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 predict responsiveness


A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning

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In recent years, immunotherapy has dramatically improved the treatment options in various cancers increasing the survival rates for treated patients. Among the most promising immunotherapeutic approaches there is the pharmacological manipulation of the physiologic immune checkpoints [1,2,3,4]. Immune-checkpoint blockade is the basis for the clinical antitumor activity of the most promising currently approved antibodies targeting the checkpoint molecules CTLA4 (Cytotoxic T-Lymphocyte Antigen 4), PD1 (Programmed Cell Death 1) and PD-L1 (Programmed cell death ligand 1).Nevertheless, there are heterogeneous response rates to immune checkpoint inhibitors (ICI) [4,5,6] among the different cancer types, and also in the context of patients affected by a specific cancer.