This project will support a mulfidisciplinary team that combines mathematical modeling and in-vivo experiments with the following goals to: (1) investigate the interactions between tumor vascularity, blood flow, metabolic phenotype that govern physical parameters in the microenvironment, and (2) gain understanding on the role of physical parameters (particulariy the p02 and extracellular pH) in tumor growth and metastases. Experimental work will be performed in the PIs laboratory as well as the Small Animal Imaging Laboratory, SAIL, as part of Core A (Imaging). The modeling work will be performed within the Integrative Mathematical Oncology (IMO) program as well as within Core B (Math). In previous work, mathemafical models of the interactions of the tumor microenvironment and cellular phenotype and behavior have been built upon and integrated with in-vitro and in-vivo experiments. However, the current models are limited by the absence of tumor vascular dynamics that incorporate angiogenesis, vessel maturation and decay, and chaofic blood flow. To achieve this, our experimental approach will be guided by biologically realistic mathemafical models. Consistent with this research strategy, all of the mathematical models will be parameterized with experimentally determined values, and model predictions will be tested in-vivo using MRI ora dorsal skin-fold chamber. The accuracy and limitations of signal extraction from images and their relationship to the mathematical models will also be examined in collaboration with Drs. Barrett and Kupinski at the Centerfor Gamma Ray Imaging (CGRI). Some of these experiments will employ a new imaging technology that combines fluorescent microscopy with high (40 micron) resolution imaging through a novel /S*'

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA143970-04
Application #
8555188
Study Section
Special Emphasis Panel (ZCA1-SRLB-9 (O1))
Project Start
2009-09-30
Project End
2014-08-31
Budget Start
2012-09-01
Budget End
2013-08-31
Support Year
4
Fiscal Year
2012
Total Cost
$601,192
Indirect Cost
$214,392
Name
H. Lee Moffitt Cancer Center & Research Institute
Department
Type
DUNS #
139301956
City
Tampa
State
FL
Country
United States
Zip Code
33612
Brown, Joel S; Cunningham, Jessica J; Gatenby, Robert A (2017) Aggregation Effects and Population-Based Dynamics as a Source of Therapy Resistance in Cancer. IEEE Trans Biomed Eng 64:512-518
de Groot, Amber E; Roy, Sounak; Brown, Joel S et al. (2017) Revisiting Seed and Soil: Examining the Primary Tumor and Cancer Cell Foraging in Metastasis. Mol Cancer Res 15:361-370
McFarland, Christopher D; Yaglom, Julia A; Wojtkowiak, Jonathan W et al. (2017) The Damaging Effect of Passenger Mutations on Cancer Progression. Cancer Res 77:4763-4772
Ibrahim-Hashim, Arig; Robertson-Tessi, Mark; Enriquez-Navas, Pedro M et al. (2017) Defining Cancer Subpopulations by Adaptive Strategies Rather Than Molecular Properties Provides Novel Insights into Intratumoral Evolution. Cancer Res 77:2242-2254
Gatenby, Robert A; Frieden, B Roy (2017) CellularĀ information dynamics through transmembrane flow of ions. Sci Rep 7:15075
Zhang, Jingsong; Cunningham, Jessica J; Brown, Joel S et al. (2017) Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat Commun 8:1816
Gravenmier, Curtis A; Siddique, Miriam; Gatenby, Robert A (2017) Adaptation to Stochastic Temporal Variations in Intratumoral Blood Flow: The Warburg Effect as a Bet Hedging Strategy. Bull Math Biol :
Kim, Eunjung; Rebecca, Vito W; Smalley, Keiran S M et al. (2016) Phase i trials in melanoma: A framework to translate preclinical findings to the clinic. Eur J Cancer 67:213-222
Gillies, Robert J; Kinahan, Paul E; Hricak, Hedvig (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology 278:563-77
Scott, Jacob G; Fletcher, Alexander G; Anderson, Alexander R A et al. (2016) Spatial Metrics of Tumour Vascular Organisation Predict Radiation Efficacy in a Computational Model. PLoS Comput Biol 12:e1004712

Showing the most recent 10 out of 118 publications