Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX.
Biostatistics Program, Fred Hutchinson Cancer Research Center, Seattle, WA.
J Clin Oncol. 2022 Mar 10;40(8):876-883. doi: 10.1200/JCO.21.01460. Epub 2022 Jan 7.
To investigate whether a panel of circulating protein biomarkers would improve risk assessment for lung cancer screening in combination with a risk model on the basis of participant characteristics.
A blinded validation study was performed using prostate lung colorectal ovarian (PLCO) Cancer Screening Trial data and biospecimens to evaluate the performance of a four-marker protein panel (4MP) consisting of the precursor form of surfactant protein B, cancer antigen 125, carcinoembryonic antigen, and cytokeratin-19 fragment in combination with a lung cancer risk prediction model (PLCO) compared with current US Preventive Services Task Force (USPSTF) screening criteria. The 4MP was assayed in 1,299 sera collected preceding lung cancer diagnosis and 8,709 noncase sera.
The 4MP alone yielded an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.77 to 0.82) for case sera collected within 1-year preceding diagnosis and 0.74 (95% CI, 0.72 to 0.76) among the entire specimen set. The combined 4MP + PLCO model yielded an area under the receiver operating characteristic curve of 0.85 (95% CI, 0.82 to 0.88) for case sera collected within 1 year preceding diagnosis. The benefit of the 4MP in the combined model resulted from improvement in sensitivity at high specificity. Compared with the USPSTF2021 criteria, the combined 4MP + PLCO model exhibited statistically significant improvements in sensitivity and specificity. Among PLCO participants with ≥ 10 smoking pack-years, the 4MP + PLCO model would have identified for annual screening 9.2% more lung cancer cases and would have reduced referral by 13.7% among noncases compared with USPSTF2021 criteria.
A blood-based biomarker panel in combination with PLCO significantly improves lung cancer risk assessment for lung cancer screening.
研究一组循环蛋白生物标志物是否可以与基于参与者特征的风险模型相结合，改善肺癌筛查的风险评估。
采用前列腺肺结直肠卵巢（PLCO）癌症筛查试验的数据和生物标本进行了一项盲法验证研究，以评估由表面活性剂蛋白 B 前体、癌抗原 125、癌胚抗原和细胞角蛋白 19 片段组成的四标志物蛋白组（4MP）与目前美国预防服务工作组（USPSTF）筛查标准相比，在结合肺癌风险预测模型（PLCO）时的性能。该 4MP 在 1299 份在肺癌诊断前收集的血清和 8709 份非病例血清中进行了检测。
4MP 单独用于在诊断前 1 年内收集的病例血清时，其受试者工作特征曲线下面积为 0.79（95%CI，0.77 至 0.82），而在整个标本集中为 0.74（95%CI，0.72 至 0.76）。4MP+PLCO 联合模型在诊断前 1 年内收集的病例血清中，受试者工作特征曲线下面积为 0.85（95%CI，0.82 至 0.88）。联合模型中 4MP 的优势在于提高了高特异性下的灵敏度。与 USPSTF2021 标准相比，联合 4MP+PLCO 模型在灵敏度和特异性方面均有显著提高。在 PLCO 参与者中，≥10 包年吸烟量者中，与 USPSTF2021 标准相比，4MP+PLCO 模型每年可识别 9.2%更多的肺癌病例，而非病例的转诊率可降低 13.7%。
血液生物标志物组与 PLCO 相结合可显著提高肺癌筛查的肺癌风险评估。
