Gene-expression profiling elucidates molecular signaling networks that can be therapeutically targeted in vestibular schwannoma
Bibliographic record
Abstract
OBJECT: Vestibular schwannomas (VS) are common benign tumors of the vestibular nerve that cause significant morbidity. The current treatment strategies for VS include surgery or radiation, with each treatment option having associated complications and side effects. The transcriptional landscape of schwannoma remains largely unknown. METHODS: In this study the authors performed gene-expression profiling of 49 schwannomas and 7 normal control vestibular nerves to identify tumor-specific gene-expression patterns. They also interrogated whether schwannomas comprise several molecular subtypes using several transcription-based clustering strategies. The authors also performed in vitro experiments testing therapeutic inhibitors of over-activated pathways in a schwannoma cell line, namely the PI3K/AKT/mTOR pathway. RESULTS: The authors identified over 4000 differentially expressed genes between controls and schwannomas with network analysis, uncovering proliferation and anti-apoptotic pathways previously not implicated in VS. Furthermore, using several distinct clustering technologies, they could not reproducibly identify distinct VS subtypes or significant differences between sporadic and germline NF2-associated schwannomas, suggesting that they are highly similar entities. The authors identified overexpression of PI3K/AKT/mTOR signaling networks in their gene-expression study and evaluated this pathway for therapeutic targeting. Testing the 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 pharmacological inhibition of the PI3K/AKT/mTOR pathway with next-generation compounds led to decreased cell viability and increased cell death. CONCLUSIONS: These findings implicate aberrant activation of the PI3K/AKT/mTOR pathway as a molecular mechanism of pathogenesis in VS and suggest inhibition of this pathway as a potential treatment strategy.
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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".