The process of using mechanistic models to assist with understanding experimental results and informing clinical decisions depends on a suitable framework for developing, analyzing, implementing, adjusting and presenting the models and results. Furthermore, models must be calibrated so that they recapitulate experimental a n d c l i n i c a l observations both in terms of their range of behavior as well as the intrinsic variability of outcomes before they can be used to specifically impact one or more clinical decisions (e.g. predict novel treatment strategies). There is also a practical aspect of turning theoretical predictions into actionable clinical decisions that requires tools to generate patient cohorts, mimic clinical trials and present results in a visual and easy to use manner such that clinicians can immediately understand and manipulate them. The central focus of the Bench-to-Bedside Core is therefore to integrate e x p e r i m e n t s , clinical data, and models to assist with hypothesis generation, testing, and validation (Project 1), and translate successes into a clinical setting using clinically-relevant tools (Project 2). This will be accomplished through:
Aim 1, core models that explore unifying principles of evolution, heterogeneity, and response to treatment;
Aim 2, Radiomics, Pathomics, and a generalized set of analysis tools for integrating experimental and clinical data to facilitate mathematical model development and calibration;
and Aim 3, decision support tools including Phase ?i trials and dynamically optimized therapy for use in clinical trials. The tools and methods we develop here are generalized and therefore suitable to a wider class of experimental and clinical data and will serve as a key legacy from this project to drive bench-to-beside science.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA193489-02
Application #
9158476
Study Section
Special Emphasis Panel (ZCA1-TCRB-T)
Program Officer
Hanlon, Sean E
Project Start
Project End
Budget Start
2016-09-01
Budget End
2017-08-31
Support Year
2
Fiscal Year
2016
Total Cost
$598,790
Indirect Cost
$245,195
Name
H. Lee Moffitt Cancer Center & Research Institute
Department
Type
DUNS #
139301956
City
Tampa
State
FL
Country
United States
Zip Code
33612
Subramanian, Hemachander; Gatenby, Robert A (2018) Evolutionary advantage of directional symmetry breaking in self-replicating polymers. J Theor Biol 446:128-136
Shah, Seema; Brock, Ethan J; Jackson, Ryan M et al. (2018) Downregulation of Rap1Gap: A Switch from DCIS to Invasive Breast Carcinoma via ERK/MAPK Activation. Neoplasia 20:951-963
Karolak, Aleksandra; Rejniak, Katarzyna A (2018) Micropharmacology: An In Silico Approach for Assessing Drug Efficacy Within a Tumor Tissue. Bull Math Biol :
Shah, Seema; Brock, Ethan J; Ji, Kyungmin et al. (2018) Ras and Rap1: A tale of two GTPases. Semin Cancer Biol :
Gallaher, Jill A; Enriquez-Navas, Pedro M; Luddy, Kimberly A et al. (2018) Spatial Heterogeneity and Evolutionary Dynamics Modulate Time to Recurrence in Continuous and Adaptive Cancer Therapies. Cancer Res 78:2127-2139
Maley, Carlo C; Aktipis, Athena; Graham, Trevor A et al. (2017) Classifying the evolutionary and ecological features of neoplasms. Nat Rev Cancer 17:605-619
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
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
Gatenby, Robert A; Brown, Joel (2017) Mutations, evolution and the central role of a self-defined fitness function in the initiation and progression of cancer. Biochim Biophys Acta Rev Cancer 1867:162-166
Silva, Ariosto; Silva, Maria C; Sudalagunta, Praneeth et al. (2017) An Ex Vivo Platform for the Prediction of Clinical Response in Multiple Myeloma. Cancer Res 77:3336-3351

Showing the most recent 10 out of 22 publications