Modeling Cancer Using Ecological Principles
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The invasion of a new species into an established ecosystem can be directly compared to the steps involved in cancer metastasis.
New research published in BioMed Central's open access journal Theoretical Biology and Medical Modelling uses the Tilman model of competition between invasive species to study the metastasis of prostate cells into bone.
Approximately 40,000 men a year in the US who have apparently successful surgery or radiotherapy for prostate cancer will suffer incurable metastasis of their disease in bone.
The prostate cells invade the bone marrow and, sometimes after years of remaining dormant, compete with hematopoietic stem cells and take over the bone microenvironment.
The Tilman model examines various components of invasion of species into an environment. Kun-Wan Chen and Kenneth J. Pienta from the University of Michigan substituted steps in cancer progression into the Tilman equations.
After initially multiplying and undergoing genetic mutation (equivalent to evolution) within the prostate, the cancer cells leave the prostate and enter the circulation. The next step is a period of survival in circulation followed by invasion into bone.
Once in the bone there is a 'lag period' while the cells establish themselves. Eventually, the cancer cells begin to multiply and out-compete the hematopoietic stem cells.
Kun-Wan Chen explained, "The invading cancer cells could be thought of as several species, and the fittest mutants become dominant and multiply. Eventually there is a massive impact on the biosphere (human host)."
Prof Pienta continued, "Devastating ecological invasions are well known. For example introduction of domestic animals to Mauritius resulted in extinction of the dodo and Caulerpa taxifolia, a mutated killer algae, is plaguing the Mediterranean. Cancer metastasis follows a similar path to that of ecological invasions and our adaptation of the Tilman equations shows how invading cancer cells can destroy the normal body habitat. Use of ecological modeling can help us understand the complex biology of metastasis."
