1. Boolean models of CRC progression trained with public patient data
- Author
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Khan, Faiz, Vera, Julio, Wolkenhauer, Olaf, and Salehzadeh-Yazdi, Ali
- Abstract
Boolean models of CRC progression calibrated with patient data. Models are developed in a software tool CellNetAnalyzer (CNA). The models uploaded here are ready to simulate in CNA. To gain an in-depth understanding of mechanisms underlying CRC tumorigenesis as well as to identify novel drug targets, we constructed a molecular interaction map (MIM) combining key signaling pathways from the cancer cell and the immune cells in the microenvironment. The MIM was dynamically simulated using stimulus-response analysis for two CRC patient datasets GSE1323 (primary tumor to metastasis progression) and GSE8671 (normal colonic mucosa to colorectal adenomas progression), here used as experimental conditions, and the analysis revealed disease signatures for each condition. More specifically, the identified disease signatures for the dataset GSE1323 were: (1) simultaneous activation of TNF/TNFRSF1A,B; EGF/EGFR and inactivation of ERE/ESR, (2) simultaneous activation of TNF/TNFRSF1A,B; TLR4 and inactivation of ERE/ESR, and the signature for the dataset GSE8671 was simultaneous activation of TNF/TNFRSF1A,B, and TLR41. Further, in silico perturbation analyses were performed and identified that concurrently inhibiting: (1) MAPK3 and STAT3 in case of the GSE1323, (2) ELK1/ATF2 and STAT3, or (3) MAPK14 and STAT3 in case of GSE8671, can achieve significant anti-cancer effects (i.e., lower EMT, proliferation and inflammation, and increase apoptosis). Disease signatures and therapeutic targets were validated through patient data using Kaplan Meier survival analysis and several machine learning methods. In conclusion, this holistic study mimics known characteristics of cancer, systematically predicts disease signatures and therapeutic targets, and can be applied to other immunogenic cancers.
- Published
- 2022
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