84 Recent advancements in genomic research have provided unprecedented insights into the biological and clinical dynamics of thyroid cancer. This leap in understanding stems from extensive sequencing data and sophisticated experimental models, shedding light on the genetic simplicity and mutation-specific mechanisms that drive this disease. These discoveries are poised to revolutionise the clinical management of thyroid cancer, enabling more refined strategies for treatment and prognosis. The findings were published in the journal Nature Reviews Endocrinology. Thyroid cancers, which originate from thyroid follicular cells, generally exhibit a low mutation burden and genomic simplicity compared to other cancers. This has facilitated the correlation between specific genotypes and phenotypes. Thyroid cancer is mostly caused by changes that turn on the mitogen-activated protein kinase (MAPK) pathway. BRAF and RAS mutations are the main ones responsible for this. These mutations not only initiate the tumour but also influence its histology, differentiation, and metastatic behaviour. According to the Cancer Genome Atlas (TCGA) study, MAPK pathway-activating events are responsible for initiating about 90% of papillary thyroid carcinomas (PTCs). BRAFV600E mutations are the most common, particularly in classic and tall-cell PTCs, while RAS mutations are more prevalent in the follicular variant of PTC. These mutations establish distinct transcriptomic features, captured by the BRAFV600E-RAS score, which serves as a proxy for MAPK activation. Interestingly, thyroid cancers initiated by these mutations display varying levels of thyroid differentiation. It is common for BRAFV600E-like PTCs to have higher MAPK outputs and lower thyroid differentiation scores, which means they take in and use iodine less efficiently. RAS-driven PTCs, on the other hand, keep the differentiation level of the thyroid higher, which has important clinical implications for targeted and radioactive iodine (RAI) therapies. While BRAF and RAS mutations are important for the start of a tumour, more genetic changes are needed for thyroid cancer to progress to more aggressive types like poorly differentiated thyroid cancer (PDTC) and anaplastic thyroid cancer (ATC). These include mutations in the PI3K-AKT pathway, disruption of p53 function, and TERT promoter mutations. Each of these genetic changes contributes to the tumour’s aggressiveness and its ability to evade standard treatments. The loss of p53 function, for example, has been shown to fast-track the progression of PTCs and follicular thyroid carcinomas (FTCs) to ATCs. This is done by getting rid of important barriers to oncogene-induced senescence and cell cycle checkpoints, which leads to more DNA damage and genomic instability. The integration of genomic data into clinical practice has opened new avenues for managing thyroid cancer. Molecular testing is now recommended for patients with aggressive thyroid cancers and for all patients with ATC. This genomic understanding aids in refining prognosis, informing RAI treatment, and guiding targeted therapies. Selective RET inhibitors, for instance, have shown promise in treating advanced or metastatic RET-fusion-positive thyroid cancer. Trials with drugs like pralsetinib and selpercatinib have reported high objective response rates, demonstrating the potential of targeted therapies to improve patient outcomes. Moreover, the identification of TERT promoter mutations has emerged as a crucial marker for poor prognosis. People with differentiated thyroid cancers (DTCs) who have high-risk clinical and pathological features, such as higher rates of distant metastases and persistent disease, can benefit the most from these mutations. Despite these advancements, several challenges remain in the genomic landscape of thyroid cancer. Researchers are still working to fully understand the genetic determinants and functional underpinnings of certain tumour subsets. There is also a need for improved human-derived experimental models that retain the complexity of original tumours, enabling more accurate mechanistic studies and rapid testing of new treatments. Clinically, the optimal stratification of patient risk, overcoming unresponsiveness to approved therapies, and ensuring equitable access to treatments are ongoing challenges. However, the continuous generation and integration of genomic and biological data promise to enhance traditional treatments and immunotherapies, accelerating progress towards better clinical applications.