A closeup of a large tumor. Photo courtesy of Wikimedia Commons
Dec. 22 (UPI) -- New research out of the University of Southern California suggests the growth patterns specific to a tumor can predict which cancer therapies are likely to work best.
Like the patients in which they grow, every tumor is unique. Doctors and researchers are becoming increasingly aware of this fact as they devise individualized cancer treatments. Because each tumor is unique, cancerous growths respond differently to various drugs.
Cancer drugs can have deleterious side effects, so finding the most effective drug -- or combination of drugs -- on the first try is imperative.
"Identifying a measurement or quantity that predicts how specific tumors will respond, called a predictive biomarker, is extremely valuable to cancer research," Stacey Finley, a assistant professor of biomedical engineering at USC, said in a news release.
Tumors need nutrients to fuel their growth. To get what they need, the cancer growth hijacks a process known as angiogenesis, which generates new blood vessels from the existing vasculature. Previous studies have shown tumor growth can be slowed by blocking or curbing vascular endothelial growth factor, or VEGF, a protein that promotes angiogenesis.
To better understand why some tumors reposed better to VEGF-blocking treatments than others, researchers built a computational model of tumor-bearing mice. They used the model, which was designed using real experimental and clinical data, to identify relationships between the drug's efficacy and certain properties of tumor growth.
"We found that certain parameters about the way the tumor grows could successfully and accurately predict the response to anti-angiogenic treatment that inhibits VEGF signaling in the mouse," Finley said. "Using the characteristics of the tumor's growth, we can predict how effective the anti-angiogenic therapy will be, or whether the tumor's growth will slow down, even before treatment begins."
Scientists published the results of their modeling work in the journal PLOS Computational Biology.
The next step is to reverse engineer the model and use it to analyze mice tumors in order to predict the efficacy of VEGF-inhibtor drugs.