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LFSPROShiny:一个用于预测和可视化具有有害种系突变的家族中癌症风险的交互式 R/Shiny 应用程序。

LFSPROShiny: An Interactive R/Shiny App for Prediction and Visualization of Cancer Risks in Families With Deleterious Germline Mutations.

机构信息

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX.

Department of Statistics, Rice University, Houston, TX.

出版信息

JCO Clin Cancer Inform. 2024 Feb;8:e2300167. doi: 10.1200/CCI.23.00167.

Abstract

PURPOSE

LFSPRO is an R library that implements risk prediction models for Li-Fraumeni syndrome (LFS), a genetic disorder characterized by deleterious germline mutations in the gene. To facilitate the use of these models in clinics, we developed LFSPROShiny, an interactive R/Shiny interface of LFSPRO that allows genetic counselors (GCs) to perform risk predictions without any programming components and further visualize the risk profiles of their patients to aid the decision-making process.

METHODS

LFSPROShiny implements two models that have been validated on multiple LFS patient cohorts: a competing risk model that predicts cancer-specific risks for the first primary and a recurrent-event model that predicts the risk of a second primary tumor. Starting with a visualization template, we keep regular contact with GCs, who ran LFSPROShiny in their counseling sessions, to collect feedback and discuss potential improvement. On receiving the family history as input, LFSPROShiny renders the family into a pedigree and displays the risk estimates of the family members in a tabular format. The software offers interactive overlaid side-by-side bar charts for visualization of the patients' cancer risks relative to the general population.

RESULTS

We walk through a detailed example to illustrate how GCs can run LFSPROShiny in clinics from data preparation to downstream analyses and interpretation of results with an emphasis on the utilities that LFSPROShiny provides to aid decision making.

CONCLUSION

Since December 2021, we have applied LFSPROShiny to over 100 families from counseling sessions at the MD Anderson Cancer Center. Our study suggests that software tools with easy-to-use interfaces are crucial for the dissemination of risk prediction models in clinical settings, hence serving as a guideline for future development of similar models.

摘要

目的

LFSPRO 是一个 R 库,用于实现 Li-Fraumeni 综合征(LFS)的风险预测模型,LFS 是一种遗传疾病,其特征是基因中的有害种系突变。为了促进这些模型在临床中的应用,我们开发了 LFSPROShiny,这是一个 LFSPRO 的交互式 R/Shiny 界面,允许遗传咨询师(GC)在无需任何编程组件的情况下进行风险预测,并进一步可视化患者的风险概况,以帮助决策过程。

方法

LFSPROShiny 实现了两个已在多个 LFS 患者队列中验证过的模型:一个竞争风险模型,用于预测首次原发性癌症的特异性风险,以及一个复发事件模型,用于预测第二个原发性肿瘤的风险。从可视化模板开始,我们与 GC 保持定期联系,他们在咨询会议中运行 LFSPROShiny,收集反馈并讨论潜在的改进。在收到家族史作为输入后,LFSPROShiny 将家族绘制成系谱,并以表格形式显示家族成员的风险估计。该软件提供了交互式并排条形图,用于可视化患者相对于一般人群的癌症风险。

结果

我们通过一个详细的示例来演示 GC 如何在临床中使用 LFSPROShiny,从数据准备到下游分析和结果解释,重点介绍 LFSPROShiny 提供的辅助决策的实用程序。

结论

自 2021 年 12 月以来,我们已经在 MD 安德森癌症中心的咨询会议中应用 LFSPROShiny 对超过 100 个家庭进行了分析。我们的研究表明,具有易于使用界面的软件工具对于在临床环境中传播风险预测模型至关重要,因此可以作为未来类似模型开发的指南。

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