Metastatic cancer growth is one of the most challenging areas in cancer treatment. However, metastasis is difficult to study systematically in the laboratory largely due to discrepancies between cell culture models and tumor growth in vivo. Much research has been devoted to defining molecular and biochemical changes during tumor progression, but a deeper understanding of the interaction between cancer cells and the organ microenvironment is crucial to future advances in cancer therapy. Our overall goal is to develop an integrated bioengineered/computational model of metastatic tumor growth to probe the relationships between growth dynamics, heterogeneous microenvironments, and the underlying biophysics. This proposal applies an interdisciplinary approach to cancer metastasis by directly merging the methods of the physical sciences, regenerative medicine, and tissue engineering. A University of Southern California-led multi-institutional team has developed mechanistic, multiscale computational models of vascularized tumor growth in complex virtual tissues. Wake Forest University has developed tissue bioengineering techniques to create functional liver organoids that can be injected with cancer cells and will be used to recapitulate the in vivo milieu of cancer metastasis. We propose to use bioengineering to create living liver tissues in situ with the native structure and function of human livers. The proposed integrated bioengineered/computational platform should give unprecedented spatiotemporal resolution and microenvironmental control of metastatic colon cancer growth.
In Aim 1 of this proposal the computational model will be calibrated to data from bioengineered hepatic disc and in situ organoid experiments. Simulation predictions of colon cancer metastatic development will be compared to experiments to quantify accuracy and determine need for model refinements.
In Aim 2, the calibrated model will be used to systematically investigate colon tumor growth dynamics under diverse microenvironmental conditions, in which we modulate biophysical parameters by applying mechanical forces, altering oxygenation, and administering therapeutics. We will validate the model's predictions against in situ organoid experiments under these same conditions.
In Aim 3, we will calibrate the simulator to patient-derived metastatic colon tumor explants and determine if simulations of tumor growth correspond with imaging and outcome data from the same patients. This project will create a first-of-its-kind integrated computational/bioengineered liver metastasis model, providing a reproducible, controllable system for probing and manipulating the dynamics of metastasis, testing and refining hypotheses, and making predictions that can be extrapolated to human cancer. These integrative modeling efforts will give a new dimension to understanding tumor spread and yield important information about treating cancer metastases.
Most cancer-related deaths are the result of uncontrolled tumor invasion and growth of metastases throughout the body. This metastatic tumor spread depends crucially on the ability of cancer cells to thrive in the site of the metastases called the microenvironment. We will use bioengineered livers seeded with metastatic colon cancer to systematically investigate the microenvironmental factors contributing to metastatic progression in colon cancer patients and develop computational models to predict outcome and identify new targets for treatment.
|Ghaffarizadeh, Ahmadreza; Friedman, Samuel H; Macklin, Paul (2016) BioFVM: an efficient, parallelized diffusive transport solver for 3-D biological simulations. Bioinformatics 32:1256-8|
|Juarez, Edwin F; Lau, Roy; Friedman, Samuel H et al. (2016) Quantifying differences in cell line population dynamics using CellPD. BMC Syst Biol 10:92|
|Baptista, Pedro M; Moran, Emma C; Vyas, Dipen et al. (2016) Fluid Flow Regulation of Revascularization and Cellular Organization in a Bioengineered Liver Platform. Tissue Eng Part C Methods 22:199-207|
|Skardal, Aleksander; Devarasetty, Mahesh; Forsythe, Steven et al. (2016) A reductionist metastasis-on-a-chip platform for in vitro tumor progression modeling and drug screening. Biotechnol Bioeng 113:2020-32|
|Garvey, Colleen M; Spiller, Erin; Lindsay, Danika et al. (2016) A high-content image-based method for quantitatively studying context-dependent cell population dynamics. Sci Rep 6:29752|
|Nishii, Kenichiro; Reese, Greg; Moran, Emma C et al. (2016) Multiscale computational model of fluid flow and matrix deformation in decellularized liver. J Mech Behav Biomed Mater 57:201-14|
|Kani, Kian; Faca, Vitor M; Hughes, Lindsey D et al. (2012) Quantitative proteomic profiling identifies protein correlates to EGFR kinase inhibition. Mol Cancer Ther 11:1071-81|