Most solid tumors in cancer are resistant to immunotherapy because of the immunosuppressive effect of the tumor microenvironment. In the present study, the researchers developed an in silico 3D model of human solid tumor tissue that can comprise over a million cells, including the different categories of cells usually present in solid tumors, and over clinically relevant timeframes. This model could be informed by individual patient data to generate individual in silico tumor explants. The stratification of growth kinetics of these explants could reasonably predict survival in a cohort of patients. Further, the model was used to simulate the effect of chemotherapy, immunotherapies, and cell migration inhibitors alone and in combination to try to find optimal treatment strategies. This platform can complement other patient-specific ex vivo models and can be used for high-throughput screening of combinatorial immunotherapies.
High-throughput screening of combinatorial immunotherapies with patient-specific in silico models of metastatic colorectal cancer
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