ReviewJournal of the Royal Society, Interface2017
Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.
Review in Journal of the Royal Society, Interface, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed.
- CAR T-cell and oncolytic virus dynamics and determinants of combination therapy success for glioblastoma.Mathematical biosciences · 2025Article
- Optimizing the Efficacy of Vaccine-Induced Immunotherapy in Melanomas.Bulletin of mathematical biology · 2025Article
- Analysis of time-fractional cancer-tumor immunotherapy model using modified He-Laplace algorithm.Scientific reports · 2025Article
- Mathematical modeling of the effect of early detection in breast cancer treatment.Frontiers in oncology · 2025Article
- Spatial organization and stochastic fluctuations of immune cells impact clinical responsiveness to immunotherapy in melanoma patients.PNAS nexus · 2024Article
- Dose optimization of an adjuvanted peptide-based personalized neoantigen melanoma vaccine.PLoS computational biology · 2024Article
- BioNetGMMFit: estimating parameters of a BioNetGen model from time-stamped snapshots of single cells.NPJ systems biology and applications · 2023Article
- Review
- In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome.Nature communications · 2023Article
- Immune-checkpoint inhibitor therapy response evaluation using oncophysics-based mathematical models.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology · 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
- Limitations of Immunotherapy in Cancer.Cureus · 2022Review
- Cancerous Tumor Controlled Treatment Using Search Heuristic (GA)-Based Sliding Mode and Synergetic Controller.Cancers · 2022Article
- Comprehensive Analysis of mTORC1 Signaling Pathway-Related Genes in the Prognosis of HNSCC and the Response to Chemotherapy and Immunotherapy.Frontiers in molecular biosciences · 2022Article
- Analysis of the Equilibrium Phase in Immune-Controlled Tumors Provides Hints for Designing Better Strategies for Cancer Treatment.Frontiers in oncology · 2022Article
- Engineered in vitro tumor models for cell-based immunotherapy.Acta biomaterialia · 2021Review
- Multiscale modeling in disease.Current opinion in systems biology · 2021Article
- Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data.Cancers · 2021Review
- CARTCancers · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.