Aurora Kinases as Druggable Targets in Pediatric Leukemia: Heterogeneity in Target Modulation Activities and Cytotoxicity by Diverse Novel Therapeutic Agents
Bibliographic record
Abstract
Leukemia is the most common pediatric malignancy, constituting more than 30% of all childhood cancers. Although cure rates have improved greatly, approximately one in five children relapse and poor survival rates post relapse remain a challenge. Given this, more effective and innovative therapeutic strategies are needed in order to improve prognosis. Aurora kinases, a family of serine/threonine kinases essential for the regulation of several mitotic processes, have been identified as potential targets for cancer therapeutics. Elevated expression of Aurora kinases has been demonstrated in several malignancies and is associated with aberrant mitotic activity, aneuploidy and alterations in chromosomal structure and genome instability. Based on this rationale, a number of small molecule inhibitors have been formulated and advanced to human studies in the recent past. A comparative analysis of these agents in cytotoxicity and target modulation analyses against a panel of leukemia cells provides novel insights into the unique mechanisms and codependent activity pathways involved in targeting Aurora kinases, constituting a distinctive preclinical experimental framework to identify appropriate agents and combinations in future clinical studies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".