EPCO-17. UNMASKING CLONAL EVOLUTION OF DIFFUSE INTRINSIC GLIOMA USING MULTI-MODAL GENOMIC DATA
Bibliographic record
Abstract
Abstract Diffuse intrinsic pontine glioma (DIPG) is an infiltrative incurable tumor affecting children. DIPG tumors often harbor a recurrent H3K27M mutation which leads to a global loss of H3K27me2/3 and overall DNA hypomethylation, suggesting an important role of the epigenome, and consequent transcriptome in DIPG pathogenesis. To thoroughly characterize the clonal evolution of DIPG, we collected 33 samples from 7 DIPG patients using a multi-region sampling strategy and generated whole-exome and transcriptome sequencing, and DNA methylation profiling data. Using our novel bioinformatics approach, 28 distinct tumor sub-clones were identified and characterized in our cohort of DIPG patients whilst simultaneously interrogating the tumor’s ability to migrate and disseminate. When present, initiating tumor clone (Clone 1) exclusively contained H3K27M with significant DNA methylation changes whereas the divergent clones that arise later in DIPG evolutionary trees were typically driven by the copy number aberrations. Further characterization of DIPG sub-clones identified unique gene expression profiles (i.e. cell migration and angiogenesis programs) that support and enable DIPG dissemination. In this study, we uncovered how DIPG evolves at the genetic, epigenetic, and transcriptional levels in parallel and in doing so, reveal novel evolutionary phenotypes at the sub-clonal level related to the tumor’s behavior. We are further validating these results in single-cell RNA sequencing experiments and gaining further insights into the underlying molecular mechanisms responsible for invasive and aggressive DIPG phenotypes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".