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Asian Pacific Journal of Cancer... Mar 2022Breast cancer is one of the most frequently diagnosed malignancy among women. Turmeric is isolated from Curcuma longa. Curcumin is main curcuminoid of the turmeric which...
OBJECTIVE
Breast cancer is one of the most frequently diagnosed malignancy among women. Turmeric is isolated from Curcuma longa. Curcumin is main curcuminoid of the turmeric which is a member of Zingiberaceae. In this current study antiproliferative effects of curcumin were investigated in luminal A breast cancer cell line MCF-7 and triple negative breast cancer cell line MDA-MB-231.
METHODS
For this purpose cell viability, cell index values by xCELLigence Real-Time Cell Analysis DP instrument, mitotic index and apoptotic index analysis were used.
RESULTS
Cell viability and cell index values showed that 75 µM concentration of curcumin was IC50 concentration. When IC50 concentration was applied to both cell lines, a significant decrease was observed in the mitotic index values, while a significant increase was observed in the apoptotic index values (p<0.05).
CONCLUSION
Curcumin, which has antiproliferative effects on breast cancer cells, is thought to be effective in cancer treatment.
Topics: Cell Survival; Curcumin; Female; Humans; Mitotic Index; Triple Negative Breast Neoplasms
PubMed: 35345363
DOI: 10.31557/APJCP.2022.23.3.911 -
Modern Pathology : An Official Journal... Mar 2024In recent years, artificial intelligence (AI) has demonstrated exceptional performance in mitosis identification and quantification. However, the implementation of AI in...
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 mm 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.
Topics: Humans; Female; Breast Neoplasms; Ki-67 Antigen; Artificial Intelligence; Mitosis; Mitotic Index
PubMed: 38154653
DOI: 10.1016/j.modpat.2023.100416 -
Anais Da Academia Brasileira de Ciencias Sep 2016The objective of this study was to evaluate the antiproliferative, cytotoxic and genotoxic potential of salty liquid synthetic flavorings of Butter, Cheddar Cheese and...
The objective of this study was to evaluate the antiproliferative, cytotoxic and genotoxic potential of salty liquid synthetic flavorings of Butter, Cheddar Cheese and Onion. The antiproliferative potential (2.9-1500 µg/mL) was assessed by MTT assay after 72h using the human tumor lines SF-295 (glioblastoma), OVCAR-8 (ovarian), HCT-116 (colon) and HL-60 (promyelocytic leukemia) and primary cultures of murine Sarcoma 180 (S180) and peripheral blood mononuclear cells (PBMC). Allium cepa bulbs were exposed to growing respective doses (1 mL and 2 mL). Only Butter and Cheddar flavorings revealed cytotoxic activity on cancer cells, with IC50 values ranging from 125.4 µg/mL (Cheddar - HCT-116) to 402.6 µg/mL (Butter - OVCAR-8). Butter flavoring was the most cytotoxic on PBMC (136.3 µg/mL) and increased cell division rate in relation to the mitotic index but did not cause cellular aberrations. Onion and Cheddar flavorings reduced the mitotic index after 24h and 48h exposure, but only Onion flavoring resulted in cellular aberrations and mitotic spindle abnormalities, such as anaphase and telophase bridges, micronucleated cells, conchicine-metaphases and amplifications. So, Butter, Onion and/or Cheddar flavorings caused significant changes in the division of meristematic cells of A. cepa and presented cytotoxic action even on decontrolled proliferating human tumor cells.
Topics: Animals; Antineoplastic Agents; Butter; Cell Line, Tumor; Cheese; Cytotoxins; Flavoring Agents; Formazans; Humans; Leukocytes, Mononuclear; Meristem; Mice; Mitosis; Mitotic Index; Mutagens; Onions; Tetrazolium Salts
PubMed: 27627067
DOI: 10.1590/0001-3765201620150553 -
Modern Pathology : An Official Journal... Sep 2020Pulmonary neuroendocrine neoplasms are classified by WHO as either typical or atypical carcinoids, large cell (LCNEC) or small cell (SCLC) neuroendocrine carcinoma based...
Pulmonary neuroendocrine neoplasms are classified by WHO as either typical or atypical carcinoids, large cell (LCNEC) or small cell (SCLC) neuroendocrine carcinoma based on mitotic count, morphology, and necrosis assessment. LCNEC with low mitotic count and sharing morphologic features with carcinoids are in a gray zone for classification and their rare prevalence and the paucity of studies precludes proper validation of the current grading system. In this study, we aim to investigate their clinicopathological and transcriptomic profiles. Lung resection specimens obtained from 18 patients diagnosed with carcinoids or LCNEC were selected. Four of them were characterized as borderline tumors based on a mitotic rate ranging between 10 and 30 mitoses per 2 mm. Comprehensive morphological and immunohistochemical (IHC) evaluation was performed and tumor-based transcriptomic profiles were analyzed through unsupervised clustering. Clustering analysis revealed two distinct molecular groups characterized by low (C1) and high (C2) proliferation. C1 was comprised of seven carcinoids and three borderline tumors, while C2 was comprised of seven LCNEC and one borderline tumor. Furthermore, patients in cluster C1 had a better recurrence-free survival compared with patients in cluster C2 (20% vs 75%). Histological features, IHC profile, and molecular analysis showed that three out of four borderline tumors showed features consistent with carcinoids. Therefore, our findings convey that the current diagnostic guidelines are suboptimal for classification of pulmonary neuroendocrine tumors with increased proliferative index and carcinoid-like morphology. These results support the emerging concept that neuroendocrine tumors with carcinoid-like features and mitotic count of <20 mitoses per 2 mm should be regarded as pulmonary carcinoids instead of LCNEC.
Topics: Aged; Biomarkers, Tumor; Carcinoid Tumor; Female; Gene Expression Regulation, Neoplastic; Humans; Lung; Lung Neoplasms; Male; Middle Aged; Mitosis; Mitotic Index; Retrospective Studies; Transcriptome
PubMed: 32291397
DOI: 10.1038/s41379-020-0538-8 -
Archives of Pathology & Laboratory... Nov 2020Mitotic count is an important histologic criterion for grading and prognostication in phyllodes tumors (PTs). Counting mitoses is a routine practice for pathologists...
CONTEXT.—
Mitotic count is an important histologic criterion for grading and prognostication in phyllodes tumors (PTs). Counting mitoses is a routine practice for pathologists evaluating neoplasms, but different microscopes, variable field selection, and areas have led to possible misclassification.
OBJECTIVE.—
To determine whether 10 high-power fields (HPFs) or whole slide mitotic counts correlated better with PT clinicopathologic parameters using digital pathology (DP). We also aimed to find out whether this study might serve as a basis for an artificial intelligence (AI) protocol to count mitosis.
DESIGN.—
Representative slides were chosen from 93 cases of PTs diagnosed between 2014 and 2015. The slides were scanned and viewed with DP. Mitotic counting was conducted on the whole slide image, before choosing 10 HPFs and demarcating the tumor area in DP. Values of mitoses per millimeter squared were used to compare results between 10 HPFs and the whole slide. Correlations with clinicopathologic parameters were conducted.
RESULTS.—
Both whole slide counting of mitoses and 10 HPFs had similar statistically significant correlation coefficients with grade, stromal atypia, and stromal hypercellularity. Neither whole slide mitotic counts nor mitoses per 10 HPFs showed statistically significant correlations with patient age and tumor size.
CONCLUSIONS.—
Accurate mitosis counting in breast PTs is important for grading. Exploring machine learning on digital whole slides may influence approaches to training, testing, and validation of a future AI algorithm.
Topics: Adult; Artificial Intelligence; Breast Neoplasms; Cytodiagnosis; Female; Humans; Microscopy; Middle Aged; Mitosis; Mitotic Index; Pathology, Clinical; Phyllodes Tumor; Reproducibility of Results; Sensitivity and Specificity
PubMed: 32150458
DOI: 10.5858/arpa.2019-0435-OA -
Journal of Clinical Pathology Jan 1994To verify the correlation between MIB-1, Ki67, and proliferating cell nuclear antigen (PCNA-PC10) scores and S-phase fraction in intermediate grade non-Hodgkin's...
AIMS
To verify the correlation between MIB-1, Ki67, and proliferating cell nuclear antigen (PCNA-PC10) scores and S-phase fraction in intermediate grade non-Hodgkin's lymphomas (Working Formulation F); and their reliability in differently processed tissues.
METHODS
Forty one non-Hodgkin's lymphomas were classified as (F) intermediate grade malignant lymphomas according to the Working Formulation; mitotic counts and percentage of large cells were assessed for each case. Sections from formalin fixed, paraffin wax embedded tissues were stained with anti MIB-1 monoclonal antibody, after microwave oven processing, and anti-PCNA (PC10) monoclonal antibody using an avidin-biotin immunoperoxidase (ABC) method. One thousand cells from 10 representative fields were scored. Frozen sections from surgical specimens were stained with Ki67 monoclonal antibody using the ABC method; the fraction of Ki67 positive cells was calculated scoring 1000 cells. Flow cytometry analysis (FCM) was performed on cell suspensions from fresh tissues. Correlations between data were estimated using linear regression.
RESULTS
A linear correlation was found between MIB-1 and Ki67 scores (r = 0.92; p < 0.00001); between MIB-1 and PCNA scores (r = 0.79; p < 0.00001); and between MIB-1 score and S-phase fraction (r = 0.51; p = 0.0006). A linear correlation was also found between Ki67 and PCNA scores (r = 0.85; p < 0.00001); between Ki67 score and S-phase fraction (r = 0.6; p = 0.0002); and between PCNA score and S-phase fraction (r = 0.74; p < 0.00001). A correlation was found between mitotic counts and MIB-1 (r = 0.56; p = 0.0001), PCNA (r = 0.51; p = 0.0007), or Ki67 scores (r = 0.47; p = 0.002); between the percentage of large cells and MIB-1 (r = 0.49; p = 0.0009), PCNA (r = 0.6; p = 0.00003), and Ki67 scores (r = 0.53; p = 0.0003) and S-phase fraction (r = 0.55; p = 0.0002).
CONCLUSION
MIB-1, Ki67, and PCNA (PC10) scores and S-phase fraction are highly correlated and equally well represent the proliferative activity of intermediate grade non-Hodgkin's lymphomas in differently processed material. MIB-1 and PCNA stains can be applied even on small biopsy specimens. MIB-1 produces homogenous staining without background; it also strongly stains mitotic figures. It can be performed on routinely processed tissues, permitting the simultaneous evaluation of the morphology and tumour cell kinetics. The wide standard deviations of the proliferative indices found for intermediate grade NHL suggest that this category probably includes various degrees of malignancy.
Topics: Antibodies, Monoclonal; Antigens, Neoplasm; DNA, Neoplasm; Female; Flow Cytometry; Humans; Immunoenzyme Techniques; Ki-67 Antigen; Lymphoma, Non-Hodgkin; Male; Middle Aged; Mitotic Index; Neoplasm Proteins; Nuclear Proteins; Proliferating Cell Nuclear Antigen; S Phase
PubMed: 7907607
DOI: 10.1136/jcp.47.1.18 -
Virchows Archiv : An International... Jan 2024Oral epithelial dysplasia (OED) is diagnosed and graded using a range of histological features, making grading subjective and challenging. Mitotic counting and...
Oral epithelial dysplasia (OED) is diagnosed and graded using a range of histological features, making grading subjective and challenging. Mitotic counting and phosphohistone-H3 (PHH3) staining have been used for the prognostication of various malignancies; however, their importance in OED remains unexplored. This study conducts a quantitative analysis of mitotic activity in OED using both haematoxylin and eosin (H&E)-stained slides and immunohistochemical (IHC) staining for PHH3. Specifically, the diagnostic and prognostic importance of mitotic number, mitotic type and intra-epithelial location is evaluated. Whole slide images (WSI) of OED (n = 60) and non-dysplastic tissue (n = 8) were prepared for analysis. Five-year follow-up data was collected. The total number of mitosis (TNOM), mitosis type and intra-epithelial location was manually evaluated on H&E images and a digital mitotic count performed on PHH3-stained WSI. Statistical associations between these features and OED grade, malignant transformation and OED recurrence were determined. Mitosis count increased with grade severity (H&E: p < 0.005; IHC: p < 0.05), and grade-based differences were seen for mitosis type and location (p < 0.05). The ratio of normal-to-abnormal mitoses was higher in OED (1.61) than control (1.25) and reduced with grade severity. TNOM, type and location were better predictors when combined with histological grading, with the most prognostic models demonstrating an AUROC of 0.81 for transformation and 0.78 for recurrence, exceeding conventional grading. Mitosis quantification and PHH3 staining can be an adjunct to conventional H&E assessment and grading for the prediction of OED prognosis. Validation on larger multicentre cohorts is needed to establish these findings.
Topics: Humans; Histones; Prognosis; Mitotic Index; Biomarkers, Tumor; Neoplasm Grading; Mitosis; Phosphorylation
PubMed: 37882821
DOI: 10.1007/s00428-023-03668-6 -
Hormones & Cancer Jun 201417β-Estradiol (estrogen), through receptor binding and activation, is required for mammary gland development. Estrogen stimulates epithelial proliferation in the...
17β-Estradiol (estrogen), through receptor binding and activation, is required for mammary gland development. Estrogen stimulates epithelial proliferation in the mammary gland, promoting ductal elongation and morphogenesis. In addition to a developmental role, estrogen promotes proliferation in tumorigenic settings, particularly breast cancer. The proliferative effects of estrogen in the normal breast and breast tumors are attributed to estrogen receptor α. Although in vitro studies have demonstrated that the G protein-coupled estrogen receptor (GPER, previously called GPR30) can modulate proliferation in breast cancer cells both positively and negatively depending on cellular context, its role in proliferation in the intact normal or malignant breast remains unclear. Estrogen-induced GPER-dependent proliferation was assessed in the immortalized nontumorigenic human breast epithelial cell line, MCF10A, and an ex vivo organ culture model employing human breast tissue from reduction mammoplasty or tumor resections. Stimulation by estrogen and the GPER-selective agonist G-1 increased the mitotic index in MCF10A cells and proportion of cells in the cell cycle in human breast and breast cancer explants, suggesting increased proliferation. Inhibition of candidate signaling pathways that may link GPER activation to proliferation revealed a dependence on Src, epidermal growth factor receptor transactivation by heparin-bound EGF and subsequent ERK phosphorylation. Proliferation was not dependent on matrix metalloproteinase cleavage of membrane-bound pro-HB-EGF. The contribution of GPER to estrogen-induced proliferation in MCF10A cells and breast tissue was confirmed by the ability of GPER-selective antagonist G36 to abrogate estrogen- and G-1-induced proliferation, and the ability of siRNA knockdown of GPER to reduce estrogen- and G-1-induced proliferation in MCF10A cells. This is the first study to demonstrate GPER-dependent proliferation in primary normal and malignant human tissue, revealing a role for GPER in estrogen-induced breast physiology and pathology.
Topics: Breast; Breast Neoplasms; Cell Line, Tumor; Cell Proliferation; Epithelial Cells; Estradiol; Extracellular Signal-Regulated MAP Kinases; Female; Humans; Mitotic Index; Phosphorylation; Receptors, Estrogen; Receptors, G-Protein-Coupled; Signal Transduction; Transcriptional Activation
PubMed: 24718936
DOI: 10.1007/s12672-014-0174-1 -
Cancer Apr 2002Cell proliferation is a major determinant of the biologic behavior of breast carcinoma. MIB-1 monoclonal antibody is a promising tool for determining cell proliferation... (Comparative Study)
Comparative Study Review
BACKGROUND
Cell proliferation is a major determinant of the biologic behavior of breast carcinoma. MIB-1 monoclonal antibody is a promising tool for determining cell proliferation on routine histologic material. The objectives of this study were to compare MIB-1 evaluation to other methods of measuring cell proliferation, with a view to refining the cutoff used to classify tumors with low and high proliferation rates in therapeutic trials.
METHODS
One hundred eighty-five invasive breast carcinomas were evaluated for cell proliferation by determining monoclonal antibody MIB-1 staining, histologic parameters (Scarff-Bloom-Richardson grade and mitotic index) on paraffin sections, S-phase fraction (SPF) by flow cytometry, and thymidine-kinase (TK) content of frozen samples.
RESULTS
There was a high correlation (P = 0.0001) between the percentage of MIB-1 positive tumor cells and SPF, TK, histologic grade, and the mitotic index. Multivariate analyses including MIB-1 at 5 different cutoffs (10%, 15%, 17% [median], 20%, 25%) and the other proliferative markers showed that the optimal MIB-1 cutoff was 25% and that the mitotic index was the proliferative variable that best discriminated between low and high MIB-1 samples. A MIB-1 cutoff of 25% adequately identified highly proliferative tumors. Conversely, with a MIB-1 cutoff of 10%, few tumors with low proliferation were misclassified.
CONCLUSIONS
The choice of MIB-1 cutoff depends on the following clinical objective: if MIB-1 is used to exclude patients with slowly proliferating tumors from chemotherapeutic protocols, a cutoff of 10% will help to avoid overtreatment. In contrast, if MIB-1 is used to identify patients sensitive to chemotherapy protocols, it is preferable to set the cutoff at 25%. The MIB-1 index should be combined with some other routinely used proliferative markers, such as the mitotic index.
Topics: Adult; Aged; Aged, 80 and over; Antibodies, Monoclonal; Antigens, Nuclear; Biomarkers, Tumor; Biopsy; Breast Neoplasms; Carcinoma, Ductal, Breast; Carcinoma, Lobular; Cell Division; DNA, Neoplasm; Female; Flow Cytometry; Humans; Immunoenzyme Techniques; Ki-67 Antigen; Middle Aged; Mitotic Index; Neoplasm Invasiveness; Neoplasm Staging; Nuclear Proteins; Predictive Value of Tests; Receptors, Estrogen; Receptors, Progesterone; Thymidine Kinase
PubMed: 12001111
DOI: 10.1002/cncr.10458 -
A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images.Scientific Reports Mar 2021The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates...
The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep CNN based multi-phase mitosis detection framework "MP-MitDet" for mitotic nuclei identification in breast cancer histopathological images. The workflow constitutes: (1) label-refiner, (2) tissue-level mitotic region selection, (3) blob analysis, and (4) cell-level refinement. We developed an automatic label-refiner to represent weak labels with semi-sematic information for training of deep CNNs. A deep instance-based detection and segmentation model is used to explore probable mitotic regions on tissue patches. More probable regions are screened based on blob area and then analysed at cell-level by developing a custom CNN classifier "MitosRes-CNN" to filter false mitoses. The performance of the proposed "MitosRes-CNN" is compared with the state-of-the-art CNNs that are adapted to cell-level discrimination through cross-domain transfer learning and by adding task-specific layers. The performance of the proposed framework shows good discrimination ability in terms of F-score (0.75), recall (0.76), precision (0.71) and area under the precision-recall curve (0.78) on challenging TUPAC16 dataset. Promising results suggest good generalization of the proposed framework that can learn characteristic features from heterogenous mitotic nuclei.
Topics: Automation; Benchmarking; Breast Neoplasms; Cell Nucleus; Datasets as Topic; Female; Humans; Image Processing, Computer-Assisted; Mitosis; Mitotic Index; Neoplasm Grading; Neural Networks, Computer
PubMed: 33737632
DOI: 10.1038/s41598-021-85652-1