GO-CRISPR: a highly controlled workflow to discover gene essentiality in loss-of-function screens

SUMMARYGenome-wide CRISPR screens are an effective discovery tool for genes that underlie diverse cellular mechanisms that can be scored through cell fitness. Loss-of-function screens are particularly challenging compared to gain-of-function because of the limited dynamic range of decreased sgRNA sequence detection. Here we describe Guide-Only control CRISPR (GO-CRISPR), an improved loss-of-function screening workflow, and its companion software package, Toolset for the Ranked Analysis of GO-CRISPR Screens (TRACS). We demonstrate a typical GO-CRISPR workflow in a non-proliferative 3D spheroid model of dormant high grade serous ovarian cancer and demonstrate superior performance to standard screening methods. The unique integration of the pooled sgRNA library quality and guide-only controls allows TRACS to identify novel molecular pathways that were previously unidentified in tumor dormancy. Together, GO-CRISPR and TRACS can robustly improve the discovery of essential genes in challenging biological scenarios.

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Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier

10.1101/2020.05.30.125526 ◽

2020 ◽

Cited By ~ 2

Author(s):

Eiru Kim ◽

Traver Hart

Keyword(s):

Dynamic Range ◽

Quality Data ◽

Loss Of Function ◽

Genetic Screens ◽

Gene Essentiality ◽

Guide Rna ◽

Genome Wide ◽

Improved Model ◽

Target Effects

AbstractIdentifying essential genes in genome-wide loss of function screens is a critical step in functional genomics and cancer target finding. We previously described the Bayesian Analysis of Gene Essentiality (BAGEL) algorithm for accurate classification of gene essentiality from short hairpin RNA and CRISPR/Cas9 genome wide genetic screens. Here, we introduce an updated version, BAGEL2, which employs an improved model that offers greater dynamic range of Bayes Factors, enabling detection of tumor suppressor genes, and a multi-target correction that reduces false positives from off-target CRISPR guide RNA. We also suggest a metric for screen quality at the replicate level and demonstrate how different algorithms handle lower-quality data in substantially different ways. BAGEL2 is written in Python 3 and source code, along with all supporting files, are available on github (https://github.com/hart-lab/bagel).

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Abstract 1380: Genome-wide fingerprinting of regulatory chromatin to evaluate the tissue specific origins of high-grade serous ovarian cancer

10.1158/1538-7445.am2014-1380 ◽

2014 ◽

Author(s):

Howard C. Shen ◽

Simon Coetzee ◽

Dennis J. Hazelett ◽

Gerhard A. Coetzee ◽

Houtan Noushmehr ◽

...

Keyword(s):

Ovarian Cancer ◽

High Grade ◽

Serous Ovarian Cancer ◽

Tissue Specific ◽

Genome Wide

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Whence High-Grade Serous Ovarian Cancer

American Society of Clinical Oncology Educational Book ◽

10.1200/edbk_174718 ◽

2017 ◽

pp. 443-448 ◽

Cited By ~ 1

Author(s):

Elise C. Kohn ◽

S. Percy Ivy

Keyword(s):

Ovarian Cancer ◽

Dna Repair ◽

Rational Design ◽

Driver Mutations ◽

High Grade ◽

Serous Ovarian Cancer ◽

Loss Of Function ◽

Brca1 And Brca2 ◽

Unique Type ◽

Cellular Environment

Our understanding of epithelial ovarian cancer has blossomed, and we now recognize that it is a collection of varied histologic and molecularly different malignancies, many of which may not derive from a true ovarian anatomic precursor. High-grade serous ovarian cancer (HGSOC) is a unique type of epithelial cancer. It is characterized by nearly universal mutation in and dysfunction of p53, genomic instability rather than driver mutations, advanced stage at onset, and probable fallopian tube epithelium origin, with a serous tubal in situ carcinoma precursor. Germline deleterious mutations in BRCA1 and BRCA2, as well as other less prevalent genes involved in DNA repair, such as PALB2 and RAD51c, are associated with its carcinogenesis and may predict susceptibility to classes of treatment agents, including DNA-damaging agents and DNA repair inhibitors. Loss of function of these genes is associated with homologous recombination dysfunction (HRD). It is now recognized that there may be HGSOC with wild-type BRCA1 and BRCA2 with an identifiable HRD phenotype. Such HRD tumors also may be more susceptible to certain classes of treatments and may be phenotypically detectable with a composite molecular biomarker that has been shown to be predictive for response to PARP inhibitors. Use of this new knowledge of the anatomic and molecular background of HGSOC has led to the rational design of novel combinations of treatment classes to create an HRD-like cellular environment and thus drive treatment benefits.

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Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier

Genome Medicine ◽

10.1186/s13073-020-00809-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Eiru Kim ◽

Traver Hart

Keyword(s):

Dynamic Range ◽

Quality Data ◽

Loss Of Function ◽

Genetic Screens ◽

Gene Essentiality ◽

Guide Rna ◽

Genome Wide ◽

And Performance ◽

Improved Model ◽

Sensitivity Specificity

Abstract Background Identifying essential genes in genome-wide loss-of-function screens is a critical step in functional genomics and cancer target finding. We previously described the Bayesian Analysis of Gene Essentiality (BAGEL) algorithm for accurate classification of gene essentiality from short hairpin RNA and CRISPR/Cas9 genome-wide genetic screens. Results We introduce an updated version, BAGEL2, which employs an improved model that offers a greater dynamic range of Bayes Factors, enabling detection of tumor suppressor genes; a multi-target correction that reduces false positives from off-target CRISPR guide RNA; and the implementation of a cross-validation strategy that improves performance ~ 10× over the prior bootstrap resampling approach. We also describe a metric for screen quality at the replicate level and demonstrate how different algorithms handle lower quality data in substantially different ways. Conclusions BAGEL2 substantially improves the sensitivity, specificity, and performance over BAGEL and establishes the new state of the art in the analysis of CRISPR knockout fitness screens. BAGEL2 is written in Python 3 and source code, along with all supporting files, are available on github (https://github.com/hart-lab/bagel).

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Loss of LKB1-NUAK1 Signalling Enhances NF-κB Activity in a Spheroid Model of High-Grade Serous Ovarian Cancer

10.21203/rs.3.rs-951554/v1 ◽

2021 ◽

Author(s):

Adrian Buensuceso ◽

Jamie Lee Fritz ◽

Olga Collins ◽

Yudith Ramos Valdes ◽

Gabriel E. DiMattia ◽

...

Keyword(s):

Ovarian Cancer ◽

Cell Survival ◽

Residual Disease ◽

Immunohistochemical Analysis ◽

Signalling Pathways ◽

Nuclear Staining ◽

High Grade ◽

Serous Ovarian Cancer ◽

Loss Of Function ◽

Inflammatory Signalling

Abstract High-grade serous ovarian cancer (HGSOC) is an aggressive malignancy often diagnosed at an advanced stage. Although most HGSOC patients respond initially to debulking surgery combined with cytotoxic chemotherapy, many ultimately relapse with platinum-resistant disease. Thus, improving outcomes requires new ways of limiting metastasis and eradicating residual disease. We identified previously that Liver kinase B1 (LKB1) and its substrate NUAK1 are implicated in EOC spheroid cell viability and are required for efficient metastasis in orthotopic mouse models. Here, we sought to identify additional signalling pathways altered in EOC cells due to LKB1 or NUAK1 loss-of-function. Transcriptome analysis revealed that inflammatory signalling mediated by NF-κB transcription factors is hyperactive due to LKB1-NUAK1 loss in HGSOC cells and spheroids. Upregulated NF-κB signalling due to NUAK1 loss suppresses reactive oxygen species (ROS) production and sustains cell survival in spheroids. NF-κB signalling is also activated in HGSOC precursor fallopian tube secretory epithelial cell spheroids, and is further enhanced by NUAK1 loss. Finally, immunohistochemical analysis of OVCAR8 xenograft tumors lacking NUAK1 displayed increased RelB expression and nuclear staining. Our results support the idea that NUAK1 and NF-κB signalling pathways together regulate ROS and inflammatory signalling, supporting cell survival during each step of HGSOC pathogenesis. We propose that their combined inhibition may be efficacious as a novel therapeutic strategy for advanced HGSOC.

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Minimal genome-wide human CRISPR-Cas9 library

Genome Biology ◽

10.1186/s13059-021-02268-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Emanuel Gonçalves ◽

Mark Thomas ◽

Fiona M. Behan ◽

Gabriele Picco ◽

Clare Pacini ◽

...

Keyword(s):

Large Scale ◽

Dynamic Range ◽

Loss Of Function ◽

Assay Sensitivity ◽

Guide Rna ◽

Minimal Genome ◽

Backward Compatibility ◽

Large Size ◽

Genome Wide ◽

Complex Models

AbstractCRISPR guide RNA libraries have been iteratively improved to provide increasingly efficient reagents, although their large size is a barrier for many applications. We design an optimised minimal genome-wide human CRISPR-Cas9 library (MinLibCas9) by mining existing large-scale gene loss-of-function datasets, resulting in a greater than 42% reduction in size compared to other CRISPR-Cas9 libraries while preserving assay sensitivity and specificity. MinLibCas9 provides backward compatibility with existing datasets, increases the dynamic range of CRISPR-Cas9 screens and extends their application to complex models and assays.

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Minimal genome-wide human CRISPR-Cas9 library

10.1101/848895 ◽

2019 ◽

Cited By ~ 1

Author(s):

Emanuel Gonçalves ◽

Mark Thomas ◽

Fiona M Behan ◽

Gabriele Picco ◽

Clare Pacini ◽

...

Keyword(s):

Large Scale ◽

Gene Loss ◽

Dynamic Range ◽

Loss Of Function ◽

Assay Sensitivity ◽

Guide Rna ◽

Minimal Genome ◽

Large Size ◽

Genome Wide ◽

Complex Models

AbstractCRISPR guide-RNA libraries have been iteratively optimised to provide increasingly efficient reagents, although their large size is a barrier for many applications. We designed an optimised minimal genome-wide human CRISPR-Cas9 library (MinLibCas9), by mining existing large-scale gene loss-of-function datasets, resulting in a greater than 42% reduction in size compared to other libraries while preserving assay sensitivity and specificity. MinLibCas9 increases the dynamic range of CRISPR-Cas9 loss-of-function screens and extends their application to complex models and assays.

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