Invasive Ductal Carcinoma Tissues Research Articles

The functional role of Nudt2 in human triple negative breast cancer.

The main known function of Nudix hydrolase 2 (Nudt2) is to hydrolyze the secondary messenger diadenosine 5', 5'''-p1, p4-tetraphosphate (Ap4A). In this study we examined the role of Nudt2 in breast carcinoma through its expression in human invasive ductal carcinoma tissues, and its functions in human triple negative breast cancer (TNBC) cell lines. A significantly higher expression of Nudt2 was observed in human invasive ductal carcinoma tissues compared to that in normal breast tissue. Knockdown of Nudt2 in TNBC cell lines resulted in a significant reduction in cellular proliferation via the Ki67 marker, accompanied by G0/G1 phase cell cycle arrest, in the migration and invasion of these cells and in tumorigenicity and anchorage-independent growth. It can therefore be concluded that Nudt2 plays a significant role in promoting TNBC growth.

Intraoperative Evaluation of Breast Tissues During Breast Cancer Operations Using the MasSpec Pen

Surgery with complete tumor resection remains the main treatment option for patients with breast cancer. Yet, current technologies are limited in providing accurate assessment of breast tissue in vivo, warranting development of new technologies for surgical guidance. To evaluate the performance of the MasSpec Pen for accurate intraoperative assessment of breast tissues and surgical margins based on metabolic and lipid information. In this diagnostic study conducted between February 23, 2017, and August 19, 2021, the mass spectrometry-based device was used to analyze healthy breast and invasive ductal carcinoma (IDC) banked tissue samples from adult patients undergoing breast surgery for ductal carcinomas or nonmalignant conditions. Fresh-frozen tissue samples and touch imprints were analyzed in a laboratory. Intraoperative in vivo and ex vivo breast tissue analyses were performed by surgical staff in operating rooms (ORs) within 2 different hospitals at the Texas Medical Center. Molecular data were used to build statistical classifiers. Prediction results of tissue analyses from classification models were compared with gross assessment, frozen section analysis, and/or final postoperative pathology to assess accuracy. All data acquired from the 143 banked tissue samples, including 79 healthy breast and 64 IDC tissues, were included in the statistical analysis. Data presented rich molecular profiles of healthy and IDC banked tissue samples, with significant changes in relative abundances observed for several metabolic species. Statistical classifiers yielded accuracies of 95.6%, 95.5%, and 90.6% for training, validation, and independent test sets, respectively. A total of 25 participants enrolled in the clinical, intraoperative study; all were female, and the median age was 58 years (IQR, 44-66 years). Intraoperative testing of the technology was successfully performed by surgical staff during 25 breast operations. Of 273 intraoperative analyses performed during 25 surgical cases, 147 analyses from 22 cases were subjected to statistical classification. Testing of the classifiers on 147 intraoperative mass spectra yielded 95.9% agreement with postoperative pathology results. The findings of this diagnostic study suggest that the mass spectrometry-based system could be clinically valuable to surgeons and patients by enabling fast molecular-based intraoperative assessment of in vivo and ex vivo breast tissue samples and surgical margins.

Using hybrid pre-trained models for breast cancer detection.

Breast cancer is a prevalent and life-threatening disease that affects women globally. Early detection and access to top-notch treatment are crucial in preventing fatalities from this condition. However, manual breast histopathology image analysis is time-consuming and prone to errors. This study proposed a hybrid deep learning model (CNN+EfficientNetV2B3). The proposed approach utilizes convolutional neural networks (CNNs) for the identification of positive invasive ductal carcinoma (IDC) and negative (non-IDC) tissue using whole slide images (WSIs), which use pre-trained models to classify breast cancer in images, supporting pathologists in making more accurate diagnoses. The proposed model demonstrates outstanding performance with an accuracy of 96.3%, precision of 93.4%, recall of 86.4%, F1-score of 89.7%, Matthew's correlation coefficient (MCC) of 87.6%, the Area Under the Curve (AUC) of a Receiver Operating Characteristic (ROC) curve of 97.5%, and the Area Under the Curve of the Precision-Recall Curve (AUPRC) of 96.8%, which outperforms the accuracy achieved by other models. The proposed model was also tested against MobileNet+DenseNet121, MobileNetV2+EfficientNetV2B0, and other deep learning models, proving more powerful than contemporary machine learning and deep learning approaches.

Label-free multimodal electro-thermo-mechanical (ETM) phenotyping as a novel biomarker to differentiate between normal, benign, and cancerous breast biopsy tissues.

Technologies for quick and label-free diagnosis of malignancies from breast tissues have the potential to be a significant adjunct to routine diagnostics. The biophysical phenotypes of breast tissues, such as its electrical, thermal, and mechanical properties (ETM), have the potential to serve as novel markers to differentiate between normal, benign, and malignant tissue. We report a system-of-biochips (SoB) integrated into a semi-automated mechatronic system that can characterize breast biopsy tissues using electro-thermo-mechanical sensing. The SoB, fabricated on silicon using microfabrication techniques, can measure the electrical impedance (Z), thermal conductivity (K), mechanical stiffness (k), and viscoelastic stress relaxation (%R) of the samples. The key sensing elements of the biochips include interdigitated electrodes, resistance temperature detectors, microheaters, and a micromachined diaphragm with piezoresistive bridges. Multi-modal ETM measurements performed on formalin-fixed tumour and adjacent normal breast biopsy samples from N = 14 subjects were able to differentiate between invasive ductal carcinoma (malignant), fibroadenoma (benign), and adjacent normal (healthy) tissues with a root mean square error of 0.2419 using a Gaussian process classifier. Carcinoma tissues were observed to have the highest mean impedance (110018.8 ± 20293.8 Ω) and stiffness (0.076 ± 0.009 kNm-1) and the lowest thermal conductivity (0.189 ± 0.019 Wm-1K-1) amongst the three groups, while the fibroadenoma samples had the highest percentage relaxation in normalized load (47.8 ± 5.12%). The work presents a novel strategy to characterize the multi-modal biophysical phenotype of breast biopsy tissues to aid in cancer diagnosis from small-sized tumour samples. The methodology envisions to supplement the existing technology gap in the analysis of breast tissue samples in the pathology laboratories to aid the diagnostic workflow.

B-199 Performance of the MasSpec Pen for Intraoperative Margin Evaluation During Breast Conserving Surgeries

Abstract Background Breast cancer is the second leading cause of cancer deaths among females worldwide. Surgical resection is the most common treatment option for women diagnosed with breast cancers. Many women with in situ disease or early invasive breast cancer are candidates for breast conserving surgery (BCS), which involves removing the lesion of interest and preserving the rest of the breast. One of the greatest challenges a breast cancer surgeon faces is to achieve negative margins while optimizing aesthetic outcomes. Negative margins are highly desirable as they offer the greatest potential for prolonged disease-free survival. Accurate intraoperative surgical margin evaluation is therefore critical during BSC. Molecular technologies offer the opportunity to incorporate cancer-specific biomarkers into clinical decision-making for improved cancer detection. Our team has reported the development of an innovative handheld mass spectrometry (MS) probe, the MasSpec Pen (MSPen), for rapid and gentle molecular analysis of human tissues, including breast cancer tissues. The MSPen uses water to gently extract molecules from tissues upon contact, which are then analyzed by MS and statistical classifiers to provide a predictive classification within seconds. Here, we evaluate the performance of the MSPen for intraoperative detection of breast cancer. Methods The MSPen coupled to an Orbitrap Exploris 120 (Thermo Scientific) is being used to analyze both normal breast and invasive lobular carcinoma (ILC) banked tissues. Tissue samples are first thawed prior to analysis. After analysis, the region analyzed is demarcated, and the samples snap-frozen, sectioned, H&E stained, following evaluation by a pathologist. We will then apply the least absolute shrinkage and selector operator (lasso) method to build classification models for distinguishing normal breast and ILC using the histologically validated mass spectra. The MSPen system is also implemented in the operating room for testing within surgical workflow. Analyses will be carried out during BCS surgeries for patients with ILC. The ILC classifier generated from banked tissues will be used to predict on data obtained from the intraoperative analyses and validated against final histopathological reports. Results To date, we have analyzed 213 banked human breast tissues using the MSPen, including healthy breast (n = 79), invasive ductal carcinoma (IDC) (n = 64), healthy lymph nodes (n = 26), and lymph nodes with metastatic IDC (n = 44). The rich mass spectral data obtained from breast tissues showed high relative abundance of various metabolites and lipid species that have been previously described as potentially diagnostic of breast cancer. Using this data, we built classifiers that discriminate cancer and normal breast tissues with high accuracy (overall 94%) for primary breast IDC. In a pilot clinical study, we demonstrated feasibility for ex vivo and in vivo tissue analysis using the MSPen platform during 29 breast surgeries. We are currently expanding our study to detect ILC using banked tissues and in patient tissues intraoperatively. Conclusion Our study in breast cancer provides evidence that the MSPen enables molecular-based diagnosis of IDC tissues using excised specimens and in in vivo and ex vivo analysis performed by surgeons intraoperatively. Current effort including ILC is expected to show similar capabilities for patients with ILC.

Breast tissue analysis using a clinically compatible combined time-resolved fluorescence and diffuse reflectance (TRF-DR) system.

This work aims to develop a clinically compatible system that can perform breast tissue analysis in a more time efficient process than conventional histopathological assessment. The potential for such a system to be used in vivo in the operating room or surgical suite to improve patient outcome is investigated. In this work, 80 matched pairs of invasive ductal carcinoma and adjacent normal breast tissue were measured in a combined time-resolved fluorescence and diffuse reflectance (DA) system. Following measurement, the fluorescence intensity of collagen and flavin adenine dinucleotide (FAD); the fluorescence lifetime of collagen, nicotinamide adenine dinucleotide (NADH), and FAD; the DA; absorption coefficient; and reduced scattering coefficient were extracted. Samples then underwent histological processing and H&E staining to classify composition as tumor, fibroglandular, and/or adipose tissue. Statistically significant differences in the collagen and FAD fluorescence intensity, collagen and FAD fluorescence lifetime, DA, and scattering coefficient were found between each tissue group. The NADH fluorescence lifetime and absorption coefficient were statistically different between the tumor and fibroglandular groups, and the tumor and adipose groups. While many breast tissue analysis studies label fibroglandular and adipose together as "normal" breast tissue, this work indicates that some differences between tumor and fibroglandular tissue are not the same as differences between tumor and adipose tissue. Observations of the reduced scatter coefficient may also indicate further classification to include fibro-adipose may be necessary. Future work would benefit from the additional tissue classification. With observable differences in optical parameters between the three tissue types, this system shows promise as a breast analysis tool in a clinical setting. With further work involving samples of mixed composition, this combined system could potentially be used intraoperatively for rapid margin assessment.

Assessing pathological features of breast cancer via the multimodal information of multiphoton and Raman imaging

For unveiling the pathological evolution of breast cancer, nonlinear multiphoton microscopic (MPM) and confocal Raman microspectral imaging (CRMI) techniques were both utilized to address the structural and constitutional characteristics of healthy (H), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC) tissues. MPM-based techniques, including two-photon excited fluorescence (TPEF) and second harmonic generation (SHG), visualized label-free and the fine structure of breast tissue. Meanwhile, CRMI not only presented the chemical images of investigated samples with the K-mean cluster analysis method (KCA), but also pictured the distribution of components in the scanned area through univariate imaging. MPM images illustrated that the cancer cells first arranged around the basement membrane of the duct, then proliferated to fill the lumens of the duct, and finally broke through the basement membrane to infiltrate into the stroma. Although the Raman imaging failed to visualize the cell structure with high resolution, it explained spectroscopically the gradual increase of nucleic acid and protein components inside the ducts as cancer cells proliferated, and displayed the distribution pattern of each biological component during the evolution of breast cancer. Thus, the combination of MPM and CRMI provided new insights into the on-site pathological diagnosis of malignant breast cancer, also ensured technical support for the development of multimodal optical imaging techniques for precise histopathological analysis.

Transcriptome analysis reveals differences in cell cycle, growth and migration related genes that distinguish fibroblasts derived from pre-invasive and invasive breast cancer.

As the most common form of pre-invasive breast cancer, ductal carcinoma in situ (DCIS) affects over 50,000 women in the US annually. Despite standardized treatment involving lumpectomy and radiation therapy, up to 25% of patients with DCIS experience disease recurrence often with invasive ductal carcinoma (IDC), indicating that a subset of patients may be under-treated. As most DCIS cases will not progress to invasion, many patients may experience over-treatment. By understanding the underlying processes associated with DCIS to IDC progression, we can identify new biomarkers to determine which DCIS cases may become invasive and improve treatment for patients. Accumulation of fibroblasts in IDC is associated with disease progression and reduced survival. While fibroblasts have been detected in DCIS, little is understood about their role in DCIS progression. We sought to determine 1) whether DCIS fibroblasts were similar or distinct from normal and IDC fibroblasts at the transcriptome level, and 2) the contributions of DCIS fibroblasts to breast cancer progression. Fibroblasts underwent transcriptome profiling and pathway analysis. Significant DCIS fibroblast-associated genes were further analyzed in existing breast cancer mRNA databases and through tissue array immunostaining. Using the sub-renal capsule graft model, fibroblasts from normal breast, DCIS and IDC tissues were co-transplanted with DCIS.com breast cancer cells. Through transcriptome profiling, we found that DCIS fibroblasts were characterized by unique alterations in cell cycle and motility related genes such as PKMYT1, TGF-α, SFRP1 and SFRP2, which predicted increased cell growth and invasion by Ingenuity Pathway Analysis. Immunostaining analysis revealed corresponding increases in expression of stromal derived PKMYT1, TGF-α and corresponding decreases in expression of SFRP1 and SFRP2 in DCIS and IDC tissues. Grafting studies in mice revealed that DCIS fibroblasts enhanced breast cancer growth and invasion associated with arginase-1+ cell recruitment. DCIS fibroblasts are phenotypically distinct from normal breast and IDC fibroblasts, and play an important role in breast cancer growth, invasion, and recruitment of myeloid cells. These studies provide novel insight into the role of DCIS fibroblasts in breast cancer progression and identify some key biomarkers associated with DCIS progression to IDC, with important clinical implications.

Comprehensive proteome, phosphoproteome and kinome characterization of luminal A breast cancer.

Breast cancer is one of the most frequently occurring malignant cancers worldwide. Invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) are the two most common histological subtypes of breast cancer. In this study, we aimed to deeply explore molecular characteristics and the relationship between IDC and ILC subtypes in luminal A subgroup of breast cancer using comprehensive proteomics and phosphoproteomics analysis. Cancer tissues and noncancerous adjacent tissues (NATs) with the luminal A subtype (ER- and PR-positive, HER2-negative) were obtained from paired IDC and ILC patients respectively. Label-free quantitative proteomics and phosphoproteomics methods were used to detect differential proteins and the phosphorylation status between 10 paired breast cancer and NATs. Then, the difference in protein expression and its phosphorylation between IDC and ILC subtypes were explored. Meanwhile, the activation of kinases and their substrates was also revealed by Kinase-Substrate Enrichment Analysis (KSEA). In the luminal A breast cancer, a total of 5,044 high-confidence proteins and 3,808 phosphoproteins were identified from 10 paired tissues. The protein phosphorylation level in ILC tissues was higher than that in IDC tissues. Histone H1.10 was significantly increased in IDC but decreased in ILC, Conversely, complement C4-B and Crk-like protein were significantly decreased in IDC but increased in ILC. Moreover, the increased protein expression of Septin-2, Septin-9, Heterogeneous nuclear ribonucleoprotein A1 and Kinectin but reduce of their phosphorylation could clearly distinguish IDC from ILC. In addition, IDC was primarily related to energy metabolism and MAPK pathway, while ILC was more closely involved in the AMPK and p53/p21 pathways. Furthermore, the kinomes in IDC were primarily significantly activated in the CMGC groups. Our research provides insights into the molecular characterization of IDC and ILC and contributes to discovering novel targets for further drug development and targeted treatment.

Leucine-rich repeats and immunoglobulin-like domains protein 1 (LRIG1) is downregulated in Invasive ductal carcinoma and potential prognostic marker of breast cancer.

LRIG1 belongs to the family of transmembrane proteins containing leucine-rich repeats. LRIGs are considered as tumor suppressors as they negatively regulate receptor tyrosine kinases. The role of LRIG1 as an EGFR regulator makes it an important marker to be studied in various epithelial-derived cancers. LRIG1 expression was determined in Erbb2 + cell lines by western blotting, and cell motility was examined by cell migration assay. The AKT/GSK3-β/β-catenin pathway was determined in the presence of LRIG1 and Erbb2 by using western blotting. So far, no study has reported the expression of LRIG1 in benign forms of tumor such as fibroadenoma. The current study aims to analyze LRIG1 expression in fibroadenoma and invasive ductal carcinoma (IDC) tissues. In this study, we compared the LRIG1 expression with different clinicopathological parameters of patients having IDC or fibroadenoma. LRIG1 expression was low in Erbb2+ cell lines, and more cell motility was observed. The AKT/GSK3-β/β-catenin pathway was activated when LRIG1 was downregulated; consequently, Erbb2 was upregulated. Our results indicated that LRIG1 expression can be significantly correlated with age, Nottingham index, and Her2/neu status of cancer. The expression of LRIG1 in IDC and fibroadenoma were found to be significantly different. The fibroadenoma tissue sections were found to express LRIG1 more intensely as compared to the IDC sections, which are in line with the studies reporting reduced copy number of the gene either due to gene deletion or transcriptional inhibition. This further supports that the downregulation of LRIG1 may lead to malignant tumor acting as a tumor suppressor.

Comparison of mammosphere formation from stem-like cells of normal breast, malignant primary breast tumors, and MCF-7 cell line

BackgroundMammosphere formation assay has become a versatile tool to quantify the activity of putative breast cancer stem cells in non-adherent in vitro cultures. However, optimizing the suspension culture system is crucial to establish mammosphere cultures from primary breast tumors.MethodsThis study aimed at determining the self-renewal and sphere-forming potential of breast cancer stem-like cells derived from human primary invasive ductal carcinoma and normal breast tissue samples, and MCF-7 breast cancer cell line using an optimal suspension culture system. Mammosphere-forming efficiency of the mammospheres generated from the tissue samples and cell line were compared. We evaluated the expression of CD44+/CD24−/low and CD49f+/EpCAM−/low phenotypes in the stem-like cells by flow cytometry. CK-18, CK-19, α-SMA, and EpCAM marker expression was assessed using immunohistochemical staining.ResultsBreast epithelial cells isolated from the three samples formed two-dimensional spheroids in suspension cultures. Interestingly, mammospheres formed from patient-derived primary breast tumors were enriched in breast cancer stem-like cells with the phenotype CD44+/CD24−/low and exhibited a relatively more number of large spheres when compared to the normal breast stem cells. MCF-7-derived SCs were more aggressive and resulted in the formation of a significantly higher number of spheroids. The expression of CK-18/CK-19 and α-SMA/EpCAM proteins was confirmed in breast cancer tissues.ConclusionsThus, the use of primary tumor specimens and breast cancer cell lines as suitable models for elucidating the breast cancer stem cell activity was validated using mammosphere culture system.

Pro-Apoptotic and Anti-Invasive Properties Underscore the Tumor-Suppressing Impact of Myoglobin on a Subset of Human Breast Cancer Cells.

The expression of myoglobin (MB), well known as the oxygen storage and transport protein of myocytes, is a novel hallmark of the luminal subtype in breast cancer patients and correlates with better prognosis. The mechanisms by which MB impacts mammary tumorigenesis are hitherto unclear. We aimed to unravel this role by using CRISPR/Cas9 technology to generate MB-deficient clones of MCF7 and SKBR3 breast cancer cell lines and subsequently characterize them by transcriptomics plus molecular and functional analyses. As main findings, loss of MB at normoxia upregulated the expression of cell cyclins and increased cell survival, while it prevented apoptosis in MCF7 cells. Additionally, MB-deficient cells were less sensitive to doxorubicin but not ionizing radiation. Under hypoxia, the loss of MB enhanced the partial epithelial to mesenchymal transition, thus, augmenting the migratory and invasive behavior of cells. Notably, in human invasive mammary ductal carcinoma tissues, MB and apoptotic marker levels were positively correlated. In addition, MB protein expression in invasive ductal carcinomas was associated with a positive prognostic value, independent of the known tumor suppressor p53. In conclusion, we provide multiple lines of evidence that endogenous MB in cancer cells by itself exerts novel tumor-suppressive roles through which it can reduce cancer malignancy.

Transcription factor SOX3 upregulated pro-apoptotic genes expression in human breast cancer.

Sex-determining region Y-box 3 (SOX3) protein, a SOX transcriptions factors group, has been identified as a key regulator in several diseases, including cancer. Downregulation of transcriptions factors in invasive ductal carcinoma (IDC) can interfere in neoplasia development, increasing its aggressiveness. We investigated SOX3 protein expression and its correlation with apoptosis in the MDA-MB-231 cell line, as SOX3 and Pro-Caspase-3 immunoexpression in paraffin-embedded invasive ductal carcinoma tissue samples from patients (n = 27). Breast cancer cell line MDA-MD-231 transfected with pEF1-SOX3 + and pEF1-Empty vector followed by cytotoxicity assay (MTT), Annexin-V FITC PI for apoptosis percentage assessment by flow cytometry, qPCR for apoptotic-related gene expression, immunofluorescence, and immunohistochemistry to SOX3 immunolocalization in culture cells, and paraffin-embedded invasive ductal carcinoma tissue samples. Apoptotic rate was higher in cells transfected with pEF1-SOX3 + (56%) than controls (10%). MDA-MB-231 transfected with pEF1-SOX3 + presented upregulation of pro-apoptotic mRNA from CASP3, CASP8, CASP9, and BAX genes, contrasting with downregulation antiapoptotic mRNA from BCL2, compared to non-transfected cells and cells transfected with pEF1-empty vector (p < 0.005). SOX3 protein nuclear expression was detected in 14% (4/27 cases) of ductal carcinoma cases, and pro-Caspase-3 expression was positive in 50% of the cases. Data suggest that SOX3 transcription factor upregulates apoptosis in breast cancer cell line MDA-MB-231, and has a down nuclear expression in ductal carcinoma cases, and need to be investigated as a tumor suppressor protein, and its loss of expression and non-nuclear action turn the cells resistant to apoptosis. Further studies are necessary to understand how SOX3 protein regulates the promoter regions of genes involved in apoptosis.

Detection of High-Risk Human Papillomavirus DNA in Invasive Ductal Carcinoma Specimens.

According to several studies, there is an association between human papillomavirus (HPV) and breast cancer. Therefore, detection and genotyping of HPV seem important. The present study aimed to investigate the presence of HPV DNA in breast tissues by analyzing the L1 gene. This case-control study was conducted on 63 formalin-fixed paraffin-embedded (FFPE) tissues of invasive ductal carcinoma (IDC) as the case group and 32 FFPE tissues of fibroadenoma as the control group. HPV DNA was detected using the polymerase chain reaction assay. Positive samples were then subjected to genotyping. All statistical analyses were performed in SPSS version 22.0. The patients' age ranged from 15 to 92 years, with a mean age of 43.54±16.36 years. HPV DNA was detected in 17/95 (17.89%) samples, including 9/32 (28.12%) fibroadenoma samples and 8/63 (12.69%) IDC samples. No significant difference was observed regarding the presence of HPV DNA between the IDC and fibroadenoma tissues (P=0.08). However, a significant difference was found in the detection of high-risk HPV (HR-HPV) between the case and control groups (P=0.03). In the case group, 87.5% of the detected viruses (7/8 samples) were HR-HPV, while in the control group, 22.22% of positive samples (2/9 samples) were HR-HPV (P=0.03). Based on the results, HR-HPV and low-risk HPV genotypes were detected in 53% (9/17) and 47% (8/17) of positive samples, respectively. In this study, 12.69% of IDC samples were positive for HPV genomes, and HR-HPV was detected in 87.5% of these samples. The present results suggest the important role of HR-HPV in the development of breast cancer.

Immunohistochemical Survey of Invasive Ductal Carcinoma of the Breast, using ER, PR, HER2 and KI-67 biomarkers, in Uyo, Nigeria

Background: Breast’s Invasive Ductal Carcinoma (IDC), which is the commonest type of malignancy in females worldwide, can be characterized using immunohistochemistry in view of personalized cancer therapy. In this study, we aimed to determine the pattern of immunohistochemical profiles of IDC using oestrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 receptor (HER2) and proliferative index (Ki-67) biomarkers in our tertiary healthcare facility in Uyo, Akwa Ibom State, Nigeria given the dearth of its data in our environment.&#x0D; Materials and methods: We carried out a retrospective hospital-based immunohistochemical study of archival IDC tissue blocks over a four- and half-year period. Using systematic random sampling method, 64 formalin fixed paraffin embedded (FFPE) IDC tissue blocks were selected for this study. We carried out immunohistochemical evaluation using ER, PR, HER2 and Ki-67 biomarkers. Subsequently, we presented the results and classification schemes as text, tables, graphs, and photomicrographs.&#x0D; Results: We found that the proportion of expressions were ER-negative (88.7%), PR-negative (87.3%), HER2-negative (68.3%) and Ki-67 (&lt;20%) being 83.6% respectively. The immunohistochemical-based classification which was done using combined immunohistochemical profiles of ER/PR/HER2 and ER/PR/HER2/Ki-67 biomarkers respectively, revealed five immunohistochemical-based subtypes. These subtypes were ER-positive luminal A (ER+/±PR+/HER2-) [5.56%], ER-positive luminal B (ER+/±PR+/HER2+) [5.56%], HER2-overexpression (ER-/±PR+/HER2+) [16.66%], Triple negative (ER-/PR-/HER2-) [66.67%] and Unclassified subtypes (ER-/PR+/HER2-) [5.56%]. Furthermore, these five subtypes were further subcategorized into low (Ki-67 &lt;20%) and high (Ki-67 ≥20%) proliferation subtypes accordingly.&#x0D; Conclusion: The commonest pattern of immunohistochemical profile expression of IDC in Uyo was found to be the Triple negative subtype.

The Use of Convolutional Neural Networks in the Prediction of Invasive Ductal Carcinoma in Histological Images of Breast Cancer.

This paper presents a deep learning approach for automatic detection and visual analysis of Invasive Ductal Carcinoma (IDC) tissue regions. The method proposed in this work is a convolutional neural network (CNN) for visual semantic analysis of tumor regions for diagnostic support. Detection of IDC is a time-consuming and challenging task, mainly because a pathologist needs to examine large tissue regions to identify areas of malignancy. Deep Learning approaches are particularly suitable for dealing with this type of problem, especially when many samples are available for training, ensuring high quality of the learned features by the classifier and, consequently, its generalization capacity. A 3-hidden-layer CNN with data balancing reached both accuracy and F1-Score of 0.85 and outperforming other approaches from the literature. Thus, the proposed method in this article can serve as a support tool for the identification of invasive breast cancer.

Evaluation PD-L1, CD8 and CD20 as early predictor and tracking markers for breast cancer (BC) in Egypt

BackgroundBreast cancer (BC) is considered as a common type of cancer threatening women throughout the world. Therefore, development of early predication biomarkers for BC got more concern especially for Egyptian females. This study was aimed to evaluate PD-L1, CD8, and CD20 as early prediction breast cancer biomarkers. MethodsFlow cytometry (FC), immunohistochemistry (IHC), Western Blot, and q-PCR were used to compare PD-L1, CD20, and CD8 levels in tissues and blood samples of Breast Cancer and controls. ResultsBlood samples showed a significant increase in PD-L1, CD20, and CD8 compared to controls (p˂0.005). A Significant correlation was shown between PD-L1, CD8, and CD20 in tissue and breast cancer subtypes. Whereas, invasive lobular carcinoma (ILC) was characterized by superior PD-L1 and CD20 levels compared to invasive ductal carcinoma (IDC). FC studies on Blood showed 83% and 45.7% PD-L1 expressions for IDC and ILC, respectively. CD20 in ILC and IDC were 78.2% and 62.5%, respectively. Nevertheless, CD8 was 74.2% for IDC and 67.7% for ILC. Whereas, FC studies for PD-L1, CD20, and CD8 in ILC in tissues gave 34.4%, 30.2% and 35.1%, respectively. In addition, IDC tissue samples showed 16%, 12.5, and 13.5% for PD-L1, CD20, and CD8. The moderate stage of adenocarcinoma caused expression of PD-L1 within inflammatory cells, while expression was within neoplastic glandular cells in late stage. ConclusionPD-L1, CD8, and CD20 are considered as early predictor and tracking markers for breast cancer.

Extracellular Vesicles Derived from SIPA1high Breast Cancer Cells Enhance Macrophage Infiltration and Cancer Metastasis through Myosin-9

Simple SummaryThe high expression of signal-induced proliferation-associated 1 (SIPA1) in breast cancer could aggravate cancer cell metastasis, but how the tumour microenvironment is involved in this incident is unknown. In this study, we investigated whether breast cancer cells with high SIPA1 expression recruited macrophages into the tumour microenvironment. We also found that extracellular vesicles (EVs) derived from MDA-MB-231 cells significantly enhanced macrophage migration, compared with that from SIPA1-knockdown MDA-MB-231 cells both in vitro and in vivo. In terms of the mechanism, SIPA1 in cancer cells modulated the key protein myosin-9 in EVs and promoted macrophage infiltration via EVs. We confirmed that either down-regulating SIPA1 expression or blocking myosin-9 by its inhibitor, blebbistatin, led to the suppression of macrophage infiltration. These findings contribute to a deep understanding of how SIPA1 regulates the tumour microenvironment in breast cancer to facilitate tumour metastasis and provide a basis for the development of therapeutics against breast cancer metastasis.Tumour cell metastasis can be genetically regulated by proteins contained in cancer cell-derived extracellular vesicles (EVs) released to the tumour microenvironment. Here, we found that the number of infiltrated macrophages was positively correlated with the expression of signal-induced proliferation-associated 1 (SIPA1) in invasive breast ductal carcinoma tissues and MDA-MB-231 xenograft tumours. EVs derived from MDA-MB-231 cells (231-EVs) significantly enhanced macrophage migration, compared with that from SIPA1-knockdown MDA-MB-231 cells (231/si-EVs) both in vitro and in vivo. We revealed that SIPA1 promoted the transcription of MYH9, which encodes myosin-9, and up-regulated the expression level of myosin-9 in breast cancer cells and their EVs. We also found that blocking myosin-9 by either down-regulating SIPA1 expression or blebbistatin treatment led to the suppression of macrophage infiltration. Survival analysis showed that breast cancer patients with high expression of SIPA1 and MYH9 molecules had worse relapse-free survival (p = 0.028). In summary, SIPA1high breast cancer can enhance macrophage infiltration through EVs enriched with myosin-9, which might aggravate the malignancy of breast cancer.

Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer.

Diffusion-weighted imaging plays a key role in magnetic resonance imaging (MRI) of breast tumors. However, it remains unclear how to interpret single diffusion encoding with respect to its link with tissue microstructure. The purpose of this retrospective cross-sectional study was to use tensor-valued diffusion encoding to investigate the underlying microstructure of invasive ductal carcinoma (IDC) and evaluate its potential value in a clinical setting. We retrospectively reviewed biopsy-proven breast cancer patients who underwent preoperative breast MRI examination from July 2020 to March 2021. We reviewed the MRI of 29 patients with 30 IDCs, including analysis by diffusional variance decomposition enabled by tensor-valued diffusion encoding. The diffusion parameters of mean diffusivity (MD), total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), macroscopic fractional anisotropy (FA), and microscopic fractional anisotropy (µFA) were estimated. The parameter differences were compared between IDC and normal fibroglandular breast tissue (FGBT), as well as the association between the diffusion parameters and histopathologic items. The mean value of MD in IDCs was significantly lower than that of normal FGBT (1.07±0.27 vs. 1.34±0.29, P<0.001); however, MKT, MKA, MKI, FA, and µFA were significantly higher (P<0.005). Among all the diffusion parameters, MKI was positively correlated with the tumor size on both MRI and pathological specimen (rs=0.38, P<0.05 vs. rs=0.54, P<0.01), whereas MKT had a positive correlation with the tumor size in the pathological specimen only (rs=0.47, P<0.02). In addition, the lymph node (LN) metastasis group had significantly higher MKT, MKA, and µFA compared to the metastasis negative group (P<0.05). Tensor-valued diffusion encoding enables a useful non-invasive method for characterizing breast cancers with information on tissue microstructures. Particularly, µFA could be a potential imaging biomarker for evaluating breast cancers prior to surgery or chemotherapy.

Predicting invasive ductal carcinoma tissues in whole slide images of breast Cancer by using convolutional neural network model and multiple classifiers

Predicting invasive ductal carcinoma tissues in whole slide images of breast Cancer by using convolutional neural network model and multiple classifiers