Mayo Clinic, Phoenix, AZ.
Mayo Clinic Florida, Jacksonville, FL.
JCO Precis Oncol. 2023 Jul;7:e2200679. doi: 10.1200/PO.22.00679.
A multi-cancer detection test using a targeted methylation assay and machine learning classifiers was validated and optimized for screening in prospective, case-controlled Circulating Cell-free Genome Atlas (ClinicalTrials.gov identifier: NCT02889978) substudy 3. Here, we report test performance in a subgroup of participants with symptoms suspicious for cancer to assess the test's ability to potentially facilitate efficient diagnostic evaluation in symptomatic individuals.
We evaluated test performance (sensitivity, specificity, and accuracy of cancer signal origin [CSO] prediction accuracy) in participants with clinically presenting cancers (CPCs) and noncancer with underlying medical conditions and among two subgroups (65 years and older and GI cancers). Overall survival (OS) of participants who had a cancer signal detected/not detected was compared with SEER-based expected survival.
A total of 2,036 cancer and 1,472 noncancer participants were included. Specificity was high in all noncancer participants (99.5% [95% CI, 98.4 to 99.8]). In participants with CPCs, the overall sensitivity was 64.3% (95% CI, 62.2 to 66.4) and the overall accuracy of CSO prediction in true positives was 90.3%. For GI cancers, the overall sensitivity was 84.1% (95% CI, 80.6 to 87.1). In participants 65 years and older, test performance was similar to that of all participants. Individuals with cancers not detected had a significantly better OS than that expected from SEER ( < .01).
This test detected a cancer signal with high specificity and CSO prediction accuracy and moderate sensitivity in symptomatic individuals, with especially high performance in participants with GI cancers. The survival analysis implied that the cancers not detected were less clinically aggressive than cancers detected by the test, providing prognostic insights to physicians. This multi-cancer detection test could facilitate efficient workup and stratify cancer risk in symptomatic individuals.
使用靶向甲基化分析和机器学习分类器的多癌种检测试验已在前瞻性病例对照循环游离基因组图谱（ClinicalTrials.gov 标识符：NCT02889978）子研究 3 中进行了验证和优化，用于筛查。在此，我们报告了该检测在具有癌症可疑症状的参与者亚组中的检测性能，以评估该检测在有症状个体中是否具有促进有效诊断评估的潜力。
我们评估了患有临床表现癌症（CPCs）和非癌症伴潜在医疗条件的参与者以及两个亚组（65 岁及以上和胃肠道癌症）中检测性能（癌症信号来源[CSO]预测准确性的灵敏度、特异性和准确性）。比较了检测到/未检测到癌症信号的参与者的总体生存（OS）与 SEER 基于预期的生存。
共纳入 2036 例癌症和 1472 例非癌症患者。所有非癌症患者的特异性均较高（99.5%[95%CI，98.4 至 99.8]）。在 CPC 患者中，总体灵敏度为 64.3%（95%CI，62.2 至 66.4），真阳性中 CSO 预测的总体准确性为 90.3%。对于胃肠道癌症，总体灵敏度为 84.1%（95%CI，80.6 至 87.1）。在 65 岁及以上的参与者中，检测性能与所有参与者相似。未检测到癌症的个体的 OS 明显优于 SEER 预期（<0.01）。
该检测在有症状的个体中具有高特异性和 CSO 预测准确性以及中等灵敏度，能够检测出癌症信号，尤其在胃肠道癌症患者中具有较高的性能。生存分析表明，未检测到的癌症比该检测检测到的癌症侵袭性更小，为医生提供了预后信息。这种多癌种检测试验可以为有症状的个体提供有效的检查和分层癌症风险。
