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引用本文的文献

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Validating Risk Prediction Models for Multiple Primaries and Competing Cancer Outcomes in Families With Li-Fraumeni Syndrome Using Clinically Ascertained Data.利用临床确定的数据验证 Li-Fraumeni 综合征家族中多种原发性和竞争癌症结局的风险预测模型。
J Clin Oncol. 2024 Jun 20;42(18):2186-2195. doi: 10.1200/JCO.23.01926. Epub 2024 Apr 3.

本文引用的文献

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Validating Risk Prediction Models for Multiple Primaries and Competing Cancer Outcomes in Families With Li-Fraumeni Syndrome Using Clinically Ascertained Data.利用临床确定的数据验证 Li-Fraumeni 综合征家族中多种原发性和竞争癌症结局的风险预测模型。
J Clin Oncol. 2024 Jun 20;42(18):2186-2195. doi: 10.1200/JCO.23.01926. Epub 2024 Apr 3.
2
Unique Transcriptional Profiles Underlie Osteosarcomagenesis Driven by Different p53 Mutants.不同 p53 突变驱动的成骨肉瘤发生的独特转录特征。
Cancer Res. 2023 Jul 14;83(14):2297-2311. doi: 10.1158/0008-5472.CAN-22-3464.
3
Fundamental immune-oncogenicity trade-offs define driver mutation fitness.基本的免疫肿瘤学权衡定义了驱动突变的适应性。
Nature. 2022 Jun;606(7912):172-179. doi: 10.1038/s41586-022-04696-z. Epub 2022 May 11.
4
The Common Germline Mutation Is Hypomorphic and Confers Incomplete Penetrance and Late Tumor Onset in a Mouse Model.常见的种系突变是功能不全的,并在小鼠模型中导致不完全外显和肿瘤晚期发生。
Cancer Res. 2021 May 1;81(9):2442-2456. doi: 10.1158/0008-5472.CAN-20-1750. Epub 2021 Feb 26.
5
Penetrance of Different Cancer Types in Families with Li-Fraumeni Syndrome: A Validation Study Using Multicenter Cohorts.家族性李-佛美尼综合征中不同癌症类型的外显率:多中心队列的验证研究。
Cancer Res. 2020 Jan 15;80(2):354-360. doi: 10.1158/0008-5472.CAN-19-0728. Epub 2019 Nov 12.
6
Penetrance Estimates Over Time to First and Second Primary Cancer Diagnosis in Families with Li-Fraumeni Syndrome: A Single Institution Perspective.Li-Fraumeni 综合征家系中首次和二次原发癌诊断的穿透率估计:单机构视角。
Cancer Res. 2020 Jan 15;80(2):347-353. doi: 10.1158/0008-5472.CAN-19-0725. Epub 2019 Nov 12.
7
Bayesian Semiparametric Estimation of Cancer-specific Age-at-onset Penetrance with Application to Li-Fraumeni Syndrome.癌症特异性发病年龄外显率的贝叶斯半参数估计及其在李-佛美尼综合征中的应用
J Am Stat Assoc. 2019;114(526):541-552. doi: 10.1080/01621459.2018.1482749. Epub 2018 Aug 15.
8
Bayesian estimation of a semiparametric recurrent event model with applications to the penetrance estimation of multiple primary cancers in Li-Fraumeni syndrome.贝叶斯估计半参数复发事件模型及其在 Li-Fraumeni 综合征中多种原发性癌症外显率估计中的应用。
Biostatistics. 2020 Jul 1;21(3):467-482. doi: 10.1093/biostatistics/kxy066.
9
Mutational processes shape the landscape of TP53 mutations in human cancer.突变过程塑造了人类癌症中 TP53 突变的景观。
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Estimating Mutation Carrier Probability in Families with Li-Fraumeni Syndrome Using LFSPRO.使用LFSPRO估计李-佛美尼综合征家族中的突变携带者概率。
Cancer Epidemiol Biomarkers Prev. 2017 Jun;26(6):837-844. doi: 10.1158/1055-9965.EPI-16-0695. Epub 2017 Jan 30.

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.

DOI:10.1200/CCI.23.00167
PMID:38346271
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10871774/
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。