8 genes that help predict melanoma patient’s response to treatment identifiedMay 31st, 2009 - 12:25 pm ICT by ANI
Washington, May 31 (ANI): Eight genes that help predict a melanoma patient’s response to treatment have been identified by researchers from the University of Pittsburgh Cancer Institute (UPCI).
The findings have been presented at the 45th annual meeting of the American Society of Clinical Oncology (ASCO).
“Approximately 70,000 people will be diagnosed with metastatic melanoma this year,” said principal investigator Hussein Tawbi, M.D., M.Sc., assistant professor of medicine, University of Pittsburgh School of Medicine, and with UPCI’s Melanoma Program.
“This form of cancer is aggressive and often resistant to chemotherapy. In fact, only 7 to 10 percent of patients are likely to respond to the current standard of care. We wanted to see if there was a way to predict which patients would respond to treatment and which ones would not,” the expert added.
To reach the conclusion, Tawbi and his colleagues examined the tumor tissues of 21 patients with metastatic melanoma, some of whom responded to chemotherapy and some who did not. Once the cases were divided, the researchers used a mathematical tool called Neural Network Analysis to survey over 25,000 genes and the regulators that turn the genes on and off to see if they could identify ones that could distinguish responders from nonresponders.
“Cancer cells contain massive amounts of information that, if analyzed appropriately, may inform us how to kill them,” said Tawbi.
“They contain thousands of genes, and every gene has a switch that turns it on or off. Neural Network Analysis, which utilizes pattern recognition algorithms, helped us identify a signature of eight genes and their switches that predict a patient’s likelihood of responding to treatment for metastatic melanoma,” the expert added.
“The genes that we isolated in this study could be potential targets for new therapies down the road,” explained Tawbi. (ANI)
Tags: american society of clinical oncology, assistant professor, cancer cells, chemotherapy, genes, massive amounts, mathematical tool, medicine university, melanoma, metastatic melanoma, neural network analysis, patients with metastatic, pattern recognition, pittsburgh cancer institute, pittsburgh school, principal investigator, regulators, school of medicine, university of pittsburgh, university of pittsburgh school of medicine