Novel Tools for Familial Risk Prediction
家族风险预测的新工具
基本信息
- 批准号:8530798
- 负责人:
- 金额:$ 22.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2013
- 资助国家:美国
- 起止时间:2013-03-01 至 2015-02-28
- 项目状态:已结题
- 来源:
- 关键词:AccountingAddressAlgorithmsAttentionBRCA2 geneBehavioralBiological MarkersBreastCancer InterventionCancer PatientClinicalClinical ManagementColonoscopyColorectal CancerComputer softwareCounselingDana-Farber Cancer InstituteDataData CollectionDecision MakingDevelopmentDisciplineDiseaseDisorder by SiteEarly DiagnosisEarly treatmentEnvironmentFamilyFamily history ofFoundationsFundingFutureGenesGeneticGenetic screening methodGoalsGrantIndividualInformaticsInheritedInstitutesInterventionMalignant NeoplasmsMalignant neoplasm of ovaryMalignant neoplasm of pancreasMedicineMeta-AnalysisMethodsModelingMutationOncogenesPatientsPenetrancePopulationPredispositionPreventionPublishingQuantitative EvaluationsRecording of previous eventsResearchResearch InfrastructureResearch PersonnelResourcesReview LiteratureRiskRisk AssessmentRisk FactorsRisk ManagementSkin CancerStatistical MethodsSyndromeTestingTrainingTranslatingValidationbasecancer preventioncancer riskcancer sitecancer typeclinical applicationhigh riskimprovedinterestmalignant breast neoplasmmedical specialtiesneoplasm registrynovelnovel strategiesopen sourceprogramspublic health relevancescreeningsoftware developmenttool
项目摘要
DESCRIPTION (provided by applicant): Challenges: The vast majority of individuals in the developed world have a family history of at least one type of cancer. Aside from major cancer syndromes where family histories point clearly towards a specific cancer site, family history information is not systematically used for the purpose of managing risk, of wisely using genetic testing, and of improving prevention practices. Strong evidence is emerging that syndromes once thought to be distinct, are overlapping in terms of the cancer site, and that several genetic factors increase the risk of multiple cancers. This opens important opportunities for screening and management of risk across clinical disciplines. A critical obstacle is the lack of software infrastructures and analytical approaches for capturing family history information across a large number of disease sites, for assessing whether the occurrence of multiple cancers in a family is likely to be random or hereditary; and for translating family history across multiple disease sites
data into useful clinical decision tools. Aims: Investigators in this proposal have developed the most detailed, accurate, and widely used tools for the breast-ovarian, colorectal, pancreatic, and skin cancer syndromes and the most widely used clinical tools to implement them, including CancerGene and HRA. All are freely available for research. The overall goal of this proposal is to lay the informatics and statistical foundations for both model implementation and clinical application of more comprehensive approaches. This cannot simply be addressed by juxtaposing software and algorithms that have been successful for single-syndrome models, but it requires novel strategies. Specifically, AIM 1 Is to develop software, including a) a general purpose open source risk calculator that can cover simultaneously an arbitrary number of cancer sites and, at the individual level, cancer-specific biomarkers, preventative interventions, and covariates; and b) tools for the implementation of the calculator in both primary and high risk clinical environments. AIM 2 is to develop statistical methods to estimate the population parameters required by the general purpose calculator. AIM 3 is to develop a proof-of-principle model covering about 10 disease sites, based on a comprehensive literature review of penetrance, interventions, and cancer markers. This will allow testing and troubleshooting of the clinical implementation and permit quantification of the benefits of clinical approaches using information across clinical disciplines. Impact: This research will have a direct impact by generating freely available computational and methodological resources for developing and implementing models that consider multiple syndromes. The hypothesis behind this proposal is that making these tools available can have a significant effect on: what data is collected; what use is made of this data across disease-specific programs; and whether individuals at increased risk receive appropriate attention in both early detection and treatment.
描述(由申请人提供): 挑战:发达国家的绝大多数人都有至少一种癌症的家族史。除了家族史明确指向特定癌症部位的主要癌症综合征外,家族史信息并未系统地用于风险管理、明智地使用基因检测和改进预防实践的目的。强有力的证据表明,曾经被认为是不同的综合征在癌症部位方面是重叠的,并且一些遗传因素会增加患多种癌症的风险。这为跨临床学科的风险筛查和管理提供了重要机会。一个关键障碍是缺乏软件基础设施和分析方法来捕获大量疾病地点的家族史信息,以评估一个家庭中多种癌症的发生是否可能是随机的或遗传性的;以及跨多个疾病部位的家族史翻译
将数据转化为有用的临床决策工具。 目标:本提案中的研究人员开发了最详细、准确且广泛使用的乳腺癌-卵巢癌、结直肠癌、胰腺癌和皮肤癌综合征工具以及最广泛使用的临床工具来实施这些工具,包括 CancerGene 和 HRA。所有这些都可以免费用于研究。该提案的总体目标是为更全面的方法的模型实施和临床应用奠定信息学和统计基础。这不能简单地通过并置已成功用于单一综合征模型的软件和算法来解决,而是需要新颖的策略。具体来说,目标 1 是开发软件,包括 a) 通用开源风险计算器,可以同时覆盖任意数量的癌症部位,以及在个体水平上的癌症特异性生物标志物、预防干预措施和协变量; b) 用于在初级和高风险临床环境中实施计算器的工具。目标 2 是开发统计方法来估计通用计算器所需的总体参数。 AIM 3 旨在基于外显率、干预措施和癌症标志物的综合文献综述,开发一个涵盖约 10 个疾病部位的原理验证模型。这将允许对临床实施进行测试和故障排除,并允许使用跨临床学科的信息来量化临床方法的好处。 影响:这项研究将通过生成免费的计算和方法资源来开发和实施考虑多种综合症的模型,从而产生直接影响。该提案背后的假设是,提供这些工具可以对以下方面产生重大影响:收集哪些数据;这些数据在特定疾病项目中有何用途;以及高危人群是否在早期发现和治疗方面得到适当的关注。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Giovanni Luigi PARMIGIANI其他文献
Giovanni Luigi PARMIGIANI的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Giovanni Luigi PARMIGIANI', 18)}}的其他基金
Statistical methods for cancer mutational signatures
癌症突变特征的统计方法
- 批准号:
10662461 - 财政年份:2021
- 资助金额:
$ 22.5万 - 项目类别:
Statistical methods for cancer mutational signatures
癌症突变特征的统计方法
- 批准号:
10278549 - 财政年份:2021
- 资助金额:
$ 22.5万 - 项目类别:
Statistical methods for cancer mutational signatures
癌症突变特征的统计方法
- 批准号:
10439883 - 财政年份:2021
- 资助金额:
$ 22.5万 - 项目类别:
Bioinformatics Tools for Genomic Analysis of Tumor and Stromal Pathways in Cancer
用于癌症肿瘤和基质途径基因组分析的生物信息学工具
- 批准号:
8606837 - 财政年份:2013
- 资助金额:
$ 22.5万 - 项目类别:
Bioinformatics Tools for Genomic Analysis of Tumor and Stromal Pathways in Cancer
用于癌症肿瘤和基质途径基因组分析的生物信息学工具
- 批准号:
8458359 - 财政年份:2013
- 资助金额:
$ 22.5万 - 项目类别:
Core 5 - Biostatistics and Bioinformatics Core
核心 5 - 生物统计学和生物信息学核心
- 批准号:
10555739 - 财政年份:2011
- 资助金额:
$ 22.5万 - 项目类别:
Predoctoral Biostatistics Training in Genesis/Genomics
创世纪/基因组学博士前生物统计学培训
- 批准号:
7123200 - 财政年份:2006
- 资助金额:
$ 22.5万 - 项目类别:
相似国自然基金
时空序列驱动的神经形态视觉目标识别算法研究
- 批准号:61906126
- 批准年份:2019
- 资助金额:24.0 万元
- 项目类别:青年科学基金项目
本体驱动的地址数据空间语义建模与地址匹配方法
- 批准号:41901325
- 批准年份:2019
- 资助金额:22.0 万元
- 项目类别:青年科学基金项目
大容量固态硬盘地址映射表优化设计与访存优化研究
- 批准号:61802133
- 批准年份:2018
- 资助金额:23.0 万元
- 项目类别:青年科学基金项目
针对内存攻击对象的内存安全防御技术研究
- 批准号:61802432
- 批准年份:2018
- 资助金额:25.0 万元
- 项目类别:青年科学基金项目
IP地址驱动的多径路由及流量传输控制研究
- 批准号:61872252
- 批准年份:2018
- 资助金额:64.0 万元
- 项目类别:面上项目
相似海外基金
Bayesian Statistical Learning for Robust and Generalizable Causal Inferences in Alzheimer Disease and Related Disorders Research
贝叶斯统计学习在阿尔茨海默病和相关疾病研究中进行稳健且可推广的因果推论
- 批准号:
10590913 - 财政年份:2023
- 资助金额:
$ 22.5万 - 项目类别:
Predicting firearm suicide in military veterans outside the VA health system using linked civilian electronic health record data
使用链接的民用电子健康记录数据预测退伍军人管理局卫生系统外退伍军人的枪支自杀
- 批准号:
10655968 - 财政年份:2023
- 资助金额:
$ 22.5万 - 项目类别:
Deep Learning Based Natural Language Processing Markers of Anxiety and Depression
基于深度学习的自然语言处理的焦虑和抑郁标记
- 批准号:
10723819 - 财政年份:2023
- 资助金额:
$ 22.5万 - 项目类别:
Fair risk profiles and predictive models for outcomes of obstructive sleep apnea through electronic medical record data
通过电子病历数据对阻塞性睡眠呼吸暂停结果进行公平的风险概况和预测模型
- 批准号:
10678108 - 财政年份:2023
- 资助金额:
$ 22.5万 - 项目类别:
Mining minority enriched AllofUs data for innovative ethnic specific risk prediction modeling
挖掘少数族裔丰富的 AllofUs 数据,用于创新的种族特定风险预测模型
- 批准号:
10798514 - 财政年份:2023
- 资助金额:
$ 22.5万 - 项目类别: