ArticleEvolutionary applications2013
Cancer as a moving target: understanding the composition and rebound growth kinetics of recurrent tumors.
Article in Evolutionary applications, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Model-informed experimental design recommendations for distinguishing intrinsic and acquired targeted therapeutic resistance in head and neck cancer.NPJ systems biology and applications · 2022Article
- Mathematical Approach to Differentiate Spontaneous and Induced Evolution to Drug Resistance During Cancer Treatment.JCO clinical cancer informatics · 2019Article
- Limiting the development of anti-cancer drug resistance in a spatial model of micrometastases.Mathematical biosciences and engineering : MBE · 2016Article
- Optimal treatment and stochastic modeling of heterogeneous tumors.Biology direct · 2016Review
- Article
- Spatial heterogeneity in drug concentrations can facilitate the emergence of resistance to cancer therapy.PLoS computational biology · 2015Article
- Evolution of acquired resistance to anti-cancer therapy.Journal of theoretical biology · 2014Review
- Killing me softly--future challenges in apoptosis research.International journal of molecular sciences · 2014Review
- Evolutionary foundations for cancer biology.Evolutionary applications · 2013Article
- Drug resistance missense mutations in cancer are subject to evolutionary constraints.PloS one · 2013Article
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Authors and funding
3 authors.
Funding
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Abstract
We introduce a stochastic branching process model of diversity in recurrent tumors whose growth is driven by drug resistance. Here, an initially declining population can escape certain extinction via the production of mutants whose fitness is drawn at random from a mutational fitness landscape. Using a combination of analytical and computational techniques, we study the rebound growth kinetics and composition of the relapsed tumor. We find that the diversity of relapsed tumors is strongly affected by the shape of the mutational fitness distribution. Interestingly, the model exhibits a qualitative shift in behavior depending on the balance between mutation rate and initial population size. In high mutation settings, recurrence timing is a strong predictor of the diversity of the relapsed tumor, whereas in the low mutation rate regime, recurrence timing is a good predictor of tumor aggressiveness. Analysis reveals that in the high mutation regime, stochasticity in recurrence timing is driven by the random survival of small resistant populations rather than variability in production of resistance from the sensitive population, whereas the opposite is true in the low mutation rate setting. These conclusions contribute to an evolutionary understanding of the suitability of tumor size and time of recurrence as prognostic and predictive factors in cancer.
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