DDDR-49. IGF1R inhibition arrests meningioma growth
Bibliographic record
Abstract
Abstract Meningiomas are the most common primary tumors of the central nervous system. While standard-of-care treatment—surgical resection and/or radiotherapy—is effective for most patients, a subset of tumors progress despite these interventions. To identify novel therapeutic targets, we generated single-cell transcriptomic atlases of WHO grade 1 and 2 meningiomas. This analysis revealed multiple tumor cell states and highlighted robust activation of the Insulin-like Growth Factor 2–Insulin-like Growth Factor 1 Receptor (IGF2–IGF1R) signaling axis, a key regulator of cell metabolism and growth. Inhibition of IGF1R in fresh patient-derived 2D cultures and ex vivo explant models suppressed tumor cell proliferation. Based on these findings, a patient with a rapidly progressing, IGF2–IGF1R–expressing grade 2 meningioma—previously treated with radiotherapy—was administered Ceritinib, an IGF1R inhibitor. While the tumor had been growing at a rate exceeding 1 mm/month prior to treatment, no further growth was observed after two months of Ceritinib therapy. In summary, we identified IGF2–IGF1R signaling as an active and targetable pathway in meningioma. Ceritinib demonstrated both preclinical efficacy and promising early clinical activity in a case of radiotherapy-resistant recurrent meningioma. To our knowledge, this represents the first report of clinical efficacy of Ceritinib in meningioma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".