Quantitative cancer-immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancer
Abstract Patients with advanced metastatic colorectal cancer (mCRC) typically exhibit significant interindividual differences in treatment responses and face poor survival outcomes. To systematically analyze the heterogeneous tumor progression and recurrence observed in advanced mCRC patients, we de...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-025-00513-1 |
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| author | Chenghang Li Yongchang Wei Jinzhi Lei |
| author_facet | Chenghang Li Yongchang Wei Jinzhi Lei |
| author_sort | Chenghang Li |
| collection | DOAJ |
| description | Abstract Patients with advanced metastatic colorectal cancer (mCRC) typically exhibit significant interindividual differences in treatment responses and face poor survival outcomes. To systematically analyze the heterogeneous tumor progression and recurrence observed in advanced mCRC patients, we developed a quantitative cancer-immunity cycle (QCIC) model. The QCIC model employs differential equations to capture the biological mechanisms underlying the cancer-immunity cycle and predicts tumor evolution dynamics under various treatment strategies through stochastic computational methods. We introduce the treatment response index (TRI) to quantify disease progression in virtual clinical trials and the death probability function (DPF) to estimate overall survival. Additionally, we investigate the impact of predictive biomarkers on survival prognosis in advanced mCRC patients, identifying tumor-infiltrating CD8+ cytotoxic T lymphocytes (CTLs) as key predictors of disease progression and the tumor-infiltrating CD4+ Th1/Treg ratio as a significant determinant of survival outcomes. This study presents an approach that bridges the gap between diverse clinical data sources and the generation of virtual patient cohorts, providing valuable insights into interindividual treatment variability and survival forecasting in mCRC patients. |
| format | Article |
| id | doaj-art-8b908467778b4f668a2e7fb3bfc82a31 |
| institution | OA Journals |
| issn | 2056-7189 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj-art-8b908467778b4f668a2e7fb3bfc82a312025-08-20T02:11:42ZengNature Portfolionpj Systems Biology and Applications2056-71892025-04-0111111710.1038/s41540-025-00513-1Quantitative cancer-immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancerChenghang Li0Yongchang Wei1Jinzhi Lei2School of Mathematical Sciences, Tiangong UniversityDepartment of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan UniversitySchool of Mathematical Sciences, Tiangong UniversityAbstract Patients with advanced metastatic colorectal cancer (mCRC) typically exhibit significant interindividual differences in treatment responses and face poor survival outcomes. To systematically analyze the heterogeneous tumor progression and recurrence observed in advanced mCRC patients, we developed a quantitative cancer-immunity cycle (QCIC) model. The QCIC model employs differential equations to capture the biological mechanisms underlying the cancer-immunity cycle and predicts tumor evolution dynamics under various treatment strategies through stochastic computational methods. We introduce the treatment response index (TRI) to quantify disease progression in virtual clinical trials and the death probability function (DPF) to estimate overall survival. Additionally, we investigate the impact of predictive biomarkers on survival prognosis in advanced mCRC patients, identifying tumor-infiltrating CD8+ cytotoxic T lymphocytes (CTLs) as key predictors of disease progression and the tumor-infiltrating CD4+ Th1/Treg ratio as a significant determinant of survival outcomes. This study presents an approach that bridges the gap between diverse clinical data sources and the generation of virtual patient cohorts, providing valuable insights into interindividual treatment variability and survival forecasting in mCRC patients.https://doi.org/10.1038/s41540-025-00513-1 |
| spellingShingle | Chenghang Li Yongchang Wei Jinzhi Lei Quantitative cancer-immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancer npj Systems Biology and Applications |
| title | Quantitative cancer-immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancer |
| title_full | Quantitative cancer-immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancer |
| title_fullStr | Quantitative cancer-immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancer |
| title_full_unstemmed | Quantitative cancer-immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancer |
| title_short | Quantitative cancer-immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancer |
| title_sort | quantitative cancer immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancer |
| url | https://doi.org/10.1038/s41540-025-00513-1 |
| work_keys_str_mv | AT chenghangli quantitativecancerimmunitycyclemodelingforpredictingdiseaseprogressioninadvancedmetastaticcolorectalcancer AT yongchangwei quantitativecancerimmunitycyclemodelingforpredictingdiseaseprogressioninadvancedmetastaticcolorectalcancer AT jinzhilei quantitativecancerimmunitycyclemodelingforpredictingdiseaseprogressioninadvancedmetastaticcolorectalcancer |