Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
EBioMedicine. 2024 Oct;108:105377. doi: 10.1016/j.ebiom.2024.105377. Epub 2024 Sep 30.
To determine whether an algorithm based on repeated measurements of a panel of four circulating protein biomarkers (4 MP) for lung cancer risk assessment results in improved performance over a single time measurement.
We conducted data analysis of the 4 MP consisting of the precursor form of surfactant protein B, cancer antigen 125, carcinoembryonic antigen, and cytokeratin-19 fragment in pre-diagnostic sera from 2483 ever-smoker participants (389 cases and 2094 randomly selected non-cases) in the Prostate, Lung, Colorectal, Ovarian (PLCO) Study who had at least two sequential blood collections over 6 years. A parametric empirical Bayes (PEB) algorithm, which incorporates participant biomarker history at each time point, was compared to a single-threshold (ST) method.
Among ever-smoker participants, the PEB approach yielded an additional 4% improvement in the AUC compared to the ST approach (P-value: 0.009). When considering an ≥10 PY smoking history and at a fixing the specificity corresponding to 1% 6-year lung cancer risk, PEB resulted in significant improvement in the sensitivity (Sen:96.3% vs Sen:91.0%; P-value: 6.7e-3). The PEB algorithm identified 17 of the 35 cases that remained ST negative, at an average of 1.26 years before diagnosis. Ten case individuals who were positive based on ST at an average of 1.03 years prior to diagnosis were identified earlier by PEB, at an average of 2.70 years.
An algorithm based on repeated measurements of the 4 MP improves sensitivity and results in an earlier detection of lung cancer compared to a single-threshold method.
This study was supported by NIH Grant Nos. U01CA271888, U01CA194733, U01CA213285, NCI EDRN U01 CA200468, P30CA016672, and U24CA086368; the Cancer Prevention & Research Institute of Texas RP180505 and RP160693; the SPORE P50CA140388; the CCTS TR000371; and the generous philanthropic contributions to The University of Texas MD Anderson Cancer Center Moon Shots Program and the Lyda Hill Foundation.
为了确定基于肺癌风险评估中四个循环蛋白生物标志物（4MP）的重复测量的算法是否比单次测量结果具有更好的性能。
我们对来自 2483 名曾吸烟者参与者（389 例和 2094 例随机选择的非病例）的预诊断血清中的 4MP 进行数据分析，该分析由表面活性剂蛋白 B 前体、癌抗原 125、癌胚抗原和细胞角蛋白 19 片段组成，他们在 6 年内至少有两次连续的血液采集。与单一阈值（ST）方法相比，一种参数经验贝叶斯（PEB）算法，该算法结合了参与者在每个时间点的生物标志物历史，用于比较。
在曾吸烟者参与者中，与 ST 方法相比，PEB 方法使 AUC 提高了 4%（P 值：0.009）。当考虑到 10 年以上的吸烟史，并固定 6 年肺癌风险为 1%的特异性时，PEB 显著提高了灵敏度（96.3%对 91.0%；P 值：6.7e-3）。PEB 算法在诊断前平均 1.26 年前确定了 ST 为阴性的 35 例病例中的 17 例。10 例基于 ST 在诊断前平均 1.03 年前为阳性的病例个体，通过 PEB 更早地被发现，平均提前 2.70 年。
与单阈值方法相比，基于四个生物标志物重复测量的算法可提高灵敏度，并可更早地检测到肺癌。
本研究由 NIH 资助号 U01CA271888、U01CA194733、U01CA213285、NCI EDRN U01 CA200468、P30CA016672 和 U24CA086368；德克萨斯州癌症预防与研究研究所 RP180505 和 RP160693；SPORE P50CA140388；CCTS TR000371；以及慷慨的慈善捐款给德克萨斯大学 MD 安德森癌症中心的 Moon Shots 计划和 Lyda Hill 基金会。
