Association of p27 and Cyclin D1 Expression and Benefit from Adjuvant Trastuzumab Treatment in HER2-Positive Early Breast Cancer: A TransHERA Study
Bibliographic record
Abstract
Abstract Purpose: To assess the prognostic and predictive value of selected biomarkers involved in cell-cycle regulation or proliferation in patients with HER2-positive early breast cancer. Experimental Design: Protein expression of TOP2A, Ki67, cyclin D1, and p27 was immunohistochemically determined in tissue microarrays of surgical specimens from 862 patients randomized to trastuzumab (1 or 2 years; N = 561) and observation (N = 301) arms of the HERA trial. The primary analysis endpoint was disease-free survival (DFS). Biomarkers were examined as continuous or categorical variables (predefined cutoffs). Interaction terms between biomarkers and treatment were assessed in multivariate Cox models adjusted for variables of clinical interest. Results: A significant interaction was detected between p27 and treatment (adjusted P = 0.0049). Trastuzumab effect was significant in the p27-low subgroup (≤70% p27-positive tumor cells; N = 318). HR Comb Trast vs. Obs 0.44, 95% CI, 0.29–0.65 (P < 0.001). No trastuzumab effect was observed in the p27-high subgroup N = 435; HR Comb Trast vs. Obs 0.97, 95% CI, 0.66–1.44, P = 0.89), indicating that these patients derived little or no benefit from trastuzumab treatment. A prognostic effect of p27 on DFS was observed, with p27-high patients experiencing half the hazard of a DFS event compared with low ones (HR p27 High vs. Low 0.49, 95% CI, 0.32–0.75). TOP2A, Ki67, and cyclin D1, as categorical variables were not predictive, whereas cyclin D1 as continuous variable was predictive of trastuzumab benefit. Conclusions: In TransHERA, patients with HER2-positive early breast cancer with low p27 expression in their tumors benefited from trastuzumab treatment, whereas patients with high p27 expression did not. Clin Cancer Res; 24(13); 3079–86. ©2018 AACR.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".