Phyllodes tumours (PT) are rare and distinct breast tumours, which span a morphological continuum. Classification into benign, borderline and malignant categories reflects their biology and clinical behaviour and is essential to guide management. This study aims to assess the diagnostic agreement of PT using the UK National Health Service Breast Screening Programme (NHSBSP) breast pathology external quality assurance (EQA) scheme data.Twenty-six PTs were identified in the EQA scheme, which were diagnosed by an average of 607 participants/circulation. Data on diagnostic categories were collected and representative slides were reviewed. The level of concordance between reporting pathologists was assessed. There were 14 benign, six borderline and six malignant PT. The overall rate of diagnosis agreement was 86% when analysed as benign lesions, borderline PT and malignant lesions, which decreased to 79% when diagnosed as PT (irrespective of grade) and to 63% when the diagnosis was further refined to PT categories (benign, borderline and malignant PTs). The highest agreement rate was observed in malignant PT (86%) and the lowest in borderline PT (42%). Malignant heterologous elements, stromal overgrowth and leaf-like architecture are features associated with higher concordance rates. Lower-priority features were stromal expansion, clefting and multinodularity.The concordance of PT diagnosis, as an entity, is high, but its classification into benign, borderline and malignant has variable agreement levels, with borderline tumours having the lowest concordance rate. More research to refine the diagnostic criteria for categorisation of PT is warranted to improve concordance between pathologists.
Aurora Kinase A (AURKA/STK15) has a role in centrosome duplication and is a regulator of mitotic cell proliferation. It is over-expressed in breast cancer and other cancers, however; its role in ductal carcinoma in situ (DCIS) remains to be defined. This study aims to characterize AURKA protein expression in DCIS and evaluate its prognostic significance.AURKA was assessed immunohistochemically in a large well-characterized cohort of DCIS (n = 776 pure DCIS and 239 DCIS associated with invasive breast cancer [DCIS-mixed]) with long-term follow-up data (median = 105 months) and basic molecular characterization.High AURKA expression was observed in 15% of DCIS cases and was associated with features of aggressiveness including larger tumour size, high nuclear grade, hormone receptor negativity, HER2 positivity, and high Ki67 proliferation index. AURKA expression was higher in DCIS associated with invasive breast cancer than in pure DCIS (p < 0.0001). In the DCIS-mixed cohort, the invasive component showed higher AURKA expression than the DCIS component (p < 0.0001). Outcome analysis revealed that AURKA was a predictor of invasive recurrence (p = 0.002).High AURKA expression is associated with poor prognosis in DCIS and might be a potential marker to predict DCIS progression to invasive disease.
The outcome of the luminal oestrogen receptor-positive (ER +) subtype of breast cancer (BC) is highly variable and patient stratification needs to be refined. We assessed the prognostic significance of oestrogen-regulated solute carrier family 39 member 6 (SLC39A6) in BC, with emphasis on ER + tumours.SLC39A6 mRNA expression and copy number alterations were assessed using the METABRIC cohort (n = 1980). SLC39A6 protein expression was evaluated in a large (n = 670) and annotated series of early-stage (I-III) operable BC using tissue microarrays and immunohistochemistry. The associations between SLC39A6 expression and clinicopathological parameters, patient outcomes and other ER-related markers were evaluated using Chi-square tests and Kaplan-Meier curves.High SLC39A6 mRNA and protein expression was associated with features characteristic of less aggressive tumours in the entire BC cohort and ER + subgroup. SLC39A6 protein expression was detected in the cytoplasm and nuclei of the tumour cells. High SLC39A6 nuclear expression and mRNA levels were positively associated with ER + tumours and expression of ER-related markers, including the progesterone receptor, forkhead box protein A1 and GATA binding protein 3. In the ER + luminal BC, high SLC39A6 expression was independently associated with longer BC-specific survival (BCSS) (P = 0.015, HR 0.678, 95% CI 0.472‒0.972) even in those who did not receive endocrine therapy (P = 0.001, HR 0.701, 95% CI 0.463‒1.062).SLC39A6 may be prognostic for a better outcome in ER + luminal BC. Further functional studies to investigate the role of SLC39A6 in ER + luminal BC are warranted.
Recent advances in whole slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence (AI) based diagnostic, prognostic, and predictive algorithms. Computational Pathology (CPath) offers an integrated solution to utilize information embedded in pathology WSIs beyond what we obtain through visual assessment. For automated analysis of WSIs and validation of machine learning (ML) models, annotations at the slide, tissue and cellular levels are required. The annotation of important visual constructs in pathology images is an important component of CPath projects. Improper annotations can result in algorithms which are hard to interpret and can potentially produce inaccurate and inconsistent results. Despite the crucial role of annotations in CPath projects, there are no well-defined guidelines or best practices on how annotations should be carried out. In this paper, we address this shortcoming by presenting the experience and best practices acquired during the execution of a large-scale annotation exercise involving a multidisciplinary team of pathologists, ML experts and researchers as part of the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) consortium. We present a real-world case study along with examples of different types of annotations, diagnostic algorithm, annotation data dictionary and annotation constructs. The analyses reported in this work highlight best practice recommendations that can be used as annotation guidelines over the lifecycle of a CPath project.
The glutamine metabolism has a key role in the regulation of uncontrolled tumour growth. This study aimed to evaluate the expression and prognostic significance of glutaminase in luminal breast cancer (BC). The glutaminase isoforms (GLS/GLS2) were assessed at genomic/transcriptomic levels, using METABRIC (n = 1398) and GeneMiner datasets (n = 4712), and protein using immunohistochemistry in well-characterised cohorts of Oestrogen receptor-positive/HER2-negative BC patients: ductal carcinoma in situ (DCIS; n = 206) and invasive breast cancer (IBC; n = 717). Glutaminase expression was associated with clinicopathological features, patient outcome and glutamine-metabolism-related genes. In DCIS, GLS alone and GLS+/GLS2- expression were risk factors for shorter local recurrence-free interval (p < 0.0001 and p = 0.001, respectively) and remained prognostic factors independent of tumour size, grade and comedo necrosis (p = 0.0008 and p = 0.003, respectively). In IBC, GLS gene copy number gain with high mRNA expression was associated with poor patient outcome (p = 0.011), whereas high GLS2 protein was predictive of a longer disease-free survival (p = 0.006). Glutaminase plays a role in the biological function of luminal BC, particularly GLS in the early non-invasive stage, which could be used as a potential biomarker to predict disease progression and a target for inhibition. Further validation is required to confirm these observations, and functional assessments are needed to explore their specific roles.
Abstract Background : Accurate risk stratification of breast cancer (BC) patients is critical for predicting behaviour and guiding management decision making. Despite the well-established prognostic value of proliferation in BC, the interplay between proliferation and apoptosis remains to be defined. In this study we hypothesised that the combined proliferation and apoptosis index will provide a more accurate in vivo growth rate measure and a precise prognostic indicator in the era of digital pathology and artificial intelligence. Methods and Results : Apoptotic and mitotic figures were counted in whole slide images (WSI) generated from haematoxylin and eosin-stained sections of 1545 early-stage BC cases derived from two well defined BC cohorts. Mitotic and apoptotic figures were counted in defined areas visually using the published criteria. This showed significant correlation between apoptotic and mitotic scores. The morphological scoring technique was shown to be reliable since there was a significant positive correlation between apoptosis score and cleaved caspase-3 expression. High apoptotic counts were associated with features of aggressive behaviour including high grade, high pleomorphism score, and hormonal receptor negativity. Although apoptotic index (AI) was an independent prognostic indicator in multivariate analysis, the prognostic value increased when combined with the mitotic index (MI). BC patients with high MI and high AI (HM/HA) had the shortest survival in terms of BC specific survival (BCSS), distant metastasis (DMFS) and recurrence (RFS) free survival. Differential gene expression analysis (DGE) of the cases in TCGA cohort showed several genes associated with HM/HA subgroup with transcription factor Dp-1 ( TFDP1 ) was the top gene significantly up regulated in this subgroup. Conclusions : Apoptotic cells counted in histological BC sections provides additional prognostic value in BC when combined with mitotic counts. This can be considered when using artificial intelligence algorithms to assess proliferation in BC as a prognostic indicator.
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