Abstract
Abstract BACKGROUND Pediatric low-grade gliomas (pLGGs) have diverse molecular subtypes with distinct prognoses, necessitating personalized treatments. We aimed to determine whether radiomic features of pLGGs co-segregate with molecular subtypes, establishing a proof-of-concept that distinct molecular aberrations could manifest in differentiable imaging features. METHODS Leveraging radiomic, genomic, and transcriptomic data from 201 pLGG patients from the Children’s Brain Tumor Network (CBTN), we identified imaging-based pLGG subtypes through their radiophenotypes. This involved investigating the association of imaging subtypes with 2021 WHO pLGG classifications and profiling transcriptional characteristics distinguishing pLGG clusters. We used principal component analysis and K-Means clustering on 881 radiomic features and incorporated clinical variables (age, sex, tumor location), and derived three imaging subtypes. RESULTS BRAF V600E mutations were mainly found in subtype 3, while BRAF::KIAA1549 fusion in subtype 1. To ascertain transcriptome pathways most closely associated with imaging-based subtypes, we applied a supervised machine learning model with elastic net logistic regression using a one-versus-rest strategy for each imaging cluster, considering the top 100 most variable pathways, identified through gene set enrichment and gene co-expression network analyses, molecular subtype, tumor location, age, sex, and race. The predictive accuracy (area under the receiver operating characteristic curve) of the model was 0.86 | 0.72 for imaging subtype 1, 0.93 | 0.71 for subtype 2, and 0.91 | 0.64 for subtype 3 in the training | testing sets. Notably, each imaging subtype exhibited distinct molecular mechanisms; subtype 1 showed up-regulation of oxidative phosphorylation, PDGFRB, and interleukin 4, 10, and 13 signaling, while subtype 3 was linked to histone acetylation and DNA methylation pathways, relating to BRAF V600E pLGGs. CONCLUSIONS Our radiogenomics study suggests that intrinsic molecular characteristics of tumors drive distinct imaging subgroups of pLGG, providing a basis for future multi-modal investigations that may better define progression and targetability of the disease.
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