Creaney J, Waddell N, Chin WL, Zhong X, Redwood A, Robinson B
Funding:icare Dust Diseases Board Research Stream Grants Program (2025-2028)
Lay synopsis: Mesothelioma is a devastating cancer with very limited treatment options. While new immunotherapies have shown promise, doctors currently cannot predict which patients will respond best to chemotherapy versus immunotherapy, and there are no approved treatments when first-line therapy fails.
The MAGIC consortium will bring together international researchers to create a database of de-identified information about people with mesothelioma’s treatment responses relative to genetic and immunological makeups. Using artificial intelligence to analyse this comprehensive dataset, we aim to identify molecular “fingerprints” that can predict which treatments will work best for future patients. This approach will help doctors make more informed treatment decisions and identify new therapeutic targets, ultimately leading to personalised treatment strategies that could significantly improve outcomes for mesothelioma patients.
Scientific synopsis: Mesothelioma presents significant therapeutic challenges with heterogeneous treatment responses and limited second-line options. Despite recent advances in checkpoint blockade immunotherapy, optimal treatment selection remains unclear, and most patients experience disease progression.
The MAGIC initiative aims to establish an international consortium to develop a comprehensive multi-omics database integrating clinical, genomic, proteomic, immunomic, and other molecular data from mesothelioma patients across multiple jurisdictions. Through advanced artificial intelligence and machine learning methodologies, we will analyse these complex datasets to identify predictive biomarkers and molecular patterns associated with treatment response.
Building on our team’s expertise in mesothelioma immunobiology and our established collaborations with Sir Charles Gairdner Hospital (SCGH) and QIMR Berghofer Medical Research Institute, this project leverages state-of-the-art computational techniques to analyse multidimensional datasets. Our research focus includes:
- Biomarker discovery and validation: Identifying molecular markers predictive of treatment response to chemotherapy and immunotherapy
- Predictive modelling: Developing AI-driven models for treatment selection and response prediction
- Therapeutic target identification: Discovering novel immunotherapeutic targets and synergistic treatment combinations
- Personalised treatment strategies: Enabling precision medicine approaches based on patient-specific molecular profiles