CRISPR-based functional genomics screens are a powerful tool for identifying synthetically lethal cancer drug targets. Current strategies for analyzing pooled CRISPR screens usually rely on signals from single guide RNAs (sgRNA) with differential relative abundance between two experimental conditions. However, conventional approaches are susceptible to false positives and false negatives driven by outlier cell clones, since the sgRNA abundance does not account for the heterogeneous phenotypes resulting from different editing outcomes of the same sgRNA. To overcome this, we added DNA barcodes to each sgRNA to create unique molecular identifiers (UMIs) for CRISPR libraries and developed a companion analytical platform that enables robust, industry-scale CRISPR screens. Here, we present UMIBB, a novel nonparametric Bayesian approach for analyzing UMI-CRISPR data. The number of UMIs with normalized count depletion or enrichment compared to the control experimental condition for each sgRNA is modeled by a beta-binomial distribution. The gene level statistics are derived by combining z-scores of the sgRNAs level posterior probabilities weighted by the number of UMIs in each sgRNA. This approach minimizes the impact of outlier cell clones on statistics and prioritizes genes with consistent count differentials across multiple UMIs in each gene. To assess the power of UMIBB, we benchmarked it on a low coverage (200X) genome-scale negative-selection screen, comparing with results from a high coverage (1000X) screen. These screens were conducted on KRAS mutant cancer celllines (A549) treated with trametinib or vehicle control. Despite the high noise level usually observed in lower coverage screens, our method was able to uncover most of the validated sensitizer genes for trametinib and achieved the highest sensitivity compared to conventional methods. Furthermore, we applied UMIBB on a genome-scale positive-selection screen and successfully identified novel genes (RAD18 and UBE2K) as key mediators of USP1 dependency in BRCA1 mutant cell lines. Our studies demonstrate that UMIBB is highly robust against false positives due to clonal heterogeneity and is more likely to identify true genetic interactions.

Citation Format: Ashley Choi, Samuel Meier, Silvia Fenoglio, Tianshu Feng, Justin Engel, Binzhang Shen, Shangtao Liu, Teng Teng, Tenzing Khendu, Alan Huang, Jannik Andersen, Xuewen Pan, Yi Yu. UMIBB: A novel nonparametric Bayesian method improves robustness and sensitivity of analysis in pooled CRISPR-Cas9 screens leveraging unique molecular identifiers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1224.