Positive estrogen receptor status is a poor prognostic factor in node-negative breast cancer: An observational study in Asian patients

Retrospective analysis of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), Ki67 changes and their clinical significance between primary breast cancer and metastatic tumors

Objective

To explore the relationship between receptor heterogeneity and clinicopathological characteristics in 166 patients with invasive breast cancer during metastasis.

Methods

We conducted a retrospective analysis of 166 patients diagnosed with metastatic breast cancer through biopsy, who were admitted to our hospital from January 2018 to December 2022. Statistical analysis was employed to assess the heterogeneity of receptors in both primary and metastatic lesions, including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), Ki67, as well as their association with clinicopathological features such as tumor size, lymph node metastasis, treatment regimen, and disease-free survival.

Results

The discordant expression rates of ER, PR, HER2, Ki-67 and Luminal classification between primary and metastatic lesions were 21.7%, 41.6%, 8.9%, 34.4% and 36.8%, respectively. There is a significant difference in disease-free survival between patients with consistent and inconsistent receptor status of primary and metastatic lesions, which is statistically significant. The median DFS for primary HER2(-) to metastatic HER2(+) was 84 months, which was relatively high. The Cox multivariate regression analysis revealed that the expression differences of ER, PR, HER2, and Ki67 were not influenced by endocrine therapy and chemotherapy. However, a statistically significant difference in HER2 expression was observed with targeted therapy. Tumor size was correlated with ER and Ki67 receptor status (P = 0.019, 0.016). Tumor size was not correlated with PR, and HER2 (P = 0.679, 0.440). Lymph node metastasis was not associated with changes in ER, PR, HER2, and Ki67. The discordant rates of ER, PR, HER2, and Ki-67 in patients with local recurrence were 22%, 23.7%, 5.1%, and 28.8% respectively, whereas those in patients with distant metastasis were 21.5%, 36.4%, 10.3%, and 31.8% respectively.

Conclusions

The expression levels of ER, PR, HER2, and Ki-67 in primary and metastatic breast cancer exhibit heterogeneity, which is closely associated with the prognosis and treatment outcomes of patients.

Estradiol induces bone osteolysis in triple-negative breast cancer via its membrane-associated receptor ERα36

Triple-negative breast cancer (TNBC) is thought to be an estradiol-independent, hormone therapy-resistant cancer because of lack of estrogen receptor alpha 66 (ERα66). We identified a membrane-bound splice variant, ERα36, in TNBC cells that responds to estrogen (E2) and may contribute to bone osteolysis. We demonstrated that the MDA-MB-231 TNBC cell line, which expresses ERα36 similarly to MCF7 cells, is responsive to E2, forming osteolytic tumors in vivo. MDA-MB-231 cells activate osteoclasts in a paracrine manner. Conditioned media (CM) from MDA-MB-231 cells treated with bovine serum albumin-bound E2 (E2-BSA) increased activation of human osteoclast precursor cells; this was blocked by addition of anti-ERα36 antibody to the MDA-MB-231 cultures. Osteoclast activation and bone resorption genes were elevated in RAW 264.7 murine macrophages following treatment with E2-BSA-stimulated MDA-MB-231 CM. E2 and E2-BSA increased phospholipase C (PLC) and protein kinase C (PKC) activity in MDA-MB-231 cells. To examine the role of ERα36 signaling in bone osteolysis in TNBC, we used our bone-cancer interface mouse model in female athymic homozygous Foxn1nu mice. Mice with MDA-MB-231 tumors and treated with tamoxifen (TAM), E2, or TAM/E2 exhibited increased osteolysis, cortical bone breakdown, pathologic fracture, and tumor volume; the combined E2/TAM group also had reduced bone volume. These results suggest that E2 increased osteolytic lesions in TNBC through a membrane-mediated PLC/PKC pathway involving ERα36, which was enhanced by TAM, demonstrating the role of ERα36 and its membrane-associated signaling pathway in bone tumors. This work suggests that ERα36 may be a potential therapeutic target in patients with TNBC.

A new prediction nomogram of non-sentinel lymph node metastasis in cT1-2 breast cancer patients with positive sentinel lymph nodes

We aimed to analyze the risk factors and construct a new nomogram to predict non-sentinel lymph node (NSLN) metastasis for cT1-2 breast cancer patients with positivity after sentinel lymph node biopsy (SLNB). A total of 830 breast cancer patients who underwent surgery between 2016 and 2021 at multi-center were included in the retrospective analysis. Patients were divided into training (n = 410), internal validation (n = 298), and external validation cohorts (n = 122) based on periods and centers. A nomogram-based prediction model for the risk of NSLN metastasis was constructed by incorporating independent predictors of NSLN metastasis identified through univariate and multivariate logistic regression analyses in the training cohort and then validated by validation cohorts. The multivariate logistic regression analysis revealed that the number of positive sentinel lymph nodes (SLNs) (P < 0.001), the proportion of positive SLNs (P = 0.029), lymph-vascular invasion (P = 0.029), perineural invasion (P = 0.023), and estrogen receptor (ER) status (P = 0.034) were independent risk factors for NSLN metastasis. The area under the receiver operating characteristics curve (AUC) value of this model was 0.730 (95% CI 0.676-0.785) for the training, 0.701 (95% CI 0.630-0.773) for internal validation, and 0.813 (95% CI 0.734-0.891) for external validation cohorts. Decision curve analysis also showed that the model could be effectively applied in clinical practice. The proposed nomogram estimated the likelihood of positive NSLNs and assisted the surgeon in deciding whether to perform further axillary lymph node dissection (ALND) and avoid non-essential ALND as well as postoperative complications.

Evaluation Factors Affecting on Recurrence, Metastasis, and Survival of Breast Cancer in Iranian Women by Multi-State Model Approach

Background

We used the multistate model to investigate how prognostic factors of breast cancer are seen to affect the disease process.

Methods

This cohort study was conducted at Motamed Cancer Institute of Tehran, Iran on 2363 breast cancer patients admitted from 1978 to 2017, and they were followed up until 2018. We applied the multistate models, including four states: diagnosis, recurrence, metastasis, and final absorbing mortality state.

Results

Age over 50 years, positive lymph nodes and tumor size intensified the hazard of transition from diagnosis to metastasis (P=0.002, P<0.001 and P=0.001 respectively) and they also intensified the hazard of transition from diagnosis to mortality (P=0.010, P<0.001 and P<0.001 respectively). At the same time, the educational level decreased the hazard of mentioned transitions (P<0.001). Positive estrogen receptors reduced the hazard of transition from diagnosis to metastasis (P=0.007) and positive lymph nodes also intensified the hazard of transition from metastasis to mortality (P=0.040). Tumor size had an increasing role in the transitions from diagnosis to recurrence, recurrence to metastasis, and metastasis to mortality (P=0.014, P=0.018 and P=0.002 respectively).

Conclusion

Multistate model presented the detailed effects of prognostic factors on progression of breast cancer. Implementing early diagnosis strategies and providing informational programs, especially in younger ages and lower educational level patients may be helpful in reducing the hazard of transition to higher states of breast cancer and increasing the survival of Iranian women with breast cancer by controlling tumor size growth, lymph nodes involvements and estrogen receptor status.

Expression changes of ER, PR, HER2, and Ki-67 in primary and metastatic breast cancer and its clinical significance

Objective

To explore the altered expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and cell proliferation index (Ki-67) in primary and metastatic breast cancer lesions and the correlation between the primary tumor size, lymph node metastasis, Tumor Node Metastasis (TNM) stage, molecular typing, and disease-free survival (DFS) and their clinical significance.

Methods

A retrospective analysis was conducted on the clinical data of 130 patients with metastatic breast cancer biopsy admitted to the Cancer Center of the Second Affiliated Hospital of Anhui Medical University in Hefei, China, from 2014-2019. The altered expression of ER, PR, HER2, and Ki-67 in primary and metastatic lesions of breast cancer was analyzed with respect to the site of metastasis, size of the primary tumor, lymph node metastasis, disease progression, and prognosis.

Results

The inconsistent expression rates of ER, PR, HER2, and Ki-67 in primary and metastatic lesions were 47.69%, 51.54%, 28.10%, and 29.23%, respectively. The size of the primary lesion was not, but that accompanied by lymph node metastasis was related to the altered receptor expression. Patients with positive ER and PR expression in both primary and metastatic lesions had the longest DFS, while those with negative expression had the shortest DFS. Also, changes in HER2 expression in primary and metastatic lesions were not associated with DFS. Patients with low expression of Ki-67 in both primary and metastatic lesions had the longest DFS, while patients with high expression had the shortest DFS.

Conclusion

Heterogeneity was detected in the expression levels of ER, PR, HER2, and Ki-67 in the primary and metastatic breast cancer lesions, which has a guiding significance for the treatment and prognosis of patients.

Deep learning radiomics under multimodality explore association between muscle/fat and metastasis and survival in breast cancer patients

Sarcopenia is correlated with poor clinical outcomes in breast cancer (BC) patients. However, there is no precise quantitative study on the correlation between body composition changes and BC metastasis and survival. The present study proposed a deep learning radiomics (DLR) approach to investigate the effects of muscle and fat on distant metastasis and death outcomes in BC patients. Image feature extraction was performed on 4th thoracic vertebra (T4) and 11th thoracic vertebra (T11) on computed tomography (CT) image levels by DLR, and image features were combined with clinical information to predict distant metastasis in BC patients. Clinical information combined with DLR significantly predicted distant metastasis in BC patients. In the test cohort, the area under the curve of model performance on clinical information combined with DLR was 0.960 (95% CI: 0.942-0.979, P < 0.001). The patients with distant metastases had a lower pectoral muscle index in T4 (PMI/T4) than in patients without metastases. PMI/T4 and visceral fat tissue area in T11 (VFA/T11) were independent prognostic factors for the overall survival in BC patients. The pectoralis muscle area in T4 (PMA/T4) and PMI/T4 is an independent prognostic factor for distant metastasis-free survival in BC patients. The current study further confirmed that muscle/fat of T4 and T11 levels have a significant effect on the distant metastasis of BC. Appending the network features of T4 and T11 to the model significantly enhances the prediction performance of distant metastasis of BC, providing a valuable biomarker for the early treatment of BC patients.