Biomarker trajectory for earlier detection of lung cancer.

IF 9.7 1区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL EBioMedicine Pub Date : 2024-10-01 Epub Date: 2024-09-30 DOI:10.1016/j.ebiom.2024.105377
Ehsan Irajizad, Johannes F Fahrmann, Iakovos Toumazis, Jody Vykoukal, Jennifer B Dennison, Yu Shen, Kim-Anh Do, Edwin J Ostrin, Ziding Feng, Samir Hanash
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Abstract

Background: 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.

Methods: 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.

Findings: 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 (SenPEB:96.3% vs SenST: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.

Interpretation: 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.

Funding: 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.

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早期检测肺癌的生物标志物轨迹。
背景:旨在确定基于重复测量四种循环蛋白生物标志物(4 MP)的肺癌风险评估算法是否比一次性测量更有效:目的:确定在肺癌风险评估中,基于重复测量四种循环蛋白生物标志物(4 MP)的算法是否比单次测量更有效:我们对 "前列腺、肺、结直肠、卵巢(PLCO)研究 "中2483名曾经吸烟的参与者(389名病例和2094名随机抽取的非病例)的诊断前血清中的4种MP(包括表面活性蛋白B的前体形式、癌抗原125、癌胚抗原和细胞角蛋白-19片段)进行了数据分析,这些参与者在6年内至少连续采集了两次血液。我们将参数经验贝叶斯(PEB)算法与单阈值(ST)方法进行了比较:在曾经吸烟的参与者中,PEB 方法比 ST 方法的 AUC 提高了 4%(P 值:0.009)。当考虑到吸烟史≥10PY且特异性固定为1%的6年肺癌风险时,PEB可显著提高灵敏度(SenPEB:96.3% vs SenST:91.0%;P值:6.7e-3)。PEB 算法识别出了 35 例 ST 阴性病例中的 17 例,平均诊断时间为 1.26 年。在诊断前平均 1.03 年 ST 阳性的 10 个病例在诊断前平均 2.70 年被 PEB 提前识别:与单一阈值法相比,基于 4 MP 重复测量的算法提高了灵敏度,并能更早地发现肺癌:本研究由美国国立卫生研究院拨款支持。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.
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来源期刊
EBioMedicine
EBioMedicine Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology
CiteScore
17.70
自引率
0.90%
发文量
579
审稿时长
5 weeks
期刊介绍: eBioMedicine is a comprehensive biomedical research journal that covers a wide range of studies that are relevant to human health. Our focus is on original research that explores the fundamental factors influencing human health and disease, including the discovery of new therapeutic targets and treatments, the identification of biomarkers and diagnostic tools, and the investigation and modification of disease pathways and mechanisms. We welcome studies from any biomedical discipline that contribute to our understanding of disease and aim to improve human health.
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