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% BRAFV600E-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 BRAFV600E 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.

Overall Survival for HER2-Positive Breast Cancer Patients in the HER2-Targeted Era: Evidence From a Population-Based Study.

BACKGROUND: HER2-positive breast cancer is an aggressive tumor subtype and it is usually associated with worse clinical outcomes. Given the advances in HER2-targeted therapies, we hypothesized that HER2 amplification is no longer a marker of poor prognosis. METHODS: We conducted a population-based observational study employing two independent cohorts of patients with breast cancer. Samples from the METABRIC cohort were collected before clinical availability of HER2-targeted therapies, whereas samples from the SCAN-B cohort were collected afterward. The primary endpoint was overall survival (OS). RESULTS: A total of 5121 patients were included in the analyses. In both cohorts, HER2-positive tumors were more likely to be node-positive (P < .05) and high grade (P < .001). Before HER2-targeted agents, HER2 patients had a significantly worse 5-year OS than hormone receptor-positive (HR+) patients (63.4% vs. 83.0%, HR = 2.49, P < .001). In contrast, after HER2-targeted agents entered clinical practice, 5-year OS no longer differed (88.3% vs. 90.4%, HR = 1.24, P = .17). Additionally, in an exploratory analysis using PAM50 subtypes, we identified that, after HER2-targeted therapies were implemented, patients clinically HER2-negative but PAM50-HER2-enriched have a lower OS (HR = 1.99, P = .009) than those who are both HER2-positive and PAM50-HER2-enriched, since they have not benefitted from HER2-targeted therapies. CONCLUSIONS: HER2-targeted therapies dramatically altered the natural history of HER2-positive breast cancer, with overall survival approaching those of luminal subtype. HER2 positivity is no longer a marker of poor prognosis if access to HER2-targeted therapies is granted. Future trials should assess whether HER2-negative PAM50-HER2-enriched patients may also benefit from such therapies.

Decreased expression of the thyroid hormone-inactivating enzyme type 3 deiodinase is associated with lower survival rates in breast cancer.

Thyroid hormones (THs) are critical regulators of cellular processes, while changes in their levels impact all the hallmarks of cancer. Disturbed expression of type 3 deiodinase (DIO3), the main TH-inactivating enzyme, occurs in several human neoplasms and has been associated with adverse outcomes. Here, we investigated the patterns of DIO3 expression and its prognostic significance in breast cancer. DIO3 expression was evaluated by immunohistochemistry in a primary cohort of patients with breast cancer and validated in a second cohort using RNA sequencing data from the TCGA database. DNA methylation data were obtained from the same database. DIO3 expression was present in normal and tumoral breast tissue. Low levels of DIO3 expression were associated with increased mortality in the primary cohort. Accordingly, low DIO3 mRNA levels were associated with an increased risk of death in a multivariate model in the validation cohort. DNA methylation analysis revealed that the DIO3 gene promoter is hypermethylated in tumors when compared to normal tissue. In conclusion, DIO3 is expressed in normal and tumoral breast tissue, while decreased expression relates to poor overall survival in breast cancer patients. Finally, loss of DIO3 expression is associated with hypermethylation of the gene promoter and might have therapeutic implications.

Current concepts and challenges to unravel the role of iodothyronine deiodinases in human neoplasias.

Thyroid hormones (THs) are essential for the regulation of several metabolic processes and the energy consumption of the organism. Their action is exerted primarily through interaction with nuclear receptors controlling the transcription of thyroid hormone-responsive genes. Proper regulation of TH levels in different tissues is extremely important for the equilibrium between normal cellular proliferation and differentiation. The iodothyronine deiodinases types 1, 2 and 3 are key enzymes that perform activation and inactivation of THs, thus controlling TH homeostasis in a cell-specific manner. As THs seem to exert their effects in all hallmarks of the neoplastic process, dysregulation of deiodinases in the tumoral context can be critical to the neoplastic development. Here, we aim at reviewing the deiodinases expression in different neoplasias and exploit the mechanisms by which they play an essential role in human carcinogenesis. TH modulation by deiodinases and other classical pathways may represent important targets with the potential to oppose the neoplastic process.