{"entity":{"id":"paper-depmap-tsherniak-cell-2017","kind":"paper","name":"Defining a Cancer Dependency Map: which genes each cancer cell line cannot live without","aka":[],"tldr":"Genome-scale RNAi screens across 501 cancer cell lines, analysed with the DEMETER algorithm to remove off-target noise, identified 769 genes on which subsets of cancers depend and showed that most dependencies can be predicted from the cell's molecular features.","summary":"The Broad Institute's Project Achilles knocked down 17,098 genes in 501 cell lines drawn from a broad range of cancer types using pooled shRNA libraries. A new computational method, DEMETER, separated on-target from seed-sequence off-target effects, a longstanding problem of RNAi screens.\n\nThe analysis identified 769 genes with strong differential dependency across lines; more than 90% of lines depended on at least one such gene, and 426 of the 769 dependencies could be predicted from mutation, copy number or expression features. Predictive markers were often not the gene itself but paralog loss, lineage or pathway activity, pointing to synthetic-lethal and lineage-specific targets.\n\nThe paper defined the goal of the Cancer Dependency Map (DepMap), which has since moved to genome-wide CRISPR screens (Meyers 2017, Behan 2019) in over 1,000 lines with matched multi-omics, and is the most-used resource for target discovery and biomarker hypothesis generation.","asOf":"2026-09-08","links":[{"label":"DOI","url":"https://doi.org/10.1016/j.cell.2017.06.010"},{"label":"CRISPR-based DepMap (Meyers 2017)","url":"https://doi.org/10.1038/ng.3984"},{"label":"DepMap portal","url":"https://depmap.org"}],"tags":[],"related":["depmap","high-throughput-screening-libraries"],"cancers":[],"sections":["drug-discovery"],"technologies":["crispr-screens","synthetic-lethality-approaches","functional-drug-testing"],"targets":["wrn","prmt5-mtap"],"drugs":[],"companies":[],"institutions":["broad-institute","dana-farber"],"pathways":[],"terms":["synthetic-lethality"],"trials":[],"people":[],"bottlenecks":["b-preclinical-models","b-undruggable-targets","b-translational-valley"],"keyPapers":[],"journals":["cell"],"dependsOn":[],"notes":[],"journal":"Cell","year":2017,"doi":"10.1016/j.cell.2017.06.010","pmid":"28753430","authors":"Tsherniak A, Vazquez F, Montgomery PG, et al.","paperType":"basic","findings":["501 cell lines screened with 17,098-gene shRNA library; DEMETER algorithm removed seed-based off-target effects","769 genes with differential dependency; over 90% of lines depended on at least one","426 dependencies (55%) predictable from genomic or expression features, often via paralogs or lineage factors","Dependencies on WRN in MSI-high lines and on paralogs (for example SMARCA2 in SMARCA4-mutant lines) emerged from these and follow-on screens"],"whatItMeans":"DepMap is the lookup table drug hunters use to ask: which cancers would die if we blocked this gene, and how would we recognise them? It generated targets such as WRN and PRMT5-MTAP now in clinical trials, and it is public.","caveats":["Cell lines lack microenvironment, immune context and drug pharmacology; many in vitro dependencies do not translate","RNAi knockdown is partial; CRISPR knockout can give different results for essential genes","Lineages and ancestries are unevenly represented; some cancers have few lines","Dependency does not equal druggability; most dependencies are transcription factors or lineage genes"],"changedPractice":false},"route":"/key-papers/paper-depmap-tsherniak-cell-2017/","neighbours":{"collection":[{"id":"depmap","kind":"collection","name":"DepMap (Cancer Dependency Map)","route":"/collections/depmap/"}],"technology":[{"id":"crispr-screens","kind":"technology","name":"CRISPR functional genomics","route":"/technologies/crispr-screens/"},{"id":"functional-drug-testing","kind":"technology","name":"Functional (ex vivo) drug testing","route":"/technologies/functional-drug-testing/"},{"id":"high-throughput-screening-libraries","kind":"technology","name":"High-throughput screening and DNA-encoded libraries","route":"/technologies/high-throughput-screening-libraries/"},{"id":"synthetic-lethality-approaches","kind":"technology","name":"Synthetic lethality approaches","route":"/technologies/synthetic-lethality-approaches/"}],"section":[{"id":"drug-discovery","kind":"section","name":"Drug Discovery Platforms","route":"/fronts/drug-discovery/"}],"target":[{"id":"prmt5-mtap","kind":"target","name":"PRMT5 (MTAP-deleted cancers)","route":"/targets/prmt5-mtap/"},{"id":"wrn","kind":"target","name":"WRN helicase (MSI-high cancers)","route":"/targets/wrn/"}],"institution":[{"id":"broad-institute","kind":"institution","name":"Broad Institute of MIT and Harvard","route":"/institutions/broad-institute/"},{"id":"dana-farber","kind":"institution","name":"Dana-Farber Brigham Cancer Center","route":"/institutions/dana-farber/"}],"term":[{"id":"synthetic-lethality","kind":"term","name":"Synthetic lethality","route":"/terms/synthetic-lethality/"}],"bottleneck":[{"id":"b-preclinical-models","kind":"bottleneck","name":"Lab models that fail to predict what happens in patients","route":"/bottlenecks/b-preclinical-models/"},{"id":"b-undruggable-targets","kind":"bottleneck","name":"The undruggable drivers","route":"/bottlenecks/b-undruggable-targets/"},{"id":"b-translational-valley","kind":"bottleneck","name":"The valley of death between lab and product","route":"/bottlenecks/b-translational-valley/"}],"journal":[{"id":"cell","kind":"journal","name":"Cell","route":"/journals/cell/"}]}}