The aggressiveness of tumors and their susceptibility to chemotherapy may become easier to predict based on a mathematical model developed at The University of Texas Health Science Center at Houston.
In spite of extensive experimental and clinical studies, the process of cancer growth is not well understood. Tumors are complex systems, with changes at the molecular and cellular levels influencing shape and behavior in sometimes unpredictable ways. New research by a scientist in mathematical oncology at the UT Health Science Center at Houston suggests that mathematical modeling based on data from the molecular and cellular levels could shed light on tumor development and lead to better treatments.
Cancer is the second most common cause of death in the United States, exceeded only by heart disease, according to the American Cancer Society.
At the 100th annual meeting of the American Association for Cancer Research in Denver this spring, Vittorio Cristini, Ph.D., an associate professor of health informatics at The University of Texas School of Health Information Sciences at Houston, demonstrated the predictability of tumor growth in brain cancer and chemotherapy response in breast cancer. Findings appear in two different papers in the May 15 print issue of the association's peer-reviewed journal Cancer Research.
The mathematical model developed by Cristini's lab works by defining tumor biologic and molecular properties relating to laboratory and clinical observations of cancers. In this model, the behavior of cancer cells and their surroundings is linked to tumor growth, shape and treatment response.
"The central finding of this work is that tumor growth and invasion are not erratic or unpredictable, or solely explained through genomic and molecular events, but rather are predictable processes obeying biophysical laws," the authors wrote in the paper addressing predictability of tumor growth in brain cancer.'/>"/>
|Contact: Robert Cahill|
University of Texas Health Science Center at Houston