Abstract 5019: Eliminating copy number alteration effects in the gene essentiality data from the Cancer Dependency Map project

Abstract The Cancer Dependency Map project (DepMap) aims to identify cancer vulnerabilities through the in vitro study of genetic dependencies in cancer cell lines using CRISPR/Cas9 loss-of-function screens. Cas9-mediated DNA break formation induces toxic effects that are proportional with the number of cuts. Consequently, the DepMap data displays a ‘copy number alteration effect’ (CNA effect), in which sgRNAs targeting highly amplified regions of the genome produce depletion effects. Indeed, when sgRNAs target intergenic regions of amplified regions, the observed effects are comparable to the targeting of essential genes. Hence, correcting the CNA effect is crucial to reduce false positive (and increase true negative) co-dependencies between genes. Currently, the CERES methodology is used to correct for CNA effects. By applying a consensus-independent component analysis-based (consensus ICA) algorithm on gene essentiality data after CERES correction, we observed that many CNA effects are still present in the CERES-corrected data. Subsequently, we developed a methodology that outperforms CERES in removing the CNA effect. For example, we identified a set of genes mapping to chromosome 8q of sample ACH-000542 (ovarian adenocarcinoma cell line HEYA8 having amplification at chromosome 8q) having significantly lower essentiality levels compared to the rest of the genome. Whereas the CERES algorithm did not correct this effect of the chromosome 8q amplification, it was captured by consensus ICA in a consensus estimated source and was corrected by our novel methodology. After removing all CNA effects from gene essentiality data, we observed that 12.07% of co-dependency scores reported by CERES-corrected data decreased by 0.2 (on a range of -1 to 1), and 11.34% co-dependency scores increased by 0.2. In conclusion, consensus ICA-based methodology improves the correction of the CNA effect on gene essentiality data. Application of our improved method of CNA effect correction could reduce the number of false positive targets in validating biological hypotheses leading to novel therapeutic strategies in cancer. Citation Format: Arkajyoti Bhattacharya, Carlos G. Urzúa-Traslaviña, Marcel A. T. M. van Vugt, Rudolf S. N. Fehrmann. Eliminating copy number alteration effects in the gene essentiality data from the Cancer Dependency Map project [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 5019.