GEO DataSets

(Submitter supplied) Background. 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 histological 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 = 1,103) 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. Results. 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. Conclusions. 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.

Organism:

Homo sapiens

Type:

Methylation profiling by genome tiling array

Platform:

GPL21145

28 Samples

Download data: IDAT

(Submitter supplied) 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. more...

Organism:

Homo sapiens

Type:

Methylation profiling by genome tiling array

Platform:

GPL21145

49 Samples

Download data: IDAT

(Submitter supplied) Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. more...

Organism:

Homo sapiens

Type:

Methylation profiling by array

Platform:

GPL23976

61 Samples

Download data: IDAT

(Submitter supplied) This study focused on global methylation changes between tumors, normal mucosa, primary tumor xenografts, and cell lines in order to determine epigenetic changes in cell cultures and xenografts derived from primary tumors.

Organism:

Homo sapiens

Type:

Methylation profiling by array

Platform:

GPL8490

22 Samples

Download data: TXT

(Submitter supplied) DNA methylation is one of the most studied epigenetic alterations in cancer. Genome-wide DNA methylation profiling was conducted in 6 oral tongue squamous cell carcinomas and matched normal tissues. In the present study, the Illumina Infinium HumanMethylationEPIC BeadChip (EPIC array) was used to characterize the DNA methylation pattern across approximately 850,000 CpG dinucleotide methylation loci using DNA isolated of formalin-fixed and paraffin-embedded tissue sections.

Organism:

Homo sapiens

Type:

Methylation profiling by genome tiling array

Platform:

GPL21145

12 Samples

Download data: IDAT, TXT

(Submitter supplied) Aberrant upregulation of a single oncogene FOXM1 in primary normal human oral epithelial cells orchestrated a cancer-like methylome landscape This study have identified a unique FOXM1-induced epigenetic signature which may have potentials as biomarkers for early oral cancer screening, diagnostic and/or therapeutic interventions

Organism:

Homo sapiens

Type:

Methylation profiling by genome tiling array

Platform:

GPL14361

3 Samples

Download data: GFF, PAIR, TXT