CELLector：基于基因组学的癌症体外模型选择。

CELLector: Genomics-Guided Selection of Cancer In Vitro Models.

机构信息

Open Targets, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK; Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK.

Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen 52057, Germany.

出版信息

Cell Syst. 2020 May 20;10(5):424-432.e6. doi: 10.1016/j.cels.2020.04.007.

DOI:10.1016/j.cels.2020.04.007

PMID:32437684

Abstract

Selecting appropriate cancer models is a key prerequisite for maximizing translational potential and clinical relevance of in vitro oncology studies. We developed CELLector: an R package and R Shiny application allowing researchers to select the most relevant cancer cell lines in a patient-genomic-guided fashion. CELLector leverages tumor genomics to identify recurrent subtypes with associated genomic signatures. It then evaluates these signatures in cancer cell lines to prioritize their selection. This enables users to choose appropriate in vitro models for inclusion or exclusion in retrospective analyses and future studies. Moreover, this allows bridging outcomes from cancer cell line screens to precisely defined sub-cohorts of primary tumors. Here, we demonstrate the usefulness and applicability of CELLector, showing how it can aid prioritization of in vitro models for future development and unveil patient-derived multivariate prognostic and therapeutic markers. CELLector is freely available at https://ot-cellector.shinyapps.io/CELLector_App/ (code at https://github.com/francescojm/CELLector and https://github.com/francescojm/CELLector_App).

摘要

选择合适的癌症模型是最大限度地提高体外肿瘤学研究转化潜力和临床相关性的关键前提。我们开发了 CELLector：一个 R 包和 R Shiny 应用程序，允许研究人员以患者基因组指导的方式选择最相关的癌细胞系。CELLector 利用肿瘤基因组学来识别具有相关基因组特征的复发性亚型。然后，它评估这些特征在癌细胞系中的表达情况，以确定它们的选择优先级。这使用户能够选择合适的体外模型，包括或排除在回顾性分析和未来的研究中。此外，这还可以将癌症细胞系筛选的结果与精确定义的原发性肿瘤亚群联系起来。在这里，我们展示了 CELLector 的有用性和适用性，展示了它如何帮助确定未来开发的体外模型的优先级，并揭示了基于患者的多变量预后和治疗标志物。CELLector 可在 https://ot-cellector.shinyapps.io/CELLector_App/（代码在 https://github.com/francescojm/CELLector 和 https://github.com/francescojm/CELLector_App）上免费获得。