Methodological and molecular factors underlying racial disparities in cancer outcomes

癌症结果种族差异背后的方法学和分子因素

基本信息

  • 批准号:
    10389941
  • 负责人:
  • 金额:
    $ 4.68万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-02-01 至 2024-01-31
  • 项目状态:
    已结题

项目摘要

Project Summary/Abstract Despite improvements in cancer survival, racial disparities have persisted for both men and women. Although overall incidence is lower among Black women than White women, mortality is greater; in contrast Black men have both greater incidence and mortality. Breast and ovarian cancers, which both contribute to the incongruity among women, are hormonally-driven cancers with overlapping genetic, molecular, and lifestyle risk factors. In 2020, breast cancer was the cause of over 40,000 deaths, while ovarian cancer accounted for nearly 15,000. Deaths attributable to these cancers occur disproportionately among Black women. Disparities in mortality are most pronounced among patients initially diagnosed with non-metastatic disease. Among these patients, cancer recurrence is a necessary precursor to a cancer-specific death. However, recurrence is poorly understood due to the lack of systematically-collected population-based data. Current estimates of recurrence risk are based on clinical trial data, clinic-based registries, or algorithms using administrative claims, and therefore are not representative of all patients, particularly those with less access to specialized trials or clinics. Accurate population-based estimation of recurrence is critical to fully understand mortality disparities. An important methodological consideration in population-based estimation is the follow-up of cancer survivors years after initial diagnosis. Previous studies have suggested racial minority patients are more likely to be lost to follow-up, biasing race-specific estimates. Aim 1 of this project will investigate how differences in loss to follow-up bias race-specific, population-based recurrence estimates, using the rich resources of the Georgia Cancer Registry (GCR), a population-based registry in a large, racially diverse state. The GCR is the first population-based cancer registry to systematically identify and register recurrences among breast cancer survivors. Aims 2 and 3 will use existing epidemiologic studies to investigate patient-level drivers underlying the disparities. Aim 2 will investigate the association between androgen receptor in tumor cells and breast cancer recurrence among premenopausal women, particularly those with aggressive subtypes that do not respond to available endocrine therapy and that are more commonly diagnosed in Black women. Aim 3 will investigate the role of menopausal hormone treatment, and the mediating effects of this exposure on treatment factors, in ovarian cancer progression, an association that has only been examined in White women. Results from this research will provide a framework for accurate estimation of population-based recurrence rates, additional insight into factors contributing to racial disparities in breast and ovarian cancer outcomes, and will inform future studies aimed at reducing inequities. In addition, the fellowship will provide a rich array of epidemiologic training, research, and career development that will contribute to the candidate’s ability to be a successful and independent cancer researcher, with knowledge and skills in a broad array of epidemiologic methods, familiarity with the clinical underpinnings of cancer care, and understanding of cancer disparities.
项目总结/摘要 尽管癌症存活率有所提高,但男性和女性的种族差异仍然存在。虽然 黑人妇女的总体发病率低于白色妇女,死亡率较高;相比之下,黑人男子 有更高的发病率和死亡率。乳腺癌和卵巢癌,这两者都有助于不协调 在女性中,是由遗传、分子和生活方式风险因素重叠的乳腺癌。在 2020年,乳腺癌是超过40,000人死亡的原因,而卵巢癌占近15,000人。 这些癌症造成的死亡在黑人妇女中不成比例。死亡率的差异是 在最初诊断为非转移性疾病的患者中最明显。在这些患者中, 癌症复发是癌症特异性死亡的必要前兆。然而,复发率很低, 由于缺乏系统收集的基于人口的数据,目前估计的复发率 风险基于临床试验数据、基于临床的登记或使用管理索赔的算法,以及 因此不能代表所有患者,特别是那些不太容易进入专门试验或诊所的患者。 基于人口的复发率的准确估计对于充分理解死亡率差异至关重要。一个 在基于人口的估计中,一个重要的方法学考虑因素是对癌症幸存者的随访 最初诊断后的几年。以前的研究表明,少数民族患者更容易迷路。 跟踪调查,对种族的具体估计产生偏见。本项目的目标1将研究损失与 使用格鲁吉亚丰富的资源,随访偏倚种族特异性,基于人群的复发估计 癌症登记处(GCR),一个大型的、种族多样化的州的人口登记处。大中华区是第一个 以人群为基础的癌症登记系统,以系统地识别和登记乳腺癌复发 幸存者目的2和3将使用现有的流行病学研究来调查患者水平的驱动因素, 差距。目的2探讨肿瘤细胞雄激素受体与乳腺癌的关系 绝经前妇女中的癌症复发,特别是那些具有侵袭性亚型的妇女, 对可用的内分泌治疗有反应,并且在黑人妇女中更常见。目标3将 研究绝经期激素治疗的作用,以及这种暴露对治疗的介导作用 卵巢癌进展中的相关因素,仅在白色女性中进行了研究。结果 从这项研究将提供一个框架,准确估计人口为基础的复发率, 进一步了解导致乳腺癌和卵巢癌结局种族差异的因素,并将 为今后旨在减少不平等现象的研究提供信息。此外,研究金将提供丰富的 流行病学培训,研究和职业发展,这将有助于候选人的能力,是一个 成功和独立的癌症研究人员,具有广泛的流行病学知识和技能, 方法,熟悉癌症护理的临床基础,以及对癌症差异的理解。

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 }}

Rebecca Nash其他文献

Rebecca Nash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('Rebecca Nash', 18)}}的其他基金

Methodological and molecular factors underlying racial disparities in cancer outcomes
癌症结果种族差异背后的方法学和分子因素
  • 批准号:
    10554093
  • 财政年份:
    2022
  • 资助金额:
    $ 4.68万
  • 项目类别:

相似海外基金

CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 4.68万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 4.68万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 4.68万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 4.68万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 4.68万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 4.68万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 4.68万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 4.68万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 4.68万
  • 项目类别:
    Continuing Grant
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 4.68万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了