PTPS-17LEPTOMENINGEAL METASTASIS IN MYC-DRIVEN MEDULLOBLASTOMA
Bibliographic record
Abstract
Medulloblastoma (MB) is the most common malignant brain tumor in children. Even with an intensive regimen of surgery, radiation and chemotherapy, 25-30% of MB patients still die from their disease. Recent genomic analyses of MB have shed light on the molecular basis of the disease and have begun to point to novel approaches to therapy. However, the vast majority of these studies have focused on primary tumors, and relatively few have examined the biology of leptomeningeal metastasis (LM), the spread of tumor cells through the meninges to the brain and spinal cord. LM, most commonly seen in patients with MYC-driven (Group 3) MB, is not amenable to surgical resection and is associated with extremely poor patient outcomes. To understand the mechanisms driving metastasis, we used a mouse model of MYC-driven MB in which tumor cells exhibit LM. Importantly, we found that when primary and metastatic cells from this model are injected into the cerebrospinal fluid, primary cells tend to give rise to primary tumors whereas metastatic cells are more likely to regenerate metastatic lesions. To identify pathways that mediate this difference in metastatic potential, we compared gene expression in primary vs. metastatic tumor cells from our animal model, as well as from MB patients. Our studies revealed activation of signaling pathways involved in cell motility and adhesion to the extracellular matrix, and identified genes that are commonly activated in the metastatic compartment in both mouse and human MB. Ongoing studies are focused on validating differential expression of these genes and determining their function using in vitro and in vivo assays. These studies will provide critical insight into the mechanisms of metastasis in MB and yield novel approaches for treating metastatic disease.
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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) | 0.001 | 0.000 |
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".