A study led by the ITACA Institute of the Universitat Politècnica de València, in collaboration with the Institut Català d’Oncologia (ICO), the Institut Investigació Germans Trias I Pujol (IGTP) and the Hospital Clinic de Barcelona, has opened a novel approach to improve personalized treatments for patients with glioblastoma, one of the most aggressive types of cancer today. Applying the results published in the journal „Cancers“ to clinical practice offers the potential to adapt therapies to the specific characteristics of each brain tumor.
The research focused on evaluating the effectiveness of Bevacizumab (BVZ) in the treatment of glioblastomas (GBM). This drug is designed to inhibit the formation of new blood vessels in the tumor. However, according to Dr. María del Mar álvarez-Torres of the Universitat Politècnica de València, the effectiveness of this treatment is being questioned as it is unable to improve the survival of all patients undergoing the therapy.
„The different responses of patients have raised questions about the overall benefit of this drug in this aggressive form of brain tumor. In this work, we propose using cerebral blood volume (rCBV) as a predictive marker to identify those GBM patients who could benefit from this treatment in terms of survival.“
– Dr. María del Mar álvarez-Torres, Universitat Politècnica de València
The team from UPV, IGTP, ICO and Clínic de Barcelona conducted a retrospective study involving more than 100 patients in which Bevacizumab (BVZ) was more beneficial in patients with moderately vascularized tumors, as the average survival time after treatment was 10 months longer. This suggests that initial tumor vascularity could be an important indicator to predict who would benefit most from Bevacizumab after tumor progression.
„In our research, we found that the integration of the rCBV marker allows us to identify exactly those patients with moderately vascularized tumors who would benefit more from Bevacizumab treatment. This improvement in treatment effectiveness not only offers a more targeted approach but also opens up opportunities for researching cheaper options for patients whose tumors do not respond positively to the drug. This optimized approach supports resource management and contributes to better clinical outcomes,“ explains María del Mar álvarez-Torres.
The rCBV was calculated from magnetic resonance images using an artificial intelligence-based technology developed at UPV (https://www.oncohabitats.upv.es), making it a non-invasive alternative without additional risks for the patient. Furthermore, standard diagnostic data avoids additional costs and saves time for other tests.
„Our proposal is an efficient and cost-effective option to improve treatment selection. Above all, it allows for the early identification of glioblastoma patients who will benefit the most from Bevacizumab, facilitating the personalization of treatment and improving their prospects,“ says María del Mar álvarez-Torres.
The work, now published in Cancers, is the latest result of the doctoral thesis that María del Mar carried out at UPV, specifically at the Biomedical Data Science Lab (BDSLab) of the ITACA Institute. She is currently undergoing postdoctoral training at Columbia University in New York, one of the world’s leading centers in cancer research.
Universitat Politècnica de València