Author contributions M.G., K.Y., and C.B-D. conceived the project. F.B. led CRISPR-Cas9 screening, codeveloped Project Score webportal, performed analyses, verified WRN dependency. F.I. led computational analyses and figure preparation, contributed to the Project Score webportal. G.P. performed experiments to verify WRN dependency, carried out analyses, contributed to in vivo studies. E.G. contributed to computational analysis and figures. D.vdM. contributed to developing the Project Score webportal. G.

BackgroundGenome editing by CRISPR-Cas9 technology allows large-scale screening of gene essentiality in cancer. A confounding factor when interpreting CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes within copy number amplified regions of the genome. We have developed the computational tool CRISPRcleanR which is capable of identifying and correcting gene-independent responses to CRISPR-Cas9 targeting. CRISPRcleanR uses an unsupervised approach based on the segmentation of single-guide RNA fold change values across the genome, without making any assumption about the copy number status of the targeted genes.ResultsApplying our method to existing and newly generated genome-wide essentiality profiles from 15 cancer cell lines, we demonstrate that CRISPRcleanR reduces false positives when calling essential genes, correcting biases within and outside of amplified regions, while maintaining true positive rates. Established cancer dependencies and essentiality signals of amplified cancer driver genes are detectable post-correction. CRISPRcleanR reports sgRNA fold changes and normalised read counts, is therefore compatible with downstream analysis tools, and works with multiple sgRNA libraries.ConclusionsCRISPRcleanR is a versatile open-source tool for the analysis of CRISPR-Cas9 knockout screens to identify essential genes.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4989-y) contains supplementary material, which is available to authorized users.

Genome editing by CRISPR-Cas9 technology allows large scale screening of gene essentiality in cancer. A confounding factor when interpreting CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes, particularly for those that are within copy number amplified regions of the genome. We have developed the computational tool CRISPRcleanR which is capable of identifying and correcting gene-independent responses to CRISPR-Cas9 targeting. CRISPRcleanR uses an unsupervised approach based on the segmentation of single-guide RNA (sgRNA) fold change values across the genome, without making any assumption on the copy number status of the targeted genes. Applying our method to newly generated genome-wide essentiality profiles from 15 cancer cell lines, we demonstrate that CRISPRcleanR reduces false positives when calling essential genes, correcting biases within and outside of amplified regions, while maintaining true positive rates. Established cancer dependencies and essentiality signals of amplified cancer driver genes are detectable post-correction. CRISPRcleanR reports sgRNA fold changes and normalised sgRNA read counts, and is therefore compatible with downstream analysis tools, and works with multiple sgRNA libraries. CRISPRcleanR is a versatile, open-source tool for the analysis of CRISPR-Cas9 knockout screens to identify essential genes.

SummaryFunctional genomics approaches can overcome current limitations that hamper oncology drug development such as lack of robust target identification and clinical efficacy. Here we performed genome-scale CRISPR-Cas9 screens in 204 human cancer cell lines from 12 cancer-types and developed a data-driven framework to prioritise cancer therapeutic candidates. We integrated gene cell fitness effects with genomic biomarkers and target tractability for drug development to systematically prioritise new oncology targets in defined tissues and genotypes. Furthermore, we took one of our most promising dependencies, Werner syndrome RecQ helicase, and verified it as a candidate target for tumours with microsatellite instability. Our analysis provides a comprehensive resource of cancer dependencies, a framework to prioritise oncology targets, and nominates specific new candidates. The principles described in this study can transform the initial stages of the drug development process contributing to a new, diverse and more effective portfolio of oncology targets.

Bone defects and improper healing of fractures are an increasing public health burden, and there is an unmet clinical need in their successful repair. Gene therapy has been proposed as a possible approach to improve or augment bone healing with the potential to provide true functional regeneration. While large numbers of studies have been performed in vitro or in vivo in small animal models that support the use of gene therapy for bone repair, these systems do not recapitulate several key features of a critical or complex fracture environment. Larger animal models are therefore a key step on the path to clinical translation of the technology. Herein, the current state of orthopedic gene therapy research in preclinical large animal models was investigated based on performed large animal studies. A summary and an outlook regarding current clinical studies in this sector are provided. It was found that the results found in the current research literature were generally positive but highly methodologically inconsistent, rendering a comparison difficult. Additionally, factors vital for translation have not been thoroughly addressed in these model systems, and the risk of bias was high in all reviewed publications. These limitations directly impact clinical translation of gene therapeutic approaches due to lack of comparability, inability to demonstrate non-inferiority or equivalence compared with current clinical standards, and lack of safety data. This review therefore aims to provide a current overview of ongoing preclinical and clinical work, potential bottlenecks in preclinical studies and for translation, and recommendations to overcome these to enable future deployment of this promising technology to the clinical setting.