Department of Medical Biosciences, Pathology, Umeå University, 901 87, Umeå, Sweden.
Department of Mathematics and Mathematical Statistics, Umeå University, 901 87, Umeå, Sweden.
J Transl Med. 2020 Nov 13;18(1):435. doi: 10.1186/s12967-020-02608-1.
Metastasized clear cell renal cell carcinoma (ccRCC) is associated with a poor prognosis. Almost one-third of patients with non-metastatic tumors at diagnosis will later progress with metastatic disease. These patients need to be identified already at diagnosis, to undertake closer follow up and/or adjuvant treatment. Today, clinicopathological variables are used to risk classify patients, but molecular biomarkers are needed to improve risk classification to identify the high-risk patients which will benefit most from modern adjuvant therapies. Interestingly, DNA methylation profiling has emerged as a promising prognostic biomarker in ccRCC. This study aimed to derive a model for prediction of tumor progression after nephrectomy in non-metastatic ccRCC by combining DNA methylation profiling with clinicopathological variables.
A novel cluster analysis approach (Directed Cluster Analysis) was used to identify molecular biomarkers from genome-wide methylation array data. These novel DNA methylation biomarkers, together with previously identified CpG-site biomarkers and clinicopathological variables, were used to derive predictive classifiers for tumor progression.
The "triple classifier" which included both novel and previously identified DNA methylation biomarkers together with clinicopathological variables predicted tumor progression more accurately than the currently used Mayo scoring system, by increasing the specificity from 50% in Mayo to 64% in our triple classifier at 85% fixed sensitivity. The cumulative incidence of progress (CIP) was 7.5% in low-risk vs 44.7% in high-risk in M0 patients classified by the triple classifier at diagnosis.
The triple classifier panel that combines clinicopathological variables with genome-wide methylation data has the potential to improve specificity in prognosis prediction for patients with non-metastatic ccRCC.
转移性透明细胞肾细胞癌（ccRCC）预后不良。几乎三分之一的初诊非转移性肿瘤患者随后会进展为转移性疾病。这些患者需要在诊断时就确定，以便进行更密切的随访和/或辅助治疗。目前，临床病理变量用于风险分类患者，但需要分子生物标志物来改善风险分类，以识别最受益于现代辅助治疗的高危患者。有趣的是，DNA 甲基化谱分析已成为 ccRCC 有前途的预后生物标志物。本研究旨在通过结合 DNA 甲基化谱分析和临床病理变量，为非转移性 ccRCC 肾切除术后肿瘤进展建立预测模型。
采用新的聚类分析方法（有向聚类分析）从全基因组甲基化阵列数据中识别分子生物标志物。这些新的 DNA 甲基化生物标志物与之前确定的 CpG 位点生物标志物和临床病理变量一起，用于推导用于肿瘤进展的预测分类器。
“三重分类器”包括新的和以前确定的 DNA 甲基化生物标志物以及临床病理变量，比目前使用的 Mayo 评分系统更准确地预测肿瘤进展，通过将 Mayo 评分系统的特异性从 50%提高到我们三重分类器的 64%，同时固定敏感性为 85%。在诊断时通过三重分类器分类的 M0 患者中，低危组的累积进展发生率（CIP）为 7.5%，高危组为 44.7%。
该三重分类器面板结合了临床病理变量和全基因组甲基化数据，有可能提高非转移性 ccRCC 患者预后预测的特异性。
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