Researchers ID 10 New Drug Targets for Aggressive Brain Cancer Glioblastoma

Researchers at Sweden’s Karolinska Institutet identified 10 potential drug targets for glioblastoma and published the results in the journal Cell Reports.

Glioblastoma (GBM) is the type of aggressive brain cancer that took the life of Arizona Senator John McCain in 2018. A notoriously difficult cancer to treat, there have been several high-profile drug failures recently, including Bristol-Myers Squibb’s checkpoint inhibitor Opdivo (nivolumab), which failed in May in a trial of newly diagnosed 06-methylguanine-DNA methyltransferase (MGMT)-unmethylated glioblastoma multiforme.

There’s new hope, however. Researchers at Sweden’s Karolinska Institutet identified 10 potential drug targets for glioblastoma and published the results in the journal Cell Reports.

GBM is the most aggressive and most common form of primary malignant cancer of the central nervous system. It is diagnosed in about 300,000 people around the globe each year and 241,000 patients die from brain or CNS cancer annually. Standard treatment for patients who have just been diagnosed with GBM includes surgery followed by radiation and chemotherapy. However, there are few other treatment options. The five-year survival rate of patients with GBM is less than 5%.

One reason the cancer is so difficult to treat is they don’t grow in a defined clump with well-defined borders. The cancer starts in the brain and affects glial cells, which surround neurons. The tumors have thread-like tendrils that spread into adjoining areas of the brain, making it almost impossible to surgically remove every cancer cell.

The researchers analyzed human brain tissue and GBM samples. The human body has more than 200 different cell types and not all of them are well understood or how they are uniquely affected by disease.

The authors noted, “Changes in the endothelium of the cerebral vasculature can contribute to inflammatory, thrombotic, and malignant disorders. The importance of defining cell-type-specific genes and their modification in disease is increasingly recognized. Here, we developed a bioinformatics-based approach to identify normal brain cell-enriched genes, using bulk RNA sequencing (RNA-seq) data from 238 normal human cortex samples from two independent cohorts.”

They compared the gene profiles with astrocyte, oligodendrocyte, neuron, and microglial cell profiles. They analyzed endothelial changes using this approach in 516 lower-grade gliomas and 401 glioblastoma.

They identified 10 novel glioblastoma-specific endothelial cell transcripts, none of which are found in normal brain tissue.

“We have found disease-related changes in the cells that line the tumor blood vessels, so-called endothelial cells, which have long been considered a possible clinical target for cancer treatment,” Lynn Butler, assistant professor at the Department of Molecular Medicine and Surgery for Karolinska, who led the study, reported Medical Xpress. “Proteins only expressed in the endothelial cells of the tumor vessels could be used as targets to attack the tumor’s blood supply, or for delivery of therapeutic agents, without affecting the normal brain.”

The research approach not only provides insight into glioblastoma biology and identified more potential therapeutic targets, but offers a new way to process different brain cell types and compare cell-type profiles between abnormal tissue and normal tissue.

The authors note that there are several large-scale tissue and cell profile projects, including the Human Protein Atlas and Human Cell Atlas. RNA sequencing data from unfractionated human tissue, both normal and diseased is also available via online portals like the Genotype-Tissue Expression (GTEx) Project and The Cancer Genome Atlas, “but using mixed-cell ‘bulk’ data to decipher changes in cell-type-specific transcriptome profiles in disease is challenging.”

Their new approach “generates results consistent with isolated cell and single-cell RNA-seq, does not require high-level bioinformatics expertise or complex modeling, making it an accessible tool for the wider research community.”

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