New Study Offers Personalized Strategy to Combat Glioblastoma Treatment Resistance

Glioblastoma, an aggressive and often fatal brain cancer, is notoriously resistant to standard treatments like chemotherapy and radiation. A new study from UCLA Health's Jonsson Comprehensive Cancer Center, published in Nature Communications, has identified a novel approach to address this challenge by integrating genetic and functional profiling of tumors. This strategy not only offers a clearer understanding of why glioblastoma resists treatment but also provides a path toward personalized therapies designed to improve patient outcomes.

Combining Genetic and Functional Profiling

Traditional genomic precision medicine focuses on identifying genetic mutations in tumors to match them with targeted therapies. However, this method often fails to predict how tumors will respond to treatment due to the complexity and adaptability of glioblastoma cells. To overcome this limitation, researchers at UCLA developed a dual approach that combines genetic profiling, which analyzes the tumor's genetic makeup, with functional profiling, which observes how live cancer cells react to therapies in real time.

Using a technique called BH3 profiling, the team studied how glioblastoma cells respond to treatments designed to trigger apoptosis, or programmed cell death. This analysis revealed that the effectiveness of treatments depends on specific genetic characteristics, such as the presence of a functional p53 gene. By combining these insights, researchers are now better equipped to tailor therapies to the unique behavior of individual tumors.

A Machine-Learning Tool for Tailored Treatments

Leveraging their findings, the researchers developed a machine-learning tool called GAVA. This innovative tool integrates genetic and functional data to predict how a tumor might respond to specific treatment combinations, enabling clinicians to identify the most effective therapies for each patient.

One of the key discoveries was that targeting the protein BCL-XL, which plays a critical role in preventing cancer cells from undergoing apoptosis, could significantly enhance the effectiveness of standard therapies. This finding prompted researchers to explore the use of ABBV-155, an experimental antibody-drug conjugate specifically designed to target BCL-XL while sparing healthy cells.

In preclinical models, combining ABBV-155 with standard treatments resulted in significant tumor shrinkage—an outcome rarely observed in glioblastoma research. "We found that combining standard therapies with ABBV-155 successfully induced tumor shrinkage, which is something we rarely observe in clinically relevant glioblastoma models," said Dr. David Nathanson, senior author of the study and a professor of molecular and medical pharmacology at UCLA. "The results are incredibly exciting, and we are hopeful that this approach will pave the way for a new therapy for patients with this devastating disease."

A Path Toward Personalized Care

The study represents a major step forward in precision oncology for glioblastoma. By combining functional data with genetic profiling, this approach moves beyond static genetic information to better predict tumor behavior and treatment response. Dr. Nathanson and his team believe that this dual strategy could improve outcomes for patients with this devastating disease.

The research team is now preparing to test the combination of therapies, including ABBV-155, in clinical trials to evaluate its impact on patients. If successful, this approach could offer new hope to glioblastoma patients and their families, paving the way for more effective, patient-specific treatments.

The study was supported by several foundations, including the Sheila and Stanford L. Kurland Family Foundation, the National Brain Tumor Society, and grants from the National Institutes of Health.