Abbreviations
GBM and pHGG are types of highly malignant, intrinsically resistant and inevitably recurring brain tumours. The survival rates of such tumours are extremely low, with under 2% of long-term (>10 years) survivors. Despite intensive research, their incidence and mortality rates did not change over the past 30 years. Through data-driven precision medicine using spatially resolved radio-multiomics, GLIOMATCH aims to improve the outcome of malignant brain tumours in adults and children and better understand immunotherapy for brain cancer treatment.
European citizens are diagnosed with a brain tumour annually
Median survival in children with pHGG
Median survival in adults with GBM
Challenges in reducing brain cancer mortality persist due to patient variability and the complex and heterogeneous nature of each tumour. Immunotherapy has been showing great promise, but only in subsets of patients. Identifying those patients is difficult, as biomarkers (measurable indicators, such as molecules or genetic changes, revealing information about cancer presence, progression, and treatment response) are still largely missing. As a consequence, many patients receive suboptimal or random immunotherapy applications.
The GLIOMATCH team wants to change this by developing a platform built with the help of extensive clinical trials and a data-driven approach. The project will enable immunology-based patient stratification by categorising individuals according to their immune system characteristics to customise treatments or interventions for more precise and effective healthcare strategies and make this technology accessible to clinicians.
Often, promising advancements in healthcare technology fail to scale because their impacts are not assessed in real time, and they struggle to match users’ practical needs. To make sure the GLIOMATCH platform reaches its full potential, we are conducting a health technology implementation and value assessment by real-time mixed methods.
GLIOMATCH will integrate spatially resolved, multi-layered tissue maps (using integrated single-cell multiomics), with non-invasive MRI images. This integration will fuel into a novel MRI Radio-multiomics hub, that will be made available to clinical professionals through which they can perform tumour-host based patient stratification and personalised therapy matching while interpreting longitudinal follow-up and treatment efficacy. The proposed data-driven models will be developed by analysing the largest cohort of immuno-oncology treated GBM/pHGG with matched controls and exceptionally long-term surviving GBM patients, in which various tumour-host niches will be studied in how they respond to immune-oncology perturbations and lead to improved clinical outcomes. This will be empowered by deploying an UNCAN-compatible data lake, to which incremental data collection will be used to further refine the machine learning models, while proposing novel treatment options.
will serve as a centralised platform for clinicians across Europe to upload diagnostic and follow-up MRI scans of malignant brain tumour patients to get state-of-the-art insights into patient stratification, guidance towards personalised treatment, early detection of treatment efficacy/adverse effects, and longitudinal follow-up.
is essential for creating a selection algorithm, based on data mining techniques, to match eligible GBM/pHGG patients with the most suitable immunotherapeutic treatment schemes. This will be achieved using cutting-edge methods such as deep-immunophenotyping, non-invasive radiological imaging, and extensive clinical data mining of immuno-oncology-treated patient cohorts.
with the ability to add new data incrementally to enhance our understanding of why some patients respond to specific treatments while others do not. Since all prospective patients undergo the same standard care, including tumour resection and MRI imaging, information collection can be standardised and easily added without altering the current SOC treatment.