Changes in HER2, ER, PR, and Ki-67 in HER2-Negative Breast Cancer After Neoadjuvant Chemotherapy: A Case–Control Study
Bibliographic record
Abstract
Purpose: This study investigates receptor status changes following neoadjuvant chemotherapy (NAC) in breast cancer, aiming to identify new therapeutic opportunities and improve human epidermal growth factor receptor 2 (HER2) detection and categorization methods. Methods: This retrospective analysis was conducted on patients with breast cancer who underwent NAC and surgery between July 2022 and June 2024. Chi-square tests and logistic regression models were applied to assess the associations between HER2 status changes and clinicopathological features. Results: Among 508 patients, the receptor discordance rates after NAC were 5.3% for estrogen receptor (ER), 21.3% for progesterone receptor (PR), and 43.7% for HER2. Ki-67 expression decreased in 64.6% of cases and increased in 6.8%. Of the 103 patients with HER2-0, 47 (45.6%) transitioned to IHC 1+, 9 (8.7%) to IHC 2+/ISH−, and 1 (1.0%) to IHC 2+/ISH+. Among 256 patients with HER2 IHC 1+, 58 (22.7%) transitioned to IHC 2+/ISH−, 36 (14.1%) to IHC 0, and 9 (3.5%) to IHC 2+/ISH+. For 149 patients with HER2 IHC 2+/ISH−, 50 (33.6%) transitioned to IHC 1+, 6 (4.0%) to IHC 2+/ISH+, 5 (3.4%) to IHC 0, and 1 (0.7%) to IHC 3+. Univariate analysis revealed that, when compared to grade III tumors, grade I–II tumors exhibited a higher rate of HER2-0 to HER2-low conversion (66.7% vs. 36.8%, p = 0.027). HER2-low to HER2-0 conversion was associated with ER negativity (p = 0.028), PR negativity (p = 0.021), HER2 IHC 1+ (vs. IHC 2+, p = 0.001), and TIL >10% (p = 0.049). Multivariate analysis revealed that tumors with HER2 IHC 1+ were more likely to convert to HER2-0 after NAC than those with HER2 IHC 2+ (p = 0.020). Conclusions: Following NAC, ER gain, PR loss, and Ki-67 reduction were common. HER2 and ER status changes predominantly occurred within adjacent expression intensity levels.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".