Expression of Aurora A (but Not Aurora B) Is Predictive of Survival in Breast Cancer
Bibliographic record
Abstract
PURPOSE: The cell cycle mediators Aurora A and B are targets of drugs currently in clinical development. As with other targeted therapies in breast cancer, response to therapy might be associated with target expression in tumors. We therefore assessed expression of Aurora A and B in breast tumors and studied associations with clinical/pathologic variables. EXPERIMENTAL DESIGN: Tissue microarrays containing primary specimens from 638 patients with 15-year follow-up were employed to assess expression of Aurora A and B using our automated quantitative analysis method; we used cytokeratin to define pixels as breast cancer (tumor mask) within the array spot and measured Aurora A and B expression within the mask using Cy5-conjugated antibodies. RESULTS: Aurora A and B expression was variable in primary breast tumors. High Aurora A expression was strongly associated with decreased survival (P = 0.0005). On multivariable analysis, it remained an independent prognostic marker. High Aurora A expression was associated with high nuclear grade and high HER-2/neu and progesterone receptor expression. Aurora B expression was not associated with survival. CONCLUSIONS: Aurora A expression defines a population of patients with decreased survival, whereas Aurora B expression does not, suggesting that Aurora A might be the preferred drug target in breast cancer. Aurora A expression in early-stage breast cancer may identify a subset of patients requiring more aggressive or pathway-targeted treatment. Prospective studies are needed to confirm the prognostic role of Aurora A as well as the predictive role of Aurora A expression in patients treated with Aurora A inhibitors.
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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 | 0.001 |
| 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) | 0.001 | 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".