Abstract
BackgroundCRISPR-Cas9 genome-wide screens are being increasingly performed, allowing systematic explorations of cancer dependencies at unprecedented accuracy and scale. One of the major computational challenges when analysing data derived from such screens is to identify genes that are essential for cell survival invariantly across tissues, conditions, and genomic-contexts (core-fitness genes), and to distinguish them from context-specific essential genes. This is of paramount importance to assess the safety profile of candidate therapeutic targets and for elucidating mechanisms involved in tissue-specific genetic diseases.ResultsWe have developed CoRe: an R package implementing existing and novel methods for the identification of core-fitness genes (at two different level of stringency) from joint analyses of multiple CRISPR-Cas9 screens. We demonstrate, through a fully reproducible benchmarking pipeline, that CoRe outperforms state-of-the-art tools, yielding more reliable and biologically relevant sets of core-fitness genes.ConclusionsCoRe offers a flexible pipeline, compatible with many pre-processing methods for the analysis of CRISPR data, which can be tailored onto different use-cases. The CoRe package can be used for the identification of high-confidence novel core-fitness genes, as well as a means to filter out potentially cytotoxic hits while analysing cancer dependency datasets for identifying and prioritising novel selective therapeutic targets.
Highlights
Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 genome-wide screens are being increasingly performed, allowing systematic explorations of cancer dependencies at unprecedented accuracy and scale
In [11] we have introduced the Adaptive Daisy Model (ADaM): a generalisation of the DM that is able to determine the minimal number of cell lines that should be vulnerable to knocking-out the putative Core Fitness Gene (CFG), i.e. dependent on them, in a semi-supervised manner
Comparison with existing methods and state-of-the-art sets of core-fitness genes We compared the sets of CFGs and Common Essential (CEG) predicted by CoRe when applied to the largest integrative dataset of cancer dependency assembled to date, accounting for 17,486 genes and 855 cell lines from 30 different tissue-lineages and 43 cancer types [19], with state-of-the-art sets of core-fitness genes derived from recent functional genetic screening datasets [10, 11, 30, 31]
Summary
CRISPR-Cas genome-wide screens are being increasingly performed, allowing systematic explorations of cancer dependencies at unprecedented accuracy and scale. One of the major computational challenges when analysing data derived from such screens is to identify genes that are essential for cell survival invariantly across tissues, conditions, and genomic-contexts (core-fitness genes), and to distinguish them from context-specific essential genes This is of paramount importance to assess the safety profile of candidate therapeutic targets and for elucidating mechanisms involved in tissue-specific genetic diseases. Several genome-scale CRISPR-Cas single guide RNA (sgRNA) libraries have been designed and are available to date for genetic perturbation screens in human cells, showing significantly improved precision and scale with respect to previous technologies [4,5,6,7,8] Some of these libraries have been employed in large-scale in-vitro screens assessing each gene’s potential in reducing cellular viability/fitness upon inactivation, across hundreds of immortalised human cancer cell lines [7, 9,10,11,12].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
