American Association for Cancer Research
00085472can181126-sup-200115_2_supp_4842437_p33wbb.docx (35.76 kB)

Supplementary Tables 1-6 from High-Throughput Screening of Combinatorial Immunotherapies with Patient-Specific In Silico Models of Metastatic Colorectal Cancer

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journal contribution
posted on 2023-03-31, 03:44 authored by 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

Detailed description of the parameters used in the model. Also, these tables show detailed clinical data for the patient cohorts that were used in this study.



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 in silico 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 ex vivo models and can be used for high-throughput screening of combinatorial immunotherapies.Significance: This patient-informed in silico tumor growth model allows testing of different cancer treatment strategies and immunotherapies on a cell/tissue level in a clinically relevant scenario. Cancer Res; 78(17); 5155–63. ©2018 AACR.