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      Correction of copy number induced false positives in CRISPR screens

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Cell autonomous cancer dependencies are now routinely identified using CRISPR loss-of-function viability screens. However, a bias exists that makes it difficult to assess the true essentiality of genes located in amplicons, since the entire amplified region can exhibit lethal scores. These false-positive hits can either be discarded from further analysis, which in cancer models can represent a significant number of hits, or methods can be developed to rescue the true-positives within amplified regions. We propose two methods to rescue true positive hits in amplified regions by correcting for this copy number artefact. The Local Drop Out (LDO) method uses the relative lethality scores within genomic regions to assess true essentiality and does not require additional orthogonal data (e.g. copy number value). LDO is meant to be used in screens covering a dense region of the genome (e.g. a whole chromosome or the whole genome). The General Additive Model (GAM) method models the screening data as a function of the known copy number values and removes the systematic effect from the measured lethality. GAM does not require the same density as LDO, but does require prior knowledge of the copy number values. Both methods have been developed with single sample experiments in mind so that the correction can be applied even in smaller screens. Here we demonstrate the efficacy of both methods at removing the copy number effect and rescuing hits from some of the amplified regions. We estimate a 70–80% decrease of false positive hits with either method in regions of high copy number compared to no correction.

          Author summary

          Cancer vulnerabilities have been identified by systematically disrupting individual genes in cancer cells and observing the resulting effect on cell proliferation. In recent years, a new gene editing technique called CRISPR has made it easier and cheaper to disrupt genes by precisely and completely suppressing the function of individual genes by cutting through its DNA. However, an artefact of the approach yields false positives: using CRISPR to target genes in regions of the genome which are abnormally repeated, called copy number alterations (CNA), has been shown to kill the investigated cells irrespective of the true essentiality of the amplified genes. This artefact is a particular issue when studying tumours, since CNAs are common in cancer. Additionally cancer-specific genes are known to selectively drive amplification, making the ability to assess the essentiality of genes in these regions even more important. Here we describe and provide the code to computationally correct for this artefact and recover the true essentiality of CNA genes.

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          Most cited references 22

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          The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity

          The systematic translation of cancer genomic data into knowledge of tumor biology and therapeutic avenues remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacologic annotation is available 1 . Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacologic profiles for 24 anticancer drugs across 479 of the lines, this collection allowed identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Altogether, our results suggest that large, annotated cell line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of “personalized” therapeutic regimens 2 .
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            Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models

             Simon N. Wood (2011)
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              GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers

              We describe methods with enhanced power and specificity to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth. By separating SCNA profiles into underlying arm-level and focal alterations, we improve the estimation of background rates for each category. We additionally describe a probabilistic method for defining the boundaries of selected-for SCNA regions with user-defined confidence. Here we detail this revised computational approach, GISTIC2.0, and validate its performance in real and simulated datasets.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Resources
                Role: Data curationRole: Resources
                Role: Data curationRole: Resources
                Role: ConceptualizationRole: InvestigationRole: Resources
                Role: ConceptualizationRole: InvestigationRole: Resources
                Role: ConceptualizationRole: Resources
                Role: ConceptualizationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                19 July 2018
                July 2018
                : 14
                : 7
                Affiliations
                [1 ] Novartis Institutes for Biomedical Research, Basel, Switzerland
                [2 ] Novartis Institutes for Biomedical Research, Cambridge, MA, United States of America
                Princeton University, UNITED STATES
                Author notes

                During their involvement related to this reported work, all authors were employees and shareholders of Novartis.

                PCOMPBIOL-D-17-01148
                10.1371/journal.pcbi.1006279
                6067744
                30024886
                © 2018 de Weck et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Counts
                Figures: 3, Tables: 0, Pages: 12
                Product
                Funding
                This research was funded by Novartis Institutes for BioMedical Research. The funder provided support in the form of salaries for all authors but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.
                Categories
                Research Article
                Biology and Life Sciences
                Biotechnology
                Bioengineering
                Synthetic Bioengineering
                Genome Engineering
                Synthetic Genome Editing
                Crispr
                Engineering and Technology
                Bioengineering
                Synthetic Bioengineering
                Genome Engineering
                Synthetic Genome Editing
                Crispr
                Biology and Life Sciences
                Synthetic Biology
                Synthetic Bioengineering
                Genome Engineering
                Synthetic Genome Editing
                Crispr
                Engineering and Technology
                Synthetic Biology
                Synthetic Bioengineering
                Genome Engineering
                Synthetic Genome Editing
                Crispr
                Biology and Life Sciences
                Synthetic Biology
                Synthetic Genomics
                Synthetic Genome Editing
                Crispr
                Engineering and Technology
                Synthetic Biology
                Synthetic Genomics
                Synthetic Genome Editing
                Crispr
                Medicine and Health Sciences
                Health Care
                Health Care Policy
                Screening Guidelines
                Biology and Life Sciences
                Genetics
                Gene Identification and Analysis
                Genetic Screens
                Biology and Life Sciences
                Genetics
                Gene Amplification
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Molecular Biology Assays and Analysis Techniques
                Library Screening
                Research and Analysis Methods
                Molecular Biology Techniques
                Molecular Biology Assays and Analysis Techniques
                Library Screening
                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Molecular Biology Assays and Analysis Techniques
                Library Screening
                Genomic Library Screening
                Research and Analysis Methods
                Molecular Biology Techniques
                Molecular Biology Assays and Analysis Techniques
                Library Screening
                Genomic Library Screening
                Medicine and Health Sciences
                Diagnostic Medicine
                Cancer Detection and Diagnosis
                Cancer Screening
                Medicine and Health Sciences
                Oncology
                Cancer Detection and Diagnosis
                Cancer Screening
                Engineering and Technology
                Management Engineering
                Decision Analysis
                Decision Trees
                Research and Analysis Methods
                Decision Analysis
                Decision Trees
                Custom metadata
                vor-update-to-uncorrected-proof
                2018-07-31
                The data and R scripts necessary to reproduce the results and figures have been deposited on figshare ( https://doi.org/10.6084/m9.figshare.5140057.v3).

                Quantitative & Systems biology

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