Although radiotherapy resistance is associated with locoregional recurrence and distant metastasis in breast cancers, clinically relevant molecular markers and critical signaling pathways of radioresistant breast cancer are yet to be defined. Herein, we show that HER2-STAT3-survivin regulation is associated with radiotherapy resistance in HER2-positive breast cancers. Depletion of HER2 by siRNA sensitized HER2-positive breast cancer cells to irradiation by decreasing STAT3 activity and survivin, a STAT3 target gene, expression in HER2-positive breast cancer cells. Furthermore, inhibition of STAT3 activation or depletion of survivin also sensitized HER2-positive breast cancer cells to irradiation, suggesting that the HER2-STAT3-survivin axis is a key pathway in radiotherapy resistance of HER2-positive breast cancer cells. In addition, our clinical analysis demonstrated the association between HER2-positive breast cancers and radiotherapy resistance. Notably, we found that increased expression of phosphorylated STAT3, STAT3, and survivin correlated with a poor response to radiotherapy in HER2-positive breast cancer tissues. These findings suggest that the HER2-STAT3-survivin axis might serve as a predictive marker and therapeutic target to overcome radiotherapy resistance in HER2-positive breast cancers.

This study aimed to investigate the predictive efficacy of positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) for the pathological response of advanced breast cancer to neoadjuvant chemotherapy (NAC). The breast PET/MRI image deep learning model was introduced and compared with the conventional methods. PET/CT and MRI parameters were evaluated before and after the first NAC cycle in patients with advanced breast cancer [n = 56; all women; median age, 49 (range 26–66) years]. The maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained with the corresponding baseline values (SUV0, MTV0, and TLG0, respectively) and interim PET images (SUV1, MTV1, and TLG1, respectively). Mean apparent diffusion coefficients were obtained from baseline and interim diffusion MR images (ADC0 and ADC1, respectively). The differences between the baseline and interim parameters were measured (ΔSUV, ΔMTV, ΔTLG, and ΔADC). Subgroup analysis was performed for the HER2-negative and triple-negative groups. Datasets for convolutional neural network (CNN), assigned as training (80%) and test datasets (20%), were cropped from the baseline (PET0, MRI0) and interim (PET1, MRI1) images. Histopathologic responses were assessed using the Miller and Payne system, after three cycles of chemotherapy. Receiver operating characteristic curve analysis was used to assess the performance of the differentiating responders and non-responders. There were six responders (11%) and 50 non-responders (89%). The area under the curve (AUC) was the highest for ΔSUV at 0.805 (95% CI 0.677–0.899). The AUC was the highest for ΔSUV at 0.879 (95% CI 0.722–0.965) for the HER2-negative subtype. AUC improved following CNN application (SUV0:PET0 = 0.652:0.886, SUV1:PET1 = 0.687:0.980, and ADC1:MRI1 = 0.537:0.701), except for ADC0 (ADC0:MRI0 = 0.703:0.602). PET/MRI image deep learning model can predict pathological responses to NAC in patients with advanced breast cancer.

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In the era of precision medicine, the prediction of ovarian function recovery from chemotherapy-induced amenorrhoea using feasible biological markers may be helpful to optimise the treatment strategy for young patients with hormone receptor-positive breast cancer. The purpose of this study was to investigate the accuracy of post-chemotherapy biological markers for predicting the recovery of ovarian function in breast cancer patients of the ASTRRA trial, with chemotherapy-induced amenorrhoea. Using data of 82 participants from a single institution in the ASTRRA trial, the post-chemotherapy serum levels of the anti-Müllerian hormone (AMH), oestradiol, inhibin B and other clinical factors associated with chemotherapy-induced amenorrhoea were evaluated. Recovery of ovarian function was defined by the resumption of menstruation manifested by vaginal bleeding. Fifty-two patients regained menstruation within 55 months after enrolment. In univariate analysis, <40 years of age (P = 0.009), oestradiol ≥37 pg/mL (P = 0.003) or AMH ≥800 pg/mL (P = 0.026) were associated with recovery of menstruation. On multivariate analysis, oestradiol (hazard ratio: 3.171, 95% CI: 1.306–7.699, P = 0.011) and AMH (hazard ratio: 2.853, 95% CI: 1.011–8.046, P = 0.048) remained as significant independent predictors for resumption of menstruation. The diagnostic accuracy of age, oestradiol and AMH in predicting the resumption of menstruation was 38.3, 23.3 and 86.7%, respectively. In conclusion, post-chemotherapy AMH level might be a relatively accurate predictor of the recovery of ovarian function, presented by resumption of menstruation in breast cancer patients with chemotherapy-induced amenorrhoea.