Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Clin Cancer Res. 2019 Nov 1;25(21):6357-6367. doi: 10.1158/1078-0432.CCR-18-3277. Epub 2019 Jul 12.
An unmet need in low-resource countries is an automated breast cancer detection assay to prioritize women who should undergo core breast biopsy and pathologic review. Therefore, we sought to identify and validate a panel of methylated DNA markers to discriminate between cancer and benign breast lesions using cells obtained by fine-needle aspiration (FNA). Two case-control studies were conducted comparing cancer and benign breast tissue identified from clinical repositories in the United States, China, and South Africa for marker selection/training ( = 226) and testing ( = 246). Twenty-five methylated markers were assayed by Quantitative Multiplex-Methylation-Specific PCR (QM-MSP) to select and test a cancer-specific panel. Next, a pilot study was conducted on archival FNAs (49 benign, 24 invasive) from women with mammographically suspicious lesions using a newly developed, 5-hour, quantitative, automated cartridge system. We calculated sensitivity, specificity, and area under the receiver-operating characteristic curve (AUC) compared with histopathology for the marker panel.
In the discovery cohort, 10 of 25 markers were selected that were highly methylated in breast cancer compared with benign tissues by QM-MSP. In the independent test cohort, this panel yielded an AUC of 0.937 (95% CI = 0.900-0.970). In the FNA pilot, we achieved an AUC of 0.960 (95% CI = 0.883-1.0) using the automated cartridge system.
We developed and piloted a fast and accurate methylation marker-based automated cartridge system to detect breast cancer in FNA samples. This quick ancillary test has the potential to prioritize cancer over benign tissues for expedited pathologic evaluation in poorly resourced countries.
在资源匮乏的国家，存在一个未满足的需求，即需要一种自动化的乳腺癌检测方法，以便优先选择需要进行核心乳腺活检和病理检查的女性。因此，我们试图鉴定和验证一组甲基化 DNA 标志物，以区分通过细针穿刺（FNA）获得的细胞中的癌症和良性乳腺病变。我们进行了两项病例对照研究，比较了来自美国、中国和南非的临床储存库中鉴定的癌症和良性乳腺组织，以进行标志物选择/训练（= 226）和测试（= 246）。通过定量多重甲基化特异性 PCR（QM-MSP）检测 25 个甲基化标志物，以选择和测试癌症特异性标志物面板。然后，使用新开发的 5 小时定量自动化试剂盒系统，对具有乳腺可疑病变的女性的存档 FNA（49 例良性，24 例浸润性）进行了一项试点研究。我们计算了与组织病理学相比，该标志物面板的敏感性、特异性和接受者操作特征曲线（ROC）下的面积（AUC）。
在发现队列中，通过 QM-MSP 比较良性组织，有 10 个标志物在乳腺癌中高度甲基化。在独立测试队列中，该面板的 AUC 为 0.937（95% CI = 0.900-0.970）。在 FNA 试点中，我们使用自动化试剂盒系统获得了 0.960（95% CI = 0.883-1.0）的 AUC。
我们开发并试点了一种快速准确的基于甲基化标志物的自动化试剂盒系统，用于检测 FNA 样本中的乳腺癌。这种快速辅助测试有可能优先考虑癌症而不是良性组织，以便在资源匮乏的国家加快进行病理评估。
