Integrated cross-study datasets of genetic dependencies in cancer

Abstract

CRISPR-Cas9 viability screens are being increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers, and to assemble a comprehensive map of cancer genetic vulnerabilities that might be exploited therapeutically. Here, we integrated the two largest independent CRISPR-Cas9 screens performed to date (at Broad and Sanger institutes), by assessing and selecting methods for correcting technology-specific biases and batch effects arising from differences in the underlying experimental protocols. Our integrated datasets recapitulate findings from the individual ones, provide larger statistical power allowing novel cancer-and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. Finally, we provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets and assembling large cross-study cancer dependency maps.Cancer is a complex disease that can arise from multiple different genetic alterations.The alternative mechanisms by which cancer can evolve result in a large amount of heterogeneity between patients, with the vast majority of them still not benefiting from approved targeted therapies 1 . In order to identify and prioritize new potential therapeutic targets for precision cancer therapy, analyses of cancer vulnerabilities at a genome-wide scale and across large panels of in vitro cancer models are being increasingly performed 2-11 . This has been facilitated by recent advances in genome editing technologies allowing unprecedented precision and scale via CRISPR-Cas9 screens. To date, two large pan-cancer CRISPR-Cas9 screens have been independently performed by the Broad and Sanger institutes 2,12 . The two institutes have also joined forces in a collaborative endeavor with the aim of assembling a joint comprehensive map of all the intracellular genetic dependencies and vulnerabilities of cancer: the Cancer Dependency Map (DepMap) 13,14 .Despite the two generated datasets containing so far data from over 300 cell lines each, detecting all cancer dependencies has been estimated to require the analysis of thousands of cancer models 3 . Consequently, the integration of these two datasets will be key for the DepMap and other projects aiming at systematically probing cancer dependencies.This will provide a more comprehensive representation of heterogeneous cancer types as well as an increased sample size to support the use of statistical/machine-learning methods to identify molecular features associated with differential gene dependencies. This will form the basis for the development of effective new therapies with associated biomarkers for patient stratification 15 . Furthermore, designing robust standards and computational protocols for the integration of t...

Chronos: a CRISPR cell population dynamics model

“…al. (Dempster et al, 2019) or Pacini et al(Pacini et al, 2020) However, for most results we did normalize the CERES and CRISPRCleanR data so the median of common essential gene fitness effects in each cell line is −1, as described in Meyers et. al.…”

Project Score database: a resource for investigating cancer cell dependencies and prioritizing therapeutic targets

“…The Project Score database will continue to grow and evolve as more data become available. Specifically, additional CRISPR–Cas9 screening datasets in cell lines are publicly available and, together with additional datasets generated at the Sanger Institute, these will be analysed through a consistent pipeline and integrated into the database in early 2021 ( 19 , 20 ). Additional datasets from CRISPR–Cas9 screens in patient-derived 3D organoid cultures and multi-gene CRISPR perturbations to study combinations of targets will be incorporated as they become available.…”

CEN‐tools: an integrative platform to identify the contexts of essential genes

“…All systematic analysis within CEN‐tools has been performed separately for the two projects. However, a recent preprint from Pacini et al (preprint: Pacini et al , 2020) has integrated the essentiality scores from SANGER and BROAD and we have included the integrated data sets in the CEN‐tools website as a beta‐version. We repeated our core gene analysis in this “INTEGRATED” data set (Appendix Fig S10A–D) and from a preliminary analysis, we observe that core genes identified from the core gene analysis using the integrated data set is highly concordant with the overlapping set of core genes identified from each project analysed separately.…”