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Aims: Nucleolar morphometric features have a potential role in the assessment of the aggressiveness of many cancers. However, the role of nucleoli in invasive breast cancer (BC) is still unclear. The aims of this study were to investigate the optimal method for scoring nucleoli in IBC and their prognostic significance, and to refine the grading of breast cancer (BC) by incorporating nucleolar score. Methods and results: Digital images acquired from haematoxylin and eosin-stained sections from a large BC cohort were divided into training (n = 400) and validation (n = 1200) sets for use in this study. Four different assessment methods were evaluated in the training set to identify the optimal method associated with the best performance and significant prognostic value. These were: (i) a modified Helpap method; (ii) counting prominent nucleoli (size ≥2.5 µm) in 10 field views (FVs); (iii) counting prominent nucleoli in five FVs; and (iv) counting prominent nucleoli in one FV. The optimal method was applied to the validation set and to an external validation set, i.e. data from The Cancer Genome Atlas (n = 743). Scoring prominent nucleoli in five FVs showed the highest interobserver concordance rate (intraclass correlation coefficient of 0.8) and a significant association with BC-specific survival (P < 0.0001). A high nucleolar score was associated with younger age, larger tumour size, and higher grade. Incorporation of nucleolar score in the Nottingham grading system resulted in a higher significant association with survival than the conventional grade. Conclusions: Quantification of nucleolar prominence in five FVs is a cost-efficient and reproducible morphological feature that can predict BC behaviour and can provide an alternative to pleomorphism to improve BC grading performance.

Background: The absence of a robust risk stratification tool for triple negative breast cancer (TNBC) underlies imprecise and non-selective treatment of these patients with cytotoxic chemotherapy. This study aimed to interrogate transcriptomes of TNBC resected samples using next generation sequencing (NGS) to identify novel biomarkers associated with disease outcomes. Methods: A subset of cases (n=112) from a large, diverse and well-characterised cohort of primary TNBCs (n=333) were subjected to RNA-sequencing (60M total reads/sample) and analyzed using the Illumina HiSeq 2500 platform. We identified genes associated with distant metastasis-free survival (DMFS) and breast cancer-specific survival (BCSS) by combining the application of supervised artificial neuronal network (ANN) analysis with gene selection to the RNA-sequencing data. The prognostic ability of these genes was validated using the Breast Cancer Gene-Expression Miner v4. 0 and Genotype 2 outcome datasets. Multivariate Cox regression analysis identified a prognostic gene signature that was independently associated with poor prognosis. Finally, we corroborated our results from the two-gene prognostic signature by their protein expression using immunohistochemistry. Results: ANN identified two gene panels that strongly predicted DMFS and BCSS. Univariate Cox regression analysis of 21 genes common to both panels revealed that the expression level of eight genes was independently associated with poor prognosis (p<0.05). Adjusting for clinicopathological factors including patient's age, grade, nodal stage, tumor size, and lymphovascular invasion using multivariate Cox regression analysis yielded a two-gene prognostic signature (ACSM4 and SPDYC) which was associated with poor prognosis (p<0.05) independent of other prognostic variables. We validated the protein expression of these two genes, and it was significantly associated with patient outcome in both independent and combined manner (p<0.05). Conclusion: Our study identifies a prognostic gene signature that can predict prognosis in TNBC patients and could potentially be used to guide the clinical management of TNBC patients.

Background KN motif and ankyrin repeat domains 1 (KANK1) plays an important role in cytoskeleton maintenance and contributes to the regulation of cell proliferation, adhesion and apoptosis. KANK1 is involved in progression of a variety of solid tumours; however, its role in invasive breast cancer (BC) remains unknown. This study aims to evaluate the clinicopathological and prognostic value of KANK1 expression in operable BC. Methods KANK1 expression was assessed at the transcriptomic level using multiple BC cohorts; the Molecular Taxonomy of BC International Consortium cohort (METABRIC; n = 1980), The Cancer Genome Atlas BC cohort (TCGA; n = 949) and the publicly available BC transcriptomic data hosted by BC Gene-Expression Miner (bc-GenExMiner v4.0) and Kaplan-Meier plotter?. The Nottingham BC cohort (n = 1500) prepared as tissue microarrays was used to assess KANK1 protein expression using immunohistochemistry (IHC). The association between clinicopathological variables and patient outcome was investigated. Results In the METABRIC cohort, high expression of KANK1 mRNA was associated with characteristics of good prognosis including lower grade, absence of lymphovascular invasion and HER2 negativity (all; p < 0.001) and with better outcome [p = 0.006, Hazards ratio, (HR) 0.70, 95% CI 0.54-0.91]. High KANK1 protein expression was correlated with smaller tumour size and HER2 negativity, and better outcome in terms of longer breast cancer-specific survival [p = 0.013, HR 0.7, 95% CI 0.536-0.893] and time to distant metastasis [p = 0.033, HR 0.65, 95% CI 0.51-0.819]. Conclusion These results supported that upregulation of KANK1 works as a tumour suppressor gene in BC and is associated with improved patients' outcomes.