OS08.3.A Distinct systemic and tumor microenvironment immune landscapes discriminate across sellar tumor types and controls through a methylation-based deconvolution method. (5th September 2022)

• Record Type: Journal Article
• Authors: Herrgott, G; Asmaro, K; Thomas, B; Nelson, K; Cazacu, S; Hasselbach, L; Transou, A; deCarvalho, A; Mukherjee, A; Robin, A; Lee, I; Kalkanis, S; Rock, J; Rock, J; Noushmehr, H; Castro, A
• Is Part Of: Neuro-oncology. Volume 24(2022)Supplement 2
• Page Start: ii18
• Page End: ii19
• Publication Date: 2022-09-05
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noac174.057
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Background: Systemic (Sys) and tumor microenvironment (TME) immune milieus play a pivotal role in tumor development, outcome and immunotherapy response predictions across a variety of central nervous system tumors. Genome-wide methylation profiling can reliably discriminate and estimate immune cell proportions present in the blood and within the tumor and has not been reported across sellar tumor types (STT). Material and Methods: We estimated cell composition in liquid biopsy (LB, serum/plasma) and tissue specimens from 42 STT collections (i.e., pituitary neuroendocrine tumors [PitNETs; n=37] and craniopharyngiomas [CP; n=5]), and 26 nontumor controls (LB: 11; Tissue: 15) using MethylCIBERSORT, a methylation-based deconvolution algorithm and established immune cell signatures as reference. LB methylation was profiled with EPIC array. Correlations between estimated cell proportions across sample sources were explored (Spearman). Immune cell proportion hierarchical k-means clustering was performed across tissue and LB specimens. Similarly, mean comparisons between and across sample types and subgroups of interest were performed [Non-parametric Kruskal-Wallis, Wilcoxon rank-sum tests; p<0.05]. Results: We identified three immune-clusters across tissue specimens which distinguished controls (k3-cluster) from sellar tumor specimens (k1- and k2- clusters), primarily attributable to differential B-cell and monocyte proportions. Interestingly, a subset of PitNET and CP, belonging to the k2-cluster, presented a distinct immune profile compared to their K1-sellar tumor counterparts. Analysis of plasma-derived immune clusters revealed that PitNETs were distributed across four distinct immune patterns and CP clustered together with controls and a PitNET subset. One of the PitNET clusters was enriched with patients that died during follow-up and presented an enrichment of CD4-(including the regulatory subtype), CD8 and CD56-T and depletion of natural killer cells. Differences across serum- and tissue-derived clusters were present but less prominent than their plasma counterparts. No correlation between immune cell proportions across other clinicopathological features within each tumor type (sex, age, histotypes, invasion etc) was observed. Conclusion: Our results suggest that PitNETs are characterized by differential TME and systemic immune subtypes which also distinguish these tumors from CP and controls. Additionally, distinct systemic immune composition between tissue and LB sources, more readily observed in plasma, suggest that the systemic response to the presence of the tumor is distinct from the immune response noted in the TME. Tumor immune subtyping may allow the stratification of STT according to immunotherapy response vulnerabilities.