0000000000225496
AUTHOR
Davide Gentilini
A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning
Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we …
Gene expression profiling of peripheral blood mononuclear cells in endometriosis identifies genes altered in non-gynaecologic chronic inflammatory diseases
background: Pelvic inflammatory phenomena have been suggested as critical players in the natural history of endometriosis. However, to what extent these events could affect the systemic immunologic status remains to be clarified. Here, we compared the gene expression profile in peripheral blood mononuclear cells from endometriosis patients in the severe diseased stage with the profile after a conventional surgical treatment for removal of endometriotic lesions and adhesions.   methods: Microarray analysis included four patients suffering from severe endometriosis in which blood samples were obtained few days before the surgical intervention and again 6 months later. Real-time quantitative…