IMG-11. RADIOGENOMIC ANALYSIS INFORMS ON IMMUNOLOGICAL PROFILES, PROGNOSIS, AND TREATMENT RESPONSE IN PEDIATRIC LOW-GRADE GLIOMA

Abstract

Abstract BACKGROUND Pediatric low-grade gliomas (pLGGs) have variable prognosis and treatment responses. Complete resection cannot be achieved for all tumors, especially for highly infiltrative or deep-seated tumors, necessitating additional therapy, from chemotherapy to targeted inhibitors. We integrated imaging-derived phenotypes with genotypic traits from transcriptional analysis, offering an in-depth characterization of pLGG immune microenvironment, progression risk, and likelihood of multiple treatments. METHODS Analyzing 549 treatment-naïve pLGGs with multiparametric MRI and RNA sequencing, we identified distinct immunological groups using XCell scores based on immune cell infiltration. We developed a radiomic signature using conventional MRI and machine learning techniques (support vector machines with a linear kernel and nested cross-validation) to distinguish the ‘immune-hot’ group, and incorporated diffusion MRI to improve signature accuracy. Additionally, a clinicoradiomic model predicting tumor progression risk and treatment response was trained, integrating clinical and radiomic data. Transcriptomic analysis was conducted to identify pathways correlated with clinicoradiomic risk, predictive of pLGG progression. RESULTS Three immunological groups were revealed, the ‘immune-hot’ group characterized by poor prognosis due to a high concentration of pro-tumorigenic M2-polarized macrophages, despite a higher preponderance of T-lymphocytes. The radiomic signature effectively distinguished the ‘immune-hot’ group with balanced accuracies of 76.8%/86.0% in discovery/replication sets, improved by diffusion MRI to 81.5%/84.4%. The clinicoradiomic model showed concordance indices of 0.71 (discovery) and 0.77 (replication), predicting patient progression risk. Significant differences (p=0.0010) were found in clinicoradiomic risk scores between patients undergoing one versus multiple treatments post-diagnosis, linking higher scores to a likelihood of multiple treatments. Transcriptomic pathways associated with higher clinicoradiomic risk highlighted the importance of fatty acid oxidation, a tumor-promoting mechanism that drives adaptive resistance to cytolytic immune cell effectors. CONCLUSIONS This first large-scale radiogenomic analysis in pLGGs aids in prognostication, assessing progression risk, predicting treatment response to standard-of-care therapies, and stratification of patients to identify potential candidates for novel therapies targeting aberrantly regulated pathways.