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Genetic screens in cancer cell lines inform gene function and drug discovery. More comprehensive screen datasets with multi-omics data are needed to enhance opportunities to functionally map genetic vulnerabilities. Here, we construct a second-generation map of cancer dependencies by annotating 930 cancer cell lines with multi-omic data and analyze relationships between molecular markers and cancer dependencies derived from CRISPR-Cas9 screens. We identify dependency-associated gene expression markers beyond driver genes, and observe many gene addiction relationships driven by gain of function rather than synthetic lethal effects. By combining clinically informed dependency-marker associations with protein-protein interaction networks, we identify 370 anti-cancer priority targets for 27 cancer types, many of which have network-based evidence of a functional link with a marker in a cancer type. Mapping these targets to sequenced tumor cohorts identifies tractable targets in different cancer types. This target prioritization map enhances understanding of gene dependencies and identifies candidate anti-cancer targets for drug development.
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Testes Genéticos , Neoplasias , Humanos , Fenótipo , Descoberta de Drogas , Neoplasias/genética , Neoplasias/patologia , Linhagem Celular Tumoral , Sistemas CRISPR-CasRESUMO
CRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated the two largest public independent CRISPR-Cas9 screens performed to date (at the Broad and Sanger institutes) by assessing, comparing, and selecting methods for correcting biases due to heterogeneous single-guide RNA efficiency, gene-independent responses to CRISPR-Cas9 targeting originated from copy number alterations, and experimental batch effects. Our integrated datasets recapitulate findings from the individual datasets, provide greater statistical power to cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. We provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets.
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Neoplasias/genética , Biomarcadores Tumorais , Sistemas CRISPR-Cas , Linhagem Celular Tumoral , Repetições Palindrômicas Curtas Agrupadas e Regularmente Espaçadas , Variações do Número de Cópias de DNA , Genes Essenciais/genética , Genômica/métodos , Humanos , RNA Guia de Cinetoplastídeos/genéticaRESUMO
Low success rates during drug development are due, in part, to the difficulty of defining drug mechanism-of-action and molecular markers of therapeutic activity. Here, we integrated 199,219 drug sensitivity measurements for 397 unique anti-cancer drugs with genome-wide CRISPR loss-of-function screens in 484 cell lines to systematically investigate cellular drug mechanism-of-action. We observed an enrichment for positive associations between the profile of drug sensitivity and knockout of a drug's nominal target, and by leveraging protein-protein networks, we identified pathways underpinning drug sensitivity. This revealed an unappreciated positive association between mitochondrial E3 ubiquitin-protein ligase MARCH5 dependency and sensitivity to MCL1 inhibitors in breast cancer cell lines. We also estimated drug on-target and off-target activity, informing on specificity, potency and toxicity. Linking drug and gene dependency together with genomic data sets uncovered contexts in which molecular networks when perturbed mediate cancer cell loss-of-fitness and thereby provide independent and orthogonal evidence of biomarkers for drug development. This study illustrates how integrating cell line drug sensitivity with CRISPR loss-of-function screens can elucidate mechanism-of-action to advance drug development.
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Antineoplásicos/farmacologia , Sistemas CRISPR-Cas , Desenvolvimento de Medicamentos/métodos , Ensaios de Seleção de Medicamentos Antitumorais/métodos , Redes Reguladoras de Genes/efeitos dos fármacos , Aptidão Genética/efeitos dos fármacos , Mapas de Interação de Proteínas/efeitos dos fármacos , Antineoplásicos/toxicidade , Biomarcadores/metabolismo , Linhagem Celular Tumoral , Técnicas de Inativação de Genes , Redes Reguladoras de Genes/genética , Aptidão Genética/genética , Genômica , Humanos , Modelos Lineares , Proteínas de Membrana/genética , Proteínas de Membrana/metabolismo , Proteína de Sequência 1 de Leucemia de Células Mieloides/antagonistas & inibidores , Preparações Farmacêuticas/metabolismo , Software , Ubiquitina-Proteína Ligases/genética , Ubiquitina-Proteína Ligases/metabolismoRESUMO
Drug sensitivity testing utilizing preclinical disease models such as cancer cell lines is an important and widely used tool for drug development. Importantly, when combined with molecular data such as gene copy number variation or somatic coding mutations, associations between drug sensitivity and molecular data can be used to develop markers to guide patient therapies. The use of organoids as a preclinical cancer model has become possible following recent work demonstrating that organoid cultures can be derived from patient tumors with a high rate of success. A genetic analysis of colon cancer organoids found that these models encompassed the majority of the somatic variants present within the tumor from which it was derived, and capture much of the genetic diversity of colon cancer observed in patients. Importantly, the systematic sensitivity testing of organoid cultures to anticancer drugs identified clinical gene-drug interactions, suggestive of their potential as preclinical models for testing anticancer drug sensitivity. In this chapter, we describe how to perform medium/high-throughput drug sensitivity screens using 3D organoid cell cultures.
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Antineoplásicos/farmacologia , Proliferação de Células/efeitos dos fármacos , Neoplasias do Colo/patologia , Ensaios de Seleção de Medicamentos Antitumorais/métodos , Neoplasias Esofágicas/patologia , Técnicas de Cultura de Órgãos/métodos , Organoides/patologia , Neoplasias do Colo/tratamento farmacológico , Neoplasias Esofágicas/tratamento farmacológico , Humanos , Organoides/efeitos dos fármacosRESUMO
The protocol Drug Sensitivity Assays of Human Cancer Organoid Cultures has now been made available open access under a CC BY 4.0 license.
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This corrects the article DOI: 10.1038/sdata.2017.139.
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Esophageal adenocarcinoma (EAC) incidence is increasing while 5-year survival rates remain less than 15%. A lack of experimental models has hampered progress. We have generated clinically annotated EAC organoid cultures that recapitulate the morphology, genomic, and transcriptomic landscape of the primary tumor including point mutations, copy number alterations, and mutational signatures. Karyotyping of organoid cultures has confirmed polyclonality reflecting the clonal architecture of the primary tumor. Furthermore, subclones underwent clonal selection associated with driver gene status. Medium throughput drug sensitivity testing demonstrates the potential of targeting receptor tyrosine kinases and downstream mediators. EAC organoid cultures provide a pre-clinical tool for studies of clonal evolution and precision therapeutics.
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Adenocarcinoma/tratamento farmacológico , Evolução Clonal , Neoplasias Esofágicas/tratamento farmacológico , Organoides/química , Receptores Proteína Tirosina Quinases/genética , Adenocarcinoma/metabolismo , Idoso , Idoso de 80 Anos ou mais , Variações do Número de Cópias de DNA , Análise Mutacional de DNA , Ensaios de Seleção de Medicamentos Antitumorais , Neoplasias Esofágicas/metabolismo , Feminino , Humanos , Concentração Inibidora 50 , Cariotipagem , Masculino , Pessoa de Meia-Idade , Mutação , Medicina de Precisão , Análise de Sequência de RNA , TranscriptomaRESUMO
Metastatic colorectal cancer is a leading cause of cancer death. However, current therapy options are limited to chemotherapy, with the addition of anti-EGFR antibodies for patients with RAS wild-type tumours. Novel drug targets, or drug combinations that induce a synergistic response, would be of great benefit to patients. The identification of genes that are essential for cell survival can be undertaken using functional genomics screens. Furthermore, performing such screens in the presence of a targeted agent would allow the identification of combinations that result in a synthetic lethal interaction. Here, we present a dataset containing the results of a large scale RNAi screen (815 genes) to detect essential genes as well as synergistic combinations with targeted therapeutic agents using a panel of 27 colorectal cancer cell lines. These data identify genes that are essential for colorectal cancer cell survival as well as synthetic lethal treatment combinations using novel computational approaches. Moreover, this dataset could be utilised in combination with genomic profiling to identify predictive biomarkers of response.
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Neoplasias Colorretais , Biomarcadores Tumorais , Linhagem Celular Tumoral , Neoplasias Colorretais/tratamento farmacológico , Neoplasias Colorretais/genética , Sinergismo Farmacológico , Genes Essenciais , Humanos , Interferência de RNARESUMO
The inter- and intra-tumor heterogeneity of breast cancer needs to be adequately captured in pre-clinical models. We have created a large collection of breast cancer patient-derived tumor xenografts (PDTXs), in which the morphological and molecular characteristics of the originating tumor are preserved through passaging in the mouse. An integrated platform combining in vivo maintenance of these PDTXs along with short-term cultures of PDTX-derived tumor cells (PDTCs) was optimized. Remarkably, the intra-tumor genomic clonal architecture present in the originating breast cancers was mostly preserved upon serial passaging in xenografts and in short-term cultured PDTCs. We assessed drug responses in PDTCs on a high-throughput platform and validated several ex vivo responses in vivo. The biobank represents a powerful resource for pre-clinical breast cancer pharmacogenomic studies (http://caldaslab.cruk.cam.ac.uk/bcape), including identification of biomarkers of response or resistance.