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Updated: Jan 20, 2026

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model
Published on: March 20, 2020
A population-based recurrence risk management study of patients with pT1 node-negative HER2+ breast cancer: a
Makoto Kubo1, Masaaki Kawai2, Hiraku Kumamaru3
1Department of Surgery and Oncology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, 812-8582, Japan. mkubo@tumor.med.kyushu-u.ac.jp.
Purpose:
Recurrence risk management of patients with small (≤ 2 cm), node-negative, human epidermal growth factor receptor 2 (HER2)-positive breast cancer remains challenging. We studied the effects of adjuvant chemotherapy and/or trastuzumab and survival outcomes among these patients, using data from the population-based Japanese National Clinical Database (NCD).
Methods:
We identified a cohort of 2736 breast cancer patients with HER2+ pT1N0 disease: 489 pT1a, 642 pT1b, and 1623 pT1c. The median observation period was 76 months, and the 5-year follow-up rate was 48.2%. The number of events was 212 for disease-free survival (DFS), 40 for breast cancer-specific survival, and 84 for overall survival (OS).
Results:
There were 24.5% of pT1a, 51.9% of pT1b, and 63.3% of pT1c patients who were treated systemically after surgery. OS in pT1b (logrank test; p = 0.03) and DFS in pT1c (logrank test; p < 0.001) were significantly improved in treated compared with untreated patients. In the Cox proportional hazards model, treated patients had significantly longer OS than untreated patients in pT1b (hazard ratio (HR) 0.20) and pT1c (HR 0.54) groups. Estrogen receptor-negative tumors was also a significant predictor of survival in pT1c (HR 2.01) but not pT1ab patients. Furthermore, HR was greater in patients aged ≤ 35 years (3.18) compared to that in patients aged 50-69 years in the pT1b group.
Conclusions:
NCD data revealed that systemic treatment improved OS in pT1bc but not in pT1a node-negative HER2+ breast cancer patients. Future observational research using big-sized data is expected to play an important role in optimizing treatment for patients with early-stage breast cancer.
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