Brennan Kevin, Metzner Thomas J, Kao Chia-Sui, Massie Charlie E, Stewart Grant D, Haile Robert W, Brooks James D, Hitchins Megan P, Leppert John T, Gevaert Olivier
Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA.
Department of Urology, Stanford University School of Medicine, Stanford University, Stanford, CA.
JCO Precis Oncol. 2020 Sep 28;4. doi: 10.1200/PO.20.00015. eCollection 2020.
A challenge in the diagnosis of renal cell carcinoma (RCC) is to distinguish chromophobe RCC (chRCC) from benign renal oncocytoma, because these tumor types are histologically and morphologically similar, yet they require different clinical management. Molecular biomarkers could provide a way of distinguishing oncocytoma from chRCC, which could prevent unnecessary treatment of oncocytoma. Such biomarkers could also be applied to preoperative biopsy specimens such as needle core biopsy specimens, to avoid unnecessary surgery of oncocytoma.
We profiled DNA methylation in fresh-frozen oncocytoma and chRCC tumors and adjacent normal tissue and used machine learning to identify a signature of differentially methylated cytosine-phosphate-guanine sites (CpGs) that robustly distinguish oncocytoma from chRCC.
Unsupervised clustering of Stanford and preexisting RCC data from The Cancer Genome Atlas (TCGA) revealed that of all RCC subtypes, oncocytoma is most similar to chRCC. Unexpectedly, however, oncocytoma features more extensive, overall abnormal methylation than does chRCC. We identified 79 CpGs with large methylation differences between oncocytoma and chRCC. A diagnostic model trained on 30 CpGs could distinguish oncocytoma from chRCC in 10-fold cross-validation (area under the receiver operating curve [AUC], 0.96 (95% CI, 0.88 to 1.00)) and could distinguish TCGA chRCCs from an independent set of oncocytomas from a previous study (AUC, 0.87). This signature also separated oncocytoma from other RCC subtypes and normal tissue, revealing it as a standalone diagnostic biomarker for oncocytoma.
This CpG signature could be developed as a clinical biomarker to support differential diagnosis of oncocytoma and chRCC in surgical samples. With improved biopsy techniques, this signature could be applied to preoperative biopsy specimens.
肾细胞癌（RCC）诊断中的一个挑战是区分嫌色性肾细胞癌（chRCC）与良性肾嗜酸细胞瘤，因为这些肿瘤类型在组织学和形态学上相似，但它们需要不同的临床管理。分子生物标志物可以提供一种区分嗜酸细胞瘤与chRCC的方法，这可以避免对嗜酸细胞瘤进行不必要的治疗。此类生物标志物还可应用于术前活检标本，如针芯活检标本，以避免对嗜酸细胞瘤进行不必要的手术。
我们分析了新鲜冷冻的嗜酸细胞瘤、chRCC肿瘤及相邻正常组织中的DNA甲基化情况，并使用机器学习来识别能有力区分嗜酸细胞瘤与chRCC的差异甲基化胞嘧啶-磷酸-鸟嘌呤位点（CpG）特征。
对来自斯坦福大学和癌症基因组图谱（TCGA）的现有RCC数据进行无监督聚类分析显示，在所有RCC亚型中，嗜酸细胞瘤与chRCC最为相似。然而，出乎意料的是，嗜酸细胞瘤的总体异常甲基化比chRCC更为广泛。我们鉴定出79个在嗜酸细胞瘤和chRCC之间存在较大甲基化差异的CpG。在10倍交叉验证中，基于30个CpG训练的诊断模型能够区分嗜酸细胞瘤与chRCC（受试者操作特征曲线下面积[AUC]为0.96（95%CI，0.88至1.00）），并且能够区分TCGA中的chRCC与先前一项研究中的一组独立嗜酸细胞瘤（AUC为0.87）。该特征还能将嗜酸细胞瘤与其他RCC亚型及正常组织区分开来，表明它是嗜酸细胞瘤的一种独立诊断生物标志物。
这种CpG特征可开发为一种临床生物标志物，以支持手术样本中嗜酸细胞瘤和chRCC的鉴别诊断。随着活检技术的改进，该特征可应用于术前活检标本。
