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Leveraging high-risk populations for precision prevention: A novel approach for improving risk prediction for outcomes after a breast cancer diagnosis

Leveraging high-risk populations for precision prevention: A novel approach for improving risk prediction for outcomes after a breast cancer diagnosis
利用高危人群进行精准预防:一种改善乳腺癌诊断后结果风险预测的新方法
批准号:
10300319
负责人:
Nur Zeinomar
金额:
$19.99万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-08-01 至 2025-07-31
关键词:
AccountingAddressAdvisory CommitteesAffectAfrican ancestryAustraliaBiological AssayBlack raceBreastBreast Cancer Risk FactorBreast Cancer survivorBreast Cancer survivorshipCanadaCancer PrognosisCharacteristicsClinicalClinical MarkersContralateral BreastDataDevelopmentDevelopment PlansDiagnosisEducational workshopEnrollmentEpidemiologyEstrogen receptor negativeEthnic groupEuropeanEventFamilyFamily history ofFollow-Up StudiesFutureGene Expression ProfilingGeneticGenetic VariationGenomicsGenotypeGoalsHealthHeritabilityHormone ReceptorInheritedInternationalInterventionInterviewK22 AwardKnowledgeLearning SkillLinkage DisequilibriumLongitudinal StudiesLymph Node InvolvementModelingMolecularNew JerseyOutcomePerformancePersonsPrognosisPublic HealthRNARaceRecommendationResearchResearch PersonnelResourcesRiskRisk AssessmentSocioeconomic StatusSubgroupTimeTrainingTraining SupportTranslatingTranslationsUnited StatesVariantWomanbaseblack womenbreast cancer diagnosisbreast cancer family registrybreast cancer survivalcancer diagnosiscancer recurrencecancer subtypescancer therapycareer developmentclinical careclinical implementationclinical translationcohortexperiencefollow-upgenetic associationgenetic pedigreegenome-widehealth disparityhigh riskhigh risk populationimprovedimproved outcomeindividualized preventionknowledge translationlymph nodesmalignant breast neoplasmmeetingsmortalitymortality riskneoplasm registrynovelnovel strategiesoncotypeoutcome predictionpolygenic risk scorepopulation basedprecision medicinepredictive modelingprognostic indexprognostic toolracial and ethnicrecruitrisk predictionrisk stratificationscreening guidelinesskillssurvival predictionsymposiumtertiary preventiontumor

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中文摘要
翻译
项目总结 尽管乳腺癌(BC)的预后在过去20年中有了戏剧性的改善,但主要的存活率 根据一些临床因素和肿瘤特征,诊断后的差异仍然存在。然而, 即使在相似的分子BC亚型中,存活率也存在差异,支持这一额外因素 应考虑在诊断后更好地预测结果。令人惊讶的是,与首次事件的风险模型不同 BC,目前预测BC诊断和治疗后存活率的模型没有纳入宿主种系 遗传变异。此外,与黑人女性相比,黑人女性死于BC的死亡率高出40% 白人同龄人。此外,它们在基因组研究中的代表性严重不足;因此,未来的临床 基于生殖系遗传变异的新精准医学解决方案的实施可能会加剧 现有的健康差距。这项提议旨在提供经验证据,这将是至关重要的第一步 根据针对乳腺癌高危人群的适当临床建议,改善BC预后 糟糕的结果。在目标1a中,我将调查多基因风险评分(Prs)是否改善了BC的风险预测。 乳腺癌家族的预后、超出标准的临床标记物和肿瘤特征 注册表(BCFR)。目标1b将利用BCFR来检查在现有BC预后的基础上增加PR的影响 诸如诺丁汉预后指数(NPI)这样的工具,它结合了肿瘤大小、肿瘤分级、 和淋巴受累。在目标2a中,我将研究PRS是否改善了对bc预后的风险预测 妇女健康圈后续研究(WCHFS),对不列颠哥伦比亚省黑人幸存者的纵向研究。目标2b将 使用来自WCHFS的数据,检查将PR添加到NPI的影响。我的长期目标是翻译 通过更准确的风险评估和降低风险策略,将流行病学结果纳入临床护理 癌症诊断后的结果。这个K22奖项将为我提供必要的培训和支持,以 完成以下短期目标:(1)获得统计遗传学方面的高级技能;(2)培训 将科研成果转化为临床研究成果;及(3)专业发展,包括 学习成为一名成功的独立调查员所必需的技能。为了实现这些目标,我有 提出了详细的职业发展计划,包括参加短期课程和讲习班,参加国家 会议,与我的顾问委员会的会议,并通过完成 提出研究目标。这项K22研究将解决关键知识和临床翻译方面的差距 确定谁是BC预后不良的最高风险女性。鉴于不列颠哥伦比亚省幸存者的数量不断增加 并保持某些亚组的BC生存差异,这是一个及时而重要的建议。
英文摘要
PROJECT SUMMARY Despite dramatic improvements in Breast Cancer (BC) prognosis over the past two decades, major survival differences after diagnosis persist based on a number of clinical factors and tumor characteristics. However, even within similar molecular BC subtypes there are differences in survival, supporting that additional factors should be considered to better predict outcomes after diagnosis. Surprisingly, unlike risk models for first incident BC, current models for prediction of survival after a BC diagnosis and treatment do not incorporate host germline genetic variation. In addition, Black women experience a 40% higher mortality rate due to BC compared to their White counterparts. Further, they have been greatly underrepresented in genomic studies; so future clinical implementations of new precision medicine solutions based on germline genetic variation may exacerbate existing health disparities. This proposal aims to produce empirical evidence that will be an essential first step to improve BC prognosis based on appropriate clinical recommendations targeted to those with the highest risk of poor outcomes. In Aim 1a, I will investigate if a polygenic risk score (PRS) improves risk prediction of BC prognosis, over and beyond standard clinical markers and tumor characteristics in the Breast Cancer Family Registry (BCFR). Aim 1b will utilize the BCFR to examine the impact of adding a PRS to existing BC prognostic tools such as the Nottingham Prognostic Index (NPI), which incorporates information on tumor size, tumor grade, and lymph node involvement. In Aim 2a, I will examine if the PRS improves risk prediction for BC prognosis in the Women’s Circle of Health Follow-Up Study (WCHFS), a longitudinal study of Black BC survivors. Aim 2b will examine the impact of adding PRS to NPI using data from the WCHFS. My long-term goal is to translate epidemiologic findings into clinical care through more accurate risk assessment and risk-reducing strategies for outcomes after a cancer diagnosis. This K22 award will provide me with the necessary training and support to accomplish the following short-term goals: (1) obtain advanced skills in statistical genetics; (2) training in the translation of scientific research findings in the clinical context; and (3) professional development including learning the skills necessary to be a successful independent investigator. To achieve these goals, I have proposed a detailed career development plan, including taking short courses and workshops, attending national conferences, meetings with my advisory committee, and obtaining research experience by completing the proposed research aims. This K22 research will address critical knowledge and clinical translation gaps in identifying women who are the highest risk for poor BC prognosis. Given the increasing number of BC survivors and persisting BC survival differences for certain subgroups, this is a timely and important proposal.
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Leveraging high-risk populations for precision prevention: A novel approach for improving risk prediction for outcomes after a breast cancer diagnosis
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