STEM-12. TARGETING GLIOBLASTOMA WITH A BRAIN PENETRANT DRUG THAT IMPAIRS BRAIN TUMOUR STEM CELLS
Bibliographic record
Abstract
Abstract Brain tumour stem cells (BTSCs) are a population of self-renewing malignant stem cells that play an important role in glioblastoma tumour hierarchy and contribute to tumour growth, therapeutic resistance, and tumour relapse. Thus, targeting of BTSCs within the bulk of tumours represents a crucial therapeutic strategy. Here, we report a potent brain penetrant drug (AJ5939) that impairs BTSCs and impedes glioblastoma progression. We show that AJ5939 inhibits the self-renewal and growth of BTSCs harbouring a diverse range of oncogenic mutations without affecting non-oncogenic neural stem cells. Global gene expression profiling revealed significant alterations in BTSC transcriptome and attenuation of the expression of a large panel of genes involved in cell cycle progression, stemness, and DNA repair mechanisms. Mechanistically, we show that AJ5939 impairs Notch signalling pathway via directly targeting this pathway and altering the stemness signature. In combination with ionizing radiation (IR), however, AJ5939 suppresses the DNA repair mechanisms and facilitate the response of the BTSC to ionizing radiation induced cell death. Finally, we present data that AJ5939 treatment of brain tumours in preclinical models significantly prolongs lifespan.
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 | 0.000 |
| 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".