Pediatric brain tumors are the leading cause of cancer-related death in children. Recent advances in genomic profiling have revolutionized our understanding of these tumors, revealing distinct molecular subgroups in tumor entities, which were previously thought to represent homogenous diseases. Despite significant progress in the past decades in the management of pediatric brain tumors, tumor relapse remains a critical challenge, particularly for high-risk subgroups. Relapse patterns can vary considerably among different molecular subgroups, reflecting underlying differences in tumor biology and response to therapy. Understanding these patterns is essential for improving therapeutic decision making in radiation oncology (e.g., target volume definition). By leveraging comprehensive genomic data and clinical follow-up information, we seek to identify subgroup-specific relapse dynamics. Our findings will contribute to the growing body of knowledge on the molecular underpinnings of pediatric brain tumors and inform the development of personalized treatment approaches during radiation treatment planning, ultimately with the hope to improve survival in this vulnerable population.
Reverse Translation: post-hoc identification of putative alterations associated with radioresistance
Radiation therapy represents a cornerstone in the treatment of various cancers, yet its effectiveness is often compromised by the emergence of radioresistant tumor cells. To address this challenge, we utilize a reverse translation approach to identify putative genetic and epigenetic alterations associated with radioresistance. By retrospectively analyzing clinical data alongside molecular profiles from treated patients, we aim to pinpoint specific alterations conferring resistance to radiation therapy. Our work aims to integrate comprehensive genomic data with clinical outcomes to uncover potential biomarkers and therapeutic targets for personalized treatment strategies. In collaboration with the Preclinical Program at KiTZ (AG Zuckermann), in vivo models will be generated to test and target these putative alterations, providing a robust platform for validating our findings and advancing the development of combination therapy. Our research enhances the understanding of radioresistance mechanisms and demonstrates the utility of reverse translation in identifying clinically relevant genetic alterations, ultimately aiming to improve therapeutic outcomes and patient survival.
