Characterization of Weakly Hormone Receptor (HR)-Positive, HER2-Negative Breast Cancer and Current Treatment Strategies

Potential therapies for non-coding RNAs in breast cancer

Breast cancer (BC) is one of the frequent tumors that seriously endanger the physical and mental well-being in women with strong heterogeneity, and its pathogenesis involves multiple risk factors. Depending on the type of BC, hormonal therapy, targeted therapy, and immunotherapy are the current systemic treatment options along with conventional chemotherapy. Despite significant progress in understanding BC pathogenesis and therapeutic options, there is still a need to identify new therapeutic targets and develop more effective treatments. According to recent sequencing and profiling studies, non-coding (nc) RNAs genes are deregulated in human cancers via deletion, amplification, abnormal epigenetic, or transcriptional regulation, and similarly, the expression of many ncRNAs is altered in breast cancer cell lines and tissues. The ability of single ncRNAs to regulate the expression of multiple downstream gene targets and related pathways provides a theoretical basis for studying them for cancer therapeutic drug development and targeted delivery. Therefore, it is far-reaching to explore the role of ncRNAs in tumor development and their potential as therapeutic targets. Here, our review outlines the potential of two major ncRNAs, long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) as diagnostic and prognostic biomarkers as well as targets for new therapeutic strategies in breast cancer.

Clinical Outcomes and Intrinsic Subtypes of Breast Cancer Patients with Single Hormone Receptor-positive Receiving Neoadjuvant Chemotherapy

BACKGROUND

Extensive studies have highlighted the significance of estrogen receptor (ER) and progesterone receptor (PR) in breast cancer (BRCA). However, our understanding of patients with single hormone receptor (HR)-positive (sHR+) BRCA remains limited. This lack of understanding poses challenges in predicting prognosis and selecting appropriate treatments.

PATIENTS AND METHODS

We collected data from a total of 825 human epidermal growth factor receptor 2 negative (HER2-) BRCA patients who underwent neoadjuvant chemotherapy (NAC) in two distinct cohorts. Four subgroups were created within each cohort based on their HR expression: ER+/PR+, ER+/PR-, ER-/PR+, and ER-/PR-. We conducted comparative analyses to assess clinicopathological characteristics, chemotherapy responsiveness, clinical outcomes, and intrinsic subtyping among these subgroups.

RESULTS

ER+/PR- constituted 11.1% and 14.9% of samples in two cohorts, respectively, whereas ER-/PR+ comprised 8.3% and 3.7%. Higher histologic grades were more common in the ER-/PR+ group as compared to the ER+/PR+ subgroup (P = .0075 in cohort 1; P = .026 in cohort 2). Additionally, after multivariable analysis, ER-/PR+ were more likely to achieve pathological complete response (pCR) (cohort 1: OR =6.67; 95%CI, 2.63-16.94; P < .001; cohort 2: OR =3.70; 95%CI, 1.08-11.84; P = .030;). Between ER+/PR- and ER+/PR+, the distant recurrence-free survival (DRFS) was comparable. The survival outcomes in the ER-/PR+ subgroup present a partial inconsistency between the 2 cohorts. Furthermore, the ER-/PR+ subgroup exhibited a higher incidence of the basal-like subtype, while the ER+/PR- subgroup had a higher proportion of luminal-like subtypes.

CONCLUSION

This study highlighted the distinct clinical and genetic characteristics of sHR+ BRCA, emphasizing the potential need for optimized treatment strategies.

Developing a weakly supervised deep learning framework for breast cancer diagnosis with HR status based on mammography images

The status of hormone receptors (HR) at the molecular level is crucial for accurate diagnosis and effective treatment of breast cancer. Meanwhile, mammography is an effective screening method for detecting breast cancer, which significantly improve survival. However, diagnosing the molecular status of breast cancer involves a pathological biopsy, which can affect the accuracy of the diagnosis. To non-invasively diagnose the hormone receptor (HR) status of breast cancer and reduced manual annotation, we proposed a weakly supervised deep learning framework BSNet which detected breast cancer with HR status and benign tumors. BSNet was trained on 2321 multi-view mammography cases from female undergoing digital mammography for the general population at Harbin Medical University Cancer Hospital in Heilongjiang Province during the period 2017-2018 and was validated on the external cohort. The average AUCs of BSNet on the test set and the external validation set were 0.89 and 0.92, respectively. BSNet demonstrated excellent performance in non-invasive breast cancer diagnosis with HR status, using multiple mammography views without pixel annotation. Furthermore, we developed a web server (http://bsnet.edbc.org) for easy use. BSNet described high-dimensional mammography of breast cancer subtypes, which helped inform early management options.