ArticleCancers2020
Modifying Adaptive Therapy to Enhance Competitive Suppression.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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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
20 citing papers in PubMed.
- Mathematical Biomarkers of Adaptive Therapy Outcomes in Prostate Cancer.JAMA oncology · 2026Article
- Review
- Deriving Optimal Treatment Timing for Adaptive Therapy: Matching the Model to the Tumor Dynamics.Bulletin of mathematical biology · 2025Article
- To modulate or to skip: De-escalating PARP inhibitor maintenance therapy in ovarian cancer using adaptive therapy.Cell systems · 2024Article
- Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy.Cancer research · 2024Article
- Outlook and opportunities for engineered environments of breast cancer dormancy.Science advances · 2024Review
- The effect of tumor composition on the success of adaptive therapy: The case of metastatic Castrate-Resistant Prostate Cancer.PloS one · 2024Article
- Treatment of evolving cancers will require dynamic decision support.Annals of oncology : official journal of the European Society for Medical Oncology · 2023Review
- Intermetastatic and Intrametastatic Heterogeneity Shapes Adaptive Therapy Cycling Dynamics.Cancer research · 2023Article
- Antibiotic resistance in bacterial communities.Current opinion in microbiology · 2023Review
- Effective dose window for containing tumor burden under tolerable level.NPJ systems biology and applications · 2023Article
- A survey of open questions in adaptive therapy: Bridging mathematics and clinical translation.eLife · 2023Article
- Biomarkers or biotargets? Using competition to lure cancer cells into evolutionary traps.Evolution, medicine, and public health · 2023Article
- Article
- Article
- The impact of the spatial heterogeneity of resistant cells and fibroblasts on treatment response.PLoS computational biology · 2022Article
- Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?PLoS computational biology · 2021Article
- Review
- Do mechanisms matter? Comparing cancer treatment strategies across mathematical models and outcome objectives.Mathematical biosciences and engineering : MBE · 2021Article
- Are Adaptive Chemotherapy Schedules Robust? A Three-Strategy Stochastic Evolutionary Game Theory Model.Cancers · 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
2 authors.
Funding
Abstract
Adaptive therapy is a promising new approach to cancer treatment. It is designed to leverage competition between drug-sensitive and drug-resistant cells in order to suppress resistance and maintain tumor control for longer. Prompted by encouraging results from a recent pilot clinical trial, we evaluate the design of this initial test of adaptive therapy and identify three simple modifications that should improve performance. These modifications are designed to increase competition and are easy to implement. Using the mathematical model that supported the recent adaptive therapy trial, we show that the suggested modifications further delay time to tumor progression and also increase the range of patients who can benefit from adaptive therapy.
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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.