Analysis of Molecular Networks Uncovers Potential Targets in Vestibular Schwannoma
Bibliographic record
Abstract
Objective : Understanding molecular pathway mechanisms of the formation of vestibular schwannoma may lead to potential therapeutic targets. Study Design : We performed gene expression profiling of 49 schwannomas (36 sporadic and 13 NF2-associated cases) and 7 normal control vestibular nerves. Results : We identified over 4,000 differentially expressed genes between control and schwannoma with network analysis uncovering proliferation and antiapoptotic pathways previously not implicated in vestibular schwannomas. Using several distinct clustering technologies, we could not reproducibly identify subtypes of schwannomas suggesting that our schwannoma cohort was molecularly distinct from normal tissue yet highly similar among themselves. At the molecular level the PI3K/AKT/mTOR signaling network was overexpressed in our schwannoma cohort and evaluated for therapeutic targeting. Testing compounds BEZ235 and PKI-587 both novel dual inhibitors of PI3K and mTOR attenuated tumor growth in a preclinical cell line model of schwannoma (HEI-293). In vitro findings demonstrated that ablation of the PI3K/AKT/mTOR pathway with next generation inhibitors lead to decreased cell viability and increased cell death. Conclusion : The discovery of novel molecular targets in vestibular schwannoma by transcriptional profiling as compared with appropriate controls may lead to effective therapeutic strategies and shed insight into the molecular ontogeny of this tumor.
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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 | 0.001 | 0.001 |
| 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".