Breast cancer, characterized by unique epidemiological patterns and significant heterogeneity, remains one of the leading causes of malignancy-related deaths in women. The increasingly nuanced molecular subtypes of breast cancer have enhanced the comprehension and precision treatment of this disease. The mechanisms of tumorigenesis and progression of breast cancer have been central to scientific research, with investigations spanning various perspectives such as tumor stemness, intra-tumoral microbiota, and circadian rhythms. Technological advancements, particularly those integrated with artificial intelligence, have significantly improved the accuracy of breast cancer detection and diagnosis. The emergence of novel therapeutic concepts and drugs represents a paradigm shift towards personalized medicine. Evidence suggests that optimal diagnosis and treatment models tailored to individual patient risk and expected subtypes are crucial, supporting the era of precision oncology for breast cancer. Despite the rapid advancements in oncology and the increasing emphasis on the clinical precision treatment of breast cancer, a comprehensive update and summary of the panoramic knowledge related to this disease are needed. In this review, we provide a thorough overview of the global status of breast cancer, including its epidemiology, risk factors, pathophysiology, and molecular subtyping. Additionally, we elaborate on the latest research into mechanisms contributing to breast cancer progression, emerging treatment strategies, and long-term patient management. This review offers valuable insights into the latest advancements in Breast Cancer Research, thereby facilitating future progress in both basic research and clinical application.

Purpose: The tumor microenvironment has a profound impact on prognosis and immunotherapy. However, the landscape of the triple-negative breast cancer (TNBC) microenvironment has not been fully understood. Experimental Design: Using the largest original multi-omics dataset of TNBC (n = 386), we conducted an extensive immunogenomic analysis to explore the heterogeneity and prognostic significance of the TNBC microenvironment. We further analyzed the potential immune escape mechanisms of TNBC. Results: The TNBC microenvironment phenotypes were classified into three heterogeneous clusters: cluster 1, the "immune-desert" cluster, with low microenvironment cell infiltration; cluster 2, the "innate immune-inactivated" cluster, with resting innate immune cells and nonimmune stromal cells infiltration; and cluster 3, the "immune-inflamed" cluster, with abundant adaptive and innate immune cells infiltration. The clustering result was validated internally with pathological sections and externally with TCGA and METABRIC cohorts. The microenvironment clusters had significant prognostic efficacy. In terms of potential immune escape mechanisms, cluster 1 was characterized by an incapability to attract immune cells, and MYC amplification was correlated with low immune infiltration. In cluster 2, chemotaxis but inactivation of innate immunity and low tumor antigen burden might contribute to immune escape, and mutations in the PI3K-AKT pathway might be correlated with this effect. Cluster 3 featured high expression of immune checkpoint molecules. Conclusions: Our study represents a step towards personalized immunotherapy for TNBC patients. Immune checkpoint inhibitors might be effective for "immune-inflamed" cluster, and the transformation of "cold tumors" into "hot tumors" should be considered for "immune-desert" and "innate immune-inactivated" clusters.

Approximately 15% to 20% of breast cancers are classified as triple-negative breast cancer (TNBC), which is characterized by a lack of estrogen receptor and progesterone receptor expression and no ERBB2 gene amplification.¹ Compared with other subtypes, TNBC tends to have a higher histologic grade, increased aggressiveness, and more risk of local recurrence and visceral metastasis.² The reported prevalence of BRCA1 and BRCA2 (BRCA1/2) variants in unselected patients with TNBC is 11.2%.³ Both BRCA-associated breast cancer and sporadic TNBC exhibit characteristics consistent with abnormal DNA repair and genome-wide instability, which supports the use of DNA-damaging compounds, such as platinum derivatives.²

While recognized as a generally aggressive disease, triple-negative breast cancer (TNBC) is highly diverse in different patients with variable outcomes. In this prospective observational study, we aimed to develop an RNA signature of TNBC patients to improve risk stratification and optimize the choice of adjuvant therapy. Transcriptome microarrays for 33 paired TNBC and adjacent normal breast tissue revealed tumor-specific mRNAs and long noncoding RNAs (lncRNA) that were associated with recurrence-free survival. Using the Cox regression model, we developed an integrated mRNA-lncRNA signature based on the mRNA species for FCGR1A, RSAD2, CHRDL1, and the lncRNA species for HIF1A-AS2 and AK124454. The prognostic and predictive accuracy of this signature was evaluated in a training set of 137 TNBC patients and then validated in a second independent set of 138 TNBC patients. In addition, we enrolled 82 TNBC patients who underwent taxane-based neoadjuvant chemotherapy (NCT) to further verify the predictive value of the signature. In both the training and validation sets, the integrated signature had better prognostic value than clinicopathologic parameters. We also confirmed the interaction between the administration of taxane-based NCT and different risk groups. In the NCT cohort, patients in the low-risk group were more likely to achieve pathologic complete remission after taxane-based NCT (P = 0.014). Functionally, we showed that HIF1A-AS2 and AK124454 promoted cell proliferation and invasion in TNBC cells and contributed there to paclitaxel resistance. Overall, our results established an integrated mRNA-lncRNA signature as a reliable tool to predict tumor recurrence and the benefit of taxane chemotherapy in TNBC, warranting further investigation in larger populations to help frame individualized treatments for TNBC patients. Cancer Res; 76(8); 2105–14. ©2016 AACR.

Breast cancer is now the most frequently diagnosed malignancy, and metastasis remains the leading cause of death in breast cancer. However, little is known about the dynamic changes during the evolvement of dissemination. In this study, 65 968 cells from four patients with breast cancer and paired metastatic axillary lymph nodes are profiled using single‐cell RNA sequencing (scRNA‐seq) and spatial transcriptomics. A disseminated cancer cell cluster with high levels of oxidative phosphorylation (OXPHOS), including the upregulation of cytochrome C oxidase subunit 6C and dehydrogenase/reductase 2, is identified. The transition between glycolysis and OXPHOS when dissemination initiates is noticed. Furthermore, this distinct cell cluster is distributed along the tumor's leading edge. The findings here are verified in three different cohorts of breast cancer patients and an external scRNA‐seq dataset, which includes eight patients with breast cancer and paired metastatic axillary lymph nodes. This work describes the dynamic metabolic evolvement of early disseminated breast cancer and reveals a switch between glycolysis and OXPHOS in breast cancer cells as the early event during lymph node metastasis.

Antibody‐drug conjugates (ADCs) are a rapidly developing therapeutic approach in cancer treatment that has shown remarkable activity in breast cancer. Currently, there are two ADCs approved for the treatment of human epidermal growth factor receptor 2‐positive breast cancer, one for triple‐negative breast cancer, and multiple investigational ADCs in clinical trials. However, drug resistance has been noticed in clinical use, especially in trastuzumab emtansine. Here, the mechanisms of ADC resistance are summarized into four categories: antibody‐mediated resistance, impaired drug trafficking, disrupted lysosomal function, and payload‐related resistance. To overcome or prevent resistance to ADCs, innovative development strategies and combination therapy options are being investigated. Analyzing predictive biomarkers for optimal therapy selection may also help to prevent drug resistance.