The availability of multiple datasets comprising genome-scale RNAi viability screens in hundreds of diverse cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated cell line screen-quality parameters and hierarchical Bayesian inference into DEMETER2, an analytical framework for analyzing RNAi screens (https://depmap.org/R2-D2). This model substantially improves estimates of gene dependency across a range of performance measures, including identification of gold-standard essential genes and agreement with CRISPR/Cas9-based viability screens. It also allows us to integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.

The availability of multiple datasets together comprising hundreds of genome-scale RNAi viability screens across a diverse range of cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated estimation of cell line screen quality parameters and hierarchical Bayesian inference into an analytical framework for analyzing RNAi screens (DEMETER2; https://depmap.org/R2-D2). We applied this model to individual large-scale datasets and show that it substantially improves estimates of gene dependency across a range of performance measures, including identification of goldstandard essential genes as well as agreement with CRISPR-Cas9-based viability screens. This model also allows us to effectively integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.

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We hypothesized that candidate dependencies for which there are small molecules that are either approved or in advanced development for a nononcology indication may represent potential therapeutic targets. To test this hypothesis, we performed genome-scale loss-of-function screens in hundreds of cancer cell lines. We found that knockout of EGLN1, which encodes prolyl hydroxylase domain-containing protein 2 (PHD2), reduced the proliferation of a subset of clear cell ovarian cancer cell lines in vitro. EGLN1-dependent cells exhibited sensitivity to the pan-EGLN inhibitor FG-4592. The response to FG-4592 was reversed by deletion of HIF1A, demonstrating that EGLN1 dependency was related to negative regulation of HIF1A. We also found that ovarian clear cell tumors susceptible to both genetic and pharmacologic inhibition of EGLN1 required intact HIF1A. Collectively, these observations identify EGLN1 as a cancer target with therapeutic potential.Significance: These findings reveal a differential dependency of clear cell ovarian cancers on EGLN1, thus identifying EGLN1 as a potential therapeutic target in clear cell ovarian cancer patients.