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Correction of copy number induced false positives in CRISPR screens

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      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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      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

            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.

            Contributors
            ORCID: http://orcid.org/0000-0003-0887-6771, Role: Conceptualization, Role: Data curation, Role: Formal analysis, Role: Methodology, Role: Software, Role: Visualization, Role: Writing – original draft, Role: Writing – review & editing
            Role: Data curation, Role: Resources
            Role: Data curation, Role: Resources
            Role: Data curation, Role: Resources
            ORCID: http://orcid.org/0000-0002-8859-0089, Role: Conceptualization, Role: Investigation, Role: Resources
            Role: Conceptualization, Role: Investigation, Role: Resources
            Role: Conceptualization, Role: Resources
            Role: Conceptualization, Role: Supervision, Role: Writing – original draft, Role: Writing – review & editing
            Role: Conceptualization, Role: Methodology, Role: Project administration, Role: Supervision, Role: Writing – original draft, Role: 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
            30024886 6067744 10.1371/journal.pcbi.1006279 PCOMPBIOL-D-17-01148
            © 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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