Classification of Thyroid Tumors Based on DNA Methylation Patterns

Background: Alterations in DNA methylation are stable epigenetic events that can serve as clinical biomarkers. The aim of this study was to analyze methylation patterns among various follicular cell-derived thyroid neoplasms to identify disease subtypes and help understand and classify thyroid tumors.Methods: We employed an unsupervised machine learning method for class discovery to search for distinct methylation patterns among various thyroid neoplasms. Our algorithm was not provided with any clinical or pathological information, relying exclusively on DNA methylation data to classify samples. We analyzed 810 thyroid samples (n = 256 for discovery and n = 554 for validation), including benign and malignant tumors, as well as normal thyroid tissue.Results: Our unsupervised algorithm identified that samples could be classified into three subtypes based solely on their methylation profile. These methylation subtypes were strongly associated with histological diagnosis (p < 0.001) and were therefore named normal-like, follicular-like, and papillary thyroid carcinoma (PTC)-like. Follicular adenomas, follicular carcinomas, oncocytic adenomas, and oncocytic carcinomas clustered together forming the follicular-like methylation subtype. Conversely, classic papillary thyroid carcinomas (cPTC) and tall cell PTC clustered together forming the PTC-like subtype. These methylation subtypes were also strongly associated with genomic drivers: 98.7% BRAF(V600E)-driven cancers were PTC like, whereas 96.0% RAS-driven cancers had a follicular-like methylation pattern. Interestingly, unlike other diagnoses, follicular variant PTC (FVPTC) samples were split into two methylation clusters (follicular like and PTC like), indicating a heterogeneous group likely to be formed by two distinct diseases. FVPTC samples with a follicular-like methylation pattern were enriched for RAS mutations (36.4% vs. 8.0%; p < 0.001), whereas FVPTC- with PTC-like methylation patterns were enriched for BRAF(V600E) mutations (52.0% vs. 0%, Fisher exact p = 0.004) and RET fusions (16.0% vs. 0%, Fisher exact p = 0.003).Conclusions: Our data provide novel insights into the epigenetic alterations of thyroid tumors. Since our classification method relies on a fully unsupervised machine learning approach for subtype discovery, our results offer a robust background to support the classification of thyroid neoplasms based on methylation patterns.

Automated summary

Using unsupervised machine learning, this study identified three subtypes of thyroid neoplasms based on DNA methylation patterns, which were strongly associated with histological diagnosis and genomic drivers. These findings provide a robust background for the classification of thyroid tumors based on methylation patterns.