Mutational landscape and DNA methylation-based classification of squamous cell carcinoma and urothelial carcinoma

Identification of the tissue of origin is fundamental for cancer treatment. However, squamous cell carcinomas from different sites lack representative histological and immunohistochemical features. This study aimed to identify mutational profiles and further establish a DNA methylation-based classification for squamous cell carcinoma and urothelial carcinoma. Samples of unambiguous squamous cell carcinomas and urothelial carcinomas were collected for targeted next-generation sequencing and mutational landscape analysis. Moreover, using Illumina methylation BeadChip data from public datasets and a local cohort, we developed a DNA methylation-based classifier utilizing the CatBoost algorithm to identify four common types of squamous cell carcinoma (lung, head and neck, esophagus, and cervix) as well as urothelial carcinoma.

The DNA mutational profiles of squamous cell carcinomas from different sites overlapped greatly, and there was no significant difference in tumor mutation burden or microsatellite status. On the basis of public datasets and analyses via various machine learning algorithms, a DNA methylation-based classification containing 106 features by the CatBoost algorithm was constructed and reached an accuracy of 98.79% (490/496) in the training set from PanCanAtlas datasets. The predictive accuracies of the methylation classification in the public validation set and local FUSCC validation set 1 with known primary were 86.96% (340/391) and 84.87% (101/119), respectively. The predictive accuracy for the primary samples (89.66%, 78/87) was obviously greater than that for the metastatic samples (71.88%, 23/32). FUSCC validation set 2 included ten complicated cancer of unknown primary (CUP) samples with squamous cell differentiation. When a well-established 90-gene expression assay was compared with the present classification, our methylation-based classification successfully classified two samples with no eligible RNA expression; the results for four sample were consistent with higher methylation prediction scores in three, and those for two samples were inconsistent. The methylation-based classification results of the remaining two samples were more compatible with the results of the clinical evaluation.

We successfully established a DNA methylation-based classification for squamous cell carcinomas (lung, head and neck, esophagus, and cervix) and urothelial carcinomas with outstanding diagnostic performance for the first time. This classification has high potential for clinical translation to address the dilemma of identifying the origin of squamous cell carcinoma of unknown primary.