Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening

McDonald, Ellice; DeWeck, Antoine; Schlabach, Michael R.; Billy, Éric; Mavrakis, Konstantinos J.; Hoffman, Gregory R.; Belur, Dhiren; Castelletti, Deborah; Frias, Elizabeth; Gampa, Kalyani; Golji, Javad; Kao, Iris; Li, Li; Megel, Philippe; Perkins, Thomas A.; Ramadan, Nadire; Ruddy, David A.; Silver, Serena J.; Sovath, Sosathya; Stump, Mark; Weber, Odile; Widmer, Roland; Yu, Jianjun; Yu, Kristine; Yue, Yiyang; Abramowski, Dorothée; Ackley, Elizabeth; Barrett, Rosemary; Berger, Joel P.; Bernard, Julie L.; Billig, Rebecca; Brachmann, Saskia M.; Buxton, Frank P.; Caothien, Roger; Caushi, Justina X.; Chung, Franklin; Cortés-Cros, Marta; DeBeaumont, Rosalie; Delaunay, Clara; Desplat, Aurore; Duong, William; Dwoske, Donald A.; Eldridge, Richard S.; Farsidjani, Ali; Feng, Fan; Feng, Jiajia; Flemming, Daisy; Forrester, William C.; Galli, Giorgio; Gao, Zhenhai; Gauter, François; Gibaja, Veronica; Haas, Kristy; Hattenberger, Marc; Hood, Tami; Hurov, Kristen E.; Jagani, Zainab; Jenal, Mathias; Johnson, Jennifer A.; Jones, Michael D.; Kapoor, Avnish; Korn, Joshua M.; Liu, Jilin; Liu, Qiumei; Liu, Shumei; Liu, Yue; Loo, Alice; Macchi, Kaitlin J.; Martin, Typhaine; McAllister, Gregory; Meyer, Amandine; Mollé, Sandra; Pagliarini, Raymond; Phadke, Tanushree; Repko, Brian; Schouwey, Tanja; Shanahan, Fergus; Shen, Qiong; Stamm, Christelle; Stephan, Christine; Stucke, Volker M.; Tiedt, Ralph; Varadarajan, Malini; Venkatesan, Kavitha; Vitari, Alberto C.; Wallroth, Marco; Weiler, Jan; Zhang, Jing; Mickanin, Craig; Myer, Vic E.; Porter, Jeffery A.; Lai, Albert; Bitter, Hans; Lees, Emma; Keen, Nicholas; Kauffmann, Audrey; Stegmeier, Frank; Hofmann, Francesco; Schmelzle, Tobias; Sellers, William R.

doi:10.1016/j.cell.2017.07.005

Cited by 572 publications

(663 citation statements)

References 56 publications

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“…organoids, mice), human cancer cell lines will for a long time remain the most readily accessible options for understanding the molecular basis of oncogenesis. Cell lines have notably proven to be useful for testing drug efficacy [10] and identifying synthetic lethality [12]. Therefore, a thorough characterization of the shared molecular signatures between HCC cell lines and the counterpart primary tumours is highly needed for defining core and novel alterations that can be investigated in vitro with the highest prospect of clinical translation.…”

Section: Introductionmentioning

confidence: 99%

Liver cancer cell lines distinctly mimic the metabolic gene expression pattern of the corresponding human tumours

Nwosu

Battello²,

Rothley

et al. 2018

J Exp Clin Cancer Res

108

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BackgroundAlthough metabolism is profoundly altered in human liver cancer, the extent to which experimental models, e.g. cell lines, mimic those alterations is unresolved. Here, we aimed to determine the resemblance of hepatocellular carcinoma (HCC) cell lines to human liver tumours, specifically in the expression of deregulated metabolic targets in clinical tissue samples.MethodsWe compared the overall gene expression profile of poorly-differentiated (HLE, HLF, SNU-449) to well-differentiated (HUH7, HEPG2, HEP3B) HCC cell lines in three publicly available microarray datasets. Three thousand and eighty-five differentially expressed genes in ≥2 datasets (P < 0.05) were used for pathway enrichment and gene ontology (GO) analyses. Further, we compared the topmost gene expression, pathways, and GO from poorly differentiated cell lines to the pattern from four human HCC datasets (623 tumour tissues). In well- versus poorly differentiated cell lines, and in representative models HLE and HUH7 cells, we specifically assessed the expression pattern of 634 consistently deregulated metabolic genes in human HCC. These data were complemented by quantitative PCR, proteomics, metabolomics and assessment of response to thirteen metabolism-targeting compounds in HLE versus HUH7 cells.ResultsWe found that poorly-differentiated HCC cells display upregulated MAPK/RAS/NFkB signaling, focal adhesion, and downregulated complement/coagulation cascade, PPAR-signaling, among pathway alterations seen in clinical tumour datasets. In HLE cells, 148 downregulated metabolic genes in liver tumours also showed low gene/protein expression – notably in fatty acid β-oxidation (e.g. ACAA1/2, ACADSB, HADH), urea cycle (e.g. CPS1, ARG1, ASL), molecule transport (e.g. SLC2A2, SLC7A1, SLC25A15/20), and amino acid metabolism (e.g. PHGDH, PSAT1, GOT1, GLUD1). In contrast, HUH7 cells showed a higher expression of 98 metabolic targets upregulated in tumours (e.g. HK2, PKM, PSPH, GLUL, ASNS, and fatty acid synthesis enzymes ACLY, FASN). Metabolomics revealed that the genomic portrait of HLE cells co-exist with profound reliance on glutamine to fuel tricarboxylic acid cycle, whereas HUH7 cells use both glucose and glutamine. Targeting glutamine pathway selectively suppressed the proliferation of HLE cells.ConclusionsWe report a yet unappreciated distinct expression pattern of clinically-relevant metabolic genes in HCC cell lines, which could enable the identification and therapeutic targeting of metabolic vulnerabilities at various liver cancer stages.Electronic supplementary materialThe online version of this article (10.1186/s13046-018-0872-6) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Liver cancer cell lines distinctly mimic the metabolic gene expression pattern of the corresponding human tumours

Nwosu

Battello²,

Rothley

et al. 2018

J Exp Clin Cancer Res

108

View full text Add to dashboard Cite

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“…We retrieved cancer dependency and synthetic lethal relationship data from Project Drive 46 and calculated the dependency of EZH2, SUZ12 and EED in three different breast cancer cell lines, MDA-MB-453 (MLL3-PHD domain mutation), MDA-MB-463 (MLL3-WT) and T47D (MLL3-Non-PHD domain mutation). Cells with MLL3-PHD domain mutation are more sensitive to the depletion of all the three PRC2 components compared to MLL3-WT and –non PHD domain mutation cell lines (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The plasmid and sample raw counts of sgRNA for cell lines from Project DRIVE 46 were downloaded and analyzed using an in-house script. The plasmid and sample raw counts per shRNA were normalized in pairs using Trimmed Mean of M-values (TMM) normalization.…”

Section: Methodsmentioning

confidence: 99%

Resetting the epigenetic balance of Polycomb and COMPASS function at enhancers for cancer therapy

Wang

Zhao

Ozark

et al. 2018

Nat Med

134

132

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MLL3 (also named KMT2C) is a COMPASS subunit that implements H3K4 mono-methylation at gene enhancers. KMT2C frequently incurs point-mutations across a range of human tumors, nevertheless precisely how these lesions alter MLL3 function and contribute to oncogenesis is unclear. Here we report a cancer mutational hotspot in MLL3 within its Plant Homeo Domain (PHD) repeats and demonstrate that this domain mediates association with the histone H2A deubiquitinase and tumor suppressor BAP1. Cancer-associated MLL3 PHD mutations disrupt the interaction between MLL3 and BAP1 and correlate with poor patient survival. Cancer cells bearing MLL3 PHD mutations or lacking BAP1, exhibit reduced enhancer recruitment of MLL3 and the H3K27 demethylase UTX (KDM6A). As the result, inhibiting the H3K27 methyltransferase activity of polycomb repressor complex 2 (PRC2) in tumor cells harboring BAP1 or MLL3 mutations, restores normal gene expression patterns and impairs cell proliferation in vivo. This study provides mechanistic insight for the role of MLL3 PHD mutations in cancer and points to restoration of the balanced state of polycomb-COMPASS for the treatment of cancers resulting from mutations in these epigenetic factors.

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“…We and others have observed that cancer cell lines exhibit highly variable genetic dependencies for cellular fitness (Bertomeu et al, 2018; Blomen et al, 2015; Hart et al, 2015; Hart et al, 2017a; McDonald et al, 2017; Meyers et al, 2017; Tsherniak et al, 2017; Wang et al, 2015; Wang et al, 2017b) in a manner that reflects the diverse genomic and transcriptomic alterations a cell may accumulate during tumorigenesis. Project Achilles (Broad Institute of MIT and Harvard) seeks to systematically map genetic vulnerabilities across large collections of cancer cell lines, including the Cancer Cell Line Encyclopedia (CCLE) (Barretina et al, 2012), and has recently performed genome-scale perturbation screens in 501 cancer cell lines using RNA interference (RNAi) (Tsherniak et al, 2017) and in 342 cancer cell lines using CRISPR-Cas9 (Meyers et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Interrogation of Mammalian Protein Complex Structure, Function, and Membership Using Genome-Scale Fitness Screens

et al. 2018

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Summary Protein complexes are assemblies of subunits that have co-evolved to execute one or many coordinated functions in the cellular environment. Functional annotation of mammalian protein complexes is critical to understanding biological processes as well as disease mechanisms. Here, we used genetic co-essentiality derived from genome-scale RNAi- and CRISPR-Cas9- based fitness screens performed across hundreds of human cancer cell lines to assign measures of functional similarity. From these measures, we systematically built and characterized functional similarity networks which recapitulate known structural and functional features of well-studied protein complexes and resolve novel functional modules within complexes lacking structural resolution, such as the mammalian SWI/SNF complex. Finally, by integrating functional networks with large protein-protein interaction networks, we discovered novel protein complexes involving recently-evolved genes of unknown function. Taken together, these findings demonstrate the utility of genetic perturbation screens alone and in combination with large-scale biophysical data to enhance our understanding of mammalian protein complexes in normal and disease states.

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Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening

Cited by 572 publications

References 56 publications

Liver cancer cell lines distinctly mimic the metabolic gene expression pattern of the corresponding human tumours

Liver cancer cell lines distinctly mimic the metabolic gene expression pattern of the corresponding human tumours

Resetting the epigenetic balance of Polycomb and COMPASS function at enhancers for cancer therapy

Interrogation of Mammalian Protein Complex Structure, Function, and Membership Using Genome-Scale Fitness Screens

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