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Cell lines are key tools for preclinical cancer research, but it remains unclear how well they represent patient tumor samples. Direct comparisons of tumor and cell line transcriptional profiles are complicated by several factors, including the variable presence of normal cells in tumor samples. We thus develop an unsupervised alignment method (Celligner) and apply it to integrate several large-scale cell line and tumor RNA-Seq datasets. Although our method aligns the majority of cell lines with tumor samples of the same cancer type, it also reveals large differences in tumor similarity across cell lines. Using this approach, we identify several hundred cell lines from diverse lineages that present a more mesenchymal and undifferentiated transcriptional state and that exhibit distinct chemical and genetic dependencies. Celligner could be used to guide the selection of cell lines that more closely resemble patient tumors and improve the clinical translation of insights gained from cell lines. The determination of whether cancer cell lines recapitulate the molecular features of corresponding patient tumours remains essential for the selection of appropriate cell line models for preclinical studies. The method developed here, Celligner, integrates cancer cell line and tumour RNA-seq datasets and reveals large differences in their concordance across cell lines and cancer types.

Despite considerable efforts to identify cancer metabolic alterations that might unveil druggable vulnerabilities, systematic characterizations of metabolism as it relates to functional genomic features and associated dependencies remain uncommon. To further understand the metabolic diversity of cancer, we profiled 225 metabolites in 928 cell lines from more than 20 cancer types in the Cancer Cell Line Encyclopedia (CCLE) using liquid chromatography–mass spectrometry (LC-MS). This resource enables unbiased association analysis linking the cancer metabolome to genetic alterations, epigenetic features and gene dependencies. Additionally, by screening barcoded cell lines, we demonstrated that aberrant ASNS hypermethylation sensitizes subsets of gastric and hepatic cancers to asparaginase therapy. Finally, our analysis revealed distinct synthesis and secretion patterns of kynurenine, an immune-suppressive metabolite, in model cancer cell lines. Together, these findings and related methodology provide comprehensive resources that will help clarify the landscape of cancer metabolism. Systematic metabolite profiling across cancer cell lines uncovers patterns associated with genetic and epigenetic features and reveals dysregulated metabolic states that can be exploited for anticancer therapy

CRISPR loss of function screens are powerful tools to interrogate biology but exhibit a number of biases and artifacts that can confound the results. Here, we introduce Chronos, an algorithm for inferring gene knockout fitness effects based on an explicit model of cell proliferation dynamics after CRISPR gene knockout. We test Chronos on two pan-cancer CRISPR datasets and one longitudinal CRISPR screen. Chronos generally outperforms competitors in separation of controls and strength of biomarker associations, particularly when longitudinal data is available. Additionally, Chronos exhibits the lowest copy number and screen quality bias of evaluated methods. Chronos is available at https://github.com/broadinstitute/chronos .

Genome-scale CRISPR-Cas9 viability screens performed in cancer cell lines provide a systematic approach to identify cancer dependencies and new therapeutic targets. As multiple large-scale screens become available, a formal assessment of the reproducibility of these experiments becomes necessary. We analyze data from recently published pan-cancer CRISPR-Cas9 screens performed at the Broad and Sanger Institutes. Despite significant differences in experimental protocols and reagents, we find that the screen results are highly concordant across multiple metrics with both common and specific dependencies jointly identified across the two studies. Furthermore, robust biomarkers of gene dependency found in one data set are recovered in the other. Through further analysis and replication experiments at each institute, we show that batch effects are driven principally by two key experimental parameters: the reagent library and the assay length. These results indicate that the Broad and Sanger CRISPR-Cas9 viability screens yield robust and reproducible findings. Integrating independent large-scale pharmacogenomic screens can enable unprecedented characterization of genetic vulnerabilities in cancers. Here, the authors show that the two largest independent CRISPR-Cas9 gene-dependency screens are concordant, paving the way for joint analysis of the data sets.

Systematic exploration of cancer cell vulnerabilities can inform the development of novel cancer therapeutics. Here, through analysis of genome-scale loss-of-function datasets, we identify adenosine deaminase acting on RNA ( ADAR or ADAR1) as an essential gene for the survival of a subset of cancer cell lines. ADAR1-dependent cell lines display increased expression of interferon-stimulated genes. Activation of type I interferon signaling in the context of ADAR1 deficiency can induce cell lethality in non-ADAR1-dependent cell lines. ADAR deletion causes activation of the double-stranded RNA sensor, protein kinase R (PKR). Disruption of PKR signaling, through inactivation of PKR or overexpression of either a wildtype or catalytically inactive mutant version of the p150 isoform of ADAR1, partially rescues cell lethality after ADAR1 loss, suggesting that both catalytic and non-enzymatic functions of ADAR1 may contribute to preventing PKR-mediated cell lethality. Together, these data nominate ADAR1 as a potential therapeutic target in a subset of cancers. Specific cancer cell vulnerabilities can provide an opportunity for the development of novel cancer therapeutics. In this study the authors demonstrate that targeting ADAR1 represents a potential therapeutic vulnerability in cancers with activated interferon response signatures.

Bromodomain-containing protein 9 (BRD9) is a recently identified subunit of SWI/SNF(BAF) chromatin remodeling complexes, yet its function is poorly understood. Here, using a genome-wide CRISPR-Cas9 screen, we show that BRD9 is a specific vulnerability in pediatric malignant rhabdoid tumors (RTs), which are driven by inactivation of the SMARCB1 subunit of SWI/SNF. We find that BRD9 exists in a unique SWI/SNF sub-complex that lacks SMARCB1, which has been considered a core subunit. While SMARCB1-containing SWI/SNF complexes are bound preferentially at enhancers, we show that BRD9-containing complexes exist at both promoters and enhancers. Mechanistically, we show that SMARCB1 loss causes increased BRD9 incorporation into SWI/SNF thus providing insight into BRD9 vulnerability in RTs. Underlying the dependency, while its bromodomain is dispensable, the DUF3512 domain of BRD9 is essential for SWI/SNF integrity in the absence of SMARCB1. Collectively, our results reveal a BRD9-containing SWI/SNF subcomplex is required for the survival of SMARCB1 -mutant RTs. Mutations of the SWI/SNF chromatin-remodeling complex member SMARCB1 can cause malignant rhaboid tumors. Here the authors report a BRD9-containing SWI/SNF subcomplex that lacks SMARCB1 and its requirement for the survival of rhaboid tumors.

Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate. Information-rich assays, such as gene-expression profiling, have generally not permitted efficient profiling of a given perturbation across multiple cellular contexts. Here, we develop MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of single-cell RNA-sequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines. We combine it with Cell Hashing to further multiplex additional experimental conditions, such as post-treatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and enable prediction of long-term cell viability from short-term transcriptional responses to treatment. Large-scale screens of chemical and genetic vulnerabilities in cancer are typically limited to simple readouts of cell viability. Here, the authors develop a method for profiling post-perturbation transcriptional responses across large pools of cancer cell lines, enabling deep characterization of shared and context-specific responses.

Chordoma is a primary bone cancer with no approved therapy 1 . The identification of therapeutic targets in this disease has been challenging due to the infrequent occurrence of clinically actionable somatic mutations in chordoma tumors 2 , 3 . Here we describe the discovery of therapeutically targetable chordoma dependencies via genome-scale CRISPR-Cas9 screening and focused small-molecule sensitivity profiling. These systematic approaches reveal that the developmental transcription factor T (brachyury; TBXT ) is the top selectively essential gene in chordoma, and that transcriptional cyclin-dependent kinase (CDK) inhibitors targeting CDK7/12/13 and CDK9 potently suppress chordoma cell proliferation. In other cancer types, transcriptional CDK inhibitors have been observed to downregulate highly expressed, enhancer-associated oncogenic transcription factors 4 , 5 . In chordoma, we find that T is associated with a 1.5-Mb region containing ‘super-enhancers’ and is the most highly expressed super-enhancer-associated transcription factor. Notably, transcriptional CDK inhibition leads to preferential and concentration-dependent downregulation of cellular brachyury protein levels in all models tested. In vivo, CDK7/12/13-inhibitor treatment substantially reduces tumor growth. Together, these data demonstrate small-molecule targeting of brachyury transcription factor addiction in chordoma, identify a mechanism of T gene regulation that underlies this therapeutic strategy, and provide a blueprint for applying systematic genetic and chemical screening approaches to discover vulnerabilities in genomically quiet cancers. A combination of genome-scale CRISPR-Cas9 screening and focused small-molecule sensitivity profiling enables the discovery of therapeutically targetable tumor dependencies in rare tumors.

Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome‐wide editing screenings have facilitated the discovery of clinically relevant gene–drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine‐learning‐based directions that could resolve some of the arising discrepancies. Graphical Abstract Cancer cell lines (CCLs) are widely used for analysing cancer and identifying therapies. This Review discusses the limitations of CCLs in representing tumours and considers computational methods that can systematically evaluate the suitability of CCLs as tumour proxies.

Cas9 is commonly introduced into cell lines to enable CRISPR–Cas9-mediated genome editing. Here, we studied the genetic and transcriptional consequences of Cas9 expression itself. Gene expression profiling of 165 pairs of human cancer cell lines and their Cas9-expressing derivatives revealed upregulation of the p53 pathway upon introduction of Cas9, specifically in wild-type TP53 ( TP53 -WT) cell lines. This was confirmed at the messenger RNA and protein levels. Moreover, elevated levels of DNA repair were observed in Cas9-expressing cell lines. Genetic characterization of 42 cell line pairs showed that introduction of Cas9 can lead to the emergence and expansion of p53-inactivating mutations. This was confirmed by competition experiments in isogenic TP53 -WT and TP53 -null ( TP53 −/− ) cell lines. Lastly, Cas9 was less active in TP53 -WT than in TP53 -mutant cell lines, and Cas9-induced p53 pathway activation affected cellular sensitivity to both genetic and chemical perturbations. These findings may have broad implications for the proper use of CRISPR–Cas9-mediated genome editing. Cas9 expression induces DNA damage and activates the p53 pathway, and it can lead to the selection of cells with p53-inactivating mutations. Cas9 is less active in wild-type TP53 cell lines than in TP53- mutant cell lines.