A Practical Immunohistochemistry-Based Model for Predicting Pathologic Complete Response in Estrogen Receptor-Strong Positive and HER2-Negative Breast Cancer.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 3/4)
유사 논문P · Population 대상 환자/모집단
522 patients with ER-strong positive/HER2-negative tumors who received NAC and surgery between 2008 and 2021.
I · Intervention 중재 / 시술
NAC and surgery between 2008 and 2021
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
High-scoring patients may benefit from NAC, while patients with low- or intermediate-scores may be better managed with surgery and endocrine therapy. This model may support personalized treatment decisions regarding NAC.
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[PURPOSE] While the benefit of neoadjuvant chemotherapy (NAC) has been established in human epidermal growth factor receptor 2 (HER2)-positive and triple-negative breast cancers, its effectiveness in
APA
Lee SM, Lee JE, et al. (2026). A Practical Immunohistochemistry-Based Model for Predicting Pathologic Complete Response in Estrogen Receptor-Strong Positive and HER2-Negative Breast Cancer.. Journal of breast cancer. https://doi.org/10.4048/jbc.2025.0242
MLA
Lee SM, et al.. "A Practical Immunohistochemistry-Based Model for Predicting Pathologic Complete Response in Estrogen Receptor-Strong Positive and HER2-Negative Breast Cancer.." Journal of breast cancer, 2026.
PMID
41612660 ↗
Abstract 한글 요약
[PURPOSE] While the benefit of neoadjuvant chemotherapy (NAC) has been established in human epidermal growth factor receptor 2 (HER2)-positive and triple-negative breast cancers, its effectiveness in achieving pathological complete response (pCR) and optimal patient selection in estrogen receptor (ER)-positive, HER2-negative breast cancers remain less clearly defined. This study aimed to identify immunohistochemistry (IHC)-based predictors of pCR and to develop a scoring model for ER-strong positive/HER2-negative breast cancer.
[METHODS] Data from a prospective cohort were retrospectively analyzed. We included 522 patients with ER-strong positive/HER2-negative tumors who received NAC and surgery between 2008 and 2021. IHC markers including progesterone receptor (PR), Ki-67, epidermal growth factor receptor (EGFR), cytokeratin 5/6 (CK5/6), and p53 were evaluated to identify predictors of pCR. Independent predictors of pCR from multivariate logistic regression were used to develop a weighted 4-point model. Model performance was assessed using receiver operating characteristic analysis. The prognostic impact of pCR was evaluated using Kaplan-Meier and Cox regression analyses.
[RESULTS] Independent predictors of pCR included PR-negative status, positivity for basal-like markers (EGFR or CK5/6), and Ki-67 ≥ 50%. The scoring model demonstrated good discrimination for pCR (area under the curve = 0.754). pCR rates increased stepwise, with scores of 4.9% (low), 10.7% (intermediate), and 36.2% (high). In the high-score group, pCR was significantly associated with improved disease-free survival (hazard ratio [HR], 0.09; = 0.023) and distant metastasis-free survival (HR, 0.11; = 0.035), whereas no significant survival differences according to pCR status were observed in the low and intermediate score groups.
[CONCLUSION] This IHC-based model predicts pCR and helps identify subgroups in which pCR is associated with meaningful survival benefit following NAC in ER-positive/HER2-negative breast cancers. High-scoring patients may benefit from NAC, while patients with low- or intermediate-scores may be better managed with surgery and endocrine therapy. This model may support personalized treatment decisions regarding NAC.
[METHODS] Data from a prospective cohort were retrospectively analyzed. We included 522 patients with ER-strong positive/HER2-negative tumors who received NAC and surgery between 2008 and 2021. IHC markers including progesterone receptor (PR), Ki-67, epidermal growth factor receptor (EGFR), cytokeratin 5/6 (CK5/6), and p53 were evaluated to identify predictors of pCR. Independent predictors of pCR from multivariate logistic regression were used to develop a weighted 4-point model. Model performance was assessed using receiver operating characteristic analysis. The prognostic impact of pCR was evaluated using Kaplan-Meier and Cox regression analyses.
[RESULTS] Independent predictors of pCR included PR-negative status, positivity for basal-like markers (EGFR or CK5/6), and Ki-67 ≥ 50%. The scoring model demonstrated good discrimination for pCR (area under the curve = 0.754). pCR rates increased stepwise, with scores of 4.9% (low), 10.7% (intermediate), and 36.2% (high). In the high-score group, pCR was significantly associated with improved disease-free survival (hazard ratio [HR], 0.09; = 0.023) and distant metastasis-free survival (HR, 0.11; = 0.035), whereas no significant survival differences according to pCR status were observed in the low and intermediate score groups.
[CONCLUSION] This IHC-based model predicts pCR and helps identify subgroups in which pCR is associated with meaningful survival benefit following NAC in ER-positive/HER2-negative breast cancers. High-scoring patients may benefit from NAC, while patients with low- or intermediate-scores may be better managed with surgery and endocrine therapy. This model may support personalized treatment decisions regarding NAC.
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