Rat protein tyrosine phosphatase η physically interacts with the PDZ domains of syntenin

AbstractThe tyrosine phosphatase r-PTPη is able to suppress the malignant phenotype of rat thyroid tumorigenic cell lines. To identify r-PTPη interacting proteins, a yeast two-hybrid screening was performed and an insert corresponding to the full-length syntenin cDNA was isolated. It encodes a protein containing two PDZ domains that mediates the binding of syntenin to proteins such as syndecan, proTGF-α, β-ephrins and neurofascin. We show that r-PTPη is able to interact with syntenin also in mammalian cells, and although syntenin is a tyrosine-phosphorylated protein it is not a substrate of r-PTPη. The integrity of both PDZ domains of syntenin and the carboxy-terminal region of r-PTPη are required for the interaction between syntenin and r-PTPη

A Prospective, Multicenter Study Examining the Relationship between Thyroid Cancer Treatment Outcomes and the Presence of Autoimmune Thyroiditis

: Background There is some controversy on the potential relationship between autoimmune processes and clinicopathological features as well as prognosis of differentiated thyroid cancer (DTC), and the evidence is limited by its largely retrospective nature. We examined the relationship between the presence of autoimmune thyroiditis and 1-year thyroid cancer treatment outcomes in a large, multi-center study, using prospectively collected data. Methods We included data from consecutive DTC patients enrolled in the Italian Thyroid Cancer observatory (ITCO) database (NCT04031339). We divided the groups according to the presence (AT) or absence (noAT) of associated autoimmune thyroiditis. We used propensity score matching to compare the clinical features and outcomes between the 2 groups at 1-year follow-up. Results We included data from 4233 DTC patients, including 3172 (75%) females. The American Thyroid Association (ATA) risk levels were as follows: 51% (2160/4233) low risk, 41.3% (1750/4233) intermediate risk, and 7.6% (323/4233) high risk. There were 1552 patients (36.7%) who had autoimmune thyroiditis. Before propensity score matching, AT patients were significantly younger, and had a smaller and bilateral tumor (p<0.0001). Patients with AT more frequently fell into the low and intermediate risk categories, while ATA high risk was more frequent among noAT patients (p=0.004). After propensity score matching, patients with AT more frequently showed evidence of disease (structural/biochemical incomplete response) versus excellent/indeterminate response, compared to patients without AT (7.3% versus 4.5%, p=0.001), with an OR of 1.86 (95% CI: 1.3-2.6, p=0.0001). However, when considering only structural persistence as the outcome, no statistically significant differences were observed between patients with or without AT (3.4% versus 2.7%, p=0.35). The elevated risk associated with ATA intermediate and high risk at diagnosis remained consistently statistically significant. Conclusions In this large prospective series, biochemical persistence was more frequent, at one-year follow-up, in AT patients. However, there was no significant association between the presence of AT and structural persistence of disease. These findings may be explained by the presence of a residual thyroid tissue

A data-driven approach to refine predictions of differentiated thyroid cancer outcomes: a prospective multicenter study

Context The risk stratification of patients with differentiated thyroid cancer (DTC) is crucial in clinical decision making. The most widely accepted method to assess risk of recurrent/persistent disease is described in the 2015 American Thyroid Association (ATA) guidelines. However, recent research has focused on the inclusion of novel features or questioned the relevance of currently included features. Objective To develop a comprehensive data-driven model to predict persistent/recurrent disease that can capture all available features and determine the weight of predictors. Methods In a prospective cohort study, using the Italian Thyroid Cancer Observatory (ITCO) database (NCT04031339), we selected consecutive cases with DTC and at least early follow-up data (n = 4773; median follow-up 26 months; interquartile range, 12-46 months) at 40 Italian clinical centers. A decision tree was built to assign a risk index to each patient. The model allowed us to investigate the impact of different variables in risk prediction. Results By ATA risk estimation, 2492 patients (52.2%) were classified as low, 1873 (39.2%) as intermediate, and 408 as high risk. The decision tree model outperformed the ATA risk stratification system: the sensitivity of high-risk classification for structural disease increased from 37% to 49%, and the negative predictive value for low-risk patients increased by 3%. Feature importance was estimated. Several variables not included in the ATA system significantly impacted the prediction of disease persistence/recurrence: age, body mass index, tumor size, sex, family history of thyroid cancer, surgical approach, presurgical cytology, and circumstances of the diagnosis. Conclusion Current risk stratification systems may be complemented by the inclusion of other variables in order to improve the prediction of treatment response. A complete dataset allows for more precise patient clustering