IMMU-04. Transcriptional analysis reveals distinct microenvironmental subgroups across pediatric nervous system tumors
Bibliographic record
Abstract
Abstract INTRODUCTION: Recent clinical trials of immune checkpoint inhibitors indicated 5-11% response rate in pediatric patients depending on cancer type and expression of target proteins. Currently, a systematic analysis characterizing the immune microenvironment of childhood tumors is lacking. The main objective of this study is to uncover the features of immune microenvironment in pediatric nervous system tumors (pedNST). METHODS: We compiled transcriptomes of 925 tumors from three initiatives, Therapeutically Applicable Research To Generate Effective Treatments (TARGET, n = 149), International Cancer Genome Consortium (ICGC, n = 195) and Children Brain Tumor Tissue Network (CBTN, n = 581). We analyzed the performance of immune deconvolution tools and used publicly available datasets to define immune genesets. We conducted a consensus analysis to assign genes to cell-types and identify immunological groups. RESULTS: We found wide variability in immune infiltration across and within cancer types ranging from cold tumors such as medulloblastoma (2.7% infiltrate) to infiltrated entities such as neurofibroma (22.6%). Consensus clustering revealed four distinct immune clusters. The pediatric inflamed group (10%) included MYCN non-amplified neuroblastoma and ATRT. The myeloid-predominant group (30%) showed decreased infiltration of lymphoid cells but enrichment of myeloid cell genesets. The pediatric-cold group (42%) harbored no enrichment of immune genesets and included 72% of ependymomas and 65% of medulloblastomas. The immune excluded group (18%) showed depletion of immune cell-types and included sonic-hedgehog medulloblastoma. 71% of pedNST belonged to the lymphocyte depleted or immunologically quiet clusters, indicating the cold immune microenvironment in pedNST compared to adult cancers. CONCLUSION: We report characteristics of the immune microenvironment in pedNST. We found an overall cold microenvironment with low lymphocyte infiltration in this population compared to common adult cancers. We identified ~10% of tumors harboring a relatively inflamed microenvironment. Our data uncover characteristics of immune infiltration in pediatric tumors with potential implications to guide therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".