Inhibition of aurora kinases for tailored risk-adapted treatment of multiple myeloma
Bibliographic record
Abstract
Genetic instability and cellular proliferation have been associated with aurora kinase expression in several cancer entities, including multiple myeloma. Therefore, the expression of aurora-A, -B, and -C was determined by Affymetrix DNA microarrays in 784 samples including 2 independent sets of 233 and 345 CD138-purified myeloma cells from previously untreated patients. Chromosomal aberrations were assessed by comprehensive interphase fluorescence in situ hybridization and proliferation of primary myeloma cells by propidium iodine staining. We found aurora-A and -B to be expressed at varying frequencies in primary myeloma cells of different patient cohorts, but aurora-C in testis cell samples only. Myeloma cell samples with detectable versus absent aurora-A expression show a significantly higher proliferation rate, but neither a higher absolute number of chromosomal aberrations (aneuploidy), nor of subclonal aberrations (chromosomal instability). The clinical aurora kinase inhibitor VX680 induced apoptosis in 20 of 20 myeloma cell lines and 5 of 5 primary myeloma cell samples. Presence of aurora-A expression delineates significantly inferior event-free and overall survival in 2 independent cohorts of patients undergoing high-dose chemotherapy, independent from conventional prognostic factors. Using gene expression profiling, aurora kinase inhibitors as a promising therapeutic option in myeloma can be tailoredly given to patients expressing aurora-A, who in turn have an adverse prognosis.
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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".