Prognostic and clinical significance of the proliferation marker MCM7 in breast cancer.

INTRODUCTION: Minichromosome maintenance complex component 7 (MCM7) plays an essential role in proliferation and DNA replication of cancer cells. However, the expression and prognostic significance of MCM7 in breast cancer (BC) remain to be defined. In this study, we aimed to evaluate the role of MCM7 in BC. METHODS: We conducted immunohistochemistry staining of MCM7 in 1156 operable early-stage BC samples and assessed MCM7 at the transcriptomic levels using publicly available cohorts (n=13,430). MCM7 expression was evaluated and correlated with clinicopathological parameters including Ki67 labelling index and patient outcome. RESULTS: At the transcriptomic level, there was a significant association between high MCM7 mRNA levels and shorter patient survival in the whole cohort and in luminal BC class but not in the basel-like molecular subtype. High MCM7 protein expression was detected in 43% of patients and was significantly associated with parameters characteristic of aggressive tumour behaviour. MCM7 was independently associated with shorter survival, particularly in estrogen receptor-positive (luminal) BC. MCM7 stratified luminal tumours with aggressive clinicopathological features into distinct prognostic groups. In endocrine therapy treated BC patients, high MCM7 was associated with poor outcome but such association disappeared with administration of adjuvant chemotherapy. Patients with high expression of Ki67 and MCM7 showed worst survival while patients with double low expression BC showed the best outcome compared with single expression groups. CONCLUSION: The current findings indicate that MCM7 expression has a prognostic value in BC and can be used to identify luminal BC patients who can benefit from adjuvant chemotherapy.

The characteristics and prognostic significance of histone H1 expression in breast cancer.

Histone H1 (H.H1) is involved in chromatin organisation and gene regulation and is overexpressed in many malignant tumours, including breast cancer (BC). This study proposed and evaluated the prognostic role of H.H1 expression in BC. H.H1 mRNA expression was evaluated in publicly available BC dataset bc-GenExMiner database (n=4421). H.H1 protein expression was assessed immunohistochemically in a well-characterised early-stage BC cohort (n=1311), and associations with clinicopathological data and survival outcomes were evaluated. At the mRNA level, there was a significant association between high H.H1 mRNA and basal-like BC subtype and with poor outcome. The association with shorter survival was observed in the whole cohort and in the basal-like class. H.H1 protein expression was detected in both tumour cells and surrounding stroma. Total expression was detected in 72% of the cases, including 28% in tumour cell nuclei and 44% in the stroma. There was strong association between high tumour H.H1 expression and triple-negative BC (TNBC) subtype (p=0.007) and with shorter survival (p=0.019), independent of other variables including tumour size, histologic tumour grade, and lymph node status. H.H1 expression is associated with poor prognosis in BC. Given poor prognostic role of H.H1 in TNBC, it may represent a potential therapeutic target for patients with this aggressive disease.

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.

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.

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.

AI-based intra-tumor heterogeneity score of Ki67 expression as a prognostic marker for early-stage ER+/HER2- breast cancer.

Early-stage estrogen receptor positive and human epidermal growth factor receptor negative (ER+/HER2-) luminal breast cancer (BC) is quite heterogeneous and accounts for about 70% of all BCs. Ki67 is a proliferation marker that has a significant prognostic value in luminal BC despite the challenges in its assessment. There is increasing evidence that spatial colocalization, which measures the evenness of different types of cells, is clinically important in several types of cancer. However, reproducible quantification of intra-tumor spatial heterogeneity remains largely unexplored. We propose an automated pipeline for prognostication of luminal BC based on the analysis of spatial distribution of Ki67 expression in tumor cells using a large well-characterized cohort (n = 2,081). The proposed Ki67 colocalization (Ki67CL) score can stratify ER+/HER2- BC patients with high significance in terms of BC-specific survival (p < 0.00001) and distant metastasis-free survival (p = 0.0048). Ki67CL score is shown to be highly significant compared with the standard Ki67 index. In addition, we show that the proposed Ki67CL score can help identify luminal BC patients who can potentially benefit from adjuvant chemotherapy.

Immune infiltration, aggressive pathology, and poor survival outcomes in RECQL helicase deficient breast cancers.

RECQL is essential for genomic stability. Here, we evaluated RECQL in 449 pure ductal carcinomas in situ (DCIS), 152 DCIS components of mixed DCIS/invasive breast cancer (IBC) tumors, 157 IBC components of mixed DCIS/IBC and 50 normal epithelial terminal ductal lobular units (TDLUs). In 726 IBCs, CD8+, FOXP3+, IL17+, PDL1+, PD1+ T-cell infiltration (TILs) were investigated in RECQL deficient and proficient cancers. Tumor mutation burden (TMB) was evaluated in five RECQL germ-line mutation carriers with IBC by genome sequencing. Compared with normal epithelial cells, a striking reduction in nuclear RECQL in DCIS was evident with aggressive pathology and poor survival. In RECQL deficient IBCs, CD8+, FOXP3+, IL17+ or PDL1+ TILs were linked with aggressive pathology and shorter survival. In germline RECQL mutation carriers, increased TMB was observed in 4/5 tumors. We conclude that RECQL loss is an early event in breast cancer and promote immune cell infiltration.

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.

AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer.

Breast cancer (BC) grade is a well-established subjective prognostic indicator of tumour aggressiveness. Tumour heterogeneity and subjective assessment result in high degree of variability among observers in BC grading. Here we propose an objective Haematoxylin & Eosin (H&E) image-based prognostic marker for early-stage luminal/Her2-negative BReAst CancEr that we term as the BRACE marker. The proposed BRACE marker is derived from AI based assessment of heterogeneity in BC at a detailed level using the power of deep learning. The prognostic ability of the marker is validated in two well-annotated cohorts (Cohort-A/Nottingham: n = 2122 and Cohort-B/Coventry: n = 311) on early-stage luminal/HER2-negative BC patients treated with endocrine therapy and with long-term follow-up. The BRACE marker is able to stratify patients for both distant metastasis free survival (p = 0.001, C-index: 0.73) and BC specific survival (p < 0.0001, C-index: 0.84) showing comparable prediction accuracy to Nottingham Prognostic Index and Magee scores, which are both derived from manual histopathological assessment, to identify luminal BC patients that may be likely to benefit from adjuvant chemotherapy.

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.

Deciphering the Morphology of Tumor-Stromal Features in Invasive Breast Cancer Using Artificial Intelligence.

Tumor-associated stroma in breast cancer (BC) is complex and exhibits a high degree of heterogeneity. To date, no standardized assessment method has been established. Artificial intelligence (AI) could provide an objective morphologic assessment of tumors and stroma, with the potential to identify new features not discernible by visual microscopy. In this study, we used AI to assess the clinical significance of (1) stroma-to-tumor ratio (S:TR) and (2) the spatial arrangement of stromal cells, tumor cell density, and tumor burden in BC. Whole-slide images of a large cohort (n = 1968) of well-characterized luminal BC cases were examined. Region and cell-level annotation was performed, and supervised deep learning models were applied for automated quantification of tumor and stromal features. S:TR was calculated in terms of surface area and cell count ratio, and the S:TR heterogeneity and spatial distribution were also assessed. Tumor cell density and tumor size were used to estimate tumor burden. Cases were divided into discovery (n = 1027) and test (n = 941) sets for validation of the findings. In the whole cohort, the stroma-to-tumor mean surface area ratio was 0.74, and stromal cell density heterogeneity score was high (0.7/1). BC with high S:TR showed features characteristic of good prognosis and longer patient survival in both the discovery and test sets. Heterogeneous spatial distribution of S:TR areas was predictive of worse outcome. Higher tumor burden was associated with aggressive tumor behavior and shorter survival and was an independent predictor of worse outcome (BC-specific survival; hazard ratio: 1.7, P = .03, 95% CI, 1.04-2.83 and distant metastasis-free survival; hazard ratio: 1.64, P = .04, 95% CI, 1.01-2.62) superior to absolute tumor size. The study concludes that AI provides a tool to assess major and subtle morphologic stromal features in BC with prognostic implications. Tumor burden is more prognostically informative than tumor size.

Characteristics and prognostic significance of polo-like kinase-1 (PLK1) expression in breast cancer.

AIM: Polo-like kinase-1 (PLK1) plays a crucial role in cell cycle progression, and it is considered a potential therapeutic target in many cancers. Although the role of PLK1 is well established in triple-negative breast cancer (TNBC) as an oncogene, its role in luminal BC is still controversial. In this study, we aimed to evaluate the prognostic and predictive role of PLK1 in BC and its molecular subtypes. METHODS: A large BC cohort (n = 1208) were immunohistochemically stained for PLK1. The association with clinicopathological, molecular subtypes, and survival data was analysed. PLK1 mRNA was evaluated in the publicly available datasets (n = 6774), including The Cancer Genome Atlas and the Kaplan-Meier Plotter tool. RESULTS: 20% of the study cohort showed high cytoplasmic PLK1 expression. High PLK1 expression was significantly associated with a better outcome in the whole cohort, luminal BC. In contrast, high PLK1 expression was associated with a poor outcome in TNBC. Multivariate analyses indicated that high PLK1 expression is independently associated with longer survival in luminal BC, and in poorer prognosis in TNBC. At the mRNA levels, PLK1 expression was associated with short survival in TNBC consistent with the protein expression. However, in luminal BC, its prognostic value significantly varies between cohorts. CONCLUSION: The prognostic role of PLK1 in BC is molecular subtype-dependent. As PLK1 inhibitors are introduced to clinical trials for several cancer types, our study supports evaluation of the pharmacological inhibition of PLK1 as an attractive therapeutic target in TNBC. However, in luminal BC, PLK1 prognostic role remains controversial.

Expression, assessment and significance of Ki67 expression in breast cancer: an update.

Ki67 expression is one of the most important and cost-effective surrogate markers to assess for tumour cell proliferation in breast cancer (BC). The Ki67 labelling index has prognostic and predictive value in patients with early-stage BC, particularly in the hormone receptor-positive, HER2 (human epidermal growth factor receptor 2)-negative (luminal) tumours. However, many challenges exist in using Ki67 in routine clinical practice and it is still not universally used in the clinical setting. Addressing these challenges can potentially improve the clinical utility of Ki67 in BC. In this article, we review the function, immunohistochemical (IHC) expression, methods for scoring and interpretation of results as well as address several challenges of Ki67 assessment in BC. The prodigious attention associated with use of Ki67 IHC as a prognostic marker in BC resulted in high expectation and overestimation of its performance. However, the realisation of some pitfalls and disadvantages, which are expected with any similar markers, resulted in an increasing criticism of its clinical use. It is time to consider a pragmatic approach and weigh the benefits against the weaknesses and identify factors to achieve the best clinical utility. Here we highlight the strengths of its performance and provide some insights to overcome the existing challenges.

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 oncotype DX® 21-gene recurrence score and clinicopathological parameters: a single institutional experience.

AIMS: Oncotype DX recurrence score (RS) is a clinically validated assay, which predicts the likelihood of disease recurrence in oestrogen receptor-positive/HER2-negative (ER+/HER2-) breast cancer (BC). In this study we aimed to compare the performance of Oncotype DX against the conventional clinicopathological parameters using a large BC cohort diagnosed in a single institution. METHODS AND RESULTS: A cohort (n = 430) of ER+/HER2- BC patients who were diagnosed at the Nottingham University Hospitals NHS Trust and had Oncotype DX testing was included. Correlation with the clinicopathological and other biomarkers, including the proliferation index, was analysed. The median Oncotype DX RS was 17.5 (range = 0-69). There was a significant association between high RS and grade 3 tumours. No grade 1 BC or grade 2 tumours with mitosis score 1 showed high RS. Low RS was significantly associated with special tumour types where none of the patients with classical lobular or tubular carcinomas had a high RS. There was an inverse association between RS and levels of ER and progesterone receptor (PR) expression and a positive linear correlation with Ki67 labelling index. Notably, six patients who developed recurrence had an intermediate RS; however, four of these six cases (67%) were identified as high-risk disease when the conventional clinical and molecular parameters were considered. CONCLUSION: Oncotype DX RS is correlated strongly with the conventional clinicopathological parameters in BC. Some tumour features such as tumour grade, type, PR status and Ki67 index can be used as surrogate markers in certain scenarios.

Ki67 assessment in invasive luminal breast cancer: a comparative study between different scoring methods.

BACKGROUND: Ki67 reflects the proliferation activity in breast cancer (BC). However, an optimal method for its assessment in clinical settings has yet to be robustly defined. In this study we compared several methods to score Ki67 to identify a reliable and reproducible method for routine practice. METHODS: Sections from luminal BC cohort (n = 1662) were immunohistochemically stained with Ki67 and were assessed for the percentage, pattern, and intensity of expression. Ki67 positivity was evaluated using three methods: (i) quantification of Ki67-positive cells among 1000 invasive tumour cells within hotspot, (ii) average estimation of Ki67 within a defined hotspot, and (iii) average estimation of Ki67 positivity within the whole section. Time required for scoring, interobserver agreement and association with outcome were determined. RESULTS: The mean percentage of Ki67 expression per 1000 cells method was 16%, while the mean value of Ki67 scores using the average estimation within hotspot and whole slide were 14% and 12%, respectively. Quantification of Ki67-positive cells within 1000 cells had the highest degree of consistency between observers, and the highest hazard ratio predicting patient outcome when compared to using different common Ki67 cutoffs, which was independent of the other two methods. Granular pattern of Ki67 expression was associated with poorer outcome as compared to the other patterns. CONCLUSION: Assessment of Ki67 expression using quantification positive cells among 1000 tumour cells is an optimal method to achieve high reliability and reproducibility. Comment on the predominant Ki67 expression pattern would add prognostic and predictive value in luminal BC.

The characteristics and clinical significance of atypical mitosis in breast cancer.

Atypical mitosis is considered a feature of malignancy, however, its significance in breast cancer (BC) remains elusive. Here, we aimed to assess the clinical value of atypical mitoses in BC and to explore their underlying molecular features. Atypical and typical mitotic figures were quantified and correlated with clinicopathological variables in a large cohort of primary BC tissue sections (n = 846) using digitalized hematoxylin and eosin whole-slide images (WSIs). In addition, atypical mitoses were assessed in The Cancer Genome Atlas (TCGA) BC dataset (n = 1032) and were linked to the genetic alterations and pathways. In this study, the median of typical mitoses was 17 per 3 mm2 (range 0-120 mitoses), while the median of atypical mitoses was 4 (range 0-103 mitoses). High atypical mitoses were significantly associated with parameters characteristic of aggressive tumor behavior. The total number of mitoses, and a high atypical-to-typical mitoses ratio (>0.27) were associated with poor BC specific survival (BCSS), (p = 0.04 and 0.01, respectively). The atypical-to-typical mitoses ratio dichotomized triple negative-BC (TNBC) patients into two distinct groups in terms of the association with the outcome, while the overall number of mitoses was not. Moreover, TNBC patients with high atypical-to-typical mitoses ratio treated with adjuvant chemotherapy were associated with shorter survival (p = 0.003). Transcriptomic analysis of the TCGA-BRCA cohort dichotomized based on atypical mitoses identified 2494 differentially expressed genes. These included genes linked to pathways involved in chromosomal localization and segregation, centrosome assembly, spindle and microtubule formation, regulation of cell cycle and DNA repair. To conclude, the atypical-to-typical mitoses ratio has prognostic value independent of the overall mitotic count in BC patients and could predict the response to chemotherapy in TNBC.

Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations.

Recent advances in whole-slide imaging (WSI) technology have led to the development of a myriad of computer vision and artificial intelligence-based diagnostic, prognostic, and predictive algorithms. Computational Pathology (CPath) offers an integrated solution to utilise information embedded in pathology WSIs beyond what can be obtained 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 that 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.

Assessment of mitotic activity in breast cancer: revisited in the digital pathology era.

The assessment of cell proliferation is a key morphological feature for diagnosing various pathological lesions and predicting their clinical behaviour. Visual assessment of mitotic figures in routine histological sections remains the gold-standard method to evaluate the proliferative activity and grading of cancer. Despite the apparent simplicity of such a well-established method, visual assessment of mitotic figures in breast cancer (BC) remains a challenging task with low concordance among pathologists which can lead to under or overestimation of tumour grade and hence affects management. Guideline recommendations for counting mitoses in BC have been published to standardise methodology and improve concordance; however, the results remain less satisfactory. Alternative approaches such as the use of the proliferation marker Ki67 have been recommended but these did not show better performance in terms of concordance or prognostic stratification. The advent of whole slide image technology has brought the issue of mitotic counting in BC into the light again with more challenges to develop objective criteria for identifying and scoring mitotic figures in digitalised images. Using reliable and reproducible morphological criteria can provide the highest degree of concordance among pathologists and could even benefit the further application of artificial intelligence (AI) in breast pathology, and this relies mainly on the explicit description of these figures. In this review, we highlight the morphology of mitotic figures and their mimickers, address the current caveats in counting mitoses in breast pathology and describe how to strictly apply the morphological criteria for accurate and reliable histological grade and AI models.