Machine-learning Modeling for Personalized Immunotherapy- An Evaluation Module.

Abstract

Immune-cell therapy and targeting therapy are in rapid development to treat tumor diseases. However, current immune-cell therapy and targeting immunotherapy often face three challenges (three Ss): safety challenges such as cytokine releasing syndrome (C.R.S.); specificity targeting problems such as low efficacy caused by off-targeting tumor cells; unsatisfying payment are confounded to clinical patients and physicians. We have been studying immunotherapy for more than thirty years, and recently, personalized immunotherapy to treat tumor disease has been proposed. After we discovered quiescent genes from immune cells within the tumor microenvironment, we set up single-cell genomics analysis, studying heterogeneous immune responses from multiple tumor antigens (neo-antigen); here, we further introduce a new generation of immunotherapy module by using a machine-learning model to assess optimal immunotherapy. The machine-learning model combined with single-cell genomic analysis can predict optimal immune-cell (such as T-cells) and other optimal targeting drugs such as PD1 and CTLA4 inhibitors for the patient to use.