<b> <i>Notch1</i> </b> and <b> <i>Notch2</i> </b> Have Opposite Effects on Embryonal Brain Tumor Growth
Bibliographic record
Abstract
The role of Notch signaling in tumorigenesis can vary; Notch1 acts as an oncogene in some neoplasms, and a tumor suppressor in others. Here, we show that different Notch receptors can have opposite effects in a single tumor type. Expression of truncated, constitutively active Notch1 or Notch2 in embryonal brain tumor cell lines caused antagonistic effects on tumor growth. Cell proliferation, soft agar colony formation, and xenograft growth were all promoted by Notch2 and inhibited by Notch1. We also found that Notch2 receptor transcripts are highly expressed in progenitor cell-derived brain tumors such as medulloblastomas, whereas Notch1 is scarce or undetectable. This parallels normal cerebellar development, during which Notch2 is predominantly expressed in proliferating progenitors and Notch1 in postmitotic differentiating cells. Given the oncogenic effects of Notch2, we analyzed its gene dosage in 40 embryonal brain tumors, detecting an increased copy number in 15% of cases. Notch2 gene amplification was confirmed by fluorescence in situ hybridization in one case with extremely high Notch2 mRNA levels. In addition, expression of the Notch pathway target gene Hes1 in medulloblastomas was associated with significantly shorter patient survival (P = 0.01). Finally, pharmacological inhibition of Notch signaling suppresses growth of medulloblastoma cells. Our data indicate that Notch1 and Notch2 can have opposite effects on the growth of a single tumor type, and show that Notch2 can be overexpressed after gene amplification in human tumors.
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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".