RESUMO
Pediatric low-grade gliomas (pLGG) comprise 35% of all brain tumors. Despite favorable survival, patients experience significant morbidity from disease and treatments. A deeper understanding of pLGG biology is essential to identify novel, more effective, and less toxic therapies. We utilized single cell RNA sequencing (scRNA-seq), spatial transcriptomics, and cytokine analyses to characterize and understand tumor and immune cell heterogeneity across pLGG. scRNA-seq revealed tumor and immune cells within the tumor microenvironment (TME). Tumor cell subsets revealed a developmental hierarchy with progenitor and mature cell populations. Immune cells included myeloid and lymphocytic cells. There was a significant difference between the prevalence of two major myeloid subclusters between pilocytic astrocytoma (PA) and ganglioglioma (GG). Bulk and single-cell cytokine analyses evaluated the immune cell signaling cascade with distinct immune phenotypes among tumor samples. KIAA1549-BRAF tumors appeared more immunogenic, secreting higher levels of immune cell activators and chemokines, compared to BRAF V600E tumors. Spatial transcriptomics revealed the differential gene expression of these chemokines and their location within the TME. A multi-pronged analysis of pLGG demonstrated the complexity of the pLGG TME and differences between genetic drivers that may influence their response to immunotherapy. Further investigation of immune cell infiltration and tumor-immune interactions is warranted. Key points: There is a developmental hierarchy in neoplastic population comprising of both progenitor-like and mature cell types in both PA and GG.A more immunogenic, immune activating myeloid population is present in PA compared to GG. Functional analysis and spatial transcriptomics show higher levels of immune mobilizing chemokines in KIAA1549-BRAF fusion PA tumor samples compared to BRAF V600E GG samples. Importance of the Study: While scRNA seq provides information on cellular heterogeneity within the tumor microenvironment (TME), it does not provide a complete picture of how these cells are interacting or where they are located. To expand on this, we used a three-pronged approach to better understand the biology of pediatric low-grade glioma (pLGG). By analyzing scRNA-seq, secreted cytokines and spatial orientation of cells within the TME, we strove to gain a more complete picture of the complex interplay between tumor and immune cells within pLGG. Our data revealed a complex heterogeneity in tumor and immune populations and identified an interesting difference in the immune phenotype among different subtypes.
RESUMO
Purpose: Neurocognitive deficits are common in pediatric brain tumor survivors. The use of single nucleotide polymorphism (SNP) analysis in DNA repair genes may identify children treated with radiation therapy for brain tumors at increased risk for treatment toxicity and adverse neurocognitive outcomes. Methods: The Human 660W-Quad v1.0 DNA BeadChip analysis (Illumina) was used to evaluate 1048 SNPs from 59 DNA repair genes in 46 subjects. IQ testing was measured by the Wechsler Intelligence Scale for Children. Linear regression was used to identify the 10 SNPs with the strongest association with IQ scores while adjusting for radiation type. Results: The low vs high IQ patient cohorts were well matched for time from first treatment to most recent IQ, first treatment age, gender, and treatments received. 5 SNPs on 3 different genes (CYP29, XRCC1, and BRCA1) and on 3 different chromosomes (10, 19, and 17) had the strongest association with most recent IQ score that was not modified by radiation type. Furthermore, 5 SNPs on 4 different genes (WRN, NR3C1, ERCC4, RAD51L1) on 4 different chromosomes (8, 5, 16, 14) had the strongest association with change in IQ independent of radiation type, first IQ, and years between IQ measures. Conclusions: SNP polymorphisms offer potential to predict adverse neurocognitive outcomes in pediatric brain tumor survivors. Our results require validation in a larger patient cohort. Improving the ability to identify children at risk of treatment related neurocognitive deficits could allow for better treatment stratification and early cognitive interventions.