PO-202 PI3K activation in neural stem cells drives tumourigenesis which can be ameliorated by targeting the cAMP response element binding (CREB) protein
Bibliographic record
Abstract
Introduction Hyperactivation of the PI3K signalling is common in cancers but the precise role of the pathway in glioma biology remains to be determined. Some understanding of PI3K signalling mechanisms in brain cancer comes from studies on neural stem/progenitor cells, where signals transmitted via the PI3K pathway cooperate with other intracellular pathways and downstream transcription factors to regulate critical cell functions. Material and methods To investigate the role for the PI3K pathway in glioma initiation and development, we generated a mouse model targeting the inducible expression of a PIK3CAH1047A oncogenic mutant and deletion of the PI3K negative regulator, PTEN, to neural stem/progenitor cells (NSPCs). Results and discussions Expression of a Pik3caH1047A was sufficient to generate tumours with oligodendroglial features but simultaneous loss of PTEN was required for the development of invasive, high-grade glioma. Pik3caH1047A-PTEN mutant NSPCs exhibited enhanced neurosphere formation which correlated with increased WNT signalling, while loss of CREB in Pik3ca-Pten tumours led to longer symptom-free survival in mice. Conclusion Taken together, our findings present a novel mouse model for glioma demonstrating that the PI3K pathway is important for initiation of tumourigenesis and that disruption of downstream CREB signalling attenuates tumour expansion. Recently developed experimental compounds which target CREB can inhibit tumour cell proliferation in vitro and in vivo therefore, co-targeting archetypal oncogenic pathways, including PI3K, MAPK and WNT, as well as transcription factors such as CREB, may prove to be effective therapeutic approaches for difficult to treat cancers, including high-grade glioma.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".