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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.
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Neoplasias de la Mama , Humanos , Femenino , Neoplasias de la Mama/patología , Antígeno Ki-67 , Inteligencia Artificial , Mitosis , Índice MitóticoRESUMEN
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.
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Biomarcadores de Tumor , Neoplasias de la Mama , Receptor ErbB-2 , Receptores de Estrógenos , Receptores de Progesterona , Humanos , Femenino , Neoplasias de la Mama/patología , Neoplasias de la Mama/diagnóstico , Neoplasias de la Mama/metabolismo , Neoplasias de la Mama/mortalidad , Persona de Mediana Edad , Pronóstico , Receptor ErbB-2/metabolismo , Biomarcadores de Tumor/análisis , Biomarcadores de Tumor/metabolismo , Anciano , Receptores de Estrógenos/metabolismo , Receptores de Progesterona/metabolismo , Adulto , Medición de Riesgo , Antígeno Ki-67/metabolismo , Antígeno Ki-67/análisis , Anciano de 80 o más AñosRESUMEN
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 1,156 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 basal-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 oestrogen 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.
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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.
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Neoplasias de la Mama , Humanos , Femenino , Neoplasias de la Mama/patología , Receptores de Progesterona/metabolismo , Progesterona/uso terapéutico , Antígeno Ki-67/metabolismo , Receptores de Estrógenos/metabolismo , Estudios de Seguimiento , Receptor ErbB-2/metabolismo , Pronóstico , Biomarcadores de TumorRESUMEN
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.
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Neoplasias de la Mama , Neoplasias de la Mama Triple Negativas , Humanos , Femenino , Neoplasias de la Mama/patología , Linfocitos Infiltrantes de Tumor/patología , Inteligencia Artificial , Pronóstico , Neoplasias de la Mama Triple Negativas/patología , Biomarcadores de Tumor/metabolismoRESUMEN
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.
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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.
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Neoplasias de la Mama , Neoplasias de la Mama Triple Negativas , Humanos , Femenino , Neoplasias de la Mama/patología , Neoplasias de la Mama Triple Negativas/genética , Neoplasias de la Mama Triple Negativas/metabolismo , PronósticoRESUMEN
Although counting mitoses is part of breast cancer grading, concordance studies showed low agreement. Refining the criteria for mitotic counting can improve concordance, particularly when using whole slide images (WSIs). This study aims to refine the methodology for optimal mitoses counting on WSI. Digital images of 595 hematoxylin and eosin stained sections were evaluated. Several morphological criteria were investigated and applied to define mitotic hotspots. Reproducibility, representativeness, time, and association with outcome were the criteria used to evaluate the best area size for mitoses counting. Three approaches for scoring mitoses on WSIs (single and multiple annotated rectangles and multiple digital high-power (×40) screen fields (HPSFs)) were evaluated. The relative increase in tumor cell density was the most significant and easiest parameter for identifying hotspots. Counting mitoses in 3 mm2 area was the most representative regarding saturation and concordance levels. Counting in area <2 mm2 resulted in a significant reduction in mitotic count (P = 0.02), whereas counting in area ≥4 mm2 was time-consuming and did not add a significant rise in overall mitotic count (P = 0.08). Using multiple HPSF, following calibration, provided the most reliable, timesaving, and practical method for mitoses counting on WSI. This study provides evidence-based methodology for defining the area and methodology of visual mitoses counting using WSI. Visual mitoses scoring on WSI can be performed reliably by adjusting the number of monitor screens.
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Neoplasias de la Mama , Neoplasias de la Mama/patología , Eosina Amarillenta-(YS) , Femenino , Hematoxilina , Humanos , Mitosis , Reproducibilidad de los ResultadosRESUMEN
BACKGROUND AND AIMS: Proliferation is an important indicator of breast cancer (BC) prognosis, but is assessed using different approaches. Not all cells in the cell cycle are committed to division. This study aimed to characterise quantitative differences between BC cells in the cell cycle and those in mitosis and assess their relationship with other pathological parameters. METHODS AND RESULTS: A cohort of BC sections (n = 621) was stained with haematoxylin and eosin and immunohistochemistry for Ki-67. The proportion of mitotic cells and Ki-67-positive cells was assessed in the same areas. The Cancer Genome Atlas (TCGA) BC cohort was used to assess MKI-67 transcriptome level and its association with the mitotic counts. The mean proportion of BC cells in the cell cycle was 24% (range = 1-90%), while the mean proportion of BC cells in mitosis was 5% (range = 0-73%). A low proportion of mitoses to whole cycling cells was associated with low histological grade tumours and the luminal A molecular subtype, while tumours with a high proportion of mitoses to the overall cycling cells were associated with triple-negative subtype, larger tumour size, grade 3 tumours and lymph node metastasis. The high mitosis/low Ki-67-positive cells tumours showed a significant association with variables of poor prognosis, including high-grade and triple-negative subtypes. CONCLUSION: The proportion of BC cells in the cell cycle and mitosis is variable. We show that not only the number of cells in the cell cycle or mitosis, but also the difference between them, provides valuable information on tumour aggressiveness.
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Neoplasias de la Mama/patología , Ciclo Celular , Proliferación Celular , Mitosis , Adulto , Anciano , Femenino , Humanos , Persona de Mediana Edad , Índice Mitótico , PronósticoRESUMEN
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.
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Biomarcadores de Tumor , Neoplasias de la Mama , Histonas , Humanos , Femenino , Pronóstico , Neoplasias de la Mama/patología , Neoplasias de la Mama/mortalidad , Neoplasias de la Mama/metabolismo , Neoplasias de la Mama/diagnóstico , Persona de Mediana Edad , Biomarcadores de Tumor/metabolismo , Biomarcadores de Tumor/análisis , Biomarcadores de Tumor/genética , Histonas/metabolismo , Histonas/genética , Anciano , Adulto , Neoplasias de la Mama Triple Negativas/patología , Neoplasias de la Mama Triple Negativas/mortalidad , Neoplasias de la Mama Triple Negativas/metabolismo , Neoplasias de la Mama Triple Negativas/genética , InmunohistoquímicaRESUMEN
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.
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Neoplasias de la Mama , Humanos , Femenino , Neoplasias de la Mama/patología , Pronóstico , Antígeno Ki-67 , Receptor ErbB-2/genética , Receptor ErbB-2/metabolismo , Inteligencia ArtificialRESUMEN
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.
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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.