MODL-39. ELUCIDATING THE TRANSCRIPTOMIC LANDSCAPE OF METASTATIC PEDIATRIC MEDULLOBLASTOMA
Bibliographic record
Abstract
Abstract Medulloblastoma (MB) is a highly aggressive and the most common brain tumor in childhood. MB presents a high intertumoral heterogeneity, with at least four molecular subgroups (SHH, WNT, Group 3, and Group 4) identified. MB can metastasize to the leptomeningeal space, known as Leptomeningeal Disease (LMD) and its presence is a universal predictor of poor outcome among MB patients. Metastatic MB is predominantly found in the MB Group 3 type. Although LMD represents a main clinical challenge, its molecular mechanisms remain poorly characterized. Recent research has shown that primary and MB metastasis diverge dramatically. Our work has focused on establishing therapy naïve Group 3 Patient-Derived Xenografts that faithfully replicate the nature of primary and metastatic Medulloblastoma with the aim to perform comparative genomic and transcriptomic analyses between these models to identify genetic drivers/pathways that sustain metastatic MB or leptomeningeal disease. Our results show various signaling pathways enriched across LMD models, such as protein secretion and oxidative phosphorylation. We also have identified differentially expressed genes, where sets of genes have shown to be present in more than one PDX model, such as members of the Solute Carriers family (SLC44A3 and SLC17A9) and FCGBP. Retrieval of short variants from RNAseq data has not revealed thus far mutations enriched in LMD, suggesting these changes could be attributed to epigenetic disruption rather than genetic changes. In conclusion, our results support the notion that primary and LMD are transcriptionally different, with various enriched pathways and sets of DEG among LMD Group 3 PDX models. These findings are in progress for functional validation. Work is currently being done to profile the epigenome of LMD by correlating transcriptomic data with active chromatin markers such as H3K27ac and H3K4me1. Through these approaches, we aim to elucidate the genetic dependencies of metastatic Medulloblastoma that will help for targeted therapies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".