Highlights d IL6/STAT3 signaling drives metastasis in ER + breast cancer mouse models d IL6/STAT3 establishes shared ER-FOXA1-STAT3 enhancers independent of FOXA1 d STAT3 co-opts shared enhancers to drive a distinct gene program independent of ER d JAK inhibitor ruxolitinib represses IL6/STAT3 activity and in vivo invasion Authors

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.

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.

Background: The androgen receptor (AR) has emerged as a potential therapeutic target for AR-positive triple-negative breast cancer (TNBC). However, conflicting reports regarding AR’s prognostic role in TNBC are putting its usefulness in question. Some studies conclude that AR positivity indicates a good prognosis in TNBC, whereas others suggest the opposite, and some show that AR status has no significant bearing on the patients’ prognosis. Methods: We evaluated the prognostic value of AR in resected primary tumors from TNBC patients from six international cohorts {US (n = 420), UK (n = 239), Norway (n = 104), Ireland (n = 222), Nigeria (n = 180), and India (n = 242); total n = 1407}. All TNBC samples were stained with the same anti-AR antibody using the same immunohistochemistry protocol, and samples with ≥1% of AR-positive nuclei were deemed AR-positive TNBCs. Results: AR status shows population-specific patterns of association with patients’ overall survival after controlling for age, grade, population, and chemotherapy. We found AR-positive status to be a marker of good prognosis in US and Nigerian cohorts, a marker of poor prognosis in Norway, Ireland and Indian cohorts, and neutral in UK cohort. Conclusion: AR status, on its own, is not a reliable prognostic marker. More research to investigate molecular subtype composition among the different cohorts is warranted.

The measures to control the COVID-19 outbreak will likely remain a feature of our working lives until a suitable vaccine or treatment is found. The pandemic has had a substantial impact on clinical services, including cancer pathways. Pathologists are working remotely in many circumstances to protect themselves, colleagues, family members and the delivery of clinical services. The effects of COVID-19 on research and clinical trials have also been significant with changes to protocols, suspensions of studies and redeployment of resources to COVID-19. In this article, we explore the specific impact of COVID-19 on clinical and academic pathology and explore how digital pathology and artificial intelligence can play a key role to safeguarding clinical services and pathology-based research in the current climate and in the future.

The tumor–stroma ratio (TSR) was evaluated as a promising parameter for breast cancer prognostication in clinically relevant subgroups of patients. The TSR was assessed on hematoxylin and eosin‐stained tissue slides of 1,794 breast cancer patients from the Nottingham City Hospital. An independent second cohort of 737 patients from the Netherlands Cancer Institute to Antoni van Leeuwenhoek was used for evaluation. In the Nottingham Breast Cancer series, the TSR was an independent prognostic parameter for recurrence‐free survival (RFS; HR 1.35, 95% CI 1.10–1.66, p = 0.004). The interaction term was statistically significant for grade and triple‐negative status. Multivariate Cox regression analysis showed a more pronounced effect of the TSR for RFS in grade III tumors (HR 1.89, 95% CI 1.43–2.51, p < 0.001) and triple‐negative tumors (HR 1.86, 95% CI 1.10–3.14, p = 0.020). Comparable hazard ratios and confidence intervals were observed for grade and triple‐negative status in the ONCOPOOL study. The prognostic value of TSR was not modified by age, tumor size, histology, estrogen receptor status, progesterone receptor status, human epidermal growth factor receptor 2 status or lymph node status. In conclusion, patients with a stroma‐high tumor had a worse prognosis compared to patients with a stroma‐low tumor. The prognostic value of the TSR is most discriminative in grade III tumors and triple‐negative tumors. The TSR was not modified by other clinically relevant parameters making it a potential factor to be included for improved risk stratification.

The functions of the tumor microenvironment (TME) are orchestrated by precise spatial organization of specialized cells, yet little is known about the multicellular structures that form within the TME. Here we systematically mapped TME structures in situ using imaging mass cytometry and multitiered spatial analysis of 693 breast tumors linked to genomic and clinical data. We identified ten recurrent TME structures that varied by vascular content, stromal quiescence versus activation, and leukocyte composition. These TME structures had distinct enrichment patterns among breast cancer subtypes, and some were associated with genomic profiles indicative of immune escape. Regulatory and dysfunctional T cells co-occurred in large ‘suppressed expansion’ structures. These structures were characterized by high cellular diversity, proliferating cells and enrichment for BRCA1 and CASP8 mutations and predicted poor outcome in estrogen-receptor-positive disease. The multicellular structures revealed here link conserved spatial organization to local TME function and could improve patient stratification.

During the last decade, a dramatic rise in the development and application of artificial intelligence (AI) tools for use in pathology services has occurred. This trend is often expected to continue and reshape the field of pathology in the coming years. The deployment of computational pathology and applications of AI tools can be considered as a paradigm shift that will change pathology services, making them more efficient and capable of meeting the needs of this era of precision medicine. Despite the success of AI models, the translational process from discovery to clinical applications has been slow. The gap between self-contained research and clinical environment may be too wide and has been largely neglected. In this review, we cover the current and prospective applications of AI in pathology. We examine its applications in diagnosis and prognosis, and we offer insights for considerations that could improve clinical applicability of these tools. Then, we discuss its potential to improve workflow efficiency, and its benefits in pathologist education. Finally, we review the factors that could influence adoption in clinical practices and the associated regulatory processes.