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Study: Algorithm predicts targets for brain tumor immunotherapy

(Photo from Video Aired on WISH-TV)

INDIANAPOLIS (WISH) — University of Florida researchers have developed a new algorithm to predict which proteins expressed within brain tumor cells could serve as targets to stimulate a potent anti-tumor response. The preclinical study in mice is the latest step toward overcoming common resistance to immunotherapy treatments for brain tumors.

The study detailed a novel high-tech method devised by McKnight Brain Institute researchers to create precise and individualized mRNA-based vaccines along with personalized adoptive T cell therapy treatments to zero in simultaneously on multiple newly identified targets in mouse models of glioblastoma and medulloblastoma.

With median survival of less than 15 months, glioblastoma is the most common and deadliest primary malignant brain tumor in adults and it is notoriously treatment-resistant, while survival of medulloblastoma in children is over 70%. Amid recent advances in genome-wide sequencing technology showing that individual brain tumors are unique in their genetic makeup, researchers are pursuing personalized immunotherapy treatments for patients.

The study is a new demonstration of mRNA-based therapeutics — the kind used in the COVID-19 mRNA vaccines. Researchers paired gene-sequencing technology with their new computational algorithm as well as bioinformatics, an evolving discipline combining computer science and biology, to address the challenge of tumor uniqueness.

Antigens are toxins or substances foreign to the body that evoke an immune response. The algorithm at the heart of the study was designed to identify mutations and tumor-associated genes that serve as antigens, among other key information.

The investigators demonstrated that the therapy prompted an infiltration of T cells, which can fight tumor cells and alter the immunosuppressive tumor microenvironment, and led to improved survival duration in the mouse models.

As a next step in the line of research, the investigators additionally tested the algorithm on human glioblastoma tumor samples as a proof of concept and identified tumor-specific and associated genes — results that laid the groundwork for a future clinical trial.