Kinetic modeling of tumor regression incorporating the concept of cancer stem-like cells for patients with locally advanced lung cancer. Theor Biol Med Model 2018 Dec 27;15(1):23
Date
12/28/2018Pubmed ID
30587218Pubmed Central ID
PMC6307263DOI
10.1186/s12976-018-0096-7Scopus ID
2-s2.0-85059241653 (requires institutional sign-in at Scopus site) 4 CitationsAbstract
BACKGROUND: Personalized medicine for patients receiving radiation therapy remains an elusive goal due, in part, to the limits in our understanding of the underlying mechanisms governing tumor response to radiation. The purpose of this study was to develop a kinetic model, in the context of locally advanced lung cancer, connecting cancer cell subpopulations with tumor volumes measured during the course of radiation treatment for understanding treatment outcome for individual patients.
METHODS: The kinetic model consists of three cell compartments: cancer stem-like cells (CSCs), non-stem tumor cells (TCs) and dead cells (DCs). A set of ordinary differential equations were developed to describe the time evolution of each compartment, and the analytic solution of these equations was iterated to be aligned with the day-to-day tumor volume changes during the course of radiation treatment. A least squares fitting method was used to estimate the parameters of the model that include the proportion of CSCs and their radio-sensitivities. This model was applied to five patients with stage III lung cancer, and tumor volumes were measured from 33 cone-beam computed tomography (CBCT) images for each of these patients. The analytical solution of these differential equations was compared with numerically simulated results.
RESULTS: For the five patients with late stage lung cancer, the derived proportions of CSCs are 0.3 on average, the average probability of the symmetry division is 0.057 and the average surviving fractions of CSCs is 0.967, respectively. The derived parameters are comparable to the results from literature and our experiments. The preliminary results suggest that the CSC self-renewal rate is relatively small, compared to the proportion of CSCs for locally advanced lung cancers.
CONCLUSIONS: A novel mathematical model has been developed to connect the population of cancer stem-like cells with tumor volumes measured from a sequence of CBCT images. This model may help improve our understanding of tumor response to radiation therapy, and is valuable for development of new treatment regimens for patients with locally advanced lung cancer.
Author List
Zhong H, Brown S, Devpura S, Li XA, Chetty IJAuthor
Hualiang Zhong PhD Associate Professor in the Radiation Oncology department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AgedAged, 80 and over
Cell Count
Cell Line, Tumor
Cell Survival
Cone-Beam Computed Tomography
Female
Humans
Kinetics
Lung Neoplasms
Male
Models, Biological
Neoplasm Staging
Neoplastic Stem Cells
Radiation, Ionizing
Remission Induction
Reproducibility of Results
Time Factors
Tumor Burden
