Data from High-Throughput Screening of Combinatorial Immunotherapies with Patient-Specific <i>In Silico</i> Models of Metastatic Colorectal Cancer

作者：Jakob Nikolas Kather, Pornpimol Charoentong, Meggy Suarez‐Carmona, Esther Herpel, Fee Klupp, Alexis Ulrich, Martin Schneider, Inka Zoernig, Tom Luedde, Dirk Jaeger, Jan Poleszczuk, Niels Halama · 年份：2023 · DOI：10.1158/0008-5472.c.6512214.v1 · 研究领域：Mathematical Biology Tumor Growth、Cancer Cells and Metastasis、Cancer Immunotherapy and Biomarkers

<div>Abstract<p>Solid tumors are rich ecosystems of numerous different cell types whose interactions lead to immune escape and resistance to immunotherapy in virtually all patients with metastatic cancer. Here, we have developed a 3D model of human solid tumor tissue that includes tumor cells, fibroblasts, and myeloid and lymphoid immune cells and can represent over a million cells over clinically relevant timeframes. This model accurately reproduced key features of the tissue architecture of human colorectal cancer and could be informed by individual patient data, yielding <i>in silico</i> tumor explants. Stratification of growth kinetics of these explants corresponded to significantly different overall survival in a cohort of patients with metastatic colorectal cancer. We used the model to simulate the effect of chemotherapy, immunotherapies, and cell migration inhibitors alone and in combination. We classified tumors according to tumor and host characteristics, showing that optimal treatment strategies markedly differed between these classes. This platform can complement other patient-specific <i>ex vivo</i> models and can be used for high-throughput screening of combinatorial immunotherapies.</p><p><b>Significance:</b> This patient-informed <i>in silico</i> tumor growth model allows testing of different cancer treatment strategies and immunotherapies on a cell/tissue level in a clinically relevant sce...