University of Maryland, College Park, MD.
Georgetown University, Washington, DC.
JCO Clin Cancer Inform. 2020 Jan;4:71-88. doi: 10.1200/CCI.19.00097.
In this work, we introduce CDGnet (Cancer-Drug-Gene Network), an evidence-based network approach for recommending targeted cancer therapies. CDGnet represents a user-friendly informatics tool that expands the range of targeted therapy options for patients with cancer who undergo molecular profiling by including the biologic context via pathway information.
CDGnet considers biologic pathway information specifically by looking at targets or biomarkers downstream of oncogenes and is personalized for individual patients via user-inputted molecular alterations and cancer type. It integrates a number of different sources of knowledge: patient-specific inputs (molecular alterations and cancer type), US Food and Drug Administration-approved therapies and biomarkers (curated from DailyMed), pathways for specific cancer types (from Kyoto Encyclopedia of Genes and Genomes [KEGG]), gene-drug connections (from DrugBank), and oncogene information (from KEGG). We consider 4 different evidence-based categories for therapy recommendations. Our tool is delivered via an R/Shiny Web application. For the 2 categories that use pathway information, we include an interactive Sankey visualization built on top of d3.js that also provides links to PubChem.
We present a scenario for a patient who has estrogen receptor (ER)-positive breast cancer with amplification. Although many therapies exist for patients with ER-positive breast cancer, amplifications may confer resistance to such treatments. CDGnet provides therapy recommendations, including PIK3CA, MAPK, and RAF inhibitors, by considering targets or biomarkers downstream of FGFR1.
CDGnet provides results in a number of easily accessible and usable forms, separating targeted cancer therapies into categories in an evidence-based manner that incorporates biologic pathway information.
在这项工作中,我们引入了 CDGnet(癌症药物基因网络),这是一种基于证据的网络方法,用于推荐靶向癌症治疗。CDGnet 是一个用户友好的信息学工具,通过纳入途径信息来提供生物背景,从而为接受分子分析的癌症患者扩展靶向治疗选择范围。
CDGnet 特别考虑生物途径信息,通过查看致癌基因下游的靶点或生物标志物,并通过用户输入的分子改变和癌症类型为个体患者进行个性化处理。它整合了多种不同的知识来源:患者特定的输入(分子改变和癌症类型)、美国食品和药物管理局批准的疗法和生物标志物(从 DailyMed 中提取)、特定癌症类型的途径(来自京都基因和基因组百科全书 [KEGG])、基因-药物连接(来自 DrugBank)和致癌基因信息(来自 KEGG)。我们考虑了 4 种不同的基于证据的治疗建议类别。我们的工具通过 R/Shiny Web 应用程序提供。对于使用途径信息的 2 个类别,我们包括了一个基于 d3.js 的交互式 Sankey 可视化,该可视化还提供了到 PubChem 的链接。
我们呈现了一个患有雌激素受体(ER)阳性乳腺癌并有扩增的患者的场景。尽管 ER 阳性乳腺癌患者有许多治疗方法,但扩增可能会导致对这些治疗的耐药性。CDGnet 通过考虑 FGFR1 下游的靶点或生物标志物,提供了包括 PIK3CA、MAPK 和 RAF 抑制剂在内的治疗建议。
CDGnet 以一种基于证据的方式,将靶向癌症治疗方法分为多个易于访问和使用的类别,将生物途径信息纳入其中。