Breast cancer (BC) comprises a diverse spectrum of diseases featuring distinct presentation, morphological, biological, and clinical phenotypes. BC behaviour and response to therapy also vary widely. ...Current evidence indicates that traditional prognostic and predictive classification systems are insufficient to reflect the biological and clinical heterogeneity of BC. Advancements in high-throughput molecular techniques and bioinformatics have contributed to the improved understanding of BC biology, refinement of molecular taxonomies and the development of novel prognostic and predictive molecular assays. Molecular testing has also become increasingly important in the diagnosis and treatment of BC in the era of precision medicine. Despite the enormous amount of research work to develop and refine BC molecular prognostic and predictive assays, it is still in evolution and proper incorporation of these molecular tests into clinical practice to guide patient’s management remains a challenge. With the increasing use of more sophisticated high throughput molecular techniques, large amounts of data will continue to emerge, which could potentially lead to identification of novel therapeutic targets and allow more precise classification systems that can accurately predict outcome and response to therapy. In this review, we provide an update on the molecular classification of BC and molecular prognostic assays. Companion diagnostics, contribution of massive parallel sequencing and the use of liquid biopsy are also highlighted.
The classification of neuroendocrine neoplasms (NENs) differs between organ systems and currently causes considerable confusion. A uniform classification framework for NENs at any anatomical location ...may reduce inconsistencies and contradictions among the various systems currently in use. The classification suggested here is intended to allow pathologists and clinicians to manage their patients with NENs consistently, while acknowledging organ-specific differences in classification criteria, tumor biology, and prognostic factors. The classification suggested is based on a consensus conference held at the International Agency for Research on Cancer (IARC) in November 2017 and subsequent discussion with additional experts. The key feature of the new classification is a distinction between differentiated neuroendocrine tumors (NETs), also designated carcinoid tumors in some systems, and poorly differentiated NECs, as they both share common expression of neuroendocrine markers. This dichotomous morphological subdivision into NETs and NECs is supported by genetic evidence at specific anatomic sites as well as clinical, epidemiologic, histologic, and prognostic differences. In many organ systems, NETs are graded as G1, G2, or G3 based on mitotic count and/or Ki-67 labeling index, and/or the presence of necrosis; NECs are considered high grade by definition. We believe this conceptual approach can form the basis for the next generation of NEN classifications and will allow more consistent taxonomy to understand how neoplasms from different organ systems inter-relate clinically and genetically.
The role of glutaminase in cancer Masisi, Brendah K; El Ansari, Rokaya; Alfarsi, Lutfi ...
Histopathology,
March 2020, Volume:
76, Issue:
4
Journal Article
Peer reviewed
Open access
Increased glutamine metabolism (glutaminolysis) is a hallmark of cancer and is recognised as a key metabolic change in cancer cells. Breast cancer is a heterogeneous disease with different ...morphological and molecular subtypes and responses to therapy, and breast cancer cells are known to rewire glutamine metabolism to support survival and proliferation. Glutaminase isoenzymes (GLS and GLS2) are key enzymes for glutamine metabolism. Interestingly, GLS and GLS2 have contrasting functions in tumorigenesis. In this review, we explore the role of glutaminase in cancer, primarily focusing on breast cancer, address the role played by oncogenes and tumour suppressor genes in regulating glutaminase, and discuss current therapeutic approaches to targeting glutaminase.
Spindle cell lesions of the breast comprise a heterogeneous group of lesions, ranging from reactive and benign processes to aggressive malignant tumours. Despite their rarity, they attract the ...attention of breast pathologists due to their overlapping morphological features and diagnostic challenges, particularly on core needle biopsy (CNB) specimens. Pathologists should recognise the wide range of differential diagnoses and be familiar with the diverse morphological appearances of these lesions to make an accurate diagnosis and to suggest proper management of the patients. Clinical history, immunohistochemistry, and molecular assays are helpful in making a correct diagnosis in morphologically challenging cases. In this review, we present our approach for the diagnosis of breast spindle cell lesions, highlighting the main features of each entity and the potential pitfalls, particularly on CNB. Breast spindle cell lesions are generally classified into two main categories: bland-appearing and malignant-appearing lesions. Each category includes a distinct list of differential diagnoses and a panel of immunohistochemical markers. In bland-appearing lesions, it is important to distinguish fibromatosis-like spindle cell metaplastic breast carcinoma from other benign entities and to distinguish fibromatosis from scar tissue. The malignant-appearing category includes spindle cell metaplastic carcinoma, stroma rich malignant phyllodes tumour, other primary and metastatic malignant spindle cell tumours of the breast, including angiosarcoma and melanoma, and benign mimics such as florid granulation tissue and nodular fasciitis.
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.
Recent gene expression profiling of breast cancer has identified specific subtypes with clinical, biologic, and therapeutic implications. The basal-like group of tumors is characterized by an ...expression signature similar to that of the basal/myoepithelial cells of the breast and is reported to have transcriptomic characteristics similar to those of tumors arising in BRCA1 germline mutation carriers. They are associated with aggressive behavior and poor prognosis, and typically do not express hormone receptors or HER-2 ("triple-negative" phenotype). Therefore, patients with basal-like cancers are unlikely to benefit from currently available targeted systemic therapy. Although basal-like tumors are characterized by distinctive morphologic, genetic, immunophenotypic, and clinical features, neither an accepted consensus on routine clinical identification and definition of this aggressive subtype of breast cancer nor a way of systematically classifying this complex group of tumors has been described. Different definitions are, therefore, likely to produce variable and contradictory results that may hamper consistent identification and development of treatment strategies for these tumors. In this review, we discuss definition, heterogeneity, morphologic spectrum, relation to BRCA1, and clinical significance of this important class of breast cancer.
Breast cancer is the most common cancer and second leading cause of cancer-related death worldwide. The mainstay of breast cancer workup is histopathological diagnosis - which guides therapy and ...prognosis. However, emerging knowledge about the complex nature of cancer and the availability of tailored therapies have exposed opportunities for improvements in diagnostic precision. In parallel, advances in artificial intelligence (AI) along with the growing digitization of pathology slides for the primary diagnosis are a promising approach to meet the demand for more accurate detection, classification and prediction of behaviour of breast tumours. In this article, we cover the current and prospective uses of AI in digital pathology for breast cancer, review the basics of digital pathology and AI, and outline outstanding challenges in the field.
Purpose
MMP9 is a matricellular protein associated with extracellular matrix (ECM) remodelling, that promotes tumour progression, and modulates the activity of cell adhesion molecules and cytokines. ...This study aims to assess the prognostic value of MMP9 and its association with cytoskeletal modulators in early-stage invasive breast cancer (BC).
Methods
MMP9 expression was evaluated by immunohistochemistry using a well-characterised series of primary BC patients with long-term clinical follow-up. Association with clinicopathological factors, patient outcome and ECM remodelling BC-biomarkers were investigated. METABRIC dataset, BC-GenExMiner v4.0 and TCGA were used for the external validation of
MMP9
expression. GSEA gene enrichment analyses were used to evaluate
MMP9
associated pathways.
Results
MMP9 immunopositivity was observed in the stroma and cytoplasm of BC cells. Elevated MMP9 protein levels were associated with high tumour grade, high Nottingham Prognostic Index, and hormonal receptor negativity. Elevated MMP9 protein expression correlated significantly with cytokeratin 17 (Ck17), Epidermal Growth Factor Receptor (EGFR), proliferation (Ki67) biomarkers, cell surface adhesion receptor (CD44) and cell division control protein 42 (CDC42). Cytoplasmic MMP9 expression was an independent prognostic factor associated with shorter BC-specific survival. In the external validation cohorts,
MMP9
expression was also associated with poor patients’ outcome. Transcriptomic analysis confirmed a positive association between
MMP9
and ECM remodelling biomarkers. GSEA analysis supports MMP9 association with ECM and cytoskeletal pathways.
Conclusion
This study provides evidence for the prognostic value of MMP9 in BC. Further functional studies to decipher the role of MMP9 and its association with cytoskeletal modulators in BC progression are warranted.
Tumour protein 63 (p63) is a transcription factor of the p53 gene family, encoded by the TP63 gene located at chromosome 3q28, which regulates the activity of genes involved in growth and development ...of the ectoderm and derived tissues. p63 protein is normally expressed in the nuclei of the basal cell layer of glandular organs, including breast, in squamous epithelium and in urothelium. p63 immunohistochemical (IHC) staining has several applications in diagnostic breast pathology. It is commonly used to demonstrate myoepithelial cells at the epithelial stromal interface to differentiate benign and in situ lesions from invasive carcinoma and to characterize and classify papillary lesions including the distinction of breast intraduct papilloma from skin hidradenoma. p63 IHC is also used to identify and profile lesions showing myoepithelial cell and/or squamous differentiation, e.g. adenomyoepithelioma, salivary gland‐like tumours including adenoid cystic carcinoma, and metaplastic breast carcinoma including low‐grade adenosquamous carcinoma. This article reviews the applications of p63 IHC in diagnostic breast pathology and outlines a practical approach to the diagnosis and characterization of breast lesions through the identification of normal and abnormal p63 protein expression. The biology of p63, the range of available antibodies with emphasis on staining specificity and sensitivity, and pitfalls in interpretation are also discussed. The TP63 gene in humans, which shows a specific genomic structure, resulting in either TAp63 (p63) isoform or ΔNp63 (p40) isoform. As illustrated in the figure, both isoforms contain a DNA‐binding domain (Orange box) and an oligomerization domain (Grey box). TAp63 contains an N‐terminal transactivation (TA) domain (Green box), while ΔNp63 has an alternative terminus (Yellow box). Antibodies against conventional pan‐p63 (TP63) bind to the DNA binding domain common to both isoforms (TAp63 and p40) and does not distinguish between them. Antibodies against TAp63 bind to the N‐terminal TA domain, while antibodies specific to ΔNp63 (p40) bind to the alternative terminus. Each isoform has variant isotypes (α, β, γ, δ, and ε).
The TP63 gene in humans, which shows a specific genomic structure, resulting in either TAp63 (p63) isoform or ΔNp63 (p40) isoform. As illustrated in the figure, both isoforms contain a DNA‐binding domain (Orange box) and an oligomerization domain (Grey box). TAp63 contains an N‐terminal transactivation (TA) domain (Green box), while ΔNp63 has an alternative terminus (Yellow box). Antibodies against conventional pan‐p63 (TP63) bind to the DNA binding domain common to both isoforms (TAp63 and p40) and does not distinguish between them. Antibodies against TAp63 bind to the N‐terminal TA domain, while antibodies specific to ΔNp63 (p40) bind to the alternative terminus. Each isoform has variant isotypes (α, β, γ, δ, and ε).
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