Department of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA; Department of Dermatology, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA; Lineberger Comprehensive Cancer Center (LCCC), University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Lineberger Comprehensive Cancer Center (LCCC), University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
J Invest Dermatol. 2019 Jun;139(6):1349-1361. doi: 10.1016/j.jid.2018.11.024. Epub 2018 Dec 6.
Early diagnosis improves melanoma survival, yet the histopathological diagnosis of cutaneous primary melanoma can be challenging, even for expert dermatopathologists. Analysis of epigenetic alterations, such as DNA methylation, that occur in melanoma can aid in its early diagnosis. Using a genome-wide methylation screening, we assessed CpG methylation in a diverse set of 89 primary invasive melanomas, 73 nevi, and 41 melanocytic proliferations of uncertain malignant potential, classified based on interobserver review by dermatopathologists. Melanomas and nevi were split into training and validation sets. Predictive modeling in the training set using ElasticNet identified a 40-CpG classifier distinguishing 60 melanomas from 48 nevi. High diagnostic accuracy (area under the receiver operator characteristic curve = 0.996, sensitivity = 96.6%, and specificity = 100.0%) was independently confirmed in the validation set (29 melanomas, 25 nevi) and other published sample sets. The 40-CpG melanoma classifier included homeobox transcription factors and genes with roles in stem cell pluripotency or the nervous system. Application of the 40-CpG melanoma classifier to the diagnostically uncertain samples assigned melanoma or nevus status, potentially offering a diagnostic tool to assist dermatopathologists. In summary, the robust, accurate 40-CpG melanoma classifier offers a promising assay for improving primary melanoma diagnosis.
早期诊断可提高黑色素瘤的存活率，但皮肤原发性黑色素瘤的组织病理学诊断即使对于专业的皮肤科病理学家来说也具有挑战性。分析发生在黑色素瘤中的表观遗传改变，如 DNA 甲基化，有助于其早期诊断。我们使用全基因组甲基化筛选，评估了 89 个不同侵袭性黑色素瘤、73 个痣和 41 个恶性潜能不确定的黑素细胞增生的 CpG 甲基化情况，这些病例是根据皮肤科病理学家的观察者间评估进行分类的。黑色素瘤和痣被分为训练集和验证集。在训练集中使用弹性网络进行预测建模，确定了一个 40-CpG 分类器，可以区分 60 个黑色素瘤和 48 个痣。该分类器在验证集（29 个黑色素瘤，25 个痣）和其他已发表的样本集中的高诊断准确性（接受者操作特征曲线下面积 = 0.996，敏感性 = 96.6%，特异性 = 100.0%）得到了独立确认。40-CpG 黑色素瘤分类器包括同源盒转录因子和在干细胞多能性或神经系统中发挥作用的基因。将 40-CpG 黑色素瘤分类器应用于诊断不确定的样本，确定其为黑色素瘤或痣的状态，这可能提供了一种辅助皮肤科病理学家的诊断工具。总之，稳健、准确的 40-CpG 黑色素瘤分类器为提高原发性黑色素瘤的诊断提供了一种很有前途的检测方法。
