- A stochastic operator-splitting method for simulating the development of intratumor heterogeneity
- Project Year:
2018
- REU Student (s):
Andrew Brettin | University of Minnesota-Twin Cities MN
- Student 1 Institution:
University of Minnesota-Twin Cities
- Project Mentor:
Subhajyoti De
- Project Mentor Area:
Rutgers Cancer Institute of New Jersey
- Project Abstract:
Cancer is a genetic disease which begins from a single aberrant progenitor cell, which successively divides into pairs of daughter cells with similar reproductive capacities. As the tumor grows, many distinct genetic lineages may proliferate throughout the neoplasm, resulting in significant intratumor heterogeneity. Such high levels of genetic diversity within tumors is associated with low survival rates for patients, as genetically-distinct cell variants have differential sensitivities to current therapies. Despite its importance, how intratumor heterogeneity develops is not well understood. However, mathematical models can provide insight into potential causes. This work applies a stochastic operator-splitting method developed for simulating chemical reaction-diffusion processes to model neoplastic growth. It is shown that high differential birth-rates, high diffusion rates and early onset of mutations can result in substantial levels of intratumor heterogeneity.
