Program in Genetic Epidemiology and Statistical Genetics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Cancer Epidemiol Biomarkers Prev. 2020 May;29(5):999-1008. doi: 10.1158/1055-9965.EPI-19-1389. Epub 2020 Apr 22.
Pancreatic cancer is the third leading cause of cancer death in the United States, and 80% of patients present with advanced, incurable disease. Risk markers for pancreatic cancer have been characterized, but combined models are not used clinically to identify individuals at high risk for the disease.
Within a nested case-control study of 500 pancreatic cancer cases diagnosed after blood collection and 1,091 matched controls enrolled in four U.S. prospective cohorts, we characterized absolute risk models that included clinical factors (e.g., body mass index, history of diabetes), germline genetic polymorphisms, and circulating biomarkers.
Model discrimination showed an area under ROC curve of 0.62 via cross-validation. Our final integrated model identified 3.7% of men and 2.6% of women who had at least 3 times greater than average risk in the ensuing 10 years. Individuals within the top risk percentile had a 4% risk of developing pancreatic cancer by age 80 years and 2% 10-year risk at age 70 years.
Risk models that include established clinical, genetic, and circulating factors improved disease discrimination over models using clinical factors alone.
Absolute risk models for pancreatic cancer may help identify individuals in the general population appropriate for disease interception.
在美国，胰腺癌是导致死亡的第三大癌症原因，80%的患者就诊时已处于晚期、无法治愈的疾病状态。已经对胰腺癌的风险标志物进行了特征描述，但尚未将这些标志物联合应用于临床，以识别出患有该疾病风险较高的个体。
在一项嵌套病例对照研究中，对采集血液后确诊的 500 例胰腺癌病例（病例组）和来自四个美国前瞻性队列的 1091 例匹配对照者（对照组）进行了分析，我们对包括临床因素（如体重指数、糖尿病史）、种系遗传多态性和循环生物标志物在内的绝对风险模型进行了特征描述。
通过交叉验证，模型的判别显示 ROC 曲线下面积为 0.62。我们最终的综合模型确定了 3.7%的男性和 2.6%的女性在接下来的 10 年内具有至少 3 倍于平均风险的情况。处于最高风险百分位的个体在 80 岁时患胰腺癌的风险为 4%，在 70 岁时的 10 年风险为 2%。
包含已确立的临床、遗传和循环因素的风险模型提高了疾病的判别能力，优于仅使用临床因素的模型。
胰腺癌的绝对风险模型可能有助于识别出一般人群中适合进行疾病干预的个体。
