Looking at tumors through a new lens

Neoadjuvant immune blockade (ICB), a promising treatment for melanoma, is now showing a modest survival benefit in patients with recurrent glioblastoma. Researchers are trying to find vulnerabilities in surgically removed tissues of glioblastoma. However, this is difficult because there are so many differences between patients and tumors. Researchers at Institute for Systems Biology (ISB), along with their collaborators, developed a new method to study tumors. This method uses machine learning-based image analysis, multiplex spatial protein profiling and multiplex spatially tagged microscopical compartments to build mathematical models. This approach was used to compare tumor tissue from 13 patients suffering from recurrent glioblastoma as well as 23 patients suffering high-risk melanoma. Both sets of patients were treated with neoadjuvant ICB. They used melanoma as a guide for the interpretation of glioblastoma analysis. They identified proteins that were associated with tumor-killing cells and tumor growth. Dr. Yue Lu was co-lead author on the paper that describes the research. "This framework can help to uncover pathophysiological as well as molecular features that influence the efficacy of immunotherapies," Dr. Alphonsus Nag, co-lead writer of the paper, said. Today's publication in Nature Communications is the result of a collaboration between ISB, UCLA, and MD Anderson. The brain cancer is one of the most difficult settings to achieve immunotherapy success. Scientists and clinicians can work together to improve patient care and understand cancer immunotherapy at its deepest levels. "We believe that the integrated bio, clinical, and methodological insights derived by comparing two types of tumors widely considered as being at opposite ends of immunotherapy treatment spectrums should be of interest for broad scientific and clinical audiences," stated Dr. Jim Heath (ISB President), co-author of the paper.

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