ABSTRACT
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 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. 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.
Subject(s)
Carcinoma, Squamous Cell , Head and Neck Neoplasms , Neoplasms, Unknown Primary , Humans , DNA Methylation , Neoplasms, Unknown Primary/diagnosis , Neoplasms, Unknown Primary/genetics , Neoplasms, Unknown Primary/pathology , Head and Neck Neoplasms/diagnosis , Head and Neck Neoplasms/genetics , Carcinoma, Squamous Cell/diagnosis , Carcinoma, Squamous Cell/genetics , Carcinoma, Squamous Cell/pathology , Machine LearningABSTRACT
OBJECTIVES: To assess undergraduate helmet use attitudes and behaviors in accordance with the theory of planned behavior (TPB). We predicted helmet wearers and nonwearers would differ on our subscales. METHODS: Participants (N=414, 69% female, 84% white) completed a survey. RESULTS: Principal component analysis and reliability analysis guided the creation of subscales. Group differences were detected on 9 of 10 subscales: F(18,788) = 10.721, P=.001, eta(2) = .187. Few ethnicity and sex differences were detected. CONCLUSIONS: This study supports the validity of the TPB in predicting college student helmet use and offers a new scale for future research purposes.
