We are interested in the initiation, progression, and heterogeneity of cancer. Our goal is to contribute to the improvement of cancer diagnosis, prognosis, prevention, and therapy, specifically of breast cancer and colon cancer. We have measured DNA and proteins in human biopsy specimens by computer-aided image analysis, and analyzed the quantitative data with multivariate statistics, computer simulation, and mathematical modeling.
To aid in the differential diagnosis of human breast tumors and normal tissues, discriminant functions have been derived. To predict the disease free survival of patients with pre-malignant lesions (ductal carcinoma in situ), and to predict the overall survival of young patients with invasive breast cancer, quantitative algorithms have been developed.
To study the initiation and progression of colon cancer, an agent-based computer model of human colon crypts has been developed. The virtual crypt model has been calibrated with measurements of human biopsy specimens, and its behavior verified to reproduce experimentally observed stem cell dynamics in colon crypts. Simulations are being used to evaluate different protocols for chemotherapy and chemoprevention, including drug combinations and intermittent schedules.