Agreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets

Dempster, Joshua M.; Pacini, Clare; Pantel, Sasha; Behan, Fiona M.; Green, Thomas; Krill-Burger, John M.; Beaver, Charlotte; Younger, Scott T.; Zhivich, Victor; Najgebauer, Hanna; Allen, Felicity; Gonçalvès, Emanuel; Shepherd, Rebecca; Doench, John G.; Yusa, Kosuke; Vázquez, Francisca; Parts, Leopold; Boehm, Jesse S.; Golub, Todd R.; Hahn, William C.; Root, David E.; Garnett, Mathew J.; Tsherniak, Aviad; Iorio, Francesco

doi:10.1038/s41467-019-13805-y

Cited by 208 publications

(232 citation statements)

References 34 publications

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“…We compared our set of 954 common essentials to the Core Essential Genes v2 that we previously defined as a gold standard training set for our BAGEL algorithm 305 (Hart et al, 2017), as well as the core essentials recently published Sanger dataset derived from 342 CRISPR screens performed at the Wellcome Trust Sanger Centre. (Behan et al, 2019).…”

Section: Comparing Common Essentials To Previous Gold Standardsmentioning

confidence: 99%

“…Extensive screening of cancer cell lines under the DepMap project-and, critically, the open availability of this data--affords an opportunity for re-evaluating the assumptions under which 415 these assays have been carried out. Notably, assumptions about replication and library-and batch-specific effects have been addressed in some detail Dempster et al, 2019;Rauscher et al, 2018), but questions about what might be systematically missing from these data have, to our knowledge, not been rigorously explored.…”

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confidence: 99%

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“…Behan et al, 2019) were downloaded from their corresponding supplementary data sections. Raw read count data from Sanger Institution's Project Score(Behan et al, 2019) was downloaded and processed with the 645 same pipeline using CrisprCleanR and BagelV2 algorithms to generate Bayes factors for each gene in every screen. Genes located on the X-chromosome were removed from the core essential gene set V2 (CoreV2, from(Hart et al, 2017)) and Sanger data(Behan et al, 2019) prior to comparisons.…”

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confidence: 99%

“…Raw read count data from Sanger Institution's Project Score(Behan et al, 2019) was downloaded and processed with the 645 same pipeline using CrisprCleanR and BagelV2 algorithms to generate Bayes factors for each gene in every screen. Genes located on the X-chromosome were removed from the core essential gene set V2 (CoreV2, from(Hart et al, 2017)) and Sanger data(Behan et al, 2019) prior to comparisons. The common cell lines between Avana and Sanger screens that have F-measures of greater than or equal to 0.8 were identified (N=117 cell lines).…”

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confidence: 99%

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Biases and Blind-Spots in Genome-Wide CRISPR Knockout Screens

Dede

Hart

2020

Preprint

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It is widely accepted that pooled library CRISPR knockout screens offer greater sensitivity and specificity than prior technologies in detecting genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the assumption that CRISPR 5 screens are saturating has been largely untested. Through integrated analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we show that a typical CRISPR screen has a ~20% false negative rate, beyond library-specific false negatives previously described. Replicability falls sharply as gene expression decreases, while cancer subtype-specific genes within a tissue show distinct profiles compared to false negatives. Cumulative analyses 10 across tissues suggest only a small number of lineage-specific essential genes and that these genes are highly enriched for transcription factors that define pathways of tissue differentiation.In addition, we show that half of all constitutively-expressed genes are never hits in any CRISPR screen, and that these never-essentials are highly enriched for paralogs. Together these observations strongly suggest that functional buffering masks single knockout phenotypes for a 15 substantial number of genes, describing a major blind spot in CRISPR-based mammalian functional genomics approaches. 65 negatives) and confounding off-target effects (false positives) (Boutros and Ahringer, 2008;Echeverri et al., 2006;Hart et al., 2014).More recently, adaptation of the bacterial CRISPR-Cas9 system to mammalian cells enabled genome-scale approaches to define human essential genes. Studies using this technology 70 revealed that mammalian cells have more essential genes than RNAi screens were able to detect and that, at the same false discovery rate, CRISPR screens generated 3-4 times more essential genes (Hart et al., 2014). Moreover, multiple groups revealed lists of ~2000 highly concordant human essential genes, and comparison of CRISPR technology to orthogonal techniques such as random insertion of gene traps also showed consistent results (Blomen et al., 2015;

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Section: Comparing Common Essentials To Previous Gold Standardsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Biases and Blind-Spots in Genome-Wide CRISPR Knockout Screens

Dede

Hart

2020

Preprint

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Ubiquitin receptors play redundant roles in the proteasomal degradation of the p53 repressor MDM2

2022

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Much remains to be determined about the participation of ubiquitin receptors in proteasomal degradation and their potential as therapeutic targets. Suppression of the ubiquitin receptor S5A/PSMD4/hRpn10 alone stabilises p53/ TP53 but not the key p53 repressor MDM2. Here, we observed S5A and the ubiquitin receptors ADRM1/PSMD16/hRpn13 and RAD23A and B functionally overlap in MDM2 degradation. We provide further evidence that degradation of only a subset of ubiquitinated proteins is sensitive to S5A knockdown because ubiquitin receptor redundancy is commonplace. p53 can be upregulated by S5A modulation while degradation of substrates with redundant receptors is maintained. Our observations and analysis of Cancer Dependency Map (DepMap) screens show S5A depletion/loss substantially reduces cancer cell line viability. This and selective S5A dependency of proteasomal substrates make S5A a target of interest for cancer therapy.

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Highlights from the 1st European cancer dependency map symposium and workshop

et al. 2023

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The systematic identification of tumour vulnerabilities through perturbational experiments on cancer models, including genome editing and drug screens, is playing a crucial role in combating cancer. This collective effort is known as the Cancer Dependency Map (DepMap). The 1st European Cancer Dependency Map Symposium (EuroDepMap), held in Milan last May, featured talks, a roundtable discussion, and a poster session, showcasing the latest discoveries and future challenges related to the DepMap. The symposium aimed to facilitate interactions among participants across Europe, encourage idea exchange with leading experts, and present their work and future projects. Importantly, it sparked discussions on future endeavours, such as screening more complex cancer models and accounting for tumour evolution.

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Agreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets

Cited by 208 publications

References 34 publications

Biases and Blind-Spots in Genome-Wide CRISPR Knockout Screens

Biases and Blind-Spots in Genome-Wide CRISPR Knockout Screens

Ubiquitin receptors play redundant roles in the proteasomal degradation of the p53 repressor MDM2

Highlights from the 1st European cancer dependency map symposium and workshop

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