title: Bridging population and tissue scale tumor dynamics: a new paradigm for understanding differences in tumor growth and metastatic disease.
abstract: To provide a better understanding of the relationship between primary tumor growth rates and metastatic burden, we present a method that bridges tumor growth dynamics at the population level, extracted from the SEER database, to those at the tissue level. Specifically, with this method, we are able to relate estimates of tumor growth rates and metastatic burden derived from a population-level model to estimates of the primary tumor vascular response and the circulating tumor cell (CTC) fraction derived from a tissue-level model. Variation in the population-level model parameters produces differences in cancer-specific survival and cure fraction. Variation in the tissue-level model parameters produces different primary tumor dynamics that subsequently lead to different growth dynamics of the CTCs. Our method to bridge the population and tissue scales was applied to lung and breast cancer separately, and the results were compared. The population model suggests that lung tumors grow faster and shed a significant number of lethal metastatic cells at small sizes, whereas breast tumors grow slower and do not significantly shed lethal metastatic cells until becoming larger. Although the tissue-level model does not explicitly model the metastatic population, we are able to disengage the direct dependency of the metastatic burden on primary tumor growth by introducing the CTC population as an intermediary and assuming dependency. We calibrate the tissue-level model to produce results consistent with the population model while also revealing a more dynamic relationship between the primary tumor and the CTCs. This leads to exponential tumor growth in lung and power law tumor growth in breast. We conclude that the vascular response of the primary tumor is a major player in the dynamics of both the primary tumor and the CTCs, and is significantly different in breast and lung cancer.
authors.affiliation: Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL 33612.
authors.forename: Jill
authors.initials: J
authors.lastname: Gallaher
authors.affiliation: Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305.
authors.forename: Aravind
authors.initials: A
authors.lastname: Babu
authors.affiliation: Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305.
authors.forename: Sylvia
authors.initials: S
authors.lastname: Plevritis
authors.affiliation: Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL 33612.
authors.forename: Alexander R A
authors.initials: ARA
authors.lastname: Anderson
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000453:epidemiology* / Q000401:mortality / Q000473:pathology*; D002633:Chemotaxis; D005260:Female; D006801:Humans; D000860:Hypoxia; D008175:Lung Neoplasms / Q000453:epidemiology* / Q000401:mortality / Q000473:pathology*; D015233:Models, Statistical; D008962:Models, Theoretical; D009010:Monte Carlo Method; D009336:Necrosis; D009362:Neoplasm Metastasis; D009360:Neoplastic Cells, Circulating; D010100:Oxygen / Q000378:metabolism; D011157:Population Dynamics; D018426:SEER Program; D013269:Stochastic Processes; D016896:Treatment Outcome; D014481:United States; D042461:Vascular Endothelial Growth Factor A / Q000378:metabolism
chemical_list: C467484:VEGFA protein, human; D042461:Vascular Endothelial Growth Factor A; D010100:Oxygen