ArticleNPJ systems biology and applications2025
Quantitative cancer-immunity cycle modeling for predicting disease progression in advanced metastatic colorectal cancer.
Article in NPJ systems biology and applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics for predicting microsatellite instability-high status in colorectal cancer: a systematic review and meta-analysis.World journal of surgical oncology · 2026Pooled it
- Multiscale modeling of T cell exhaustion: A mathematical framework integrating continuous dynamics with spatial heterogeneity.PLoS computational biology · 2026Article
- A risk stratification framework based on ammonia-induced cell death signatures for prognostic assessment and therapeutic insights in colorectal cancer.Discover oncology · 2026Article
- Multiscale modeling reveals synergy between CCL19 and PD-1 blockade in reshaping the TNBC microenvironment.NPJ systems biology and applications · 2026Article
- A pipeline of machine learning-driven multi-modal data fusion methods for prognostic risk analysis in bevacizumab-treated metastatic colorectal cancer.Scientific reports · 2026Article
- VIOLIN: A modular framework for scalable reconciliation of heterogeneous interaction graphs.bioRxiv : the preprint server for biology · 2026Article
- Peripheral blood immune cell subsets as non-invasive biomarkers of colorectal cancer stage, laterality, metastasis and survival.Cellular oncology (Dordrecht, Netherlands) · 2026Article
- Integrative single-cell and machine-learning analysis identifies acBMC cancer · 2026Article
- Fast Fourier transform is a training-free, ultrafast, highly efficient, and fully interpretable approach for epigenomic data compression.Scientific reports · 2025Article
- Exploring tumor dynamics and responses of prostate cancer to IL-27 based treatment combinations through biodynamic imaging and RNA sequencing analyses.Scientific reports · 2025Article
- Development and validation of an AI-augmented deep learning model for survival prediction in de novo metastatic colorectal cancer.Discover oncology · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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