Human Schwannomas Express Activated Platelet-Derived Growth Factor Receptors and c-kit and Are Growth Inhibited by Gleevec (Imatinib Mesylate)
Bibliographic record
Abstract
Schwannomas, although benign, can be fatal or give rise to significant morbidity due to an unpredictable growth rate. They can reoccur after surgery or radiation, current treatments each with significant inherent risks. These risks are further amplified in neurofibromatosis type 2 (NF2), a germ line predisposition syndrome characterized by multiple schwannomas, underlying the need for biological targeted therapies. Gleevec (STI571, imatinib mesylate), in addition to the bcr-abl oncogene in chronic myelogenous leukemia, inhibits c-kit and platelet-derived growth factor receptor (PDGFR) signaling, thereby expanding its use to several malignant and benign human diseases. In the present study, we show that human sporadic and NF2-associated schwannomas have increased expression along with activation of PDGFR-alpha, PDGFR-beta, and c-kit receptors, compared with normal or traumatic nerve. Using the human NF2-null HEI-193 schwannoma cell line, Gleevec inhibited schwannoma viability, proliferation, and anchorage-independent growth, as well as induced apoptosis in a dose-dependent manner (IC(50) 5-10 micromol/L). These antitumorigenic effects were correlated to inhibition of PDGFR-alpha, PDGFR-beta, and c-kit activation/phosphorylation and major downstream signaling pathways. Lack of robust xenograft or transgenic models of schwannomas prevents extension of these studies in vivo. However, the established long track record and tolerable toxicity of Gleevec already in clinical use and our preclinical data lead us to propose that Gleevec should be evaluated in human schwannomas with shown progressive growth.
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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 | 0.001 |
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".