ArticleEvolutionary applications2020
Cancer therapy: Attempt cure or manage drug resistance?
Article in Evolutionary applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Article
- Impacts of competition and phenotypic plasticity on the viability of adaptive therapy.Mathematical biosciences · 2025Article
- Adaptive Therapy Exploits Fitness Deficits in Chemotherapy-Resistant Ovarian Cancer to Achieve Long-Term Tumor Control.Cancer research · 2025Article
- Ataxia telangiectasia and Rad3-related (ATR) inhibition by VE-822 potently reversed 5-flourouracil resistance in colorectal cancer cells through targeting DNA damage response.Molecular biology reports · 2024Article
- Modeling resistance to the broadly neutralizing antibody PGT121 in people living with HIV-1.PLoS computational biology · 2024Article
- Dysregulated FGFR3 signaling alters the immune landscape in bladder cancer and presents therapeutic possibilities in an agent-based model.Frontiers in immunology · 2024Article
- Thymoquinone Increases the Sensitivity of SW-480 Colon Cancer Cells to 5-Fluorouracil.BioMed research international · 2024Article
- A survey of open questions in adaptive therapy: Bridging mathematics and clinical translation.eLife · 2023Article
- The future of evolutionary medicine: sparking innovation in biomedicine and public health.Frontiers in science · 2023Article
- Ecology and games in cancer: new insights into the disease.Pathologica · 2022Article
- Spatial structure impacts adaptive therapy by shaping intra-tumoral competition.Communications medicine · 2022Article
- Review
- Drug-induced resistance evolution necessitates less aggressive treatment.PLoS computational biology · 2021Article
- Metastasis Prevention: Focus on Metastatic Circulating Tumor Cells.Molecular diagnosis & therapy · 2021Review
- A theoretical analysis of tumour containment.Nature ecology & evolution · 2021Article
- Fighting microbial pathogens by integrating host ecosystem interactions and evolution.BioEssays : news and reviews in molecular, cellular and developmental biology · 2021Article
- Turnover Modulates the Need for a Cost of Resistance in Adaptive Therapy.Cancer research · 2021Article
- Review
- Article
- Cancer therapy: Attempt cure or manage drug resistance?Evolutionary applications · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer treatment is often aimed at achieving rapid, large, and sustained reductions in tumor burden. Even when these strong responses are achieved, treatment frequently fails due to the emergence of drug-resistant cell lineages. Over the last decade, a variety of authors have suggested that treatment should instead be aimed at containing resistance rather than curing the patient. That new philosophy poses a dilemma: how to choose between treatment regimens that can sometimes cure the patient and regimens that can delay progression but not cure the patient? Here, we investigate that choice. We define aspects of the evolution and ecology of tumor dynamics that determine whether it is better to attempt cure or to manage resistance. Even when it is possible to manage resistance and delay progression, this may not be the best treatment option. We show that the best option depends on how "cure" and "delaying progression" are prioritized, and how those priorities will vary among patients. We also discuss the difficulties of comparing in clinical trials traditional strategies that can sometimes successfully cure to alternative approaches where cure is not possible. More generally, where resistance management is possible, there are new challenges in communicating options to patients, setting treatment guidelines, and evaluating data from clinical trials.
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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.