Over the past twenty years, a progressively increasing number of markers biochemical, clinical, imaging, histopathological or genetic have been suggested to attempt to predict aggression and therapeutic outcome, both in secretory PitNETs and in non-secretory PitNETs. In recent years, the microenvironment tumor (TEM) has been extensively studied in different malignancies, promoting the development of therapies target and immunotherapy with checkpoint inhibitors immune system (ICI). Data on TIME in PitNETs are scarce today and derive from studies conducted mainly on heterogeneous cases for type of PitNET. Therefore, the purpose of this research project is to investigate the TIME in PitNET, to identify immune biomarkers and new molecular pathways that can promote the use of ICI and target therapies in Aggressive PitNETs.
indication as per clinical practice and in agreement to current guidelines for surgical removal of the PitNET0;
patient naïve to medical therapy before surgical removal of PitNET;
Diagnosis of PitNET confirmed through the study pathological/histological;
over 18 years of age;
History of medical therapy with analogs of somatostatin or dopamine agonist, previous the removal of the pituitary adenoma;
history of radiotherapy of the head and neck region earlier than 10 years after resection of the adenoma pituitary;
history of autoimmune diseases.
Patients with pituitary adenomas
Cohort
Prospective
Not listed
Cells immune systems
Quantify and locate cells immune systems (lymphocytes CD3, CD4, CD8, CD20, CD138, CD68+ macrophages) associated with the various PitNET subtypes in the study
Chemokines in PitNETS samples
Identify cytokines and chemokines in PitNETS samples, through Human Cytokine antibody Array ab 133997
Chemokines in PitNETS samples
Quantify cytokines and chemokines in PitNETS samples, through Human Cytokine antibody Array ab 133997
pituitary antibodies in PitNET
Quantify plasma pituitary antibodies associated with the various subtypes of PitNET under study
Identify the biomarkers associated with the various PitNET subtypes, anatomical-pathological and morphological characteristics, surgical outcome and response to medical therapy in PitNET
Identify the biomarkers associated with the various PitNET subtypes, anatomical-pathological and morphological characteristics, surgical outcome and response to medical therapy, through statistical software R. We will use clinical parameters for the measurement.
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