A study led by the ITACA Institute at the Universitat Politècnica de València, in which the Catalan Institute of Oncology (ICO), the Germans Trias i Pujol Research Institute (IGTP), and the Hospital Clinic of Barcelona also participated, opened a new way to improve personalised treatment in patients with glioblastoma, one of the most aggressive types of cancer existing today. The application of its results in clinical practice, published in the journal Cancers, would help to tailor therapies according to the specific characteristics of each brain tumour.
The research focused on evaluating the efficacy of bevacizumab (BVZ) in treating glioblastoma (GBM). This medication is designed to block the formation of new blood vessels in the tumour, although, as explained by María del Mar Álvarez-Torres, PhD from the Universitat Politècnica de València, the effectiveness of this treatment is in doubt because it does not improve the survival of all patients who receive it.
«The variability in patient responses has raised questions about the general utility of this medication in this aggressive form of brain cancer. In this work, we propose using cerebral blood volume (rCBV) as a predictive marker to identify those GBM patients who could benefit in terms of survival with this treatment,» highlights María del Mar Álvarez-Torres.
In their study, the UPV, IGTP, ICO, and the Clínic of Barcelona team conducted a retrospective study with more than 100 patients. Bevacizumab (BVZ) proved more beneficial in those patients with moderately vascular tumours, with an average survival of 10 months following treatment. This suggests that the original vascularity of the tumour could be a crucial indicator to foresee who would benefit most from bevacizumab after tumour progression.
«In our work, we have verified that introducing the rCBV marker allows us to specifically identify those patients with moderately vascularised tumours for whom treatment with bevacizumab would be better. This not only improves the efficacy of the treatment but also offers the opportunity to explore more beneficial options for patients whose tumours do not respond favourably to the medication, thus optimising resource management and improving clinical outcomes,» points out María del Mar Álvarez-Torres.
The rCBV calculation was carried out using magnetic resonance imaging using an artificial intelligence-based technology developed at the UPV (https://www.oncohabitats.upv.es). It is, therefore, a non-invasive and risk-free alternative for patients. Moreover, using standard diagnostic data avoids additional costs and saves time in further testing.
«Our proposal is an efficient and economically viable option to improve treatment selection. But above all, it allows the early identification of glioblastoma patients who will benefit most from bevacizumab, facilitating the personalisation of treatment and improving their prospects,» insists María del Mar Álvarez-Torres.
The work now published in Cancers is the last of the results of the doctoral thesis María del Mar carried out at the UPV, specifically in the Biomedical Data Science Lab (BDSLab) of the ITACA Institute. She is currently completing her training as a postdoc researcher at Columbia University in New York, one of the world’s leading centres in cancer research.
As she explains, the next steps in the research will focus on validating the results with larger patient groups. «Moreover, considering other factors, such as the patient’s age, and incorporating a continuous analysis of follow-up images could help improve patient stratification. These advances could pave the way towards a more personalised treatment approach for glioblastoma patients, improving prognosis and quality of life,» highlights María del Mar Álvarez-Torres.
GLIOCAT Project
This work is the result of the collaboration between several centres that are part of the GLIOCAT project, supported by the La Marató TV3 grant, which allowed the retrospective collection of a wide range of clinical, molecular, and radiological data from 432 patients diagnosed and treated homogeneously for glioblastoma in six centres in Catalonia between 2004 and 2013.
Specifically, the radiological images from the diagnosis were stored on a platform that allows studies to understand the characteristics of the disease and obtain data on prognosis and treatment response.
«After a study applying Artificial Intelligence-based software, we identified a radiological characteristic: the relative cerebral blood volume or rCBV in the pre-surgical MRI, as a predictive factor for response to the antiangiogenic drug bevacizumab. If this is confirmed in larger studies, it could be useful for identifying those patients who may benefit from the drug. Our research thus provides a new useful datum with the aim of personalised medicine based on predictive biomarkers,» concludes Carme Balañà from the Applied Research Group in Oncology (B-ARGO Group) of the Catalan Institute of Oncology.
Reference
Álvarez-Torres, M.d.M.; Balaña, C.; Fuster-García, E.; Puig, J.; García-Gómez, J.M. Unlocking Bevacizumab’s Potential: rCBVmax as a Predictive Biomarker for Enhanced Survival in Glioblastoma IDH-Wildtype Patients. Cancers 2024, 16, 161. https://doi.org/10.3390/cancers16010161
Source: UPV’s Communication Area