DIPG-86. INVESTIGATING THE TUMOR ENVIRONMENT AND SIGNALING DEPENDENCIES DRIVING DIFFUSE MIDLINE GLIOMA TUMOR INITIATION, PROGRESSION, AND RESISTANCE: A COMPREHENSIVE APPROACH LEVERAGING BULK MULTIOMICS DECONVOLUTION AND SINGLE-CELL OMICS VALIDATION
Bibliographic record
Abstract
Abstract BACKGROUND Diffuse Midline Glioma (DMG) is a incurable tumor affecting children. Recent genomic investigations have identified a recurrent H3K27M mutation which induces global alterations in histone methylation patterns and DNA methylation. These epigenetic modifications hint at pivotal roles in DMG pathogenesis, yet effective therapeutic strategies remain elusive, with median survival rates stagnant at approximately one year. This shortfall stems from two main factors: 1) Inadequate multiomics studies hindering our understanding of DMG evolution and tumor progression, and 2) Limited comprehension of the tumor microenvironment in DMG. METHODS To elucidate DMG’s clonal evolution, we adopted a comprehensive multi-region sampling approach, acquiring 33 specimens from seven patients. Whole-exome and transcriptome sequencing, alongside DNA methylation profiling, were performed. Additionally, we generated one of the most extensive single-cell multiomics datasets (>300,000 cells from 21 additional samples), with a focus on pre- and post-treatment, and dissemination effects in DMG. RESULTS Our analysis delineated a tumor-promoting microenvironment characterized by hypoxia and pro-inflammatory conditions, nurturing genomic alterations and specialized biological processes such as proliferation and epithelial-mesenchymal transition. Notably, a majority of immune cells exhibited M1-like polarization, bolstering pro-inflammatory programs within the tumor milieu. Subclones within the DMG cohort demonstrated dissemination potential, often exhibiting up-regulation of NOTCH, P53, and WNT beta-catenin signaling pathways. These findings suggest that DMG clones harboring dissemination capabilities outside the pons acquire additional phenotypic features, possibly mediated by epigenetic or transcriptional alterations, contributing to enhanced migratory and aggressive behaviors. CONCLUSIONS Our study unveils the parallel evolution of DMG at genetic, epigenetic, and transcriptional levels, unveiling novel subclonal phenotypes governing tumor behavior. Crucially, we identify critical environmental shifts such as hypoxia and inflammatory changes, coupled with specialized signaling programs, driving dissemination and resistance phenotypes. These insights may pave the way for generating accurate genetically mouse models and targeted therapeutic interventions to combat this challenging malignancy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".