ArticleJournal of theoretical biology2010
The evolution of tumor metastases during clonal expansion.
Article in Journal of theoretical biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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.
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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.
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Who cites it
14 citing papers in PubMed, 44 citations in OpenAlex.
- Computational modeling of ovarian cancer dynamics suggests optimal strategies for therapy and screening.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multi-model approach.Scientific reports · 2021Article
- A mathematical model of the metastatic bottleneck predicts patient outcome and response to cancer treatment.PLoS computational biology · 2020Article
- Cancer recurrence times from a branching process model.PLoS computational biology · 2019Article
- A Mathematical Framework for Modelling the Metastatic Spread of Cancer.Bulletin of mathematical biology · 2019Article
- Prediction of postoperative liver regeneration from clinical information using a data-led mathematical model.Scientific reports · 2016Article
- Mathematical Modeling of Therapy-induced Cancer Drug Resistance: Connecting Cancer Mechanisms to Population Survival Rates.Scientific reports · 2016Article
- Computational systems biology in cancer brain metastasis.Frontiers in bioscience (Scholar edition) · 2016Review
- The mathematics of cancer: integrating quantitative models.Nature reviews. Cancer · 2015Review
- The evolution of tumor metastasis during clonal expansion with alterations in metastasis driver genes.Scientific reports · 2015Article
- Modeling the connection between primary and metastatic tumors.Journal of mathematical biology · 2013Article
- The dormancy dilemma: quiescence versus balanced proliferation.Cancer research · 2013Review
- Modeling boundary conditions for balanced proliferation in metastatic latency.Clinical cancer research : an official journal of the American Association for Cancer Research · 2013Article
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Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Cancer is a leading cause of morbidity and mortality in many countries. Solid tumors generally initiate at one particular site called the primary tumor, but eventually disseminate and form new colonies in other organs. The development of such metastases greatly diminishes the potential for a cure of patients and is thought to represent the final stage of the multi-stage progression of human cancer. The concept of early metastatic dissemination, however, postulates that cancer cell spread might arise early during the development of a tumor. It is important to know whether metastases are present at diagnosis since this determines treatment strategies and outcome. In this paper, we design a stochastic mathematical model of the evolution of tumor metastases in an expanding cancer cell population. We calculate the probability of metastasis at a given time during tumor evolution, the expected number of metastatic sites, and the total number of cancer cells as well as metastasized cells. Furthermore, we investigate the effect of drug administration and tumor resection on these quantities and predict the survival time of cancer patients. The model presented in this paper allows us to determine the probability and number of metastases at diagnosis and to identify the optimum treatment strategy to maximally prolong survival of cancer patients.
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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.