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Tumor Immune Microenvironment Profiling Could Guide Therapy in Pediatric Brain Cancer

NEW YORK – Immuno-methylomic profiling of the tumor immune microenvironment of pediatric central nervous system tumors may offer prognostic data and help guide therapeutic decisions, according to a new study.

Tumors are often broken into two immunological groups, those that are "hot" and have tumor immune microenvironments that harbor immune cells and those that are "cold" and are sparsely populated by immune cells. In general, pediatric brain tumors are thought to be "cold" as they have few mutations and neoantigens.

A team of UK researchers has now applied an algorithm called methylCIBERSORT to deconvolute the makeup of the tumor immune microenvironment of about 6,000 pediatric central nervous system tumors from their DNA methylation profiles. As they reported in Nature Communications on Friday, the researchers applied this approach to three tumor types — medulloblastomas, malignant rhabdoid tumors, and pediatric high-grade gliomas. They found that the makeup of the tumor immune microenvironment appears to be related to tumor subgroups, but there are also differences between subgroups, some of which are associated with prognosis. 

"Our analysis provides an indication of the potential future therapeutic and prognostic possibilities of immuno-methylomic profiling in pediatric CNS tumor patients that may ultimately inform approach to immune-therapy," Newcastle University's Daniel Williamson and his colleagues wrote in their paper.

Using an algorithm called methylCIBERSORT — an adaption of the CIBERSORT algorithm — the researchers characterized the cells present in a tumor sample based on the immune cell-specific methylation signatures they teased out of its genome-wide DNA methylation data. 

The researchers applied the tool to a set of previously generated 3,764 panCNS tumor methylation profiles. Overall, they found that Tregs and monocytes were the most common non-cancer cells found in tumor samples, followed by B-cells. Clustering the samples based on their immune cell makeup revealed three clusters that related to tumor type and grade but were not exclusively determined by those features.

The researchers also applied methylCIBERSORT to methylation profiles from three different types of pediatric brain tumors, finding a similar pattern.

Within a set of 2,325 medulloblastomas — which can be divided into four classic subgroups of MBWNT, MBSHH, MBGrp3, and MBGrp4, as well as more recently uncovered subtypes — the researchers noted that the subtypes have varying types of immune cells present. Consensus clustering uncovered four immune clusters, dubbed MBIC1-4, that cut across those other medulloblastoma subgroups.

The different immune clusters were associated with differences in prognosis among members of the same classic subgroups. For instance, belonging to MBIC2 was associated with poorer overall survival among MBGrp4 members. Additionally, the expression of the immune checkpoint genes PDL1 and CD276 varied between clusters, as MBIC1 had high PDL1 expression and low CD276 expression. This could inform decisions to pursue immunotherapies or therapies targeted at changing the tumor immune microenvironment, the researchers noted.

Meanwhile, an analysis of 229 malignant rhabdoid tumors uncovered four immune subgroups — MRTIC1-4 — and an analysis of 401 glioma uncovered three immune subgroups — pHGGIC1-3. These immune cell-based groups again cut across other tumor subgroups, and were linked to differences in grade and prognosis.

Additionally, as immune clusters like panCNSIC3 and pHGGIC1/2 are dominated by monocytes, while others like panCNSIC1, MBIC2/3, and MRTIC2 are likely to contain CD4+ T-type and CNSIC2, MBIC1/4, and pHGGIC3 are dominated by CD8+ T-type, the researchers noted the clusters could also be used to match tumor immune microenvironments to likely immunotherapy response.

"[T]his analysis gives first indications of the potential future therapeutic and prognostic possibilities of immuno-methylomic profiling as an adjunct to methylation/expression-based sub-classification," the researchers wrote in their paper.