Predicting cell health phenotypes using image-based morphology profiling.

Genetic and chemical perturbations impact diverse cellular phenotypes, including multiple indicators of cell health. These readouts reveal toxicity and antitumorigenic effects relevant to drug discovery and personalized medicine. We developed two customized microscopy assays, one using four targeted reagents and the other three targeted reagents, to collectively measure 70 specific cell health phenotypes including proliferation, apoptosis, reactive oxygen species, DNA damage, and cell cycle stage. We then tested an approach to predict multiple cell health phenotypes using Cell Painting, an inexpensive and scalable image-based morphology assay. In matched CRISPR perturbations of three cancer cell lines, we collected both Cell Painting and cell health data. We found that simple machine learning algorithms can predict many cell health readouts directly from Cell Painting images, at less than half the cost. We hypothesized that these models can be applied to accurately predict cell health assay outcomes for any future or existing Cell Painting dataset. For Cell Painting images from a set of 1500+ compound perturbations across multiple doses, we validated predictions by orthogonal assay readouts. We provide a web app to browse predictions: http://broad.io/cell-health-app. Our approach can be used to add cell health annotations to Cell Painting datasets.

CRISPR-Cas9 screen reveals a MYCN-amplified neuroblastoma dependency on EZH2.

Pharmacologically difficult targets, such as MYC transcription factors, represent a major challenge in cancer therapy. For the childhood cancer neuroblastoma, amplification of the oncogene MYCN is associated with high-risk disease and poor prognosis. Here, we deployed genome-scale CRISPR-Cas9 screening of MYCN-amplified neuroblastoma and found a preferential dependency on genes encoding the polycomb repressive complex 2 (PRC2) components EZH2, EED, and SUZ12. Genetic and pharmacological suppression of EZH2 inhibited neuroblastoma growth in vitro and in vivo. Moreover, compared with neuroblastomas without MYCN amplification, MYCN-amplified neuroblastomas expressed higher levels of EZH2. ChIP analysis showed that MYCN binds at the EZH2 promoter, thereby directly driving expression. Transcriptomic and epigenetic analysis, as well as genetic rescue experiments, revealed that EZH2 represses neuronal differentiation in neuroblastoma in a PRC2-dependent manner. Moreover, MYCN-amplified and high-risk primary tumors from patients with neuroblastoma exhibited strong repression of EZH2-regulated genes. Additionally, overexpression of IGFBP3, a direct EZH2 target, suppressed neuroblastoma growth in vitro and in vivo. We further observed strong synergy between histone deacetylase inhibitors and EZH2 inhibitors. Together, these observations demonstrate that MYCN upregulates EZH2, leading to inactivation of a tumor suppressor program in neuroblastoma, and support testing EZH2 inhibitors in patients with MYCN-amplified neuroblastoma.

Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells.

The CRISPR-Cas9 system has revolutionized gene editing both at single genes and in multiplexed loss-of-function screens, thus enabling precise genome-scale identification of genes essential for proliferation and survival of cancer cells. However, previous studies have reported that a gene-independent antiproliferative effect of Cas9-mediated DNA cleavage confounds such measurement of genetic dependency, thereby leading to false-positive results in copy number-amplified regions. We developed CERES, a computational method to estimate gene-dependency levels from CRISPR-Cas9 essentiality screens while accounting for the copy number-specific effect. In our efforts to define a cancer dependency map, we performed genome-scale CRISPR-Cas9 essentiality screens across 342 cancer cell lines and applied CERES to this data set. We found that CERES decreased false-positive results and estimated sgRNA activity for both this data set and previously published screens performed with different sgRNA libraries. We further demonstrate the utility of this collection of screens, after CERES correction, for identifying cancer-type-specific vulnerabilities.

Defining a Cancer Dependency Map.

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

Genomic Copy Number Dictates a Gene-Independent Cell Response to CRISPR/Cas9 Targeting.

UNLABELLED: The CRISPR/Cas9 system enables genome editing and somatic cell genetic screens in mammalian cells. We performed genome-scale loss-of-function screens in 33 cancer cell lines to identify genes essential for proliferation/survival and found a strong correlation between increased gene copy number and decreased cell viability after genome editing. Within regions of copy-number gain, CRISPR/Cas9 targeting of both expressed and unexpressed genes, as well as intergenic loci, led to significantly decreased cell proliferation through induction of a G2 cell-cycle arrest. By examining single-guide RNAs that map to multiple genomic sites, we found that this cell response to CRISPR/Cas9 editing correlated strongly with the number of target loci. These observations indicate that genome targeting by CRISPR/Cas9 elicits a gene-independent antiproliferative cell response. This effect has important practical implications for the interpretation of CRISPR/Cas9 screening data and confounds the use of this technology for the identification of essential genes in amplified regions. SIGNIFICANCE: We found that the number of CRISPR/Cas9-induced DNA breaks dictates a gene-independent antiproliferative response in cells. These observations have practical implications for using CRISPR/Cas9 to interrogate cancer gene function and illustrate that cancer cells are highly sensitive to site-specific DNA damage, which may provide a path to novel therapeutic strategies. Cancer Discov; 6(8); 914-29. ©2016 AACR.See related commentary by Sheel and Xue, p. 824See related article by Munoz et al., p. 900This article is highlighted in the In This Issue feature, p. 803.