Nimg-42. radioimmunomic signatures in pediatric low-grade glioma for non-invasive identification of immunotherapeutic targets

Abstract INTRODUCTION Tumor immune microenvironment (TIME) plays a key role in response to anti-tumor therapies. Therefore, detailed characterization of TIME is crucial for patient stratification and enrollment onto emerging immunotherapies. As standard-of-care treatments are not feasible or confer devastating long-term morbidity for pediatric low-grade glioma (pLGG), non-invasive quantification of the TIME using radiomic analysis, may guide treatment decisions. On the largest available transcriptomic and imaging data in pLGGs, provided through Children’s Brain Tumor Network (CBTN), this data-driven study aims to discover immunological profiles of pLGG that are meaningfully related to biomarkers of tumor immune response, and radioimmunomic signatures that are predictive of immunological profiles. METHODS Enrichment of immune and stromal cell types and pathways in 502 subjects were inferred using xCell algorithm applied to transcriptomic data and the tumors were then clustered via consensus clustering approach. Tumor inflammation signature (TIS), as a biomarker of response to anti-PD-1 blockade, was compared across the immunological clusters. Quantitative radiomic features were extracted from the segmented tumor regions on multiparametric MRI scans of 155/502 patients. Cross-validated support vector machines with forward feature selection were trained on radiomic features using one-versus-the-rest approach to predict the immunological groups. RESULTS Three immunological clusters, i.e., immune-cold, -altered, and -hot were found, with different levels of enrichment in immune and microenvironment scores, CD4 T-cells, and macrophages. Significantly higher TIS values were found in immune-hot and -altered compared to immune-cold tumors (p= 1.2e-8 and p= 7e-16, respectively). Radioimmunomic models yielded average AUC of 0.75 for one-versus-the-rest differentiation of the immunological clusters. CONCLUSIONS The proposed analysis revealed three distinct immunological groups in pLGGs, with immune-hot tumors more likely to respond favorably to immunotherapies. Furthermore, non-invasive radioimmunomic signatures were shown to potentially stratify the patients based on their TIME, which upon further development and validation, could inform treatments for pLGGs.