Cancer might be viewed as a failure of coexistence. Yet, some recent cancer therapies strive to manage clonal cancer populations to coexist with their host and in ways that limit the more aggressive and damaging clonal phenotypes or subpopulations. The lens of ecology has provided many novel approaches to understanding cancer biology, and has even provided novel therapies for treatment of cancer. Common approaches have included game theory, population dynamic models, ABM's, and systems models. Population dynamical models have usually considered cancer cells as a single homogenous population inhabiting their tumor ecosystem, focusing on average environment, which restricts explicit consideration of how the variation in the landscape of the human body, the environment of cancer, might affect coexistence among clonal cancer populations and normal cell populations and tissues. Here, we apply the modern coexistence theory to the dynamics of cancer in patients and ask especially whether spatio-temporal environmental variability within tumors may influence cancer biology and progression.
Results/Conclusions
Modern coexistence theory includes insights into how spatial and temporal variability can promote or erode species coexistence within a community. As of yet, there have been few applications of this theory, limiting its usefulness and limiting its refinement through the challenge of empirical applications. The human body is highly variable in space and in time, with temporal variation in conditions that affect growth and reproduction at many scales that are relevant to cancer. Cancer cells are highly heterogeneous in phenotype. Quiescent states, including dormancy, are ubiquitous in cancer. Both of these traits may be associated with cancer progression, metastases and the failure of cancer therapies. We describe several applications of modern coexistence theory to understanding the population biology and control of cancer, including multiple myeloma and breast cancer. The application of coexistence theory to cancer advances understanding of the biology, disease progression, and possible control of these cancers and has helped us understand how a highly abstract body of mathematical theory can be applied to solve practical problems. In return, applications to cancer and cancer biology may provide tests of ecologists’ theories with a level of replication, speed and sophistication unavailable to many community ecologists.