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

The CRISPR–Cas9 system has revolutionized gene editing both at single genes and in multiplexed loss-of-function screens, thus enabling precise genome-scale identification of genes essential for proliferation and survival of cancer cells. However, previous studies have reported that a gene-independent antiproliferative effect of Cas9-mediated DNA cleavage confounds such measurement of genetic dependency, thereby leading to false-positive results in copy number–amplified regions. We developed CERES, a computational method to estimate gene-dependency levels from CRISPR–Cas9 essentiality screens while accounting for the copy number–specific effect. In our efforts to define a cancer dependency map, we performed genome-scale CRISPR–Cas9 essentiality screens across 342 cancer cell lines and applied CERES to this data set. We found that CERES decreased false-positive results and estimated sgRNA activity for both this data set and previously published screens performed with different sgRNA libraries. We further demonstrate the utility of this collection of screens, after CERES correction, for identifying cancer-type-specific vulnerabilities.

[1]  Phillip G. Montgomery,et al.  Defining a Cancer Dependency Map , 2017, Cell.

[2]  Jacob K. Asiedu,et al.  The Drug Repurposing Hub: a next-generation drug library and information resource , 2017, Nature Medicine.

[3]  Eric S. Lander,et al.  Gene Essentiality Profiling Reveals Gene Networks and Synthetic Lethal Interactions with Oncogenic Ras , 2017, Cell.

[4]  J. Doudna,et al.  Cornerstones of CRISPR–Cas in drug discovery and therapy , 2016, Nature Reviews Drug Discovery.

[5]  Minoru Kanehisa,et al.  KEGG: new perspectives on genomes, pathways, diseases and drugs , 2016, Nucleic Acids Res..

[6]  Julio Saez-Rodriguez,et al.  A CRISPR Dropout Screen Identifies Genetic Vulnerabilities and Therapeutic Targets in Acute Myeloid Leukemia , 2016, Cell reports.

[7]  Joshua M. Korn,et al.  CRISPR Screens Provide a Comprehensive Assessment of Cancer Vulnerabilities but Generate False-Positive Hits for Highly Amplified Genomic Regions. , 2016, Cancer discovery.

[8]  T. Golub,et al.  Genomic Copy Number Dictates a Gene-Independent Cell Response to CRISPR/Cas9 Targeting. , 2016, Cancer discovery.

[9]  Meagan E. Sullender,et al.  Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9 , 2015, Nature Biotechnology.

[10]  Minoru Kanehisa,et al.  KEGG as a reference resource for gene and protein annotation , 2015, Nucleic Acids Res..

[11]  D. Durocher,et al.  High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities , 2015, Cell.

[12]  G. Traver Hart,et al.  BAGEL: a computational framework for identifying essential genes from pooled library screens , 2015, BMC Bioinformatics.

[13]  G. Church,et al.  Supplementary Materials for Genome-wide inactivation of porcine endogenous retroviruses ( PERVs ) , 2015 .

[14]  E. Lander,et al.  Identification and characterization of essential genes in the human genome , 2015, Science.

[15]  Clifford A. Meyer,et al.  Sequence determinants of improved CRISPR sgRNA design , 2015, Genome research.

[16]  Jun S. Liu,et al.  MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens , 2014, Genome Biology.

[17]  Meagan E. Sullender,et al.  Rational design of highly active sgRNAs for CRISPR-Cas9–mediated gene inactivation , 2014, Nature Biotechnology.

[18]  Ellen T. Gelfand,et al.  Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies , 2014, Scientific Data.

[19]  J. Moffat,et al.  Measuring error rates in genomic perturbation screens: gold standards for human functional genomics , 2014, bioRxiv.

[20]  Udo Schumacher,et al.  Dual Roles of the Transcription Factor Grainyhead-like 2 (GRHL2) in Breast Cancer* , 2013, The Journal of Biological Chemistry.

[21]  Hong Jiang,et al.  Grhl2 Determines the Epithelial Phenotype of Breast Cancers and Promotes Tumor Progression , 2012, PloS one.

[22]  Adam A. Margolin,et al.  The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity , 2012, Nature.

[23]  Kevin R Brown,et al.  Essential gene profiles in breast, pancreatic, and ovarian cancer cells. , 2012, Cancer discovery.

[24]  J. Mesirov,et al.  Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer , 2011, Proceedings of the National Academy of Sciences.

[25]  Cole Trapnell,et al.  Ultrafast and memory-efficient alignment of short DNA sequences to the human genome , 2009, Genome Biology.

[26]  Xiaohua Douglas Zhang.  A pair of new statistical parameters for quality control in RNA interference high-throughput screening assays. , 2007, Genomics.

[27]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[28]  Supplemental Information 2: Kyoto Encyclopedia of genes and genomes. , 2022 .