Targeting Aurora Kinases as Therapy in Multiple Myeloma.
Bibliographic record
Abstract
Abstract The aurora kinases A and B are critical for facilitating cell cycle transit from G2 through to cytokinesis. As important regulators of the mitotic event, they are being tested as potential targets in cancer therapy. Multiple myeloma (MM) is a B cell malignancy characterized by progressive genetic instability, suggesting a disruption of cell cycle checkpoints has occurred that normally arrest cells at G2M or within mitosis when injury to the mitotic machinery occurs. Since these deficient checkpoints would prevent cell cycle arrest and potential repair and may render MM cells susceptible to apoptotic death in mitosis, we tested the anti-myeloma effecy of two separate agents that inhibit aurora kinases. Both agents induced cytoreduction of MM cell lines and primary myeloma samples at nM concentrations while normal peripheral blood lymphocytes and CLL cells were not affected. MM cells were not protected by IL-6 or activating mutations of RAS. Anti-myeloma effects were characterized by induction of tetraploidy followed by apoptosis. The myeloma apoptotic effect correlated well with the inhibition of aurora kinase activity as shown by reduction of histone 3B phosphorylation (substrate of auroras). Furthermore, stable ectopic over-expression of aurora kinase A significantly protected MM cells against aurora inhibitors but had no effect on apoptosis induced by velcade. As expression of the centrosomal protein RHAMM in MM cells may contribute to genetic instability, we tested effects of RHAMM over-expression on the sensitivity to aurora inhibitors. Although RHAMM over-expression in transfected MM cells was very modest, it significantly enhanced sensitivity to apoptosis induced by aurora kinase inhibitors. These results suggest the potential for aurora kinase inhibitors in multiple myeloma especially in patients where RHAMM is over-expressed.
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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 | 0.001 |
| 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".