Department of Otolaryngology, Head and Neck Surgery, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Clin Epigenetics. 2024 Mar 25;16(1):47. doi: 10.1186/s13148-024-01657-3.
The unknown tissue of origin in head and neck cancer of unknown primary (hnCUP) leads to invasive diagnostic procedures and unspecific and potentially inefficient treatment options for patients. The most common histologic subtype, squamous cell carcinoma, can stem from various tumor primary sites, including the oral cavity, oropharynx, larynx, head and neck skin, lungs, and esophagus. DNA methylation profiles are highly tissue-specific and have been successfully used to classify tissue origin. We therefore developed a support vector machine (SVM) classifier trained with publicly available DNA methylation profiles of commonly cervically metastasizing squamous cell carcinomas (n = 1103) in order to identify the primary tissue of origin of our own cohort of squamous cell hnCUP patient's samples (n = 28). Methylation analysis was performed with Infinium MethylationEPIC v1.0 BeadChip by Illumina.
The SVM algorithm achieved the highest overall accuracy of tested classifiers, with 87%. Squamous cell hnCUP samples on DNA methylation level resembled squamous cell carcinomas commonly metastasizing into cervical lymph nodes. The most frequently predicted cancer localization was the oral cavity in 11 cases (39%), followed by the oropharynx and larynx (both 7, 25%), skin (2, 7%), and esophagus (1, 4%). These frequencies concord with the expected distribution of lymph node metastases in epidemiological studies.
On DNA methylation level, hnCUP is comparable to primary tumor tissue cancer types that commonly metastasize to cervical lymph nodes. Our SVM-based classifier can accurately predict these cancers' tissues of origin and could significantly reduce the invasiveness of hnCUP diagnostics and enable a more precise therapy after clinical validation.
头颈部不明原发癌（hnCUP）的未知组织来源导致对患者进行侵入性诊断程序和非特异性、潜在低效的治疗选择。最常见的组织学亚型鳞状细胞癌可源自多种肿瘤原发部位，包括口腔、口咽、喉、头颈部皮肤、肺和食管。DNA 甲基化谱具有高度的组织特异性，已成功用于组织来源分类。因此，我们开发了一种支持向量机（SVM）分类器，该分类器使用广泛存在的宫颈转移鳞状细胞癌（n=1103）的公开 DNA 甲基化谱进行训练，以确定我们自己的鳞状细胞 hnCUP 患者样本（n=28）的原发组织来源。甲基化分析采用 Illumina 的 Infinium MethylationEPIC v1.0 BeadChip 进行。
SVM 算法实现了最高的整体准确性，达到 87%。鳞状细胞 hnCUP 样本在 DNA 甲基化水平上类似于常见转移至颈部淋巴结的鳞状细胞癌。最常预测的癌症定位是口腔 11 例（39%），其次是口咽和喉（均为 7 例，25%）、皮肤（2 例，7%）和食管（1 例，4%）。这些频率与流行病学研究中淋巴结转移的预期分布一致。
在 DNA 甲基化水平上，hnCUP 与常见转移至颈部淋巴结的原发性肿瘤组织癌症类型相当。我们基于 SVM 的分类器可以准确预测这些癌症的组织来源，并显著降低 hnCUP 诊断的侵入性，在临床验证后实现更精确的治疗。
