matos, G. M., Bezerra, M., He, M.-H., Oertlin, C., Record, J., Elliot, A., Kwiecinska, A., Baecklund, F., et al. (2026) · bioRxiv · doi:10.64898/2026.04.21.719850
Yang, Y.-L., Huang, Y.-H., & Lin, H.-Y. (2026) · Oncology Research · doi:10.32604/or.2026.075284
“The biological role of MYBL2 in hepatocellular carcinoma (HCC) cell viability was examined using CRISPR knockout screen datasets, which are publicly available through the Cancer Dependency Map (DepMap) portal [27].”
Fu, R., Wang, Y., Rehman, I., Bedford, E., Sharif, S., Nguyen, N. D., Powell, R. T., Adams, A., LIU, W., Wang, S., et al. (2026) · Biomolecules · doi:10.3390/biom16081198
SOX2OT as a Novel Prognostic Biomarker and Therapeutic Target in Colorectal Cancer
Ghaleb, M. S., ElSmahey, M. K., Elsokkary, H. A. R., & Hamdy, N. (2026) · 2026 IEEE International Conference on Smart Sustainable Systems for Computer and Engineering Applications (3SCEA) · doi:10.1109/3scea68071.2026.11602901
“To explore potential therapeutic relevance beyond direct drug targeting, pharmacogenomic correlation analysis was conducted using large-scale functional screening resources, namely the Genomics of Drug Sensitivity in Cancer (GDSC) [12] and the Cancer Dependency Map (DepMap) [13].”
Li, M., Xue, Y.-X., Chang, Y.-I., Xu, F., Wu, F., Tian, X.-J., Hu, J., Song, Y.-J., Yu, X.-F., Zhou, F., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72062-y
Pairwise genomic alterations identify prognostic tumor states in multiple cancer typesPreprint
Bayati, M., Mishra, J., Luhari, L., Farina-Morillas, M., & Reimand, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.14.718563
“…perturbation screens and dependency maps that link genetic vulnerabilities to molecular context, including more recent combinatorial CRISPR screens that directly assay pairwise genetic interactions (Tsherniak et al , 2017; Hart et al , 2015; Shen et al , 2017; Gonatopoulos-Pournatzis et al , 2020).”
Sex-biased gene expression shapes sex differences in gene essentialityPreprint
Rocca, C., & DeCasien, A. (2026) · bioRxiv · doi:10.64898/2026.04.13.717533
“For mediation analyses, we used gene-level CRISPR-Cas9 essentiality scores and bulk RNA-seq expression profiles from DepMap version 22Q1[15], together with sex chromosome complement and inferred sex chromosomes information from Shohat and colleagues Supplementary Table 1 [13].”
Zheng, S.-Q., Tian, R.-F., Siburian, M., Rubio, A. H., Liu, Y., Wardenaar, R., Shirzai, M., Kempe, L., Dijkstra, E., Warszawik, E., et al. (2026) · EMBO Journal · doi:10.1038/s44318-026-00766-4
“4B; dataset 23Q4; (Tsherniak et al, 2017)).”
5′ UTR length shapes alternative N-terminal protein isoforms across cancers and in rare disease
Ly, J., Smith, E. M., Bernardo, M. D., Tao, Y. F., Black, E. M., Khalizeva, E., & Cheeseman, I. M. (2026) · EMBO Reports · doi:10.1038/s44319-026-00776-7
“CFD is not essential for viability in cultured human cells (DepMap; (Tsherniak et al, 2017)), limiting functional analysis of the intracellular isoform in cultured cell lines.”
Cunniff, P. J., Fitzpatrick, C., Bauer, J., Skopelitis, D. S., Klingbeil, O., Yoshimoto, T., Joshua‐Tor, L., & Vakoc, C. R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.08.717069
“Functional genetic screening has emerged as a powerful and unbiased approach to dissect the transcriptional and lineage dependencies that underlie SCLC biology 20-23 .”
Khidhir, K. G., Sabir, D. K., Salihi, A., & Miranda, B. H. (2026) · BMC Cancer · doi:10.1186/s12885-026-15918-2
“The DepMap project uses a genome-wide pooled RNA library to identify gene essentiality across hundreds of genomically characterised cancer cell lines (A375, SK-MEL-28, HSC-1, HSC-5, SCC-25, SCC-4, COLO-16, A431, WM266-4) from the Cancer Cell Line Encyclopedia (CCLE) project [55, 56].”
The E3 ubiquitin ligase HUWE1 is required for KRAS-induced lung cancer
Searle, J., Menotti, M., McDaid, W. J., Baker, M. J., Chaturvedi, A., GINN, L. H., Reed, H., Carter, M., Blackhall, F. H., Lindsay, C. R., et al. (2026) · Cell Death and Disease · doi:10.1038/s41419-026-08672-7
Kim, J., Yeon, S. Y., Choi, K., Kim, H., Kim, H., Hwang, D., & You, S. (2026) · Journal of Extracellular Vesicles · doi:10.1002/jev2.70275
“To assess the significance of the RNAi gene effect score of PTK7, we first randomly selected 20% of genes with RNAi gene effect scores from DepMap and computed the average of the scores.”
InfluentialLi, H., Lin, C., Liu, L., Lyu, J., & Feng, Z. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014253
“The lower the Chronos score, or its predecessor DEMETER [43] score, the more crucial the gene is for cell survival.”
InfluentialGenome-wide profiling identifies the genetic dependencies of cell death following EGFR inhibition
Porto, S. A., Birdsall, G. A., Harper, N. W., Honeywell, M. E., Leighow, S. M., Naylor, T. E., Ward, K. M., Wesley, M. K., Pritchard, J. R., & Lee, M. (2026) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2026.111414
“Notably, similar analytical strategies are used effectively in the context of gene essentiality profiling (55).”
Thege, F., Hoskins, A., Kramer, A., Salim, I., Seetharaman, A., Fowlkes, N., Rajapakshe, K., Maitra, A., & Wörmann, S. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.102759
“43 To deliver Cre-lox-compatible and sgRNA-encoding lentiviral libraries to the target organs, we optimized lentiviral delivery to the murine pancreas through retrograde ductal injection, achieving sufficient exocrine specificity and transduction to enable focused in vivo screening of focused sgRNA…”
A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma
Sussman, J., Oldridge, D. A., Yu, W., Chen, C., Zellmer, A. M., Rong, J., Parvaresh-Rizi, A., Thadi, A., Yang, A., Tumulty, J. S., et al. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.102766 · Cited by 15
Exploring CRISPR/Cas9 Technology for The Modernization of Traditional Chinese Medicine
Shu-Xian, W. A. N. G., Guo, F., & Guang-Qiang, M. A. (2026) · PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS · doi:10.3724/j.pibb.2025.0505
Cell state-specific metabolic networks govern ferroptosis versus apoptosis in small cell lung cancerPreprint
Kim, J. W., Bebber, C. M., Dai, Y., Bopp, S., Edenhofer, A., Li, A. M., Rösner, T. N., Berning, L., Yang, M., Leak, L. B., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.27.714827
Commentary: GETgene-AI: a framework for prioritizing actionable cancer drug targets
Yin, L. (2026) · Frontiers in Systems Biology · doi:10.3389/fsysb.2026.1793501
HIF1α mediates resistance to radiation and to KRAS inhibitors in pancreatic adenocarcinoma
Tu, K. J., Roy, S. K., Kingsbury, T. J., & Shukla, H. D. (2026) · PLoS ONE · doi:10.1371/journal.pone.0341912
“The expression and drug sensitivity cell line data was downloaded from the DepMap dataset, which is available at https://depmap.org/portal/download/all/“ [26].”
Flower, C. T., & White, F. M. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2522090123
ERKed by too much signaling: from oncogenic driver to therapeutic vulnerability
Farnsworth, D., Naznin, F., Subramaniam, A., Sew, K., & Lockwood, W. W. (2026) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-026-06084-6
“Kamada and colleagues took a similar approach by probing the gene-dependency database Dep-Map [147].”
Abt, E. R., Wang, L., Varuzhanyan, G., Freeland, J., He, T., Peña-Garcia, G. M., Ruegg, L., McLaughlin, J., Cheng, D.-H., Balanis, N., et al. (2026) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.2532814123
Regulation Ratio: A Singular Multi-Omic Measurement of Gene Regulatory Mechanisms
Krohannon, A., Srivastava, M., Sangani, N., & Janga, S. C. (2026) · Computational and Structural Biotechnology Journal · doi:10.34133/csbj.0044 · Cited by 1
“Protein expression data were obtained from the DepMap portal ( https://depmap.org/portal ), which provides comprehensive protein abundance measurements across multiple cell lines [ 20 ].”
TBXA2R activates ERMs to drive motility, invasion, and metastatic colonization of TNBC cells
Leguay, K., Naffati, O., Kiyan, C. L., He, Y.-Y., Hogue, M., Tesnière, C., Gombos, M., Kuasne, H., Gaboury, L., Gouill, C. L., et al. (2026) · Life Science Alliance · doi:10.26508/lsa.202503601
“In the absence of an antibody that reliably detects endogenous TBXA2R in cultured cells by Western blot or immunofluorescence, we selected six TNBC cell lines based on TBXA2R mRNA expression levels reported in the Cancer Dependency Map Portal (Tsherniak et al, 2017) (Fig S2A).”
mRNA-Protein Coordination is Contextualized by Metastatic Biological PhenotypesPreprint
Sharma, R., Meimetis, N., Begzati, A., Nagar, S. J., Kellman, B. P., & Baghdassarian, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.15.638428
“In order to determine whether global gene product abundances are informative of metastasis, we leveraged DepMap’s cell line measurements 17 .”
HIDDENdb: Co-dependency database reveals a plethora of genetic and protein interactions
Silva, I. D., Bandu, S. P., Kidmose, R., Maseras, G. T., Bataillon, T., & Ros, X. B. (2026) · doi:10.48550/arxiv.2603.06903
K-Ras controls asymmetric cell divisions from the primary cilium
Chippalkatti, R., Schaffner‐Reckinger, E., Gaigneaux, A., Parisi, B., Bottone, S., Laurini, C., Rouzbahani, Y., Dijkers, M., Gómez‐Mulas, A., Sauter, T., et al. (2026) · Cell Death and Disease · doi:10.1038/s41419-026-09089-y · Cited by 1
“A list of employed materials and equipment with their sources and identi fi ers can be found in Table S1.”
The computational analysis of tumor cell sensitivity to supertarget deletion
Chetverina, D., Kozelchuk, N. Y., Lomaev, D., Shtil', A. A., & Erokhin, M. (2026) · Vavilovskii zhurnal genetiki i selektsii · doi:10.18699/vjgb-26-02
“The most large-scale project is the De-pendency Map (DepMap, https://depmap.org/ ), which analyzes inactivation of an individual gene via RNAi and CRISPR/ Cas9 technologies (Tsherniak et al., 2017; Arafeh et al., 2025).”
InfluentialOpakunle, A., Shan, Y., Huang, Y., Printzis, C., Stanhope, C. T., Stranger, B. E., & Huang, R. S. (2026) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2026.100596
“22,30”
InfluentialWang, W. S., Ma, M., Zhang, H., Zhou, Y., & Wu, G. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.113863 · Cited by 1
Gentile, C., Feng, W. W., Lenahan, S. M., Ying, A. W., Card, D. C., Wu, F. T. H., Pham, N., Radulovich, N., Cao, P. M., Hueniken, K., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.27.708377
A machine learning assay to predict disease recurrence in hormone receptor-positive breast cancer
Bielo, L. B., Trapani, D., Razavi, P., Chandarlapaty, S., Zhan, Y., & Curigliano, G. (2026) · ESMO Open · doi:10.1016/j.esmoop.2026.106064
Zeng, Y., Wang, Q., (117657), S. Z., Li, F., Chen, Q., Wang, J. (., & Tan, C. S. H. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115708
Lu, J., Liang, W.-S., Liang, Y., Ren, J., Guo, C., Li, X., Pan, J., Zhang, W.-D., & Tian, S.-S. (2026) · Chinese Herbal Medicines · doi:10.1016/j.chmed.2026.03.006
Hassani, Z. M., Goulet, F., Nguyen, D. T., Landry-Voyer, A.-M., Kwiatek, L., Gaudet, S., Boudreault, P.-L., Azad, T., & Bachand, F. (2026) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2026.111415
“Furthermore, according to the Dependency Map (DepMap) project (28), CRISPR-Cas9 loss-of-function screens indicate that PDCD2 is broadly required for the survival of many human cancer cell lines, underscoring the potential of targeting the PDCD2–uS5 interaction as an anti-cancer therapeutic strategy.”
Tang, X.-P., Wang, Y., Pu, Y., Li, K.-X., Ding, Z., Wang, M., Almeida, L., Cerezo, M., Cao, Y., Robert, C., et al. (2026) · iScience · doi:10.1016/j.isci.2026.115493
“28 By focusing on the ribosomal proteins, we observed that those upregulated propensity of RPs within ribosomes derived from DTP cells have shown strong es-sentiality in different cancer cell lines, including RPL4, RPL37, RPL23 (Figure 2G), whereas no such a correlation was observed for those small…”
Nelson, B. J., Delgado-Coka, L. A., Marchenko, N. Y., Escobar-Hoyos, L. F., Shroyer, K. R., Yurovsky, A., Ideker, T., Balázsi, G., MacCarthy, T., & Powers, S. (2026) · Molecular Oncology · doi:10.1002/1878-0261.70218
Cell type-dependent suppression of the RNA helicase DDX3Y levels by the close paralog DDX3X
Xu, X., Rainey, J., Grigoropoulos, N., & Wei, S. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1644807
“Indeed, a search against the DepMap portal (Tsherniak et al., 2017) did infer a negative impact of DDX3X loss on U87MG cells (Supplementary Figure S4).”
Dissecting FOXA1 pioneering function by acute pharmacological degradationPreprint
Hargis, L. M., Barta, P. A., Zhang, Y., Hayward, R. E., Cravatt, B. F., & Erb, M. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.25.707765
“For example, as shown by the Cancer Dependency Map, prolonged FOXA1 loss in prostate cancer models typically impairs cell growth 27–29 , which complicates the use of genetic methods to identify the immediate gene regulatory functions of this pioneer factor and the mechanisms that underly them.”
Agrawal, P., Timon, A., Gopalan, V., Singh, A., & Hannenhalli, S. S. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1711517
Hemizygous loss of helicases promotes genomic instability and cancer development
Voßgröne, K., Favero, F., Kashyap, K., Gonzalez, F. G. R., Olsen, A. V., Li, X., Mardin, B. R., Weischenfeldt, J., & Sørensen, C. S. (2026) · Science Advances · doi:10.1126/sciadv.adv4540 · Cited by 2
“In keeping with this, we found AQR to be a common essential gene in DepMap ( 32 ), a large-scale functional genomics profiling of cancer dependencies and essential genes.”
Lin, J.-C., Huang, T.-S., Chen, Y.-B., Chen, T.-Y., Wu, P.-S., Liu, T.-P., & Yang, P.-M. (2026) · Biology Direct · doi:10.1186/s13062-026-00735-1
“To test this hypothesis, we mined the DepMap database ( h t t p s : / / d e p m a p . o r g / p o r t a l / [36]) to identify small molecules with cytotoxicity profiles similar to those of established ferroptosis inducers, including 1 S,3R-RSL-3 [37], ML210 [38], and ML162 [38], then evaluated…”
NeuroD1-USP1-MYCN axis drives tumor progression in neuroblastoma
Li, G., Chen, Y., Zhuo, R., Yu, J., Bao, J., Di, W. U., Yin, H., Li, X., Li, Z., Yang, C., et al. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-026-07844-5
“To explore the role of NeuroD1, we analyzed its expression across various cancer cell lines using the DepMap portal [35] and The Human Protein Atlas [36, 37].”
Dynamic feedback control of oncogenic tyrosine kinase signaling in acute leukemia
Lee, J., Sun, R., Kume, K., Robinson, M. E., Cheng, Z., Cosgun, K. N., Ma, N., Hurtz, C., Geng, H., Luger, S. M., et al. (2026) · Science Signaling · doi:10.1126/scisignal.adw5054
Kim, J., Kovacs, H., Parthasarathy, A., Chen, S.-N., Krishnamoorthy, V., Al-Seragi, M., Zhao, M., Huang, Y.-H., Dean, P. A., Johnson, P., et al. (2026) · bioRxiv · doi:10.64898/2026.02.06.704476
“D ) Gene hits with a positive effect score were analyzed in DepMap to identify essential genes.”
InfluentialMolotkova, A., Deniz, E., Swift, M., Glasgow, E., Ma, J., Petro, J. A., Frye, I., Ozdemirli, M., Pesic, D., Ahern, G. P., et al. (2026) · Cancer Research Communications · doi:10.1158/2767-9764.crc-26-0068
Exploiting a purine imbalance to target KRAS mutant pancreatic adenocarcinomas
Mota‐Pino, J., & Fernández-Capetillo, Ó. (2026) · Gut · doi:10.1136/gutjnl-2025-337940
Allen, B. M., Gonzalez, N., Werner, E., Faundez, V., & Vrailas-Mortimer, A. D. (2026) · bioRxiv · doi:10.64898/2026.02.02.703386
“We assessed genome-scale fitness correlations between ATP7A with ARPC1A, ARPC1B, GIGYF2, HIP1, LRKK2, VPS13C and VPS35 mining the Dependency Map (DepMap) datasets 39,40 .”
Schmitt, T. M., Furiya, K., Black, C., Vazquez, A., Sharma, J., Hailemariam, M., Paushter, D. H., Trieu, L., Lam, J. L., Lee, B., et al. (2026) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2025-013520
“Importantly, the ARF 35-43 peptide was not detected in a dataset of HLA-associated peptides from a panel of more than 40 normal tissues from 12 HLA-A2 + donors (online supplemental table 3), confirming the lack of ARF presentation by normal tissues.”
Wang, F., Lu, D., Huang, R., Lu, Y., Pan, Y., Pang, X., Kan, X., Li, L., Yang, C., Zhao, G., et al. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.150651
The protein phosphatase 2A-B56α complex regulates N-Myc degradation in neuroblastoma
Tran, B., Peris, I., Wurman, E., Huang, A., Onishi, G. H., Hu, J., Avelar, R. A., Newman, E. A., DiFeo, A. V., O’Connor, C. M., et al. (2026) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2026.111298
“We next correlated N-Myc protein expression with functional dependence on N-Myc using the Cancer Dependency Map (DepMap) data-base (20), which demonstrated that the cell lines with the highest N-Myc expression were highly dependent on the protein for their survival (Fig.”
PanCNV-Explorer: Deciphering copy number alterations across human cancersPreprint
Kurz, N. S., Kornrumpf, K., Krüger, A.-R., & Dönitz, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.26.701884
“For cancer cell line data, we downloaded segment-level copy number variation data from DepMap [27] (release 25Q3).”
AstraKit: Customizable, reproducible workflows for biomedical research and precision medicinePreprint
Kurz, N. S., Kornrumpf, K., Stoves, M. K., & Dönitz, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.25.701622
Wang, Y., & Ramos, K. S. (2026) · Cancers · doi:10.3390/cancers18030459
“…RNA-Seq was performed on Illumina HiSeq 2000 or HiSeq 2500 instruments (Illumina, Inc., San Diego, CA, USA) using a large-scale, automated Illumina TruSeq RNA Sample Preparation protocol, with sequence coverage of no less than 100 million paired 101-nucleotide-long reads per sample [27,29].”
Expression-driven genetic dependency reveals targets for precision oncology
Elmas, A., Layden, H. M., Ellis, J. D., Bartlett, L. N., Zhao, X., Kawabata‐Iwakawa, R., Wang, Z., Obinata, H., Hiebert, S. W., & Huang, K.-L. (2026) · GigaScience · doi:10.1093/gigascience/giag011 · Cited by 1
“By integrating CRISPR/transcriptomic data from CCLE/DepMap [ 4 , 5 ], and global proteomic analyses [ 24 ], we ensured a robust identification of GEDs/PEDs.”
InfluentialBilik, S., Burke, O., & Elman, S. (2026) · Melanoma research · doi:10.1097/cmr.0000000000001080
Taguchi, Y.-H., & Turki, T. (2026) · bioRxiv · doi:10.1101/2025.06.12.659265
“The CRISPRInferredModelEfficacy.csv file, which included the CRISPR efficiency of the cell lines, was retrieved from DepMap [20].”
Ye, D.-X., Yuan, S.-S., Su, W., Zhang, H., Li, R., Qi, Y.-C., Lin, H., Dong, N., & Jin, Y.-T. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag349
“External generalization was assessed using an independent pan-cancer test set constructed from the current public DepMap release, referred to as DepMap-IT [ 36].”
The human DDX52 protein is a nucleic acid helicase and strand annealase that promotes cell migration
Parkes, A. J., Springer, P., & Bolt, E. (2026) · Bioscience Reports · doi:10.1042/bsr20253932
“…gene were not possible using CRISPR-Cas9 in human U2OS cells, consistent with essentiality of DDX52 suggested in Human Cancer Dependency Mapping [18], but DDX52 – / DDX52 + heterozygotes were inhibited in cellular proliferation and migration, supporting the relevance of DDX52 as a target to…”
Feature-Based Classification of Cancer Drug Target Genes Using Machine Learning Approaches
Cava, C., & Castiglioni, I. (2026) · Cell Death · doi:10.1016/j.celld.2026.100006
Mapping Gene Impact on Single-cell Transcriptomic Networks via Perturbation Response ScanningPreprint
Gupta, S., Romero, S., & Cai, J. J. (2025) · bioRxiv · doi:10.64898/2025.12.15.694358
“…off-the-shelf network centrality metrics and two dedicated virtual KO methods (scTenifoldKnk [18] and GenKI [19]) using 16 pan-cancer scRNA-seq datasets from the Cancer Cell Line Encyclopedia (CCLE) [24] and real-world CRISPR KO screens from the DepMap portal [25] and BioGRID-ORCS database [26].”
Benchmarking Chemical, Genetic, and Cell Line Encodings for Cancer Perturbation Response PredictionPreprint
Zinchenko, V., Schlicker, A., Kurilov, R., Pouplin, A., Villalba, S., & Horlacher, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.15.694331
“For gene knockouts, differential dependencies from (55) were used, defined as selectively essential in some of the cell lines using a six-standard-deviation threshold.”
Bigenzahn, J. W., Kartnig, F., Vollert, M., Sedlyarov, V., & Superti-Furga, G. (2025) · bioRxiv · doi:10.64898/2025.12.10.693430
Rosenthal, S. B., Whisenant, T. C., Šášik, R., Rivera, M., Defonso, G. I., Roy, C.-A., Enlund, S., Shraim, R., Fisch, K. M., Teachey, D. T., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.03.692208
Chromosome-arm 17p Loss Renders Breast Cancer Cells Vulnerable to AURKB InhibitionPreprint
Winkler, T., Sdeor, E., Saad, R., Okada, H., Laue, K., Leor, G., Wolf, G., Shachar, S. S., & Ben‐David, U. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.01.691489
“To experimentally validate that Del17p is associated with increased vulnerability to AURKBi, we compared two p53-deficient basal-like breast cancer cell lines, MDA-MB-231 and MDA-MB-468, which differ in their Chr17p copy number status ( 27 ).”
InfluentialHuang, C., Yu, B., Qiu, W., Cheng, H. B., & Li, S. (2025) · Advanced Therapeutics · doi:10.1002/adtp.202500270
A genome-wide RNAi screen for novel CIN genes using human artificial chromosome
Liskovykh, M. A., Kochanova, N. Y., Chiang, C.-Y., Dhall, A., Aksenova, V., Chen, Y., Reinhold, W. C., Dasso, M., Thomas, A., Cheng, K. C.-C., et al. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf345
Anand, U., Bloomberg, A., Bhattacharjee, P., Mukhopadhyay, S., Badarudeen, B., Ramakrishnan, S., Ben‐David, U., & Manna, T. K. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.111104
“To this end, we analyzed mRNA expression levels from 1517 human cancer cell lines using datasets from the DepMap portal (54, 55).”
Zhang, Y., Tan, H., & Jiang, D.-P. (2025) · Cancers · doi:10.3390/cancers17233844
“The Dependency Map (DepMap) database integrates a CRISPR-Cas9 screening dataset of 1186 cancer cell lines and applies the CERES scoring system [15,16].”
Lin, J.-C., Huang, T., Chen, Y., Chen, T.-Y., Liu, T., & Yang, P. (2025) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2025.153065
Fairall, L., Sirvydis, K., Turnbull, R. E., Knottnerus, S. J. G., Gonchar, O., Muskett, F. W., Jukes‐Jones, R., van Brussel, L., van de Geer, E., van Gassen, K. L. I., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-65472-x
“The relationship of MIDEASandDNTTIP1withp38isfurthersupported byDEPMAP,which shows reciprocal co-dependencies (Pearson correlations of co-dependency: 0.25 and 0.19, respectively) which are in the top fi ve hits for each protein 27 .”
InfluentialContext-aware gene dependency modeling via graph attention networks for precision oncology
Yu, G., Gong, Y., Fan, B., Gao, J., & Fan, Q. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-07501-3 · Cited by 2
“High-throughput CRISPR-Cas9 screens have transformed gene dependency mapping by enabling genome-scale perturbation studies across hundreds of cancer cell lines [10–12].”
TP53 and RB1 are predictive genetic biomarkers for sensitivity to cytarabine in gliomasPreprint
Laaniste, L., Luginbühl, J., Trinh, M. K., Watkins, R., Lueg, G., Neilson, G., Burgers, M., Kania, K., Reynolds, R. H., Smith, Z., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.20.688938
“Integrating perturbation data with somatic alterations has consistently revealed that genetic aberrations can generate collateral dependencies susceptible to pharmacological targeting [4], [5], [6].”
Non-coding genetic variants underlying higher prostate cancer risk in men of African ancestry
Li, S., Fatema, K., Sundarraj, N., Singh, A., Rajagopal, P. S., Notani, D., Takeda, D. Y., & Hannenhalli, S. S. (2025) · Nature Communications · doi:10.1038/s41467-025-64631-4 · Cited by 1
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
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