TARGETING CANCER STEM CELLS WITH A CDK2 INHIBITOR IN GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Glioblastoma (GB) is the most common and aggressive adult brain tumor with no cure. Brain tumor stem cells (BTSCs) are a rare population of self-renewing multipotent stem cells in GB that contribute to tumorigenesis, therapeutic resistance, and tumour recurrence. Here, we report a CDK2 inhibitor that suppresses BTSCs via OSM/OSMR/STAT3 signalling pathway. To begin with, we performed high throughput screening (HTS) of ~8400 compounds including FDA-approved drugs in patient derived human BTSCs that naturally harbour EGFRvIII mutation and elevated STAT3 phosphorylation, in search of compounds that can suppress EGFRvIII/OSMR/STAT3 oncogenic pathway. The screen led to the identification of a panel of CDK inhibitors that possessed important characteristics including a) ability to cross the blood-brain barrier with mall molecular weights of 277-566 kDa, b) low Topological Polar Surface Area (TPSA) of 76-115 A°, c) a low number of Hydrogen Bond Donors (HBDs) (1-4), and d) cLogP values ranging from 2-4. Following counter screens, we focused on a CDK2 inhibitor that significantly and most efficiently reduced OSM/OSMR signalling and STAT3 activation. Importantly, we found that the compound inhibited the self-renewal and growth of BTSCs in EGFRvIII subtype of BTSCs. This research has led to future investigation on in vivo assessment of this CDK2 inhibitor in combination with ionizing radiation and chemotherapy in preclinical models of GB.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".