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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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InfluentialOncogenic Fusions with Aberrant Transcriptional Regulation
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“The DepMap database ( www.depmap.org ), building off of the original Cancer Cell Line Encyclopedia (CCLE) project, creates data and tools that can be used and shared by researchers (18).”
Identifying prognostic targets in metastatic prostate cancer beyond AR
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Chemoproteomic Approach for Identifying Nuclear Arsenite-Binding Proteins
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Shi, X., Ramathal, C. Y., & Dezső, Z. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.21.649855 · Cited by 2
“Since drugs often have complex MOAs and typically present with off-target effects, we calculated the correlation between drug sensitivity and gene essentiality data as measured by the CRISPR screens from DepMap [26].”
Single cell resolution of an epigenetic signature of persister tumor cellsPreprint
Dumbrava, M., Ismail, W., Sandoval, L., Mazzone, A., Aalam, S. M. M., Ritting, M., Hou, X.-N., Xie, Y., Harrington, S. P., Kaufmann, S. H., et al. (2025) · bioRxiv · doi:10.1101/2025.04.16.649175 · Cited by 1
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“The CRISPR-Cas9 loss-of-function data were obtained from the Dependency Map (DepMap) portal (https://depmap.org/portal/) [29].”
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“…(e.g. minimum distance to start codon); 8) Tissue expression level, as measured by GTEx project 33 ; 9) Gene dependency score, as record by DepMap database 27 ; 10) Nearest distance to a 6-mer on transcript; 11) Maximum counts of a specific k-mer in 100 nt range flanking known m 6 A sites.”
Selective depletion of cancer cells with extrachromosomal DNA via lentiviral infection
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goloco: a web application to create genome scale information from surprisingly small experiments
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“We also conducted differential gene expression analysis in SCLC cell lines based on data available through the Dependency Map (DepMap) Portal 21Q1 release [33].”
TLE4 is a repressor of the oncogenic activity of TLX3 in T-cell acute lymphoblastic leukemia
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Deng, Y., Zhu, H.-K., & Peng, H.-L. (2025) · Clinical and Experimental Medicine (Testo stampato) · doi:10.1007/s10238-024-01526-6 · Cited by 1
“We obtained the RNAi dependency score data, which were already integrated from the Broad Institute Project Achilles, Novartis Project DRIVE, and Marcotte et al., and calculated using the DEMETER2 model [23].”
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Lee, K., Kim, D., Kim, I., Lee, J.-H., Ha, D., Lim, S., Kim, E., Im, S., Shin, K., & Kim, S. (2025) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf070 · Cited by 1
“To build a co-essentiality network, we used a dataset consisting of the genome-wide CRISPR screening data from the Achilles project 20q2 in the dependency map (DepMap) project [19].”
InfluentialWang, M., Wu, T., & Wang, D. (2026) · Gene Reports · doi:10.1016/j.genrep.2026.102651
Khorrami, M., Parvaresh, E., Asadi, J., & Tatar, M. (2026) · Functional & Integrative Genomics · doi:10.1007/s10142-026-02106-5
“Cell-line expression came from DepMap 24Q4 (Tsherniak et al. 2017; Ghandi et al. 2019; Broad Institute DepMap Portal 2024); engineered derivatives and repeat models of the same patient were removed, OE-19 was reclassified as oesophageal adenocarcinoma following Cellosaurus (Bairoch 2018), and 501…”
Lalani, S., Haddox, S., Bromley, K. S., Zahra, S., Wang, M., Ingram, M., Gadh, S., Gunalan, S., Ke, S.-L., Wang, Q., et al. (2026) · Science Advances · doi:10.1126/sciadv.aei0901
“…the biological significance of the chimeric RNAs, we took advantage of RNAi-based dependency datasets from the Dependency Map project (DepMap), which consists of two pan-cancer RNAi screening projects: Project Drive from Novartis and Project Achilles from the Broad Institute ( 22 , 23 ).”
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“Comparison with DepMap gene effect scores demonstrated that the genes targeted in our experiment encompassed a range of essentiality states [21], including genes with strong fitness effects (Figure S4A).”
MERIT: Mechanism driven model predicts drug outcomes and nominates indications for failed drugs
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Chen, G.-X., Shao, N.-H., He, H.-H., Tang, Z.-C., Hu, P.-W., & Huang, Z. (2026) · doi:10.48550/arxiv.2610.24597
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A cytoplasmic cis-acting circuit confines PI3K/AKT-driven lineage plasticity in bladder cancer.
Dong, X., Li, Y.-T., Yan, Y.-T., Xiong, H., Wu, P.-L., Chen, Y.-J., Yi, Z.-F., Zhou, C., Zuo, L., & Liu, H.-W. (2026) · Cellular Signalling · doi:10.1016/j.cellsig.2026.112952
Global signatures of Ribonucleotide Excision Repair deficiency and its therapeutic vulnerabilitiesPreprint
Toong, P. J., Boushaki, S., Zhao, S. J., Brough, R., Song, F., Krastev, D. B., Black, D., Rinaldi, G., Badja, C., Degasperi, A., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.09.757935
“To validate this finding, we identified HCC202, a breast cancer cell line within the DepMap project ( 27 ), as RERDetect-high (0.99), exhibiting all hallmark signatures of RERd.”
InfluentialGarnés-García, C., Fernández-Aroca, P., Cimas, F., Ruiz-Hidalgo, M., Belandia, B., Mayor-Garnacho, N., Fernández-Aramburo, A., Lobo, V. S. S.-A., Sánchez-Prieto, R., & Fernández-Aroca, D. (2026) · bioRxiv · doi:10.64898/2026.10.02.755908
“Gene effect (Chronos) scores from the DepMap CRISPR screen [59,60] were retrieved for 1,196 cell lines, of which 46 were annotated to the soft tissue lineage and dependency was defined by the conventional threshold of Chronos < −0.5.”
InfluentialRNA-Adjusted TUFM Protein State Captures Mitochondrial Proteome Organization in Lung Adenocarcinoma
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Tumor-specific super-enhancer drives overexpression of EVI1/MECOM in ovarian cancer.
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CellChain: Benchmarking Cross-Scale Evidence Use for Perturbation Reasoning
Xia, X., Wang, Y., Chen, S.-Z., Yu, L., Wu, H., Huang, W.-X., Yao, C.-K., Gao, Z.-Y., & Sun, S.-Q. (2026) · doi:10.48550/arxiv.2610.16346
“…ChEMBL, BindingDB, UniProtKB, Reactome, Gene Ontology/GOA, Cellosaurus, scBaseCount, CORUM, IntAct, SIGNOR, and Open Targets (Corsello et al., 2020; Tsherniak et al., 2017; Kim et al., 2023; Zdrazil et al., 2024; Gilson et al., 2016; bat, 2025; Milacic et al., 2024; Aleksander et al., 2023;…”
Ali, A. A. M., & Nelson, E. J. R. (2026) · bioRxiv · doi:10.64898/2026.10.08.757734
“It achieved an AUROC of 0.6018 (95% CI 0.587 to 0.617) on the DepMap essentiality [37] dataset across the 16,331 genes with an essentiality label, short of the 0.63 required for inclusion, so it contributes no score to any analysis.”
InfluentialThe adaptive architecture of tRNA dependencies across physiological tumor environmentsPreprint
Chen, S., Charbonneau, T., Yousefi, K., Zirak, B., Lee, S., Joshi, T., Pawluk, A., Ramani, V., & Goodarzi, H. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.08.757597
“…this question requires moving beyond measurements of tRNA abundance or molecular state to directly determine which components of the decoding machinery are functionally required in different contexts, an approach enabled by genetic dependency mapping (Gilbert et al., 2014; Tsherniak et al., 2017).”
Pınar, A., Çolak, C., & Arslan, A. K. (2026) · Intelligent Data Analysis · doi:10.1177/1088467x261490986
Subtype-Specific Transcriptional and Metabolic Liabilities in Breast Cancer
Tatli, O. (2026) · Balıkesır Health Sciences Journal · doi:10.53424/balikesirsbd.1988779
Mapping the functional dark proteome in cancer
Prensner, J. R., Freed-Pastor, W. A., Abelin, J. G., Wu, C. J., Merbl, Y., Pilpel, Y., Schumacher, T. N., Weissman, J. S., van Heesch, S., & Agami, R. (2026) · Nature reviews. Cancer · doi:10.1038/s41568-026-00985-1 · Cited by 1
Mechanism of spliceosome termination
Boreikaitė, V., Faraway, R., Vorländer, M. K., Phillips, A. W., Opitz, L., Wanke, M., Yakoub, G., Raffl, G., Fin, L., González‐Prieto, R., et al. (2026) · Nature · doi:10.1038/s41586-026-11101-6
Kong, M.-W., Li, Q.-T., Zhao, X.-W., Wang, Y., Luo, Y.-M., Yu, Y., & Zhang, C.-X. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1957511
Fırat, Y., & Kılıçaslan, Y. (2026) · Biology · doi:10.3390/biology15191782
“The 2D molecular profiles and CRISPR Gene Effect data were obtained from the DepMap Public 26Q1 release and the CCLE [3,4,13,14] (https://depmap.org/portal/data_page/?tab=allData) (accessed on 26 September 2026).”
InfluentialZhang, J., Ning, J., Fang, S., Han, Y., Li, M., Chen, W., Dong, T., Bai, Q., Jia, M., Hao, P., et al. (2026) · Genes & Diseases · doi:10.1016/j.gendis.2026.102515
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“Because co-essentiality mapping only measures phenotypes and is thus agnostic to prior knowledge of gene function, this approach can lead to the discovery of new gene functions and pathway relationships [27–30].”
Dossin, F., Attia, M., Zuniga, F. M., González‐Acosta, D., Brough, R., Song, F., Krastev, D. B., Raghunandan, M., Pettitt, S. J., Bore, A., et al. (2026) · Molecular Cell · doi:10.1016/j.molcel.2026.09.017
Li, Z., Shinohara, A., Xia, Y., Xu, X., & Takeda, S. (2026) · BioEssays · doi:10.1002/bies.70185
“DepMap assesses the effects of individual gene depletion on cell proliferation across more than 1000 cancer cell lines over five days of culture [159, 160].”
Wang, X., Sun, Y.-S., Sinha, P., Su, R.-Y., Zhang, D. Y., Park, K. H., Fu, Y., Dong, W.-F., Zhang, Z.-J., Wang, Q., et al. (2026) · Cell Reports Medicine · doi:10.1016/j.xcrm.2026.103095
“To achieve this, we utilized perturbational datasets, including the CmapR package 48 and the ConnectivityMap platform, 49 which provide gene expression profiles affected by treatment of many compounds.”
Functional identification of non-coding regulatory regions that drive breast and skin carcinogenesis
Oh, R. H., Cheng, K. C. L., Serafimova, E., Langille, E. R., Pérez‐Castro, M. A., Lowden, C., Nurtanto, J., Dessapt, J., Fotiadou, I., Haider, S., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.118111
Hang, Z., Wang, H.-K., Yu, L., Liu, S.-W., Zhang, W., Feng, T., Zhang, Y.-Q., Xiao, X.-Y., Xu, W.-H., Ye, D.-W., et al. (2026) · Cancer Genetics · doi:10.1016/j.cancergen.2026.09.010
Hippo signaling drives mesenchymal cell fate and heterogeneity in liver cancer
Souza, N. A., Kowalczyk, W., Soheily, S., Canoot, G., Ostyn, T., Corral-Ibáñez, M., Sarfi, M., Hillen, H., Sansores-García, L., Huffel, L. V., et al. (2026) · JHEP Reports · doi:10.1016/j.jhepr.2026.102064
Cracking MYC addiction: Synthetic-lethal vulnerabilities for precision cancer therapy
Deng, K.-L., Hong, Z.-Y., Wang, Y.-X., Wu, Y., Zhang, W.-D., Luan, X., & Sun, Z. (2026) · Genes & Diseases · doi:10.1016/j.gendis.2026.102534
SADRP: A structurally aware multi-modal framework for drug response prediction
Qi, R., Liu, S., Quan, T., 吴天如, Shaytan, A. К., & Liu, B. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014861
“…IC50 values, representing drug sensitivity for drug-cell line pairs, were obtained from GDSC. Multi-omics profiles, including gene expression and DNA methylation data, were retrieved from CCLE via the DepMap portal [66], which provides comprehensive molecular characterizations of cancer cell lines.”
Proteasome inhibition bidirectionally regulates ferroptosis sensitivity
Murray, M. B., Upadhyay, R., Szylo, K. J., Gautam, A., Goncalves, J., Forcina, G. C., Vinayak, A. A., Brandman, O., & Dixon, S. J. (2026) · Cell chemical biology · doi:10.1016/j.chembiol.2026.09.020
Gan, Z., Zhou, H., Liang, H., Ding, L., Wu, Q., Shen, J., Huang, J., Wang, X., Hu, N., Ma, H., et al. (2026) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2026.101691
Tumor suppressor-like mutations mark a dosage-constrained dependency in leukemia
Lee, J. D., Uyemura, M., Lamini, F., Posey, R. R., Shin, E. H., Harris, A. C., Ottone, T., Cheloni, G., Chou, D. B., Trinh, B., et al. (2026) · bioRxiv · doi:10.64898/2026.09.29.755484
EXO1 inhibition selectively kills replication repair-deficient cancer cells
Maric, M., McClelland, S. E., & Boulton, S. J. (2026) · NAR Cancer · doi:10.1093/narcan/zcag029
“One approach to address this gap is the systematic discovery of context-specific genetic dependencies based on the identification of synthetic lethality genetic interactions [ 5 ].”
HELQ & Hel308: ancient enzymes of DNA repair and recombination
Lou‐Hing, A., Downs, O., Allers, T., & Bolt, E. L. (2026) · Biochemical Society Transactions · doi:10.1042/bst20250255
Inferring cascade drivers of VEXAS syndrome by a causal machine learning tool CauNagiPreprint
Bao, D., Yao, H., Liu, J., Dong, G., Xu, S., & Cai, Z. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.23.753348
Hussein, Z., Misnik, E. V., Alaji, M., Patra, C., Mhanna, R., Habib, A., Eldeeb, A., & Kolpashchikov, D. (2026) · ChemBioChem · doi:10.1002/cbic.70544
Disentangling Heterogeneous Molecular Networks for Multi‐Omics‐Driven Cancer Driver Discovery
Gong, X., Li, J., Su, M., Yu, P., Ma, T., Zhai, R., Zhang, C., Zhang, M., & Zhang, Y. (2026) · Advanced Science · doi:10.1002/advs.77970
“Functional dependency evidence from DepMap CRISPR screens provided an independent test of candidate relevance [ 49, 50 ].”
Antibody-drug conjugate engineering: from design to efficacy and safety
Ocaña, A., Espinosa, J. R., Alonso‐Moreno, C., Győrffy, B., Tong, H., & Pandiella, A. (2026) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-026-03057-w
Zhang, D. (2026) · Biogerontology · doi:10.1007/s10522-026-10512-2
“Here we address this gap using three publicly available datasets: GTEx v8 for tissue-specific aging signatures (GTEx Consortium 2020), LINCS L1000 for drug perturbation profiles (Subramanian et al. 2017), and DepMap for external genetic concordance analysis (Tsherniak et al. 2017).”
Traffickome infers directed, compartment-resolved membrane trafficking from proteomic dataPreprint
Deshpande, R., Pinilla‐Macua, I., Paulo, J. A., Gygi, S. P., & Sorkin, A. D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.20.753016
“The CRISPR-screen and DepMap data are available from their original sources [29,49].”
Abusneina, A. M., & Elwirfli, S. M. (2026) · PLoS ONE · doi:10.1371/journal.pone.0354937
“Cell-line CRISPR gene-effect (Chronos) scores and whole-genome-sequencing copy-number data were obtained from DepMap Public 26Q1 (CRISPRGeneEffect.csv and OmicsCNGeneWGS.csv) [ 10].”
Liu, M., Song, J., Liu, X., Zhao, L., & Xiong, Y. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1923164
“DepMap 26Q1 molecular profiles were matched at the cell-line model level, including expression, copy-number and CRISPR dependency features derived from the Cancer Dependency Map resource (Tsherniak et al., 2017; Behan et al., 2019; Dempster et al., 2021).”
Premaratne, G., & Ameratunga, M. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2026.1921857
“…which utilise a direct patient biopsy for drug screening (Phan et al., 2019; Williams et al., 2022), as well as genetic perturbation strategies such as CRISPR and RNA interference knockdown screens to prioritise drug candidates for cancer therapeutics (Behan et al., 2019; Tsherniak et al., 2017).”
Integrating structural and biological evidence to rerank ESMFold2 protein-protein interactionsPreprint
Xie, J., Li, M., Chai, Y., Ou, G., Li, W., & Guo, Z. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.16.751194
“…and DepMap co-194 essentiality or dependency profiles, T5 protein-language-model embedding similarity, and AlphaMissense pathogenicity scores (Table S2) (Cheng et al., 2023; Elnaggar et al., 2022; Obayashi et al., 2023; Oughtred et al., 2021; Thumuluri et al., 2022; Tsherniak et al., 2017).”
Yan, J.-Q., Ye, J.-L., Qian, W.-B., Zhu, J.-L., Wan, Z.-P., Wang, H.-H., Meng, X.-T., Gu, J.-M., Chen, F., & Luo, C. (2026) · Functional & Integrative Genomics · doi:10.1007/s10142-026-02046-0
“Dependency data and common-essential annotations were derived from the DepMap framework and public portal resources (Meyers et al. 2017; Tsherniak et al. 2017; Pacini et al. 2021; Broad Institute n.d.).”
Uncovering pan-cancer signatures of chemoresistance
Inayatullah, M., Demirdizen, E., Keepers, Z., Correia, C. M., Hashemi, S. M., Tripathi, K. K., Sadhukhan, S., Bardhan, I., Mariappan, A., Rassool, F. V., et al. (2026) · Genome Medicine · doi:10.1186/s13073-026-01763-2
“To identify representative cellular models of chemore-sistance, transcriptomic profiles of cancer cell lines were obtained from the Cancer Cell Line Encyclopedia (CCLE) [32] available in DepMap [33].”
Solovev, I. A., & Golubev, D. (2026) · Real-World Data & Evidence · doi:10.37489/2782-3784-myrwd-114
PROTACs in Oncology: Emerging Frontiers for Next-Generation Cancer Therapeutics
Salahuddin, S., Sharma, R., Ahsan, M. J., Mazumder, A., Rana, K., Kumar, R., & Majee, C. (2026) · Onco · doi:10.3390/onco6030048
“Simultaneously, Xue et al. created photoactivatable PROTACs, also known as photo-caged PROTACs (pc-PROTACs), derived from the well-known BRD2-4 degrader bromodomain and extraterminal domain (DBet) [92].”
Elbashir, R., Abbott, K. L., Ramesh, D. L., Ali, A., Shevzov-Zebrun, A., Barbeau, A. M., Wu, M., Ward, A. P., Gültekin, Y., Do, B. T., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.13.750663
“CRISPR screening datasets, such as DepMap performed under nutrient-replete standard culture conditions, identify strong dependencies on de novo nucleotide synthesis genes in hematologic malignancies 33,57 (Extended Data Fig.”
SAF-A/HNRNPU regulates euchromatin dynamics and is required for nuclear envelope integrity
Alimenti, K., Mazzocca, M., Hansen, A. S., & Blower, M. D. (2026) · bioRxiv · doi:10.64898/2026.09.10.750682
“Loss of SAF-A is lethal in human tissue culture cells and embryonically lethal in mice 18–20 .”
Savino, A., Oikonomou, A., Perrone, F., Lucia, R. R. D., Pietri, L. D., Belattar, Y., Brown, L., Grau, M. L., McCarten, K., Najgebauer, H., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.20.700565
“Together, these data comprise nowadays over 1,000 screens across more than 18,000 genes and 900 CCLs and form the core component of The Cancer Dependency Map (DepMap) resource 27,32,33 .”
IGF2 knockout reduces but does not abolish osteosarcoma growth in vitro and in vivo
Yao, S., & Archetti, M. (2026) · FEBS Open Bio · doi:10.1002/2211-5463.70341
“Gene expression for candidate growth factors was obtained from the CCLE dataset in DepMap [31,32] (release 23Q4) and reported as log 2 (TPM + 1).”
BCL11B predetermines a persister state in breast cancer that is reversed by TNFα
Qi, Z., Gulati, G., Gleyzer, R., Kuo, A. H., Sikandar, S. S., Bai, H.-R., Ho, W. H. D., Qian, D.-L., Dirbas, F., Newman, A. M., et al. (2026) · Science Advances · doi:10.1126/sciadv.aec5436
WDR12 Promotes Glucose Metabolism via PFKFB4 Upregulation in Gastric Cancer
Zhang, R.-T., Liu, D.-H., Feng, X.-X., Cheng, A.-X., Yang, Z.-Y., & Zhang, K.-G. (2026) · Cancer Science · doi:10.1111/cas.70513
“To identify dysregulated WD40 proteins in GC, we reanalyzed proteomic data from 84 patients [31] and integrated TCGA-STAD transcriptomes and DepMap CRISPR dependency data [32–34].”
Li, X., Zhu, H., Chen, M. I., Jiang, C., Yu, W., Xiao, Y., Wei, J., Li, X., 焦臣宇, Han, S., et al. (2026) · International Immunopharmacology · doi:10.1016/j.intimp.2026.117358
KDM6A loss enhances oxidative phosphorylation uncovering tissue-level convergent evolution
Cartalemi, A. A., Rosano, D., Ferrari, C., Crupi, G., Cassina, L., Gatti, G., Corigliano, F., Punzi, S., VILLANTI, I., Dupéré-Richer, D., et al. (2026) · The EMBO Journal · doi:10.1038/s44318-026-00891-0
“Moreover, we explored the association of expression of these two genes in large datasets, using the DepMap repository (Tsherniak et al, 2017).”
Reinvigorating the possibilities: Drug repurposing as a strategic access pathway for rare diseases
Jonker, A., O'Connor, D., Mejat, A., Gribbon, P., Crombie, C., Randall, L., Parker, S., & Lo, D. C. (2026) · Translational Science of Rare Diseases · doi:10.1177/22146490261486345
“62,63 AI methods can then compare drug-induced phenotypic signatures with disease states to identify compounds that may reverse pathologies 64 without the need for mechanistic knowledge, although cellular context still remains critical.”
Qin, D., Das, R., Schallenberg, S., Riecken, K., Tinhofer, I., & Coscia, F. (2026) · bioRxiv · doi:10.1101/2025.10.11.681009
Bai, J., Faure, J. S., Khalfe, T. A., Huang, R., Burmeister, C. A., van Niekerk, A., & Prince, S. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1937655
Kim, Y., Nagy, M. R., Pollard, R. D., & Rajagopal, P. (2026) · Cancer Research Communications · doi:10.1158/2767-9764.crc-26-0401
“For targeted therapy, we perform a Wilcoxon rank-sum test to identify pairs of genes from published studies on genetic dependencies/ SL screening, from databases such as DepMap and the Cancer Cell Line Encyclopedia (37–42), and published studies on genetic relationships to pharmacology (43–45).”
Decoding cellular vulnerabilities for interpretable drug response prediction
Chen, C.-H., Du, Y.-L., Jiang, Y.-X., Wang, Y.-F., Tang, D., Ding, X.-F., & He, M. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag559
“Baseline expression and CRISPR-Cas9 gene effect scores were obtained from DepMap (24Q2) [ 15].”
InfluentialAvilés, N. M. N., Ortiz, W. I. S., Salgado-Villanueva, I. K., Tang, J., Rodríguez-Vázquez, G. O., Díaz-Quiñones, A. O., & Maldonado, H. M. (2026) · ASPET Discovery · doi:10.1016/j.aspetd.2026.100035
Rajeev, A., Veeragoni, S., & Sundar, A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.30.748149
FANCM is required for the PAX3::FOXO1-driven oncogenic program in rhabdomyosarcoma
Delaney, C. D., Lu, M., Zimmerman, S. P., Meng, Y., Vaganay, C., Cerda-Smith, C. G., Liu, A., Hoj, J. P., Killarney, S. T., Dillon, K. A., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117960
Self-citationAI virtual cells for drug discovery and pharmacology
Wang, S., Zhang, Y., Wang, D., Liu, H., & Yao, X. (2026) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2026.08.006
Lin, R., Ke, Z., Wang, J., Wei, T., Zhang, L., Fang, Y., & Tian, R. (2026) · Neuron · doi:10.1016/j.neuron.2026.08.006
Ren, Y.-Z., Chen, J.-X., Wang, M.-Y., & Zhang, Y.-S. (2026) · Journal of Computer Science · doi:10.1016/j.jocs.2026.103028
Lou, Z., Zhu, H.-P., Xu, Y.-L., Liu, X.-J., Wang, R., Dong, J.-W., Tian, J.-L., Xiang, X.-P., Zhu, L.-J., Pan, Y.-J., et al. (2026) · Genes & Diseases · doi:10.1016/j.gendis.2026.102483
Siminea, N., Popescu, V., Păun, M., Petre, I., & Păun, A. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1014785
“The genes that encode the proteins that we want to control were identified through the integration of data from extensive RNAi loss-of-function screens and CRISPR-Cas9 knockout screens [43].”
Singh, P., Kumar, A., & Dogra, N. (2026) · Biochemical and Biophysical Research Communications - BBRC · doi:10.1016/j.bbrc.2026.154613
Identifying multigenic modules under selection in the tumor genome
Kelly, M. R., Otlu, B., Sharan, R., & Ideker, T. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag668
“2017, Tsherniak et al .”
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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