Canonical <scp>TGF</scp>‐β Pathway Activity Is a Predictor of <scp>SHH</scp>‐Driven Medulloblastoma Survival and Delineates Putative Precursors in Cerebellar Development
Bibliographic record
Abstract
Medulloblastoma (MB) is the most common malignant brain tumor of childhood. Very little is known about aggressive forms of this disease, such as metastatic or recurrent MBs. In order to identify pathways involved in aggressive MB pathophysiology, we performed unbiased, whole genome microarrays on MB tumors at both the human and murine levels. Primary human MBs were compared, transcriptomically, to their patient-matched recurrent or metastatic tumors. Expression profiling was also performed on murine tumors from two spontaneously developing MB mouse models (Ptch+/- and Smo/Smo) that present with differing clinical severities. At both the human and murine levels we identified transforming growth factor-beta (TGF-β) as a potential contributor to MB progression/metastasis. Smad3, a major downstream component of the TGF-β pathway, was also evaluated using immunohistochemistry in malignant human tissues and was shown to correlate with MB metastasis and survival. Similarly, Smad3 expression during development identified a subset of cerebellar neuronal precursors as putative cells of origin for the Smad3-positive MBs. To our knowledge, this is the first study that links TGF-β to MB pathogenesis. Our research suggests that canonical activation of this pathway leads to better prognosis for patients.
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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".