Leitheiser Maximilian, Capper David, Seegerer Philipp, Lehmann Annika, Schüller Ulrich, Müller Klaus-Robert, Klauschen Frederick, Jurmeister Philipp, Bockmayr Michael
Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Institute of Pathology, Berlin, Germany.
Department of Neuropathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.
J Pathol. 2022 Apr;256(4):378-387. doi: 10.1002/path.5845. Epub 2022 Jan 20.
In head and neck squamous cell cancers (HNSCs) that present as metastases with an unknown primary (HNSC-CUPs), the identification of a primary tumor improves therapy options and increases patient survival. However, the currently available diagnostic methods are laborious and do not offer a sufficient detection rate. Predictive machine learning models based on DNA methylation profiles have recently emerged as a promising technique for tumor classification. We applied this technique to HNSC to develop a tool that can improve the diagnostic work-up for HNSC-CUPs. On a reference cohort of 405 primary HNSC samples, we developed four classifiers based on different machine learning models [random forest (RF), neural network (NN), elastic net penalized logistic regression (LOGREG), and support vector machine (SVM)] that predict the primary site of HNSC tumors from their DNA methylation profile. The classifiers achieved high classification accuracies (RF = 83%, NN = 88%, LOGREG = SVM = 89%) on an independent cohort of 64 HNSC metastases. Further, the NN, LOGREG, and SVM models significantly outperformed p16 status as a marker for an origin in the oropharynx. In conclusion, the DNA methylation profiles of HNSC metastases are characteristic for their primary sites, and the classifiers developed in this study, which are made available to the scientific community, can provide valuable information to guide the diagnostic work-up of HNSC-CUP. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
在以不明原发灶转移形式出现的头颈部鳞状细胞癌（HNSC-CUPs）中，确定原发肿瘤可改善治疗方案并提高患者生存率。然而，目前可用的诊断方法繁琐，且检测率不足。基于DNA甲基化谱的预测性机器学习模型最近已成为一种有前景的肿瘤分类技术。我们将此技术应用于HNSC，以开发一种可改善HNSC-CUPs诊断检查的工具。在405份原发性HNSC样本的参考队列中，我们基于不同的机器学习模型[随机森林（RF）、神经网络（NN）、弹性网惩罚逻辑回归（LOGREG）和支持向量机（SVM）]开发了四个分类器，这些分类器可根据HNSC肿瘤的DNA甲基化谱预测其原发部位。在一个由64例HNSC转移灶组成的独立队列中，这些分类器取得了较高的分类准确率（RF = 83%，NN = 88%，LOGREG = SVM = 89%）。此外，NN、LOGREG和SVM模型在作为口咽起源标志物方面显著优于p16状态。总之，HNSC转移灶的DNA甲基化谱具有其原发部位的特征，本研究中开发的分类器已向科学界公开，可为指导HNSC-CUP的诊断检查提供有价值的信息。© 2021作者。《病理学杂志》由John Wiley & Sons Ltd代表大不列颠及爱尔兰病理学会出版。
