Characterization of sporadic somatotropinomas with high GIP receptor expression.

PURPOSE: To analyze the expression of glucose-dependent insulinotropic polypeptide receptor (GIPR) in somatotropinomas specimens and compare clinical, biochemical, radiological, therapeutic, molecular, and pathological data among those who overexpressed (GIPR +) and those who did not overexpress (GIPR - ) GIPR. METHODS: Clinical, biochemical, radiological, molecular, and pathological data were collected. GNAS1 sequencing was performed with the Sanger method. Protein expression of somatostatin receptor subtypes 2 and 5 and CAM 5.2 were analyzed by immunohistochemistry. Quantitative real-time PCR was performed to analyze the mRNA expression of GIPR with the TaqMan® method. Positive expression was considered when the fold change (FC) was above 17.2 (GIPR +). RESULTS: A total of 74 patients (54% female) were included. Eighteen tumors (24%) were GIPR + . Gsp mutation was detected in 30 tumors (40%). GIPR + tumors were more frequently densely granulated adenomas (83% vs 47%, p = 0.028). There was no difference in clinical, biochemical, radiological, therapeutic (surgical cure or response to medical therapy), or other pathological features between GIPR + and GIPR -  tumors. Twenty-eight out of 56 (50%) GIPR -  tumors harbored a gsp mutation, whereas two out of 18 (11%) GIPR + tumors harbored a gsp mutation (p = 0.005). CONCLUSION: We described, for the first time, that GIPR + and gsp mutations are not mutually exclusive, but gsp mutations are less common in GIPR + tumors. GIPR + and GIPR -  tumors have similar clinical, biochemical, radiological, therapeutic, and pathological features, with the exception of a high frequency of densely granulated adenomas among GIPR + tumors.

Machine Learning-based Prediction Model for Treatment of Acromegaly With First-generation Somatostatin Receptor Ligands.

CONTEXT: Artificial intelligence (AI), in particular machine learning (ML), may be used to deeply analyze biomarkers of response to first-generation somatostatin receptor ligands (fg-SRLs) in the treatment of acromegaly. OBJECTIVE: To develop a prediction model of therapeutic response of acromegaly to fg-SRL. METHODS: Patients with acromegaly not cured by primary surgical treatment and who had adjuvant therapy with fg-SRL for at least 6 months after surgery were included. Patients were considered controlled if they presented growth hormone (GH) <1.0 ng/mL and normal age-adjusted insulin-like growth factor (IGF)-I levels. Six AI models were evaluated: logistic regression, k-nearest neighbor classifier, support vector machine, gradient-boosted classifier, random forest, and multilayer perceptron. The features included in the analysis were age at diagnosis, sex, GH, and IGF-I levels at diagnosis and at pretreatment, somatostatin receptor subtype 2 and 5 (SST2 and SST5) protein expression and cytokeratin granulation pattern (GP). RESULTS: A total of 153 patients were analyzed. Controlled patients were older (P = .002), had lower GH at diagnosis (P = .01), had lower pretreatment GH and IGF-I (P < .001), and more frequently harbored tumors that were densely granulated (P = .014) or highly expressed SST2 (P < .001). The model that performed best was the support vector machine with the features SST2, SST5, GP, sex, age, and pretreatment GH and IGF-I levels. It had an accuracy of 86.3%, positive predictive value of 83.3% and negative predictive value of 87.5%. CONCLUSION: We developed a ML-based prediction model with high accuracy that has the potential to improve medical management of acromegaly, optimize biochemical control, decrease long-term morbidities and mortality, and reduce health services costs.

Telomerase expression in clinically non-functioning pituitary adenomas.

PURPOSE: Non-functioning pituitary adenomas (NFPA) are benign tumors, however, some are agressive. We aimed to assess if human telomerase reverse transcriptase (hTERT) is present in NFPA and if it can be used as a marker of aggressiveness and proliferation. METHODS: Consecutive patients operated for NFPA whose fresh frozen tumors were available were included. We analyzed tumor's aggressiveness (based on radiological progression) and proliferation (based on Ki-67), as well as hTERT mRNA by quantitative real-time polymerase chain reaction (RT-qPCR). RESULTS: We included 109 samples from 86 patients followed for a median period of 60 months (5-120 months). Aggressive tumors were present in 66% cases and proliferative tumors in 47.7%. Seven (6.4%) samples expressed hTERT: 3 (42.8%) had aggressive and proliferative tumors, 2 (28.6%) only exhibited aggressiveness and the remaining 2 (28.6%) only proliferation. From the aggressive and proliferative tumors, 14% and 16%, respectively, expressed hTERT. From the non-aggressive and non-proliferative tumors, 9% and 6%, respectively, expressed hTERT. CONCLUSION: hTERT expression is present in a minority of NFPA and does not seem to be related to aggressiveness or proliferation in NFPA.