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Validation and extension of an existing risk model for lung cancer

Validation and extension of an existing risk model for lung cancer
现有肺癌风险模型的验证和扩展
批准号:
7911887
负责人:
RANDA A EL-ZEIN
金额:
$61.55万
依托单位国家:
美国
项目类别:
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-08-12 至 2013-07-31

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项目成果

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中文摘要
翻译
可靠的风险预测工具用于估计肺癌(美国癌症死亡的主要原因)的概率,对公共卫生的影响是巨大的。在本提案中,我们建立了肺癌流行病学风险模型,该模型是我们最近从1851名高加索肺癌患者和2001名对照患者的肺癌病例对照研究中建立的,与父母资助的性别,年龄(±5岁),吸烟状况(当前,以前,从不吸烟)和种族相匹配(ROl CA55679, P1: M. Spitz)。这个风险模型还可以估算出一个人患肺癌的绝对风险。使用来自本研究的独立数据集和来自David Christiani博士(哈佛大学公共卫生学院CA74386的共同研究者)的肺癌病例和对照的外部数据集,我们建议验证肺癌风险模型,并通过纳入风险的遗传生物标志物来扩展它。具体:1)。在使用亲本资助招募机制的800例预期累积病例和800例对照中,我们将验证我们的原始模型,然后评估一种有前景的新型细胞遗传学生物标志物的附加区分能力,细胞分裂阻断微核(CBMN)测定,这是一种多终点测定,不仅可以测量染色体损伤(微核反映染色体断裂;反映染色体重排的核质桥和反映基因扩增的核芽)以及其他细胞事件(凋亡和坏死)。我们将在基线和使用烟草特异性亚硝胺NNK后测量这些终点。我们将推导出一种方法,将这些不同的染色体/遗传不稳定性措施整合到流行病学肺癌风险模型中。2). 利用目标1的发现,我们将构建一个扩展的风险模型,包括染色体不稳定性和基因-环境相互作用的措施。我们的初步数据表明,我们目前的模型具有中等的区分能力(70%),我们相信,将模型扩展到包括这些染色体不稳定性的生物标志物以及基因环境相互作用只会提高我们模型的区分能力。这个新开发的模型可能对识别高风险人群有用,这些人群可以成为强化戒烟计划的目标,并可以参加化学预防筛查试验。3). 在内部验证原始和扩展的肺癌风险模型,使用额外的一组500例前瞻性登记的肺癌病例和500例使用父母资助的招募机制的对照,并比较扩展模型与原始Spitz模型之间的歧视权力。这也将包括独立的,内部的,CBMN测定的验证。在独立样本中评估这些染色体端点将为随后将其他功能表型和基因型纳入模型提供原理证明。
英文摘要
The public health implications of reliable risk prediction tools for estimating the probability of lung cancer, the leading cause of cancer mortality in the US, are immense. In this proposal, we build upon an epidemiologic risk model for lung cancer that we recently developed from a lung cancer case-control study of 1851 Caucasian lung cancer patients and 2001 controls, matched to the cases on sex, age (±5 years), smoking status (current, former, never) and ethnicity from the parent grant (ROl CA55679, P1: M. Spitz). This risk model also estimates an individual's absolute risk for lung cancer. Using an independent data set from this study and an external data set of lung cancer cases and controls from Dr. David Christiani (co-investigator, Harvard School of Public Health, CA74386), we propose to validate the lung cancer risk model and extend it by incorporating a genetic biomarker of risk. Specifically: 1). In 800 prospectively accrued cases and 800controls using the recruitment mechanisms of parent grant, we will validate our original model and then assess the added discriminatory ability of a promising novel cytogenetic biomarker, the cytokinesis-block micronucleus (CBMN) assay, a multi-endpoint assay that measures not only chromosome damage (micronuclei reflecting chromosome breaks; nucleoplasmic bridges reflecting chromosome rearrangements and nuclear buds reflecting gene amplification) but also other cellular events (apoptosis and necrosis). We will measure these endpoints at baseline and following challenge with the tobacco-specific nitrosamine, NNK. We will derive a method to integrate these different measures of chromosome/genetic instability into the epidemiologic lung cancer risk model. 2). Using the findings from aim 1, we will construct an extended risk model to include measures of chromosome instability and gene-environment interactions. Our preliminary data show that our current model has moderate discriminatory power (70%), we believe that extending the model to includes these biomarkers of chromosome instability as well as gene environment interactions will only improve the discriminatory power of our model. This newly developed model may be useful to identify high-risk populations who could then be targeted for intensive smoking-cessation programs and could be enrolled into chemopreven-tion screening trials. 3). Internally validate the original and extended lung cancer risk models using an additional set of 500 prospectively enrolled lung cancer cases and 500 controls using the recruitment mechanisms of parent grant and compare the discriminatory power between the extended model to that of the original Spitz model between the two models. This will also include independent, internal, validation of the CBMN assay. Evaluation of these chromosomal endpoints in an independent sample will provide proof-of-principle for subsequent inclusion of additional functional phenotypes and genotypes into the model.
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会议论文
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