Stromal lymphocytes are associated with upgrade of B3 breast lesions.

Various histopathological, clinical and imaging parameters have been evaluated to identify a subset of women diagnosed with lesions with uncertain malignant potential (B3 or BIRADS 3/4A lesions) who could safely be observed rather than being treated with surgical excision, with little impact on clinical practice. The primary reason for surgery is to rule out an upgrade to either ductal carcinoma in situ or invasive breast cancer, which occurs in up to 30% of patients. We hypothesised that the stromal immune microenvironment could indicate the presence of carcinoma associated with a ductal B3 lesion and that this could be detected in biopsies by counting lymphocytes as a predictive biomarker for upgrade. A higher number of lymphocytes in the surrounding specialised stroma was observed in upgraded ductal and papillary B3 lesions than non-upgraded (p < 0.01, negative binomial model, n = 307). We developed a model using lymphocytes combined with age and the type of lesion, which was predictive of upgrade with an area under the curve of 0.82 [95% confidence interval 0.77-0.87]. The model can identify some patients at risk of upgrade with high sensitivity, but with limited specificity. Assessing the tumour microenvironment including stromal lymphocytes may contribute to reducing unnecessary surgeries in the clinic, but additional predictive features are needed.

Intraoperative spectroscopic evaluation of sentinel lymph nodes in breast cancer surgery.

BACKGROUND AND OBJECTIVES: Sentinel lymph node (SLN) biopsy is a standard procedure for patients with breast cancer and normal axilla on imaging. Positive SLNs on histological examination can lead to a subsequent surgery for axillary lymph node clearance (ALNC). Here we report a non-destructive technique based on autofluorescence (AF) imaging and Raman spectroscopy for intra-operative assessment of SLNs excised in breast cancer surgery. METHODS: A microscope integrating AF imaging and Raman spectroscopy modules was built to allow scanning of lymph node biopsy samples. During AF-Raman measurements, AF imaging determined optimal sampling locations for Raman spectroscopy measurements. After optimisation of the AF image analysis and training of classification models based on data from 85 samples, the AF-Raman technique was tested on an independent set of 81 lymph nodes comprising 58 fixed and 23 fresh specimens. The sensitivity and specificity of AF-Raman were calculated using post-operative histology as a standard of reference. RESULTS: The independent test set contained 66 negative lymph nodes and 15 positive lymph nodes according to the reference standard, collected from 78 patients. For this set of specimens, the area under the receiver operating characteristic (ROC) curve for the AF-Raman technique was 0.93 [0.83-0.98]. AF-Raman was then operated in a regime that maximised detection specificity, producing a 94% detection accuracy: 80% sensitivity and 97% specificity. The main confounders for SLN metastasis were areas rich in histiocytes clusters, for which only few Raman spectra had been included in the training dataset. DISCUSSION: This preliminary study indicates that with further development and extension of the training dataset by inclusion of additional Raman spectra of histiocytes clusters and capsule, the AF-Raman may become a promising technique for intra-operative assessment of SLNs. Intra-operative detection of positive biopsies could avoid second surgery for axillary clearance.

Performance of a novel spectroscopy-based tool for adjuvant therapy decision-making in hormone receptor-positive breast cancer: a validation study.

PURPOSE: Digistain Index (DI), measured using an inexpensive mid-infrared spectrometer, reflects the level of aneuploidy in unstained tissue sections and correlates with tumor grade. We investigated whether incorporating DI with other clinicopathological variables could predict outcomes in patients with early breast cancer. METHODS: DI was calculated in 801 patients with hormone receptor-positive, HER2-negative primary breast cancer and ≤ 3 positive lymph nodes. All patients were treated with systemic endocrine therapy and no chemotherapy. Multivariable proportional hazards modeling was used to incorporate DI with clinicopathological variables to generate the Digistain Prognostic Score (DPS). DPS was assessed for prediction of 5- and 10-year outcomes (recurrence, recurrence-free survival [RFS] and overall survival [OS]) using receiver operating characteristics and Cox proportional hazards regression models. Kaplan-Meier analysis evaluated the ability of DPS to stratify risk. RESULTS: DPS was consistently highly accurate and had negative predictive values for all three outcomes, ranging from 0.96 to 0.99 at 5 years and 0.84 to 0.95 at 10 years. DPS demonstrated statistically significant prognostic ability with significant hazard ratios (95% CI) for low- versus high-risk classification for RFS, recurrence and OS (1.80 [CI 1.31-2.48], 1.83 [1.32-2.52] and 1.77 [1.28-2.43], respectively; all P < 0.001). CONCLUSION: DPS showed high accuracy and predictive performance, was able to stratify patients into low or high-risk, and considering its cost and rapidity, has the potential to offer clinical utility.

Novel 2 Gene Signatures Associated With Breast Cancer Proliferation: Insights From Predictive Differential Gene Expression Analysis.

The use of proliferation markers provides valuable information about the rate of tumor growth, which can guide treatment decisions. However, there is still a lack of consensus regarding the optimal molecular markers or tests to use in clinical practice. Integrating gene expression data with clinical and histopathologic parameters enhances our understanding of disease processes, facilitates the identification of precise prognostic predictors, and supports the development of effective therapeutic strategies. The purpose of this study was to apply an integrated approach that combines morphologic, clinical, and bioinformatic data to reveal effective regulators of proliferation. Whole-slide images generated from hematoxylin-and-eosin-stained sections of The Cancer Genome Atlas (TCGA) breast cancer (BC) database (n = 1053) alongside their transcriptomic and clinical data were used to identify genes differentially expressed between tumors with high and low mitotic scores. Genes enriched in the cell-cycle pathway were used to predict the protein-protein interaction (PPI) network. Ten hub genes (ORC6, SKP2, SMC1B, CDKN2A, CDC25B, E2F1, E2F2, ORC1, PTTG1, and CDC25A) were identified using CytoHubba a Cytoscape plugin. In a multivariate Cox regression model, ORC6 and SKP2 were predictors of survival independent of existing methods of proliferation assessment including mitotic score and Ki67. The prognostic ability of these genes was validated using the Molecular Taxonomy of Breast Cancer International Consortium, Nottingham cohort, Uppsala cohort, and a combined multicentric cohort. The protein expression of these 2 genes was investigated on a large cohort of BC cases, and they were significantly associated with poor prognosis and patient outcome. A positive correlation between ORC6 and SKP2 mRNA and protein expression was observed. Our study has identified 2 gene signatures, ORC6 and SKP2, which play a significant role in BC proliferation. These genes surpassed both mitotic scores and Ki67 in multivariate analysis. Their identification provides potential opportunities for the development of targeted treatments for patients with BC.

Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application.

In recent years, artificial intelligence (AI) has demonstrated exceptional performance in mitosis identification and quantification. However, the implementation of AI in clinical practice needs to be evaluated against the existing methods. This study is aimed at assessing the optimal method of using AI-based mitotic figure scoring in breast cancer (BC). We utilized whole slide images from a large cohort of BC with extended follow-up comprising a discovery (n = 1715) and a validation (n = 859) set (Nottingham cohort). The Cancer Genome Atlas of breast invasive carcinoma (TCGA-BRCA) cohort (n = 757) was used as an external test set. Employing automated mitosis detection, the mitotic count was assessed using 3 different methods, the mitotic count per tumor area (MCT; calculated by dividing the number of mitotic figures by the total tumor area), the mitotic index (MI; defined as the average number of mitotic figures per 1000 malignant cells), and the mitotic activity index (MAI; defined as the number of mitotic figures in 3 mm2 area within the mitotic hotspot). These automated metrics were evaluated and compared based on their correlation with the well-established visual scoring method of the Nottingham grading system and Ki67 score, clinicopathologic parameters, and patient outcomes. AI-based mitotic scores derived from the 3 methods (MCT, MI, and MAI) were significantly correlated with the clinicopathologic characteristics and patient survival (P < .001). However, the mitotic counts and the derived cutoffs varied significantly between the 3 methods. Only MAI and MCT were positively correlated with the gold standard visual scoring method used in Nottingham grading system (r = 0.8 and r = 0.7, respectively) and Ki67 scores (r = 0.69 and r = 0.55, respectively), and MAI was the only independent predictor of survival (P < .05) in multivariate Cox regression analysis. For clinical applications, the optimum method of scoring mitosis using AI needs to be considered. MAI can provide reliable and reproducible results and can accurately quantify mitotic figures in BC.

Tumour 63 protein (p63) in breast pathology: biology, immunohistochemistry, diagnostic applications, and pitfalls.

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, ß, γ, δ, and ε).

Nottingham prognostic x (NPx): a risk stratification tool in ER-positive HER2-negative breast cancer: a validation study.

AIMS: In this study, we validate the use of Nottingham Prognostic x (NPx), consisting of tumour size, tumour grade, progesterone receptor (PR) and Ki67 in luminal BC. MATERIALS AND METHODS: Two large cohorts of luminal early-stage BC (n = 2864) were included. PR and Ki67 expression were assessed using full-face resection samples using immunohistochemistry. NPx was calculated and correlated with clinical variables and outcome, together with Oncotype DX recurrence score (RS), that is frequently used as a risk stratifier in luminal BC. RESULTS: In the whole cohort, 38% of patients were classified as high risk using NPx which showed significant association with parameters characteristics of aggressive tumour behaviour and shorter survival (P < 0.0001). NPx classified the moderate Nottingham Prognostic Index (NPI) risk group (n = 1812) into two distinct prognostic subgroups. Of the 82% low-risk group, only 3.8% developed events. Contrasting this, 14% of the high-risk patients developed events during follow-up. A strong association was observed between NPx and Oncotype Dx RS (P < 0.0001), where 66% of patients with intermediate risk RS who had subsequent distant metastases also had a high-risk NPx. CONCLUSION: NPx is a reliable prognostic index in patients with luminal early-stage BC, and in selected patients may be used to guide adjuvant chemotherapy recommendations.

Human epidermal growth factor receptor 2 (HER2) status in breast cancer: practice points and challenges.

Human epidermal growth factor receptor 2 (HER2)-enriched breast cancer benefits significantly from anti-HER2 targeted therapies. This highlights the critical need for precise HER2 immunohistochemistry (IHC) interpretation serving as a triage tool for selecting patients for anti-HER2 regimens. Recently, the emerging eligibility of patients with HER2-low breast cancers for a novel HER2-targeted antibody-drug conjugate (T-DXd) adds challenges to HER2 IHC scoring interpretation, notably in the 0-1+ range, which shows high interobserver and interlaboratory staining platform variability. In this review, we navigate evolving challenges and suggest practical recommendations for HER2 IHC interpretation.

Morphological and molecular changes of oestrogen receptor-positive breast cancer following bridging endocrine therapy: a United Kingdom multicentre study.

AIMS: Standard neoadjuvant endocrine therapy (NAET) is used for 6-9 months to downstage hormone-receptor-positive breast cancer. Bridging ET was introduced during the COVID-19 pandemic to delay surgical intervention. There are no data in the literature on the effect of short course therapy on tumour response. We aimed to analyse the effect of bridging ET and validate the previously proposed neoadjuvant ET pathological reporting criteria. METHODS AND RESULTS: This was a multicentre cohort of 256 patients who received bridging ET between March and October 2020. Assessment of paired pre- and post-NAET hormone receptors and HER2 and posttherapy Ki67 expression was done. The median duration of NAET was 45 days. In all, 86% of cases achieved partial pathological response and 9% showed minimal residual disease. Histological response to ET was observed from as early as day 6 posttherapy. Central scarring was noted in 32.8% of cases and lymphocytic infiltrate was seen in 43.4% of cases. Significant changes associated with the duration of ET were observed in tumour grade (21%), with downgrading identified in 12% of tumours (P < 0.001), progesterone receptor (PR) expression with switch to PR-negative status in 26% of cases (P < 0.001), and HER2 status with a switch from HER2-low to HER2-negative status in 32% of cases (P < 0.001). The median patient survival was 475 days, with an overall survival rate of 99.6%. CONCLUSIONS: Changes characteristic of tumour regression and significant changes in PR and HER2 occurred following a short course of NAET. The findings support biomarker testing on pretreatment core biopsies and retesting following therapy.

Digital pathology for reporting histopathology samples, including cancer screening samples - definitive evidence from a multisite study.

AIMS: To conduct a definitive multicentre comparison of digital pathology (DP) with light microscopy (LM) for reporting histopathology slides including breast and bowel cancer screening samples. METHODS: A total of 2024 cases (608 breast, 607 GI, 609 skin, 200 renal) were studied, including 207 breast and 250 bowel cancer screening samples. Cases were examined by four pathologists (16 study pathologists across the four speciality groups), using both LM and DP, with the order randomly assigned and 6 weeks between viewings. Reports were compared for clinical management concordance (CMC), meaning identical diagnoses plus differences which do not affect patient management. Percentage CMCs were computed using logistic regression models with crossed random-effects terms for case and pathologist. The obtained percentage CMCs were referenced to 98.3% calculated from previous studies. RESULTS: For all cases LM versus DP comparisons showed the CMC rates were 99.95% [95% confidence interval (CI) = 99.90-99.97] and 98.96 (95% CI = 98.42-99.32) for cancer screening samples. In speciality groups CMC for LM versus DP showed: breast 99.40% (99.06-99.62) overall and 96.27% (94.63-97.43) for cancer screening samples; [gastrointestinal (GI) = 99.96% (99.89-99.99)] overall and 99.93% (99.68-99.98) for bowel cancer screening samples; skin 99.99% (99.92-100.0); renal 99.99% (99.57-100.0). Analysis of clinically significant differences revealed discrepancies in areas where interobserver variability is known to be high, in reads performed with both modalities and without apparent trends to either. CONCLUSIONS: Comparing LM and DP CMC, overall rates exceed the reference 98.3%, providing compelling evidence that pathologists provide equivalent results for both routine and cancer screening samples irrespective of the modality used.

ATF4 as a Prognostic Marker and Modulator of Glutamine Metabolism in Oestrogen Receptor-Positive Breast Cancer.

INTRODUCTION: ATF4, a stress-responsive transcription factor that upregulates adaptive genes, is a potential prognostic marker and modulator of glutamine metabolism in breast cancer. However, its exact role remains to be elucidated. METHODS: ATF4 expression was evaluated at genomic and transcriptomic levels using METABRIC (n = 1,980), GeneMiner (n = 4,712), and KM-Plotter datasets. Proteomic expression was assessed via immunohistochemistry (n = 2,225) in the Nottingham Primary Breast Cancer Series. ATF4 genomic copy number (CN) variation and mRNA/protein in association with clinicopathological parameters, amino acid transporters (AATs), and patient outcome were investigated. RESULTS: Genomic, transcriptomic, and proteomic overexpression of ATF4 was associated with more aggressive ER-negative tumours. ATF4 mRNA and protein expression were significantly associated with increased expression of glutamine related AATs including SLC1A5 (p < 0.01) and SLC7A11 (p < 0.02). High ATF4 and SLC1A5 protein expression was significantly associated with shorter breast cancer-specific survival (p < 0.01), especially in ER+ tumours (p < 0.01), while high ATF4 and SLC7A11 protein expression was associated with shorter survival (p < 0.01). CONCLUSION: These findings suggest a complex interplay between ATF4 and AATs in breast cancer biology and underscore the potential role for ATF4 as a prognostic marker in ER+ breast cancer, offering a unique opportunity for risk stratification and personalized treatment strategies.

Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.

Breast cancer is the most prevalent cancer among women, and its diagnosis requires the accurate identification and classification of histological features for effective patient management. Artificial intelligence, particularly through deep learning, represents the next frontier in cancer diagnosis and management. Notably, the use of convolutional neural networks and emerging Vision Transformers (ViT) has been reported to automate pathologists' tasks, including tumor detection and classification, in addition to improving the efficiency of pathology services. Deep learning applications have also been extended to the prediction of protein expression, molecular subtype, mutation status, therapeutic efficacy, and outcome prediction directly from hematoxylin and eosin-stained slides, bypassing the need for immunohistochemistry or genetic testing. This review explores the current status and prospects of deep learning in breast cancer diagnosis with a focus on whole-slide image analysis. Artificial intelligence applications are increasingly applied to many tasks in breast pathology ranging from disease diagnosis to outcome prediction, thus serving as valuable tools for assisting pathologists and supporting breast cancer management.

The Clinicopathological Significance of the Cyclin D1/E1-Cyclin-Dependent Kinase (CDK2/4/6)-Retinoblastoma (RB1/pRB1) Pathway in Epithelial Ovarian Cancers.

Cyclin-dependent kinases (CDK2, CDK4, CDK6), cyclin D1, cyclin E1 and phosphorylated retinoblastoma (pRB1) are key regulators of the G1/S cell cycle checkpoint and may influence platinum response in ovarian cancers. CDK2/4/6 inhibitors are emerging targets in ovarian cancer therapeutics. In the current study, we evaluated the prognostic and predictive significance of the CDK2/4/6-cyclin D1/E1-pRB1 axis in clinical ovarian cancers (OC). The CDK2/4/6, cyclin D1/E1 and RB1/pRB1 protein expression were investigated in 300 ovarian cancers and correlated with clinicopathological parameters and patient outcomes. CDK2/4/6, cyclin D1/E1 and RB1 mRNA expression were evaluated in the publicly available ovarian TCGA dataset. We observed nuclear and cytoplasmic staining for CDK2/4/6, cyclins D1/E1 and RB1/pRB1 in OCs with varying percentages. Increased nuclear CDK2 and nuclear cyclin E1 expression was linked with poor progression-free survival (PFS) and a shorter overall survival (OS). Nuclear CDK6 was associated with poor OS. The cytoplasmic expression of CDK4, cyclin D1 and cyclin E1 also has predictive and/or prognostic significance in OCs. In the multivariate analysis, nuclear cyclin E1 was an independent predictor of poor PFS. Tumours with high nuclear cyclin E1/high nuclear CDK2 have a worse PFS and OS. Detailed bioinformatics in the TCGA cohort showed a positive correlation between cyclin E1 and CDK2. We also showed that cyclin-E1-overexpressing tumours are enriched for genes involved in insulin signalling and release. Our data not only identified the prognostic/predictive significance of these key cell cycle regulators but also demonstrate the importance of sub-cellular localisation. CDK2 targeting in cyclin-E1-amplified OCs could be a rational approach.

Clinicopathological Significance of Cyclin-Dependent Kinase 2 (CDK2) in Ductal Carcinoma In Situ and Early-Stage Invasive Breast Cancers.

Cyclin-dependent kinase 2 (CDK2) is a key cell cycle regulator, with essential roles during G1/S transition. The clinicopathological significance of CDK2 in ductal carcinomas in situ (DCIS) and early-stage invasive breast cancers (BCs) remains largely unknown. Here, we evaluated CDK2's protein expression in 479 BC samples and 216 DCIS specimens. Analysis of CDK2 transcripts was completed in the METABRIC cohort (n = 1980) and TCGA cohort (n = 1090), respectively. A high nuclear CDK2 protein expression was significantly associated with aggressive phenotypes, including a high tumour grade, lymph vascular invasion, a poor Nottingham prognostic index (all p-values < 0.0001), and shorter survival (p = 0.006), especially in luminal BC (p = 0.009). In p53-mutant BC, high nuclear CDK2 remained linked with worse survival (p = 0.01). In DCIS, high nuclear/low cytoplasmic co-expression showed significant association with a high tumour grade (p = 0.043), triple-negative and HER2-enriched molecular subtypes (p = 0.01), Comedo necrosis (p = 0.024), negative ER status (p = 0.004), negative PR status (p < 0.0001), and a high proliferation index (p < 0.0001). Tumours with high CDK2 transcripts were more likely to have higher expressions of genes involved in the cell cycle, homologous recombination, and p53 signaling. We provide compelling evidence that high CDK2 is a feature of aggressive breast cancers. The clinical evaluation of CDK2 inhibitors in early-stage BC patients will have a clinical impact.

Pathological Changes Following Neoadjuvant Endocrine Therapy (NAET): A Multicentre Study of 391 Breast Cancers.

Oestrogen receptor (ER)-positive breast cancer (BC) is generally well responsive to endocrine therapy. Neoadjuvant endocrine therapy (NAET) is increasingly being used for downstaging ER-positive tumours. This study aims to analyse the effect of NAET on a well-characterised cohort of ER-positive BC with particular emphasis on receptor expression. This is a retrospective United Kingdom (UK) multicentre study of 391 patients who received NAET between October 2012 and October 2020. Detailed analyses of the paired pre- and post-NAET morphological changes and hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) expression were performed. The median duration of NAET was 86 days, with median survival and overall survival rates of 380 days and 93.4%, respectively. A total of 90.3% of cases achieved a pathological partial response, with a significantly higher rate of response in the HER2-low cancers. Following NAET, BC displayed some pathological changes involving the tumour stroma including central scarring and an increase in tumour infiltrating lymphocytes (TILs) and tumour cell morphology. Significant changes associated with the duration of NAET were observed in tumour grade (30.6% of cases), with downgrading identified in 19.3% of tumours (p < 0.001). The conversion of ER status from positive to low or negative was insignificant. The conversion of progesterone receptor (PR) and HER2 status to negative status was observed in 31.3% and 38.1% of cases, respectively (p < 0.001). HER2-low breast cancer decreased from 63% to 37% following NAET in the paired samples. Significant morphological and biomarker changes involving PR and HER2 expression occurred following NAET. The findings support biomarker testing on pre-treatment core biopsies and post-treatment residual carcinoma.

The clinical value of progesterone receptor expression in luminal breast cancer: A study of a large cohort with long-term follow-up.

BACKGROUND: The routine assessment of progesterone receptor (PR) expression in breast cancer (BC) remains controversial. This study aimed to evaluate the role of PR expression in luminal BC, with emphasis on the definition of positivity and its prognostic significance as compared to Ki67 expression. METHODS: A large cohort (n = 1924) of estrogen receptor (ER)-positive/HER2-negative BC was included. PR was immunohistochemically (IHC) stained on full face sections and core needle biopsies (CNB) where the optimal scoring cutoff was evaluated. In addition, the association of PR with other clinicopathological factors, cellular proliferation, disease outcome, and response to adjuvant therapy were analyzed. RESULTS: Although several cutoffs showed prognostic significance, the optimal cutoff to categorize PR expression into two clinically distinct prognostic groups on CNB was 10%. PR negativity showed a significant association with features of aggressive tumor behavior and poor outcome. Multivariate analyses indicated that the association between PR negativity and poor outcome was independent of tumor grade, size, node stage, and Ki67. PR negativity showed independent association with shorter survival in patients who received endocrine therapy whereas Ki67did not. CONCLUSION: PR IHC expression provides independent prognostic value superior to Ki67. Routine assessment of PR expression in BC using 10% cutoff in the clinical setting is recommended. PLAIN LANGUAGE SUMMARY: In this study, we have established an optimal approach to determine the prognostic value of progesterone receptor expression in estrogen receptor-positive breast cancer patients. To do this, the levels of progesterone receptor were measured in a large cohort of estrogen receptor-positive breast cancer patients. We have refined the definition of progesterone receptor positivity in estrogen receptor-positive breast cancer. We show that progesterone receptor expression adds prognostic and predictive value of endocrine therapy in estrogen receptor-positive breast cancer patients, and our results show that the absence of progesterone receptor is associated with poorer outcomes independent of tumor grade, size, node stage, and Ki67 expression.

Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence.

BACKGROUND: Tumour infiltrating lymphocytes (TILs) are a prognostic parameter in triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). However, their role in luminal (oestrogen receptor positive and HER2 negative (ER + /HER2-)) BC remains unclear. In this study, we used artificial intelligence (AI) to assess the prognostic significance of TILs in a large well-characterised cohort of luminal BC. METHODS: Supervised deep learning model analysis of Haematoxylin and Eosin (H&E)-stained whole slide images (WSI) was applied to a cohort of 2231 luminal early-stage BC patients with long-term follow-up. Stromal TILs (sTILs) and intratumoural TILs (tTILs) were quantified and their spatial distribution within tumour tissue, as well as the proportion of stroma involved by sTILs were assessed. The association of TILs with clinicopathological parameters and patient outcome was determined. RESULTS: A strong positive linear correlation was observed between sTILs and tTILs. High sTILs and tTILs counts, as well as their proximity to stromal and tumour cells (co-occurrence) were associated with poor clinical outcomes and unfavourable clinicopathological parameters including high tumour grade, lymph node metastasis, large tumour size, and young age. AI-based assessment of the proportion of stroma composed of sTILs (as assessed visually in routine practice) was not predictive of patient outcome. tTILs was an independent predictor of worse patient outcome in multivariate Cox Regression analysis. CONCLUSION: AI-based detection of TILs counts, and their spatial distribution provides prognostic value in luminal early-stage BC patients. The utilisation of AI algorithms could provide a comprehensive assessment of TILs as a morphological variable in WSIs beyond eyeballing assessment.

The clinical significance of cyclin B1 (CCNB1) in invasive breast cancer with emphasis on its contribution to lymphovascular invasion development.

BACKGROUND: Lymphovascular invasion (LVI) is regulated through complex molecular mechanisms. Cyclin B1 (CCNB1) was previously determined as being associated with LVI using large cohorts of breast cancer (BC) and artificial neural network (ANN) technique. In this study, we aimed to assess the association between CCNB1 and LVI, other clinicopathological and other LVI-related biomarkers at the molecular (RNA transcriptomic) and proteomic levels in BC. METHODS: Two transcriptomic BC cohorts (n = 2834) were used to assess the association between the expression of CCNB1 at the mRNA level and clinicopathological characteristics and patient outcome. Tissue microarrays (TMAs) from a well-characterised BC cohort (n = 2480) with long-term outcome were also used to assess the clinical significance of CCNB1 protein expression using immunohistochemistry. RESULTS: High CCNB1 mRNA expression was associated with aggressive tumour behaviour, including LVI, larger size, higher tumour grade, high lymph nodal stage, hormonal receptor negativity, HER2 positivity and poor clinical outcome (all p < 0.0001). Similarly, high CCNB1 protein expression was associated with higher tumour grade, hormonal receptor negativity and HER2 positivity (all p < 0.0001). Additionally, there was a significant association between CCNB1- and LVI-related biomarkers including N-cadherin, P-cadherin and TWIST2 at the transcriptomic and proteomic level. Multivariate analysis revealed that CCNB1 was an independent predictor of shorter BC-specific survival (HR = 1.3; 95% CI 1.2-1.5; p = 0.010). CONCLUSION: CCNB1 is a key gene associated with LVI in BC and has prognostic value. More functional studies are warranted to unravel the mechanistic role of CCNB1 in the development of LVI.

Prognostic significance of microinvasion with ductal carcinoma in situ of the breast: a meta-analysis.

PURPOSE: Ductal carcinoma in situ (DCIS) associated with invasive carcinoma ≤ 1 mm in size is defined as DCIS with microinvasion (DCIS/microinvasion) rather than as invasive breast carcinoma. The number of patients with microinvasion accounts for < 1% of all breast cancer in published studies. As the numbers are limited, the prognostic significance of DCIS/microinvasion has not been clearly elucidated. This meta-analysis aimed to investigate the survival differences between patients with DCIS/microinvasion and those with pure DCIS. METHODS: A meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was performed. We searched three electronic databases (MEDLINE, Cochrane Library, and EMBASE) and included observational studies published in English that contained survival details of patients with either DCIS or DCIS/microinvasion. RESULTS: This study identified 26 studies that described the clinicopathological characteristics of patients in both the DCIS and DCIS/microinvasion groups. Survival differences were evaluated in 10 of 26 studies. Disease-free survival and loco-regional recurrence-free survival were significantly shorter in patients with DCIS/microinvasion than in those with DCIS (Hazard ratio, 1.52; 95% confidence interval, 1.11-2.08; p = 0.01 and hazard ratio, 2.53; 95% confidence interval, 1.45-4.41; p = 0.001, respectively). Both overall survival and distant metastasis-free survival tended to be shorter in patients with DCIS/microinvasion than in patients with DCIS (Hazard ratio, 1.63; 95% CI, 0.63-4.23; p = 0.31 and hazard ratio, 1.85; 95% confidence interval, 0.74-4.66; p = 0.19, respectively) but the difference was not statistically significant. CONCLUSION: Our meta-analysis suggests that DCIS/microinvasion may display more aggressive biological and clinical behavior than pure DCIS, highlighting the potential need for closer follow-up and consideration of adjuvant treatment strategies in DCIS patients with microinvasive disease.

The Clinical and Biological Significance of Estrogen Receptor-Low Positive Breast Cancer.

Estrogen receptor (ER) status in breast cancer (BC) is determined using immunohistochemistry (IHC) with nuclear expression in ≥1% of cells defined as ER-positive. BC with 1%-9% expression (ER-low-positive), is a clinically and biologically unique subgroup. In this study, we hypothesized that ER-low-positive BC represents a heterogeneous group with a mixture of ER-positive and ER-negative tumor, which may explain their divergent clinical behavior. A large BC cohort (n = 8171) was investigated and categorized into 3 groups: ER-low-positive (1%-9%), ER-positive (≥10%), and ER-negative (<1%) where clinicopathological and outcome characteristics were compared. A subset of ER-low-positive cases was further evaluated using IHC, RNAscope, and RT-qPCR. PAM50 subtyping and ESR1 mRNA expression levels were assessed in ER-low-positive cases within The Cancer Genome Atlas data set. The reliability of image analysis software in assessment of ER expression in the ER-low-positive category was also assessed. ER-low-positive tumors constituted <2% of BC cases examined and showed significant clinicopathological similarity to ER-negative tumors. Most of these tumors were nonluminal types showing low ESR1 mRNA expression. Further validation of ER status revealed that 45% of these tumors were ER-negative with repeated IHC staining and confirmed by RNAscope and RT-qPCR. ER-low-positive tumors diagnosed on needle core biopsy were enriched with false-positive ER staining. BCs with 10% ER behaved similar to ER-positive, rather than ER-negative or low-positive BCs. Moderate concordance was found in assessment of ER-low-positive tumors, and this was not improved by image analysis. Routinely diagnosed ER-low-positive BC includes a proportion of ER-negative cases. We recommend repeat testing of BC showing 1%-9% ER expression and using a cutoff ≥10% expression to define ER positivity to help better inform treatment decisions.