Blog
About

  • Record: found
  • Abstract: found
  • Article: found
Is Open Access

Identification of oncogenic driver mutations by genome-wide CRISPR-Cas9 dropout screening

Read this article at

Bookmark
      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

      Background

      Genome-wide CRISPR-Cas9 dropout screens can identify genes whose knockout affects cell viability. Recent CRISPR screens detected thousands of essential genes required for cellular survival and key cellular processes; however discovering novel lineage-specific genetic dependencies from the many hits still remains a challenge.

      Results

      To assess whether CRISPR-Cas9 dropout screens can help identify cancer dependencies, we screened two human cancer cell lines carrying known and distinct oncogenic mutations using a genome-wide sgRNA library. We found that the gRNA targeting the driver mutation EGFR was one of the highest-ranking candidates in the EGFR-mutant HCC-827 lung adenocarcinoma cell line. Likewise, sgRNAs for NRAS and MAP2K1 (MEK1), a downstream kinase of mutant NRAS, were identified among the top hits in the NRAS-mutant neuroblastoma cell line CHP-212. Depletion of these genes targeted by the sgRNAs strongly correlated with the sensitivity to specific kinase inhibitors of the EGFR or RAS pathway in cell viability assays. In addition, we describe other dependencies such as TBK1 in HCC-827 cells and TRIB2 in CHP-212 cells which merit further investigation.

      Conclusions

      We show that genome-wide CRISPR dropout screens are suitable for the identification of oncogenic drivers and other essential genes.

      Electronic supplementary material

      The online version of this article (doi:10.1186/s12864-016-3042-2) contains supplementary material, which is available to authorized users.

      Related collections

      Most cited references 27

      • Record: found
      • Abstract: found
      • Article: not found

      Fast gapped-read alignment with Bowtie 2.

      As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
        Bookmark
        • Record: found
        • Abstract: found
        • Article: not found

        Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

        Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
          Bookmark
          • Record: found
          • Abstract: found
          • Article: not found

          Differential expression analysis for sequence count data

          High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
            Bookmark

            Author and article information

            Affiliations
            [1 ]Department of Gastroenterology and Hepatology, University Hospital Zürich, Zürich, Switzerland
            [2 ]Novartis Institutes for Biomedical Research, Novartis Pharma AG, Basel, Switzerland
            [3 ]Institute of Medical Virology, University of Zürich, Zürich, Switzerland
            [4 ]Department of Oncology, Children University Hospital Zürich, Zürich, Switzerland
            Contributors
            michael.kiessling@usz.ch
            sven.schuierer@novartis.com
            stertz.silke@virology.uzh.ch
            martin.beibel@novartis.com
            sebastian.bergling@novartis.com
            judith.knehr@novartis.com
            walter.carbone@novartis.com
            Cheryl.DeValliere@usz.ch
            Joelle.Tchinda@kispi.uzh.ch
            tewis.bouwmeester@novartis.com
            klaus.seuwen@novartis.com
            Gerhard.rogler@usz.ch
            Guglielmo.roma@novartis.com
            Journal
            BMC Genomics
            BMC Genomics
            BMC Genomics
            BioMed Central (London )
            1471-2164
            9 September 2016
            9 September 2016
            2016
            : 17
            : 1
            27613601 5016932 3042 10.1186/s12864-016-3042-2
            © The Author(s). 2016

            Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

            Categories
            Methodology Article
            Custom metadata
            © The Author(s) 2016

            Genetics

            driver mutations, kinase, nras, egfr, negative selection, dropout, whole genome crispr screen

            Comments

            Comment on this article