Project description:Several molecular signatures are able to predict the activity of adjuvant chemotherapy in breast cancer patients. However, no molecular data are already available to detect microenvironment molecular signature that can identify patients who are good candidates for treatment with immune checkpoint inhibitors (ICIs). For this reason, in a series of women with operable high grade breast cancer (HGBC), we tried to combine the ECM3 and IFN molecular signatures reflecting different aspects of tumor microenvironment that can better draw the picture of the tumor microenvironment in which the immune cells are in close contact with extracellular matrix. Grade 3 primary tumors
Project description:Several molecular signatures are able to predict the activity of adjuvant chemotherapy in breast cancer patients. However, no molecular data are already available to detect microenvironment molecular signature that can identify patients who are good candidates for treatment with immune checkpoint inhibitors (ICIs). For this reason, in a series of women with operable high grade breast cancer (HGBC), we tried to combine the ECM3 and IFN molecular signatures reflecting different aspects of tumor microenvironment that can better draw the picture of the tumor microenvironment in which the immune cells are in close contact with extracellular matrix. Subgroup of G3 tumors extracted from the ECTO1 study (Gianni L. et al, JCO 2009)
Project description:Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are rare and heterogeneous tumors presenting a wide spectrum of different clinical and biological characteristics. In these tumors, the histological evaluation is a crucial element of clinical management. Currently, tumor grading, determined by Ki-67 staining and mitotic counts, is the most reliable predictor of prognosis. This scoring method is time-consuming and a high reproducibility cannot be achieved. Novel approaches are needed to support histological evaluation and prognosis. In this study, starting from a microarray analysis, we defined the miRNAs signature for poorly differentiated NETs (G3) compared to well differentiated NETs (G1 and G2) consisting of 56 deregulated miRNAs. Moreover, we identified 8 miRNAs that were expressed in all GEP-NETs grades but at different level. Among these miRNAs, we found miR-96-5p that raised its expression levels from grade 1 to grade 3; inversely, its target FOXO1 was decrease from grade 1 to grade 3. Our results reveal that the miRNAs expression profile of GEP-NET correlates their expression with grading showing a potential advantage of miRNA quantification to aid clinicians in the classification of common GEP-NETs subtypes.
Project description:Expression profile of human GEP-NET tumors, including 113 fresh frozen biopsies of primary and metastatic tumours originating from pancreas (P-NET, 83 primary and 30 metastasis), 81 from small intestine (SI-NET, 44 primary and 37 metastasis), and 18 from rectum (RE-NET, 3 primary and 15 metastasis).