High cellular plasticity state of medulloblastoma local recurrence and distant dissemination
Bibliographic record
Abstract
Medulloblastoma (MB), a heterogeneous pediatric brain tumor, poses challenges in the treatment of tumor recurrence and dissemination. To characterize cellular diversity and genetic features, we comprehensively analyzed single-cell/nucleus RNA sequencing (sc/snRNA-seq), single-nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq), and spatial transcriptomics profiles and identified distinct cellular populations in SHH (sonic hedgehog) and Group_3 subgroups, with varying proportions in local recurrence or dissemination. Local recurrence showed higher cycling tumor cell enrichment, whereas disseminated lesions had a relatively notable presence of differentiated subsets. Chromosomal alteration evaluation revealed distinct genetic subclones during MB progression, such as chr7q gain and chr11 loss in Group_3 disseminations. A subpopulation termed “high cellular plasticity (HCP)” emerged during MB progression and was associated with increased dividing potential and chromatin accessibility, contributing to recurrence. Inhibiting HCP-associated markers, like protein tyrosine phosphatase receptor type Z1 ( PTPRZ1 ), efficiently suppressed MB progression in preclinical models. These findings address critical gaps in understanding the cellular diversity, chromosomal alterations, and biological dynamics of recurrent MB, offering potential therapeutic insights. • Multiomics reveals distinct cellular and genetic events associated with MB recurrence • MB cells with high cellular plasticity (HCP) state contribute to tumor progression • Targeting MB HCP cells improved the outcomes Liu et al. demonstrates that PTPRZ1 is crucial for maintaining the high cellular plasticity (HCP) state during Group_3 medulloblastoma recurrence. Targeting this subpopulation of tumor cells with HCP state effectively mitigates medulloblastoma progression offers a potential treatment for this aggressive disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".