PURPOSE: To determine the updated long-term outcomes of the Addition of Ovarian Suppression to Tamoxifen in Young Women With Hormone-Sensitive Breast Cancer Who Remain Premenopausal or Regain Vaginal Bleeding After Chemotherapy (ASTRRA) trial. PATIENTS AND METHODS: This study is a post-trial follow-up of the ASTRRA trial, involving 1,483 premenopausal women younger than 45 years treated with definitive surgery after completing adjuvant or neoadjuvant chemotherapy for estrogen receptor–positive breast cancer. Patients were randomly assigned in a 1:1 ratio to complete 5 years of tamoxifen (TAM) alone (TAM-only) or 5 years of TAM with ovarian function suppression (OFS) for 2 years (TAM + OFS). The primary end point was disease-free survival (DFS), and the secondary end point was overall survival (OS). RESULTS: At 106.4 months of median follow-up, there was a continuous significant reduction in the DFS event rate in the TAM + OFS group. The 8-year DFS rate was 85.4% in the TAM + OFS group and 80.2% in the TAM-only group (hazard ratio [HR], 0.67; 95% CI, 0.51 to 0.87). There were no significant differences in OS between the two groups. The OS rate was 96.5% in the TAM + OFS group and 95.3% in the TAM-only group (HR, 0.78; 95% CI, 0.49 to 1.25). CONCLUSION: Adding OFS for 2 years to adjuvant TAM with a longer follow-up resulted in consistent DFS benefits, suggesting that adding OFS to TAM should be considered for patients who remain in a premenopausal state or resume ovarian function after chemotherapy.

Lee YK, et al. J Korean Med Sci. 2020 Oct;35(46):e403. https://doi.org/10.3346/jkms.2020.35.e403

Kim H, et al. J Breast Cancer. 2021 Dec;24:e48. https://doi.org/10.4048/jbc.2021.24.e48

Background/Objectives: Breast cancer is the most common cancer in women worldwide, requiring strategic efforts to reduce its mortality. This study aimed to develop a predictive classification model for breast cancer mortality using real-world data, including various clinical features. Methods: A total of 11,286 patients with breast cancer from the National Cancer Center were included in this study. The mortality rate of the total sample was approximately 6.2%. Propensity score matching was used to reduce bias. Several machine learning models, including extreme gradient boosting, were applied to 31 clinical features. To enhance model interpretability, we used the SHapley Additive exPlanations method. ML analyses were also performed on the samples, excluding patients who developed other cancers after breast cancer. Results: Among the ML models, the XGB model exhibited the highest discriminatory power, with an area under the curve of 0.8722 and a specificity of 0.9472. Key predictors of the mortality classification model included occurrence in other organs, age at diagnosis, N stage, T stage, curative radiation treatment, and Ki-67(%). Even after excluding patients who developed other cancers after breast cancer, the XGB model remained the best-performing, with an AUC of 0.8518 and a specificity of 0.9766. Additionally, the top predictors from SHAP were similar to the results for the overall sample. Conclusions: Our models provided excellent predictions of breast cancer mortality using real-world data from South Korea. Explainable artificial intelligence, such as SHAP, validated the clinical applicability and interpretability of these models.

Purpose This study investigated pathological complete response (pCR) according to androgen receptor (AR) in breast cancer patients undergoing neoadjuvant chemotherapy and estimated the relationship between AR expression and clinicopathological factors.Materials and Methods We identified 624 breast cancer patients who underwent surgery after neoadjuvant chemotherapy at the National Cancer Center in Goyang, Korea from April 2016 to October 2019. We retrospectively collected the clinicopathologic information and AR expression results and analyzed the data according to cancer stage, hormonal receptor (HR) status, human epidermal growth factor receptor 2 (HER2) status, tumor subtype, and pCR.Results Among the 624 breast cancer patients, 529 (84.8%) were AR-positive (AR+) patients and 95 (15.2%) were AR-negative (AR–) patients. AR+ patients showed more estrogen receptor (ER) positivity, progesterone receptor (PR) positivity, HER2-positivity, and HR-positive and HER2-negative (HR+/HER2–) subtype. The rate of pCR was 31.4% (196/624). AR– patients had a significantly higher rate of pCR than AR+ patients (AR– 43.2% vs. AR+ 29.3%, p=0.007). The tumor factors associated with pCR were early stage, histologic grade 3, ER-negative, PR-negative, AR-negative, HER2-positive, and high Ki-67 values. In univariable analysis, AR+ significantly decreased the state of pCR (odds ratio, 0.546; 95% confidence interval, 0.349 to 0.853; p=0.008). According to tumor subtype, AR– tumor showed higher pCR rate in HR+/HER2– subtype (AR– 28.6% vs. AR+ 7.3%, p=0.022).Conclusion AR expression is predominant in the HR+/HER2– subtype. AR– is significantly associated with the pCR rate in breast cancer patients, especially within HR+/HER2– subtype. When determining neoadjuvant chemotherapy for the HR+/HER2– subtype, AR expression can be considered as a pCR predictive marker.

Oncotype DX (ODX), a 21-gene assay, predicts the recurrence risk in early breast cancer; however, it has high costs and long testing times. We aimed to identify clinicopathological factors that can predict the ODX risk group and serve as alternatives to the ODX test. This retrospective study included 547 estrogen receptor-positive, human epidermal growth factor receptor 2-negative, and lymph node-negative breast cancer patients who underwent ODX testing. Based on the recurrence scores, three ODX risk categories (low: 0–15, intermediate: 16–25, and high: 26–100) were established in patients aged ≤50 years (n = 379), whereas two ODX risk categories (low: 0–25 and high: 26–100) were established in patients aged >50 years (n = 168). Factors selected for analysis included body mass index, menopausal status, type of surgery, and pathological and immunohistochemical features. The ODX risk groups showed significant association with histologic grade (p = 0.0002), progesterone receptor expression (p < 0.0001), Ki-67 (p < 0.0001), and p53 expression (p = 0.023) in patients aged ≤50 years. In patients aged >50 years, tumor size (p = 0.022), Ki-67 (p = 0.001), and p53 expression (p = 0.001) were significantly associated with the risk group. Certain clinicopathological factors can predict the ODX risk group and enable decision-making on adjuvant chemotherapy; these factors differ according to age.