Thyroid Differentiation Score as a Molecular Bridge Between Genotype, Tumor Biology, and Radioiodine Theranostics in...

Differentiated thyroid cancer (DTC) is characterized by retention of thyroid-specific functions, particularly iodide uptake mediated by the sodium-iodide symporter (NIS), which underlies the effectiveness of radioiodine (RAI) imaging and therapy. Loss of iodine avidity reflects tumor dedifferentiation and is a defining feature of aggressive, RAI-refractory disease. The Thyroid Differentiation Score (TDS), introduced by The Cancer Genome Atlas (TCGA) program, provides a quantitative transcriptomic measure of thyroid lineage identity based on coordinated expression of genes involved in thyroid hormone biosynthesis and transport. This comprehensive narrative review synthesizes current evidence supporting TDS as a unifying framework linking oncogenic drivers, signaling pathways, tumor microenvironment, metabolic reprogramming, and clinical behavior across thyroid cancer subtypes, including papillary, follicular, poorly differentiated, anaplastic, and pediatric tumors. MAPK pathway activation, particularly via BRAFV600E, is associated with suppression of thyroid differentiation programs, whereas RAS-like and fusion-driven tumors tend to retain higher differentiation states. Emerging data connect TDS with tumor aggressiveness, recurrence risk, immune and inflammatory signaling, and resistance to RAI therapy. The role of TDS in guiding redifferentiation strategies using targeted tyrosine kinase inhibitors is also discussed, highlighting its potential as a biomarker for patient selection in RAI re-sensitization approaches. TDS is a well-established biological measure of thyroid-lineage differentiation and has shown prognostic associations in retrospective studies. However, its value as an individual predictive or theranostic biomarker remains investigational. Future work should focus on developing a personalized theranostic TDS that integrates matched normal tissue, molecular profiling, functional imaging, and machine learning to potentially better predict iodine-handling capacity and optimize precision management in thyroid cancer.