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The pMTnet technology detailed online in "Natural Machine Intelligence" may lead to new methods for predicting cancer prognosis and potential responses to immunotherapy
"Determining which neoantigens bind to T cell receptors and which do not seem to be an impossible task
Mutations in the genome of cancer cells cause them to display different neoantigens on the surface
"For the immune system, the presence of neoantigens is one of the biggest differences between normal cells and tumor cells," Wang Tao's laboratory uses the most advanced bioinformatics and biostatistics methods to study the effects of tumor immunity on various cancers.
Predicting neoantigens recognized by T cells can help researchers develop personalized cancer vaccines, design better T cell-based therapies, or predict patient response to other types of immunotherapies
With funding support from the National Institutes of Health (NIH) and the Cancer Prevention Institute of Texas (CPRIT), the research team found a better technology and set its sights on machine learning
Researchers use this new tool to collect information on neoantigens classified in The Cancer Genome Atlas, a public database containing information on more than 11,000 primary tumors
"As an immunologist, the biggest obstacle facing immunotherapy at present is to determine which antigens are recognized by which T cells in order to use these pairings for therapeutic purposes," said the corresponding author, Dr.
Other UTSW researchers involved in this study include James Zhu, Yunguan Wang, Xue Xiao and Lin Xu
The Simmons Cancer Center and MD Anderson Cancer Center at the University of Texas Southwest are 51 comprehensive centers exclusively designated by the National Cancer Institute, including a joint effort with the National Human Genome Institute to oversee the Cancer Genome Atlas project
Journal Reference :
Tianshi Lu, Ze Zhang, James Zhu, Yunguan Wang, Peixin Jiang, Xue Xiao, Chantale Bernatchez, John V.
