Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens

Behan, Fiona M.; Iorio, Francesco; Picco, Gabriele; Gonçalvès, Emanuel; Beaver, Charlotte; Migliardi, Giorgia; Santos, Rita; Rao, Yanhua; Sassi, Francesco; Pinnelli, Marika; Ansari, Rizwan; Harper, Sarah; Jackson, David A.; McRae, Rebecca; Pooley, Rachel; Wilkinson, Piers; Meer, Dieudonne van der; Dow, David; Buser-Doepner, Carolyn; Bertotti, Andrea; Trusolino, Livio; Stronach, Euan A.; Sáez-Rodríguez, Julio; Yusa, Kosuke; Garnett, Mathew J.

doi:10.1038/s41586-019-1103-9

Cited by 1,061 publications

(1,420 citation statements)

References 47 publications

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“…Indeed, the analysis of the mutational landscape of cancer has also uncovered the existence of mutual exclusivity and co-occurrence patterns among driver gene alterations [16,69]. Many computational tools have been developed to identify those combinatorial patterns experimentally (i.e via CRISPR-Cas9 screens [70,71]) or computationally [72][73][74][75][76][77][78][79]. Patterns of mutual exclusivity can arise from functional redundancy, context-specific dependencies (i.e tumor type or sub-type specific driving alterations), or synthetic lethality interactions.…”

Section: Resultsmentioning

confidence: 99%

“…Patterns of mutual exclusivity can arise from functional redundancy, context-specific dependencies (i.e tumor type or sub-type specific driving alterations), or synthetic lethality interactions. While functional redundancy has been used to reveal unknown functional interactions [79], the synthetic lethality concept has been very successfully applied to the identification of novel therapeutic targets [70,71] or rational drug combinations [71], and to the prediction of drug response in cell lines [71] and patients [78].…”

Section: Resultsmentioning

confidence: 99%

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Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns

Mateo

Duran‐Frigola

Gris-Oliver³

et al. 2019

Preprint

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Molecular profiling of personal cancer genomes, and the identification of actionable vulnerabilities and drug-response biomarkers, are the basis of precision oncology. Tumors often present several driver alterations that might be connected by cross-talk and feedback mechanisms, making it difficult to mark single oncogenic variations as reliable predictors of therapeutic outcome. In the current work, we uncover and exploit driver alteration cooccurrence patterns from a recently published in vivo screening in patient-derived xenografts (PDXs), including 187 tumors and 53 drugs. For each treatment, we compare the mutational profiles of sensitive and resistant PDXs to statistically define Driver Co-Occurrence (DCO) networks, which capture both genomic structure and putative oncogenic synergy. We then use the DCO networks to train classifiers that can prioritize, among the available options, the best possible treatment for each tumor based on its oncogenomic profile. In a cross-validation setting, our drug-response models are able to correctly predict 66% of sensitive and 77% of resistant drug-tumor pairs, based on tumor growth variation. Perhaps more interesting, our models are applicable to several tumor types and drug classes for which no biomarker has yet been described. Additionally, we experimentally Grant Support L.M. is a recipient of an FPI fellowship. P.A. acknowledges the support of the Spanish Ministerio de Economía y Competitividad (BIO2016-77038-R) and the European Research Council (SysPharmAD: 614944). V.S. is recipient of a Miguel Servet grant from ISCIII (CP14/00228) and receives funds from AGAUR (2017 SGR 540). The PDX program is supported by a GHD-Pink (FERO foundation) grant to V.S.. A.G.-O. and M.P. received a FI-AGAUR and a Juan de la Cierva (MJCI-2015-25412) fellowship, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns

Mateo

Duran‐Frigola

Gris-Oliver³

et al. 2019

Preprint

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“…PC has been used to analyze transcriptomics, proteomics, and metabolomics data in a large number of projects across diseases to further our understanding of human biology in health and disease (4,(11)(12)(13)(14)(15)(16)(17)(18) . Since our original report in 2011, significant advances have been made with regard to the breadth and volume of data available along with software tools to support data creation, validation, and accessibility in the wider research community.…”

Section: And the Proteomics Standards Initiative Molecularmentioning

confidence: 99%

Pathway Commons: 2019 Update

Rodchenkov

Babur

Luna

et al. 2019

Preprint

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Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g., metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (1) tools to search this comprehensive resource, (2) a download site offering integrated bulk sets of pathway data (e.g., tables of interactions and gene sets), (3) reusable software libraries for working with pathway information in several programming languages (Java, R, Python, and Javascript), and (4) a web service for programmatically querying the entire dataset.Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4,794 detailed human biochemical processes (i.e., pathways) and~2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.

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“…Targeted drug therapy has become a major cancer treatment beyond surgery, radiation therapy, chemotherapy and immunotherapy 1 . Validating primary targets and developing targeted drugs has received increasing attention 2 . In concert, over the last 20 years, the kinase family has been recognized as important drug targets [3][4] .…”

Section: Introductionmentioning

confidence: 99%

Revealing acquired resistance mechanisms of kinase-targeted drugs using an on-the-fly, function-site interaction fingerprint approach

Zhao

Bourne

2019

Preprint

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Although kinase-targeted drugs have achieved significant clinical success, they are frequently subject to the limitations of drug resistance, which has become a primary vulnerability to targeted drug therapy. Therefore, deciphering resistance mechanisms is an important step in designing more efficacious, anti-resistant, drugs. Here we studied two FDA-approved kinase drugs: Crizotinib and Ceritinib, which are first-and second-generation anaplastic lymphoma kinase (ALK) targeted inhibitors, to unravel drug-resistance mechanisms. We used an on-the-fly, function-site interaction fingerprint (on-the-fly Fs-IFP) approach by combining binding free energy surface calculations with the Fs-IFPs. Establishing the potentials of mean force and monitoring the atomic-scale protein-ligand interactions, before and after the L1196M-induced drug resistance, revealed insights into drug-resistance/anti-resistant mechanisms. Crizotinib prefers to bind the wild type ALK kinase domain, whereas Ceritinib binds more favorably to the mutated ALK kinase domain, in agreement with experimental results. We determined that ALK kinase-drug interactions in the region of the front pocket are associated with drug resistance.Additionally, we find that the L1196M mutation does not simply alter the binding modes of inhibitors, but also affects the flexibility of the entire ALK kinase domain. Our work provides an understanding of the mechanisms of ALK drug resistance, confirms the usefulness of the on-thefly Fs-IFP approach and provides a practical paradigm to study drug-resistance mechanisms in prospective drug discovery.

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Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens

Cited by 1,061 publications

References 47 publications

Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns

Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns

Pathway Commons: 2019 Update

Revealing acquired resistance mechanisms of kinase-targeted drugs using an on-the-fly, function-site interaction fingerprint approach

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