Computational correction of copy-number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells

Abstract

Major efforts using loss-of-function genetic screens to systematically identify genes essential to the proliferation and survival of cancer cells have been reported [1][2][3][4][5][6][7][8][9] . Genes identified by these approaches may represent specific genetic vulnerabilities of cancer cells, suggesting treatment strategies and directing the development of novel therapeutics. The CRISPR-Cas9 genome editing system has proven to be a powerful tool to interrogate gene essentiality in cancer cell lines. Its relative ease of application, high rates of target validation, and increased specificity compared to RNA interference technology make it an ideal instrument for use in high-throughput functional genomic screening 10 .However, we and others have recently observed that measurements of genetic dependency in genome-scale CRISPR-Cas9 loss-of-function screens are influenced by the genomic copy number (CN) of the region targeted by the sgRNA-Cas9 complex [1][2][3][4] . Targeting Cas9 to DNA sequences within regions of high CN gain creates multiple DNA double-strand breaks (DSBs), inducing a gene-independent DNA damage response and a G2 cell-cycle arrest phenotype 2 .This systematic, sequence-independent effect due to DNA cleavage (copy-number effect)confounds the measurement of the consequences of gene deletion on cell viability (geneknockout effect) and is detectable even among low-level CN amplifications and deletions. In particular, this phenomenon hinders interpretation of CRISPR-Cas9 experiments in cancer cell

CRISPR-Cas9 screen reveals a MYCN-amplified neuroblastoma dependency on EZH2

“…To identify specific dependencies in high-risk, MYCN-amplified neuroblastoma, we analyzed our genome-scale CRISPR-Cas9 loss-of-function screening of a collection of 341 human cancer cell lines from 26 tumor types (24), using the Avana library (25) PRC2 gene signatures. The PRC2 complex has histone methyltransferase activity and is responsible for trimethylating histone H3 on lysine 27 (H3K27me3), causing transcriptional repression.…”

“…To identify genes more essential in neuroblastoma compared with the other cancer cell lines, we analyzed the loss-of-function CRISPR-Cas9-based screen of 341 cancer cell lines, including 9 MYCN-amplified and 2 MYCN-nonamplified neuroblastoma cell lines (24). Our analapplied to pediatric cancers.…”

“…Stable cell lines were then transduced with the Avana library and passaged in triplicate for 21 days, and the relative enrichment or depletion of guides was assessed using massively parallel sequencing and compared with the initial plasmid pool. Data were normalized across cell lines, and a gene-level dependency score was calculated using the recently described CERES algorithm that takes into account the increased "cutting toxicity" observed in regions of copy number gain (24,26). We then identified preferential genetic dependencies in neuroblastoma cell lines by applying independent component analysis (ICA) (27,28) to the CRISPR-Cas9 data.…”

Interrogation of Mammalian Protein Complex Structure, Function, and Membership Using Genome-Scale Fitness Screens

“…We and others have observed that cancer cell lines exhibit highly variable genetic dependencies for cellular fitness (Bertomeu et al, 2018; Blomen et al, 2015; Hart et al, 2015; Hart et al, 2017a; McDonald et al, 2017; Meyers et al, 2017; Tsherniak et al, 2017; Wang et al, 2015; Wang et al, 2017b) in a manner that reflects the diverse genomic and transcriptomic alterations a cell may accumulate during tumorigenesis. Project Achilles (Broad Institute of MIT and Harvard) seeks to systematically map genetic vulnerabilities across large collections of cancer cell lines, including the Cancer Cell Line Encyclopedia (CCLE) (Barretina et al, 2012), and has recently performed genome-scale perturbation screens in 501 cancer cell lines using RNA interference (RNAi) (Tsherniak et al, 2017) and in 342 cancer cell lines using CRISPR-Cas9 (Meyers et al, 2017).…”

Ordino: a visual cancer analysis tool for ranking and exploring genes, cell lines, and tissue samples

“…Users can interactively add (i) raw experimental data or metadata stored in a database, like the expression data for a single cell line or the biotype of all listed genes, (ii) dynamically computed scores, such as the average gene expression of tissue samples from a specific tumor type, and (iii) uploaded custom data attributes. We preloaded mRNA expression, DNA copy number, and mutation data from The Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov) and the Cancer Cell Line Encyclopedia (CCLE) [1], as well as two depletion screen data sets from McDonald et al [2] and Meyers et al [3] (Supplementary Table 1). A description of the data pre-processing can be found in the supplementary material (Supplementary Notes).…”