Breast cancer is a heterogeneous disease with varied morphological appearances, molecular features, behavior, and response to therapy. Current routine clinical management of breast cancer relies on the availability of robust clinical and pathological prognostic and predictive factors to support clinical and patient decision making in which potentially suitable treatment options are increasingly available. One of the best-established prognostic factors in breast cancer is histological grade, which represents the morphological assessment of tumor biological characteristics and has been shown to be able to generate important information related to the clinical behavior of breast cancers. Genome-wide microarray-based expression profiling studies have unraveled several characteristics of breast cancer biology and have provided further evidence that the biological features captured by histological grade are important in determining tumor behavior. Also, expression profiling studies have generated clinically useful data that have significantly improved our understanding of the biology of breast cancer, and these studies are undergoing evaluation as improved prognostic and predictive tools in clinical practice. Clinical acceptance of these molecular assays will require them to be more than expensive surrogates of established traditional factors such as histological grade. It is essential that they provide additional prognostic or predictive information above and beyond that offered by current parameters. Here, we present an analysis of the validity of histological grade as a prognostic factor and a consensus view on the significance of histological grade and its role in breast cancer classification and staging systems in this era of emerging clinical use of molecular classifiers.

Breast cancer (BC) is a heterogeneous disease, encompassing a diverse spectrum of tumours with varying morphological, biological, and clinical phenotypes. Although tumours may show phenotypic overlap, they often display different biological behaviour and response to therapy. Advances in high‐throughput molecular techniques and bioinformatics have contributed to improved understanding of BC biology and refinement of molecular taxonomy with the identification of specific molecular subclasses. Although the traditional pathological morphological classification of BC is of paramount importance and provides diagnostic and prognostic information, current interest focusses on the use of a single gene and multigene assays to stratify BC into distinct groups to guide decisions on systemic therapy. This review considers approaches to the classification of BC, including their limitations, and with particular emphasis on the fundamental role of morphology in establishing an accurate diagnosis of primary invasive carcinoma of breast origin. This forms the basis for further morphological characterization and for all other approaches to BC classification that are used to provide prognostic and therapeutic predictive information.

The tumor microenvironment (TME) in breast cancer comprises local factors, cancer cells, immune cells and stromal cells of the local and distant tissues. The interaction between cancer cells and their microenvironment plays important roles in tumor proliferation, propagation and response to therapies. There is increasing research in exploring and manipulating the non-cancerous components of the TME for breast cancer treatment. As the TME is now increasingly recognized as a treatment target, its pathologic assessment has become a critical component of breast cancer management. The latest WHO classification of tumors of the breast listed stromal response pattern/fibrotic focus as a prognostic factor and includes recommendations on the assessment of tumor infiltrating lymphocytes and PD-1/PD-L1 expression, with therapeutic implications. This review dissects the TME of breast cancer, describes pathologic assessment relevant for prognostication and treatment decision, and details therapeutic options that interacts with and/or exploits the TME in breast cancer.

Background: Mammary metaplastic carcinoma encompasses epithelial-only carcinoma (high-grade adenosquamous carcinoma or pure squamous cell carcinoma), biphasic epithelial and sarcomatoid carcinoma and monophasic spindle cell carcinoma. Aim: To evaluate the clinicopathological features of a large series of 34 metaplastic carcinomas. Methods: 10 epithelial-only, 14 biphasic and 10 monophasic metaplastic carcinomas were assessed for nuclear grade, hormone receptor status, HER2/neu (cerbB2) oncogene expression, Ki-67 and p53, lymph node status and recurrence on follow-up. Results: Intermediate to high nuclear grade were assessed in most (33/34) tumours. Oestrogen and progesterone receptors were negative in 8 of 10 epithelial-only, all 14 biphasic, and 9 of 10 monophasic tumours, cerbB2 was negative in 7 of 10 epithelial-only, all 14 biphasic and 8 of 10 monophasic tumours. Ki-67 was found to be positive in 6 of 10 epithelial-only, 6 of 14 biphasic, and 7 of 10 monophasic tumours, whereas p53 was positive in 6 of 10 epithelial-only, 7 of 14 biphasic, and 8 of 10 monophasic tumours. Lymph node metastases were seen in 7 of 7 epithelial-only, 7 of 11 biphasic, and 3 of 7 monophasic tumours. Recurrences were seen in 4 of 7 epithelial-only, 8 of 9 biphasic, and 4 of 9 monophasic tumours. Conclusions: All three subtypes of metaplastic carcinoma are known to behave aggressively, and should be differentiated from the low-grade fibromatosis-like metaplastic carcinoma, which does not metastasise. Oncological treatment options may be limited by the frequently negative status of hormonal receptor and cerbB2.

Triple negative breast cancer is associated with poorer prognosis and unresponsiveness to endocrine and anti-HER2 directed agents. Despite emerging data supporting the use of polyADP-ribose polymerase (PARP) inhibitors, complete and durable responses are rare and exploration of additional targeted therapies is needed. Epidermal growth factor receptor (EGFR) is expressed in triple negative breast cancer and several clinical trials are testing the role of anti-EGFR directed therapy. However, the rate of EGFR mutations is poorly defined. We, therefore, sought to characterize EGFR mutations in triple negative breast cancers.

Background: The presence of tumor infiltrating lymphocytes (TIL) is associated with favorable prognosis. Recent evidence suggested that not only their density, but also the spatial organization as tertiary lymphoid structures (TLS), play a key role in determining patient survival. Materials and Methods: In a cohort of 248 breast cancers, the clinicopathologic association and prognostic role of TLS was examined. Results: Tertiary lymphoid structures were associated with higher tumor grade, apocrine phenotype, necrosis, extensive in situ component, lymphovascular invasion (LVI), and high TIL. For biomarkers, TLS were associated with hormone receptors negativity, HER2 positivity, and c-kit expression. Tertiary lymphoid structures were significantly related to better disease-free survival (DFS) in HER2 positive (HER2+) breast cancers (log-rank = 4.054), which was not dependent on high TIL status. The combined TLS and TIL status was an independent favorable factor associated with DFS in those cases. Interestingly, tumor cell infiltration into the TLS was found in 41.9% of TLS positive cases. It was associated with LVI in HER2 negative (HER2−) TLS positive (particularly estrogen receptor positive [ER+] HER2−) cases. In the ER+ HER2− cases, tumor cell infiltration into TLS was also associated with increased pathologic nodal stage (pN) stage and nodal involvement. Conclusion: Tertiary lymphoid structures showed a similar relationship with clinicopathologic features and biomarkers as TIL. The presence of TLS, irrespective of TIL level, could be an important favorable prognostic indicator in HER2+ breast cancer patients. Given the significance of TLS in promoting effective antitumor immunity, further understanding of its organization and induction may provide new opportunities to improve the current immunotherapy strategies.