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解析致癌转录特征以生成不同细胞状态的图谱。

Decomposing Oncogenic Transcriptional Signatures to Generate Maps of Divergent Cellular States.

机构信息

Cancer Program, Eli and Edythe Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA 02215, USA.

Cancer Program, Eli and Edythe Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.

出版信息

Cell Syst. 2017 Aug 23;5(2):105-118.e9. doi: 10.1016/j.cels.2017.08.002.

Abstract

The systematic sequencing of the cancer genome has led to the identification of numerous genetic alterations in cancer. However, a deeper understanding of the functional consequences of these alterations is necessary to guide appropriate therapeutic strategies. Here, we describe Onco-GPS (OncoGenic Positioning System), a data-driven analysis framework to organize individual tumor samples with shared oncogenic alterations onto a reference map defined by their underlying cellular states. We applied the methodology to the RAS pathway and identified nine distinct components that reflect transcriptional activities downstream of RAS and defined several functional states associated with patterns of transcriptional component activation that associates with genomic hallmarks and response to genetic and pharmacological perturbations. These results show that the Onco-GPS is an effective approach to explore the complex landscape of oncogenic cellular states across cancers, and an analytic framework to summarize knowledge, establish relationships, and generate more effective disease models for research or as part of individualized precision medicine paradigms.

摘要

癌症基因组的系统测序导致了大量癌症相关遗传改变的发现。然而,为了指导合适的治疗策略,我们有必要更深入地了解这些改变的功能后果。在这里,我们描述了 Onco-GPS(致癌定位系统),这是一个数据驱动的分析框架,可将具有共享致癌改变的个体肿瘤样本组织到由其潜在细胞状态定义的参考图谱上。我们将该方法应用于 RAS 通路,并鉴定出九个不同的成分,这些成分反映了 RAS 下游的转录活性,并定义了几个与转录成分激活模式相关的功能状态,这些状态与基因组标志和对遗传和药理学扰动的反应有关。这些结果表明,Onco-GPS 是一种探索癌症中致癌细胞状态复杂景观的有效方法,也是一种分析框架,可以总结知识、建立关系,并为研究或作为个体化精准医疗范例的一部分生成更有效的疾病模型。

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