In Silico Simulation of a Translational Human Breast Cancer Model in Mice March 25 th, 2013 Mark Dawidek Department of Medical Biophysics

Introduction Improvements in ultrasound  Improvements in monitoring angiogenic growth  Improvements in quantifying tumor- induced angiogenesis Yet to unravel tumor-induced angiogenesis Multiple feed-back and feed-forward mechanisms Department of Medical Biophysics

Introduction In silico model of tumor angiogenesis Search for mechanisms to target therapeutically Predict treatment effects Consists of separate angiogenesis and tumor growth models Project focus is tumor growth model Department of Medical Biophysics

Hahnfeldt Model Lattice where each point represents a cell Cell activity governed by four parameters: p s probability of stem cell  probability of spontaneous death  proliferation capacity  max migration distance Department of Medical Biophysics

Hahnfeldt Model “Conglomerates of self-metastases” Model algorithm implemented in MATLAB Department of Medical Biophysics

Target Curve Department of Medical Biophysics

Limitations of MATLAB Code Additional parameters: Size of lattice (3D) Number of iterations Both can drastically affect computation time Both held constant: 100x100x100 lattice 100 iterations Department of Medical Biophysics

Scaling 50 days / 100 iterations = 0.5 day mitotic cycle period Assumption: volume  # of cells # of cells in silico << # of cells in vivo # of cells after 100 iterations varies with parameters Comparing shape of curve, not volume  Normalize Department of Medical Biophysics

Fixing Proliferation Capacity and Spontaneous Death Department of Medical Biophysics

Optimizing Migration Distance and Stem Cell Probability Department of Medical Biophysics

Trend Along p s Department of Medical Biophysics

Trend Along  Department of Medical Biophysics

Trend Along  Department of Medical Biophysics

p s = 0.02,  = 1 Department of Medical Biophysics

p s = 0.02,  = 3 Department of Medical Biophysics

p s = 0.06,  = 1 Department of Medical Biophysics

p s = 0.06,  = 3 Department of Medical Biophysics

US Image of Actual Tumor Department of Medical Biophysics

Limitations and Next Steps Increase scale Angiogenesis simulation ~1/4 of actual cancer size Initialized with one cell versus millions Improve speed Dead cells simply “disappear” Effects of surrounding tissue Department of Medical Biophysics

Summary Constant lattice size and fixed number of iterations  essentially negligible, p s controls metastatic tumor size, both parameters fixed Increasing  improves curve fit Improvements decay predictably, exponentially Negligible gain for  > ~0.4 No clear effect of  on fit  controls shape and size (Hahnfeldt) Department of Medical Biophysics

Acknowledgements Thank You to Dr. James Lacefield & Matthew Lowerison Department of Medical Biophysics