New Computer Model May Aid Personalized Cancer Care

Dana-Farber Cancer Institute scientists in Boston and colleagues have developed a mathematical model to predict how a patient's tumor is likely to behave and which of several possible treatments is most likely to be effective. Reporting online on January 23, 2014 in an open-access article in the journal Cell Reports, researchers combined several types of data from pre- and post-treatment biopsies of breast tumors to obtain a molecular picture of how the cancer evolved as a result of chemotherapy. "Better understanding of tumor evolution is key to improving the design of cancer therapies and for truly individualized cancer treatment," said Kornelia Polyak, M.D., Ph.D., a breast cancer researcher in the Susan F. Smith Center for Women's Cancers at Dana-Farber. The model was developed by Dr. Polyak and Franziska Michor, Ph.D., a computational biologist at Dana-Farber. The study analyzed breast cancer samples from 47 patients who underwent pre-operative chemotherapy to shrink the tumor so it could be removed more easily. The biopsy samples, representing the major types of breast cancer, included specimens taken at diagnosis and again after the chemotherapy was completed. As has been increasingly recognized, a tumor contains a varied mix of cancer cells and the mix is constantly changing. This is known as tumor heterogeneity. The cells may have different sets of genes turned on and off – phenotypic heterogeneity – or have different numbers of genes and chromosomes – genetic heterogeneity. These characteristics, and the location of different types of cells with the tumor, shape how the cancer evolves and are a factor in the patient's outcome. In generating their predictive model, Drs.
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