Combination Effects of Integrin-linked Kinase and Abelson Kinase Inhibition on Aberrant Mitosis and Cell Death in Glioblastoma Cells
Bibliographic record
Abstract
In cancer cells, inhibition of integrin-linked kinase (ILK) increases centrosome declustering causing mitotic arrest and cell death. Yet, not all cancer cells are susceptible to anti-ILK treatment alone. We investigate a combination drug strategy targeting ILK and another oncogenic kinase, Abelson kinase (ABL). Drug-concentration viability assays (i.e., MTT assays) indicate that ILK and ABL inhibitors in combination decreased the viability of glioblastoma cells over the ILK drug QLT-0267 alone. Combination strategies also increased aberrant mitoses and cell death over QLT-0267 alone. This was evident from an increase in mitotic arrest, apoptosis and a sub-G1 peak following FAC analysis. In vitro, ILK and ABL localized to the centrosome and the putative ILK kinase domain was important for this localization. Increased levels of cytosolic ABL are associated with its transformative abilities. ILK inhibitor effects on survival correlated with its ability to decrease cytosolic ABL levels and inhibit ABL's localization to mitotic centrosomes in glioblastoma cells. ILK inhibitor effects on ABL's centrosomal localization were reversed by the proteasomal inhibitor MG132 (a drug that inhibits ABL degradation). These results indicate that ILK regulates ABL at mitotic centrosomes and that combination treatments targeting ILK and ABL are more effective then QLT-0267 alone at decreasing the survival of dividing glioblastoma cells.
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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) | 0.001 | 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".