Abstract Hundreds of genome-wide loss-of-function screens have been performed, as part of efforts such as The Cancer Dependency Map, to create a catalog of genetic dependencies in a diverse set of cancer contexts. In recent years, large-scale screening efforts have shifted perturbation technology from RNAi to CRISPR-Cas9, due to the superior efficacy and specificity of CRISPR-Cas9-mediated approaches. However, questions remain about the extent to which partial suppression of gene targets could result in selective dependency across cell lines, potentially revealing a larger set of targetable cancer vulnerabilities than can be identified using CRISPR knockout alone. Here, we use CRISPR-Cas9 and RNAi screening data for more than 400 shared cell lines to represent knockout and partial suppression genetic perturbation modalities and evaluate the utility of each for therapeutic target discovery and the inference of gene function. We find that CRISPR screens identify more dependencies, and yield more accurate predictive models and co-dependency relationships overall. However, RNAi outperforms CRISPR in identifying associations (omics, drug, co-dependencies) with genes that are common dependencies for most cell lines (pan-dependencies). As pan-dependencies occur frequently in the CRISPR dataset (~2,000 genes), using results from both RNAi and CRISPR analyses facilitates the discovery of predictive models and associated co-dependencies for a wider range of gene targets than could be detected using either dataset alone. These findings can aid in the interpretation of contrasting results obtained from CRISPR and RNAi screens and reinforce the importance of partial gene suppression methods in building a cancer dependency map.

Partial gene suppression improves identification of cancer vulnerabilities when CRISPR-Cas9 knockout is pan-lethal