Department of Pathology, Henan Key Laboratory of Tumor Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Research and Development Division, Oriomics Biotech Inc, Hangzhou, China.
Clin Epigenetics. 2024 Feb 9;16(1):25. doi: 10.1186/s13148-024-01638-6.
Cancer of unknown primary (CUP) is a group of rare malignancies with poor prognosis and unidentifiable tissue-of-origin. Distinct DNA methylation patterns in different tissues and cancer types enable the identification of the tissue of origin in CUP patients, which could help risk assessment and guide site-directed therapy.
Using genome-wide DNA methylation profile datasets from The Cancer Genome Atlas (TCGA) and machine learning methods, we developed a 200-CpG methylation feature classifier for CUP tissue of origin prediction (MFCUP). MFCUP was further validated with public-available methylation array data of 2977 specimens and targeted methylation sequencing of 78 Formalin-fixed paraffin-embedded (FFPE) samples from a single center.
MFCUP achieved an accuracy of 97.2% in a validation cohort (n = 5923) representing 25 cancer types. When applied to an Infinium 450 K array dataset (n = 1052) and an Infinium EPIC (850 K) array dataset (n = 1925), MFCUP achieved an overall accuracy of 93.4% and 84.8%, respectively. Based on MFCUP, we established a targeted bisulfite sequencing panel and validated it with FFPE sections from 78 patients of 20 cancer types. This methylation sequencing panel correctly identified tissue of origin in 88.5% (69/78) of samples. We also found that the methylation levels of specific CpGs can distinguish one cancer type from others, indicating their potential as biomarkers for cancer diagnosis and screening.
Our methylation-based cancer classifier and targeted methylation sequencing panel can predict tissue of origin in diverse cancer types with high accuracy.
癌症原发灶不明（CUP）是一组罕见的恶性肿瘤，预后差，组织起源不明。不同组织和癌症类型中独特的 DNA 甲基化模式可识别 CUP 患者的组织起源，这有助于风险评估和指导靶向治疗。
我们使用来自癌症基因组图谱（TCGA）的全基因组 DNA 甲基化谱数据集和机器学习方法，开发了用于 CUP 组织起源预测的 200-CpG 甲基化特征分类器（MFCUP）。MFCUP 进一步通过公共可用的 2977 个标本的甲基化阵列数据和来自单个中心的 78 个福尔马林固定石蜡包埋（FFPE）样本的靶向甲基化测序进行验证。
MFCUP 在代表 25 种癌症类型的验证队列（n=5923）中达到了 97.2%的准确性。当应用于 Infinium 450 K 阵列数据集（n=1052）和 Infinium EPIC（850 K）阵列数据集（n=1925）时，MFCUP 分别达到了 93.4%和 84.8%的总体准确性。基于 MFCUP，我们建立了一个靶向亚硫酸氢盐测序面板，并在 20 种癌症类型的 78 名患者的 FFPE 切片上进行了验证。该甲基化测序面板正确识别了 88.5%（69/78）的样本的组织起源。我们还发现，特定 CpG 的甲基化水平可以区分一种癌症类型与其他类型，表明它们具有作为癌症诊断和筛查的生物标志物的潜力。
我们的基于甲基化的癌症分类器和靶向甲基化测序面板可以高精度预测多种癌症类型的组织起源。
