Most Searched
Originally published January 31, 2025
Last updated January 31, 2025
Reading Time: 6 minutes
Search more articles
News & Magazine
Topics
The glioma subtype known as glioblastoma is notoriously difficult to treat. Current treatments for glioblastoma are unable to successfully address the heterogeneity between different glioblastoma tumors. The fact is that glioblastomas differ markedly between patients, and even within an individual patient, a glioblastoma does not stay the same. Instead, it mutates over time, making treatment difficult.
Promising new research, however, is aimed at identifying commonalities in glioblastomas and using that information to develop heterogeneity-agnostic treatment that will be effective across multiple glioblastomas. Lead investigator David D. Tran, MD, PhD, chief of neuro-oncology and co-director of the USC Brain Tumor Center, part of Keck Medicine of USC, explains how his team’s work could unlock new hope for glioblastoma treatment.
One of the hallmarks of glioblastoma is its diversity. “One patient’s glioblastoma is different from another patient’s glioblastoma,” Tran says. “Not only that, even when you look at a glioblastoma tumor itself, different regions of a tumor are different as well.”
These differences arise because glioblastomas genetically mutate in a random fashion. “There is really no rhyme or reason why a mutation occurs in one patient and not in another,” Tran says.
For this reason, finding a treatment that can benefit many glioblastoma patients has been elusive. “Even if you develop a therapy to target a mutation in one glioblastoma, it won’t help with another glioblastoma if that glioblastoma doesn’t have the same mutations,” he says.
Not only that but treating a single patient’s glioblastoma is difficult because the patient’s own tumor will change. “Even if we sequence a tumor and identify a set of mutations specific to that patient, then quickly develop a therapy for the patient in a couple of months, by then, however, the tumor might mutate and no longer be the same tumor it was two months ago — meaning the therapy you developed for this tumor two months ago might not work as the tumor accumulates new mutations,” Tran explains.
“You can almost never predict what the next mutations will be,” he adds. “As physicians, we’re basically just chasing our tail with treatment. That’s why it’s so difficult to treat this tumor.”
Developing a treatment suited for multiple glioblastomas would require researchers to identify some sort of commonality in glioblastomas in general. This is what Tran and his colleagues are doing. In their research, they observed that even though glioblastomas appear very different when sequenced, there are still some ways in which they are the same.
“They behave similarly,” Tran says. “They’re invariably deadly. They’re resistant to the same therapy. They infiltrate in a similar fashion and interact with the brain in a similar way. Clinically, they behave largely identically.” He draws a comparison with human beings: Although humans are all different, as a species we all tend to react to certain drugs similarly.
“It’s the same with glioblastomas,” he continues. “There must be something common among these tumors. And if we can find out what this commonality is, and this commonality is a possible target for therapy, then that therapy would become almost a universal therapy because it would attack a vast majority of glioblastomas.”
Tran and his research team developed AI algorithms, which they used to scan vast data sets of glioblastomas to study commonalities among glioblastomas. What they ended up identifying were some specific genes — most notably developmental genes — that appear common among these tumors’ cells.
As Tran explains, to reproduce, cancer cells weaponize developmental genes that otherwise lie dormant in healthy cells. In healthy cells, these genes become inactive post-utero once they are no longer needed for fetal development. In healthy adult cells, these genes are turned off. Cancer cells, however, harness these genes, which enable the cancer cells to grow uncontrollably. Because this gene state is unique to cancer cells, Tran and his colleagues pinpointed these genes as targets for destroying glioblastomas.
Using AI, Tran and his colleagues identified nine “master genes” they say regulate and control the thousands of genes in each glioblastoma tumor cell. Among these master genes, seven are developmental genes. The team’s goal is now to develop gene therapy that destroys three or four of these genes.
“If you develop a gene therapy to target these genes, the whole gene network that defines GBM tumors collapses and the tumor cells will die,” he says. “Without these genes, the tumor cell is not able to survive.”
Additionally, he says, because these genes are inactive or present in low levels in normal, healthy cells, targeting these genes with therapy won’t affect healthy cells significantly.
To prove their theory that these genes are critical to a glioblastoma’s survival, the team conducted tests in which they removed these genes from glioblastoma stem cells. By doing so, “we showed that you can actually kill these glioblastoma tumors across the wide spectrum of tumors we looked at,” he says.
In other tests, they conducted animal studies in which they altered normal brain cells with these genes, which caused the cells to turn into glioblastomas.
Tran and his team are already able to inject a modified virus, or vector, containing the DNA construct that will destroy the targeted genes in the glioblastoma and ultimately kill the tumor cells. They are currently using convection-enhanced delivery, a minimally invasive probe to inject the virus into a tumor. Using tumor mapping, they can pinpoint exactly where the injection should be introduced into each patient’s tumor to deliver maximum coverage of the entire tumor. The results of their early research have been positive. In their tests, their treatments were shown to even infect tumor cells spreading beyond the border of the tumor, Tran says.
The team is now optimizing this novel vector to make it suitable for human patients. They were recently awarded a grant from the California Institute for Regenerative Medicine (CIRM) to further drug development and translational research. Through the grant, they will be working to optimize vectors and to develop a workflow to scale up production in a GMP environment at the University of Southern California. They will also be conducting safety and efficacy confirmation studies to bring them closer to clinical trial.
Tran hopes this research will lead to the next frontier in glioblastoma treatment: a first-of-its kind, heterogeneity-agnostic gene therapy for treating glioblastomas. It would be a critical advancement given the poor survival rate for glioblastomas today. Currently, the mean survival is less than 15 months, and the five-year survival rate is 5%.
“The ultimate goal of this therapeutic program is to develop and provide a novel, effective, heterogeneity-agnostic treatment option for patients with [glioblastomas], especially recurrent [glioblastomas], who currently have limited therapeutic options and a poor prognosis,” the researchers’ grant description explains.
So far, Tran says their research has shown that “if we are able to deliver the target genes to the vast majority of tumor cells, we can achieve a very high cure rate of even up to 90%.”
For glioblastoma patients facing poor survival rates today, this would make a big difference. “If we can develop any therapy that makes up to 50% of patients survive long-term, it’s a huge success,” he says.
This first-of-its-kind treatment may also one day extend to other types of tumors, too. “Right now, we are going after glioblastomas, which is one of the most difficult tumors to treat,” Tran says. “If we can prove that this technology can be used to treat a very difficult cancer like glioblastomas, it will be proof that it can also be used to treat other cancers as well.”
Share
