ArticleNature biomedical engineering2021
A mathematical model for the quantification of a patient's sensitivity to checkpoint inhibitors and long-term tumour burden.
Article in Nature biomedical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Simulating Cancer Recurrence Patterns From Post-Treatment Viable Tumor Burden Distributions.JCO clinical cancer informatics · 2026Article
- The theory of tumor immuno-ecodynamics.Frontiers in immunology · 2026Article
- NFATC2 target gene signature correlates with immune checkpoint blockade resistance in melanoma.American journal of cancer research · 2025Article
- Digital twins in healthcare: a comprehensive review and future directions.Frontiers in digital health · 2025Review
- Optimized patient-specific immune checkpoint inhibitor therapies for cancer treatment based on tumor immune microenvironment modeling.Briefings in bioinformatics · 2024Article
- Gradient-induced instability in tumour spheroids unveils the impact of microenvironmental nutrient changes.Scientific reports · 2024Article
- Hybridizing mechanistic modeling and deep learning for personalized survival prediction after immune checkpoint inhibitor immunotherapy.NPJ systems biology and applications · 2024Article
- Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer.Molecular cancer · 2024Article
- Hybridizing mechanistic mathematical modeling with deep learning methods to predict individual cancer patient survival after immune checkpoint inhibitor therapy.Research square · 2024Article
- Dose optimization of an adjuvanted peptide-based personalized neoantigen melanoma vaccine.PLoS computational biology · 2024Article
- Global stability and parameter analysis reinforce therapeutic targets of PD-L1-PD-1 and MDSCs for glioblastoma.Journal of mathematical biology · 2023Article
- On fast simulation of dynamical system with neural vector enhanced numerical solver.Scientific reports · 2023Article
- Immune-checkpoint inhibitor therapy response evaluation using oncophysics-based mathematical models.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology · 2023Review
- Incorporating lesion-to-lesion heterogeneity into early oncology decision making.Frontiers in immunology · 2023Review
- Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy.STAR protocols · 2022Article
- Mathematical modeling of cancer immunotherapy for personalized clinical translation.Nature computational science · 2022Article
- Article
- Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy.Advanced drug delivery reviews · 2022Review
- Dedifferentiation-mediated stem cell niche maintenance in early-stage ductal carcinoma in situ progression: insights from a multiscale modeling study.Cell death & disease · 2022Article
- Mathematical Modeling to Simulate the Effect of Adding Radiation Therapy to Immunotherapy and Application to Hepatocellular Carcinoma.International journal of radiation oncology, biology, physics · 2022Article
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16 authors.
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Abstract
A large proportion of patients with cancer are unresponsive to treatment with immune checkpoint blockade and other immunotherapies. Here, we report a mathematical model of the time course of tumour responses to immune checkpoint inhibitors. The model takes into account intrinsic tumour growth rates, the rates of immune activation and of tumour-immune cell interactions, and the efficacy of immune-mediated tumour killing. For 124 patients, four cancer types and two immunotherapy agents, the model reliably described the immune responses and final tumour burden across all different cancers and drug combinations examined. In validation cohorts from four clinical trials of checkpoint inhibitors (with a total of 177 patients), the model accurately stratified the patients according to reduced or increased long-term tumour burden. We also provide model-derived quantitative measures of treatment sensitivity for specific drug-cancer combinations. The model can be used to predict responses to therapy and to quantify specific drug-cancer sensitivities in individual patients.
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