Extracting Biological Insights from the Project Achilles Genome-Scale CRISPR Screens in Cancer Cell Lines

Dempster, Joshua M.; Rossen, Jordan; Kazachkova, Mariya; Pan, Joshua; Kugener, Guillaume; De, Root; Tsherniak, Aviad

doi:10.1101/720243

Cited by 315 publications

(440 citation statements)

References 18 publications

(19 reference statements)

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“…The CRISPR viability data were downloaded from the Cancer Dependency Map portal (https://depmap.org/portal/download/). The "gene effect corrected" scores from the 19Q1 Avana data were used [5,4].…”

Section: Crispr Viability Datamentioning

confidence: 99%

Post-perturbational transcriptional signatures of cancer cell line vulnerabilities

Jones

Tsherniak

2020

Preprint

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While chemical and genetic viability screens in cancer cell lines have identified many promising cancer vulnerabilities, simple univariate readouts of cell proliferation fail to capture the complex cellular responses to perturbations. Complementarily, gene expression profiling offers an informationrich measure of cell state that can provide a more detailed account of cellular responses to perturbations. Relatively little is known, however, about the relationship between transcriptional responses to perturbations and the long-term cell viability effects of those perturbations. To address this question, we integrated thousands of post-perturbational transcriptional profiles from the Connectivity Map with large-scale screens of cancer cell lines' viability response to genetic and chemical perturbations. This analysis revealed a generalized transcriptional signature associated with reduced viability across perturbations, which was consistent across post-perturbation time-points, perturbation types, and viability datasets. At a more granular level, we lay out the landscape of treatment-specific expression-viability relationships across a broad panel of drugs and genetic reagents, and we demonstrate that these postperturbational expression signatures can be used to infer long-term viability. Together, these results help unmask the transcriptional changes that are associated with perturbation-induced viability loss in cancer cell lines. Co-senior authors

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Section: Crispr Viability Datamentioning

confidence: 99%

Post-perturbational transcriptional signatures of cancer cell line vulnerabilities

Jones

Tsherniak

2020

Preprint

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“…The scikit-learn's RandomForestRegressor82 was used to predict Reversine AUC values for 502 cell lines. The input features were (19Q4 release): RNA-Seq expression data for Like in Dempster et al, we used tenfold cross-validation, filtered features to the 1,000 having the highest Pearson correlation with the Reversine AUC values in the training set, and reported accuracy via Pearson correlation between the measured AUC values and the complete set of out-of-sample predications83 . To estimate feature importance values we retrained the model on all the samples and used…”

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confidence: 99%

Selective vulnerability of aneuploid human cancer cells to inhibition of the spindle assembly checkpoint

Cohen-Sharir

McFarland

Abdusamad

et al. 2020

Preprint

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Selective targeting of aneuploid cells is an attractive strategy for cancer treatment. Here, we mapped the aneuploidy landscapes of ~1,000 human cancer cell lines and classified them by their degree of aneuploidy. Next, we performed a comprehensive analysis of large-scale genetic and chemical perturbation screens, in order to compare the cellular vulnerabilities between neardiploid and highly-aneuploid cancer cells. We identified and validated an increased sensitivity of aneuploid cancer cells to genetic perturbation of core components of the spindle assembly checkpoint (SAC), which ensures the proper segregation of chromosomes during mitosis. Surprisingly, we also found highly-aneuploid cancer cells to be less sensitive to short-term exposures to multiple inhibitors of the SAC regulator TTK. To resolve this paradox and to uncover its mechanistic basis, we established isogenic systems of near-diploid cells and their aneuploid derivatives. Using both genetic and chemical inhibition of BUB1B, MAD2 and TTK, we found that the cellular response to SAC inhibition depended on the duration of the assay, as aneuploid cancer cells became increasingly more sensitive to SAC inhibition over time. The increased ability of aneuploid cells to slip from mitotic arrest and to keep dividing in the presence of SAC inhibition was coupled to aberrant spindle geometry and dynamics. This resulted in a higher prevalence of mitotic defects, such as multipolar spindles, micronuclei formation and failed cytokinesis. Therefore, although aneuploid cancer cells can overcome SAC inhibition more readily than diploid cells, the proliferation of the resultant aberrant cells is jeopardized. At the molecular level, analysis of spindle proteins identified a specific mitotic kinesin, KIF18A, whose levels were drastically reduced in aneuploid cancer cells. Aneuploid cancer cells were particularly vulnerable to KIF18A depletion, and KIF18A overexpression restored the sensitivity of aneuploid cancer cells to SAC inhibition. In summary, we identified an increased vulnerability of aneuploid cancer cells to SAC inhibition and explored its cellular and molecular underpinnings. Our results reveal a novel synthetic lethal interaction between aneuploidy and the SAC, which may have direct therapeutic relevance for the clinical application of SAC inhibitors.

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“…genes identified in the DepMap project(40,41) using CRISPR knock-out screens across a variety of cell lines. The DepMap project identified 2164 genes that were essential for survival.…”

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confidence: 99%

Stable gene expression for normalisation and single-sample scoring

Bhuva

Cursons

Davis

2020

Preprint

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BackgroundTranscriptomic signatures are useful in defining the molecular phenotypes of cells, tissues, and patient samples. Their most successful and widespread clinical application is the stratification of breast cancer patients into molecular (PAM50) subtypes. In most cases, gene expression signatures are developed using transcriptome-wide measurements, thus methods that match signatures to samples typically require a similar degree of measurements. The cost and relatively large amounts of fresh starting material required for whole-transcriptome sequencing has limited clinical applications, and accordingly thousands of existing gene signatures are unexplored in a clinical context.ResultsGenes in a molecular signature can provide information about molecular phenotypes and their underlying transcriptional programs from tissue samples, however determining the transcriptional state of these genes typically requires the measurement of all genes across multiple samples to allow for comparison. An efficient assay and scoring method should quantify the relative abundance of signature genes with a minimal number of additional measurements. We identified genes with stable expression across a range of abundances, and with a preserved relative ordering across large numbers (thousands) of samples, allowing signature scoring, and supporting general data normalisation for transcriptomic data. Based on singscore, we have developed a new method, stingscore, which quantifies and summarises relative expression levels of signature genes from individual samples through the inclusion of these “stably-expressed genes”.ConclusionWe show that our proposed list of stable genes has better stability across cancer and normal tissue data than previously proposed stable or housekeeping genes. Additionally, we show that signature scores computed from whole-transcriptome data are comparable to those calculated using only values for signature genes and our panel of stable genes. This new approach to gene expression signature analysis may facilitate the development of panel-type tests for gene expression signatures, thus supporting clinical translation of the powerful insights gained from cancer transcriptomic studies.

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Extracting Biological Insights from the Project Achilles Genome-Scale CRISPR Screens in Cancer Cell Lines

Cited by 315 publications

References 18 publications

Post-perturbational transcriptional signatures of cancer cell line vulnerabilities

Post-perturbational transcriptional signatures of cancer cell line vulnerabilities

Selective vulnerability of aneuploid human cancer cells to inhibition of the spindle assembly checkpoint

Stable gene expression for normalisation and single-sample scoring

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