Radioactive iodine (RAI) therapy is one of the cornerstones in the management of differentiated thyroid cancer (DTC) and it is the first-line systemic treatment in patients with advanced disease. However, 5-15% of patients are refractory to RAI treatment and the prognosis for these patients is poor.
There are different possible scenarios related to RAI-refractoriness: i) the metastatic tissue does not concentrate radioiodine at the time of the first I131 treatment; ii) the tumor tissue loses the ability to concentrate radioiodine after previous evidence of uptake; iii) radioiodine is concentrated in some lesions but not in others; iv) metastatic disease progresses despite significant uptake of RAI. In general, RAI resistance is addressed to the loss of the ability to absorb, concentrate and metabolize iodine, caused in most cases by the loss of functional differentiation markers as NIS and TPO and by oncogenic activation of downstream signal transducers which in turn dysregulate cell growth, survival, motility and invasion. However, the biology of the four classes of cancer described above is not fully understood.
The purpose of the present project is to identify a molecular signature associated with RAI refractory DTCs, which could be used as predictive markers to improve clinical management of advanced thyroid cancer patients, thus avoiding the administration of ineffective RAI treatment and address the clinician to alternative and new molecular targeted drugs.
Preliminary data on miRNA signatures of RAI-refractory and RAI-sensitive PTCs were obtained in a screening cohort of 26 PTCs. Data will be validated by digital PCR and correlated with genetic alterations. Genetic variants and miRNA expression levels will be integrated to understand how they can act together to alter regulation of Iodine metabolism and finally lead to RAI resistance in thyroid cancers. The identified association will be subsequently explored by in vitro studies (cell lines and organoids).
Molecular biomarkers were identified as predictors of radiation sensitivity in some cancers. Unfortunately, no predictive biomarker is currently available for identifying the thyroid cancer patients who should benefit from RAI therapy. Identification of a molecular signature associated with RAI refractory DTCs can improve clinical management of advanced thyroid cancer patients. Early patient classification could avoid the administration of ineffective RAI treatment that may lead to severe adverse events and consider alternative local or systemic therapies.
It is likely that mapping of genetic alterations in cancer specimens will help to determine how patients should be treated. DNA sequencing technologies play a growing role in the implementation of cancer predictors and prognosticators. Moreover, recent studies suggested that miRNAs may help to better classify cancer subtypes and predict their outcomes, due to their cancer-specific expression and stability. A spectrum of dysregulated miRNAs was identified to be involved in thyroid cancer genesis and progression. However, few studies reported the association between RAI-uptake and miRNA expression profiles and none has evaluated their association with response to RAI-treatment.
Both mutational data and miRNA expression levels will be analyzed in RAI sensitive and RAI resistant DTC patients. Genetic variants and miRNA expression results will be integrated to understand how they can act together to alter regulation of Iodine metabolism and finally lead to RAI resistance in thyroid cancers.
Two different in vitro models, cell lines and organoids, will be used to improve the knowledge of the molecular processes causing RAI resistance. In particular, the organoids are a more accurate representation of the natural environment experienced by cells in the living organism as opposed to growing cells on 2D flat surfaces. Moreover, until now, no organoids from fresh thyroid cancer tissues were established. To date, organoids cultured by only a few types of human tissues exist. The methods to grow the organoids proposed by this project will open new frontiers to develop this culture method for all human tissues in order to early predict the responses to specific cancer treatment.