Predictive value of pathological and immunohistochemical parameters for axillary lymph node metastasis in breast carcinoma

Perineural Invasion Predicts Unfavorable Prognosis in Patients With Invasive Breast Cancer

BACKGROUND/AIM

Perineural invasion (PNI) is a poor prognostic factor in a variety of cancers. However, the frequency of PNI in invasive breast carcinoma varies among studies, and the prognostic significance of PNI remains unclear. Therefore, we aimed to explore the prognostic value of PNI in breast cancer patients.

PATIENTS AND METHODS

The cohort included 191 consecutive female patients who underwent surgical resection of invasive carcinoma of no special type (NOS). The correlations between PNI and clinicopathological characteristics including prognosis were investigated.

RESULTS

The frequency of PNI was 14.1% (27/191) and the PNI-positive status was significantly correlated with large pathological tumor size (p=0.005), lymph node metastasis (p=0.001), and lymphatic invasion (p=0.009). The log-rank test showed that PNI-positive patients had shorter distant metastasis-free survival (DMFS) (p=0.002) and disease-specific survival (DSS) (p<0.001). According to the multivariate analysis, PNI had a significant adverse effect on DMFS (p=0.037) and DSS (p=0.003).

CONCLUSION

PNI could be used as an independent poor prognostic indicator in patients with invasive breast carcinoma.

Artificial Intelligence for Breast US

US is a widely available, commonly used, and indispensable imaging modality for breast evaluation. It is often the primary imaging modality for the detection and diagnosis of breast cancer in low-resource settings. In addition, it is frequently employed as a supplemental screening tool via either whole breast handheld US or automated breast US among women with dense breasts. In recent years, a variety of artificial intelligence systems have been developed to assist radiologists with the detection and diagnosis of breast lesions on US. This article reviews the background and evidence supporting the use of artificial intelligence tools for breast US, describes implementation strategies and impact on clinical workflow, and discusses potential emerging roles and future directions.

Nomogram models for stratified prediction of axillary lymph node metastasis in breast cancer patients (cN0)

Objectives

To determine the predictors of axillary lymph node metastasis (ALNM), two nomogram models were constructed to accurately predict the status of axillary lymph nodes (ALNs), mainly high nodal tumour burden (HNTB, > 2 positive lymph nodes), low nodal tumour burden (LNTB, 1-2 positive lymph nodes) and negative ALNM (N0). Accordingly, more appropriate treatment strategies for breast cancer patients without clinical ALNM (cN0) could be selected.

Methods

From 2010 to 2015, a total of 6314 patients with invasive breast cancer (cN0) were diagnosed in the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to the training and internal validation groups at a ratio of 3:1. As the external validation group, data from 503 breast cancer patients (cN0) who underwent axillary lymph node dissection (ALND) at the Second Affiliated Hospital of Chongqing Medical University between January 2011 and December 2020 were collected. The predictive factors determined by univariate and multivariate logistic regression analyses were used to construct the nomograms. Receiver operating characteristic (ROC) curves and calibration plots were used to assess the prediction models' discrimination and calibration.

Results

Univariate analysis and multivariate logistic regression analyses showed that tumour size, primary site, molecular subtype and grade were independent predictors of both ALNM and HNTB. Moreover, histologic type and age were independent predictors of ALNM and HNTB, respectively. Integrating these independent predictors, two nomograms were successfully developed to accurately predict the status of ALN. For nomogram 1 (prediction of ALNM), the areas under the receiver operating characteristic (ROC) curve in the training, internal validation and external validation groups were 0.715, 0.688 and 0.876, respectively. For nomogram 2 (prediction of HNTB), the areas under the ROC curve in the training, internal validation and external validation groups were 0.842, 0.823 and 0.862. The above results showed a satisfactory performance.

Conclusion

We established two nomogram models to predict the status of ALNs (N0, 1-2 positive ALNs or >2 positive ALNs) for breast cancer patients (cN0). They were well verified in further internal and external groups. The nomograms can help doctors make more accurate treatment plans, and avoid unnecessary surgical trauma.

Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer

The second largest cause of mortality worldwide is breast cancer, and it mostly occurs in women. Early diagnosis has improved further treatments and reduced the level of mortality. A unique deep learning algorithm is presented for predicting breast cancer in its early stages. This method utilizes numerous layers to retrieve significantly greater amounts of information from the source inputs. It could perform automatic quantitative evaluation of complicated image properties in the medical field and give greater precision and reliability during the diagnosis. The dataset of axillary lymph nodes from the breast cancer patients was collected from Erasmus Medical Center. A total of 1050 images were studied from the 850 patients during the years 2018 to 2021. For the independent test, data samples were collected for 100 images from 95 patients at national cancer institute. The existence of axillary lymph nodes was confirmed by pathologic examination. The feed forward, radial basis function, and Kohonen self-organizing are the artificial neural networks (ANNs) which are used to train 84% of the Erasmus Medical Center dataset and test the remaining 16% of the independent dataset. The proposed model performance was determined in terms of accuracy (Ac), sensitivity (Sn), specificity (Sf), and the outcome of the receiver operating curve (Roc), which was compared to the other four radiologists' mechanism. The result of the study shows that the proposed mechanism achieves 95% sensitivity, 96% specificity, and 98% accuracy, which is higher than the radiologists' models (90% sensitivity, 92% specificity, and 94% accuracy). Deep learning algorithms could accurately predict the clinical negativity of axillary lymph node metastases by utilizing images of initial breast cancer patients. This method provides an earlier diagnostic technique for axillary lymph node metastases in patients with medically negative changes in axillary lymph nodes.

Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning

Background Deep learning (DL) algorithms are gaining extensive attention for their excellent performance in image recognition tasks. DL models can automatically make a quantitative assessment of complex medical image characteristics and achieve increased accuracy in diagnosis with higher efficiency. Purpose To determine the feasibility of using a DL approach to predict clinically negative axillary lymph node metastasis from US images in patients with primary breast cancer. Materials and Methods A data set of US images in patients with primary breast cancer with clinically negative axillary lymph nodes from Tongji Hospital (974 imaging studies from 2016 to 2018, 756 patients) and an independent test set from Hubei Cancer Hospital (81 imaging studies from 2018 to 2019, 78 patients) were collected. Axillary lymph node status was confirmed with pathologic examination. Three different convolutional neural networks (CNNs) of Inception V3, Inception-ResNet V2, and ResNet-101 architectures were trained on 90% of the Tongji Hospital data set and tested on the remaining 10%, as well as on the independent test set. The performance of the models was compared with that of five radiologists. The models' performance was analyzed in terms of accuracy, sensitivity, specificity, receiver operating characteristic curves, areas under the receiver operating characteristic curve (AUCs), and heat maps. Results The best-performing CNN model, Inception V3, achieved an AUC of 0.89 (95% confidence interval [CI]: 0.83, 0.95) in the prediction of the final clinical diagnosis of axillary lymph node metastasis in the independent test set. The model achieved 85% sensitivity (35 of 41 images; 95% CI: 70%, 94%) and 73% specificity (29 of 40 images; 95% CI: 56%, 85%), and the radiologists achieved 73% sensitivity (30 of 41 images; 95% CI: 57%, 85%; P = .17) and 63% specificity (25 of 40 images; 95% CI: 46%, 77%; P = .34). Conclusion Using US images from patients with primary breast cancer, deep learning models can effectively predict clinically negative axillary lymph node metastasis. Artificial intelligence may provide an early diagnostic strategy for lymph node metastasis in patients with breast cancer with clinically negative lymph nodes. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Bae in this issue.

Invasive Breast Carcinoma Tumor Size on Core Needle Biopsy: Analysis of Practice Patterns and Effect on Final Pathologic Tumor Stage

INTRODUCTION

In the absence of nodal metastasis, pathologic tumor (pT) size remains one of the most important factors in adjuvant treatment decisions and patient prognosis in breast cancer. The aim of this study was to evaluate the effect of core needle biopsy (CNB) tumor size on final pT stage.

MATERIALS AND METHODS

Our information system was searched to identify all patients who underwent excisional procedures for invasive breast carcinoma from January 1^, 2014 to December 31, 2015. The tumor size on CNB and final excision, the number of cases in which the CNB size was larger, and the percentage of cases in which using the CNB tumor size changed the final pT stage were recorded.

RESULTS

From 1380 primary breast excisions/mastectomies, a total of 870 cases were included. In 82 (9.4%) the CNB tumor size was larger (63 of 82 cases) or no residual tumor was identified on excision (19 of 82 cases). From these 82 cases, 40 (48.7%) were properly staged on the basis of CNB tumor size, 16 (19.5%) were not staged, and 26 (31.7%) were staged using the final excision tumor size. Change in stage occurred in 7 of these 26 patients.

CONCLUSION

Our study revealed that in most cases, the largest tumor size is found in the excision/mastectomy specimen. However, in 9.4% (82 of 870), the CNB contains the most accurate tumor size for pT staging. On the basis of our results, including the largest linear tumor extent on the CNB report is recommended.

Association of conventional ultrasound, elastography and clinicopathological factors with axillary lymph node status in invasive ductal breast carcinoma with sizes > 10 mm

Background

To evaluate whether conventional ultrasound, elastography [conventional strain elastography of elasticity imaging, acoustic radiation force impulse induced strain elastography of virtual touch tissue imaging, and a novel two-dimensional shear wave elastography of virtual touch tissue imaging quantification] and clinicopathological factors are associated with axillary lymph node metastasis in invasive ductal breast carcinoma with sizes > 10 mm.

Materials and Methods

We evaluated 150 breast lesions from 148 patients using the above methods and the clinicopathological factors. Univariate and multivariate logistic regression analysis were performed to determine the axillary lymph node metastasis risk factors. Diagnostic performance was evaluated using receiver operating characteristic curve analysis.

Results

Sixty-three tumors (42%) were node-positive, 87 (58%) were node-negative. Aspect ratio, virtual touch tissue imaging grade, shear wave velocity, pathological invasive tumor size, and histological grade maintained independent significance in predicting nodal involvement. The mean tumor shear wave velocitys (4.60, 6.49, 7.16) increased in proportion to metastatic node number (0, 1-3, ≥ 4, respectively; P < 0.001). For all tumors in this study, the cut-off shear wave velocity was 6.16 m/s and was associated with 64.1% sensitivity, 78.0% specificity and an area under the ROC curve of 0.799 (95% confidence interval, 0.731-0.868).

Conclusions

Aspect ratio, virtual touch tissue imaging grade, shear wave velocity, pathological invasive tumor size and histological grade are independently associated with axillary lymph node metastasis in invasive ductal breast carcinoma with sizes > 10 mm.

Correlation of epidermal growth factor receptor (EGFR), androgen receptor (AR) and 14-3-3 sigma expression in breast cancer

Epidermal growth factor receptor (EGFR), androgen receptor (AR) and 14-3-3 sigma have been reported to be implicated in breast tumorigenesis. Their correlations, however, remain elusive in this condition. In order to examine the correlation of EGFR, AR and 14-3-3 sigma in breast cancer, and analyze their relationships with molecular subtypes of breast cancer and their impacts on overall survival, we immunohistochemistrically detected EGFR, AR and 14-3-3 sigma expression in 139 cases of breast cancer. We found that EGFR expression was negatively correlated with AR (r=-0.223, P=0.008) and positively with 14-3-3 sigma expression (r=0.181, P=0.033). There were significant differences in EGFR and AR expression between different molecular subtypes (P=0.000 and P=0.000 respectively). Kaplan-Meier cumulative survival analysis showed that none of the three biomarkers had significant impacts on overall survival of breast cancer patients (P=0.315, P=0.709, P=0.789 respectively). Univariate survival analysis revealed that tumor size (P=0.044), lymph node status (P=0.006) and clinical stage (P=0.008) were significantly associated with overall survival. Multivariate analysis demonstrated that lymph node status was the only statistically significant independent prognostic factor for overall survival [P=0.006, exp (B) =1.511, CI (1.124-2.032)]. In conclusion, EGFR expression is negatively correlated with AR and positively with 14-3-3 sigma expression in breast cancer. Furthermore, there are significant differences in EGFR and AR expression between various molecular subtypes of breast cancer. Lastly, EGFR, AR and 14-3-3 sigma have no significant impacts on overall survival of breast cancer patients.

Predictive Value of Clinicopathological Characteristics for Sentinel Lymph Node Metastasis in Early Breast Cancer

BACKGROUND Sentinel lymph node biopsy (SLNB) is one of the preferred treatments for breast cancer including clinically negative lymph node breast cancer. However, for 60-70% of patients this invasive axilla surgery is unnecessary. Our study aimed to identify the predictors for sentinel lymph node (SLN) metastasis in early breast cancer patients and provide evidence for rational decision-making in specified clinical situations. MATERIAL AND METHODS Medical records of 417 breast cancer patients who were treated with a breast surgical procedure and SLNB in Ningbo Medical Center Lihuili Eastern Hospital were retrospectively reviewed. Univariate analysis and multivariate logistic regression analysis were used to analyze the correlation between SLN metastasis and clinicopathological characteristics, including patient age, menstrual status, body mass index (BMI), family history, tumor size, laterality of tumor, histological grade, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), Ki67 index, and molecular subtypes of the tumor. RESULTS In the cohort of 417 cases, the ratio of SLNM was 23.0%. Univariate analysis found that age, tumor size, histological grade, and Ki67 index were associated with SLN metastasis. However, age, tumor size, and histological grade were the only three independent predictors for SLN metastasis by multivariate logistic regression analysis. When these three factors were considered together, three different levels of SLN metastasis groups could be classified: low-risk group with the ratio of 14.3%, moderate-risk group with the ratio of 31.4%, and high-risk group with the ratio of 66.7%. CONCLUSIONS Our study demonstrated that age, tumor size, and histological grade were three independent predictive factors for SLN metastasis in early breast cancer patients. This finding may help surgeons in the decision-making process for early breast cancer patients before considering axilla surgical procedure.

Analysis of molecular markers as predictive factors of lymph node involvement in breast carcinoma

Nodal status is the most significant independent prognostic factor in breast cancer. Identification of molecular markers would allow stratification of patients who require surgical assessment of lymph nodes from the large numbers of patients for whom this surgical procedure is unnecessary, thus leading to a more accurate prognosis. However, up to now, the reported studies are preliminary and controversial, and although hundreds of markers have been assessed, few of them have been used in clinical practice for treatment or prognosis in breast cancer. The purpose of the present study was to determine whether protein phosphatase Mg2+/Mn2+ dependent 1D, β-1,3-N-acetylglucosaminyltransferase, neural precursor cell expressed, developmentally down-regulated 9, prohibitin, phosphoinositide-3-kinase regulatory subunit 5 (PIK3R5), phosphatidylinositol-5-phosphate 4-kinase type IIα, TRF1-interacting ankyrin-related ADP-ribose polymerase 2, BCL2 associated agonist of cell death, G2 and S-phase expressed 1 and PAX interacting protein 1 genes, described as prognostic markers in breast cancer in a previous microarray study, are also predictors of lymph node involvement in breast carcinoma Reverse transcription-quantitative polymerase chain reaction analysis was performed on primary breast tumor tissues from women with negative lymph node involvement (n=27) compared with primary tumor tissues from women with positive lymph node involvement (n=23), and was also performed on primary tumors and paired lymph node metastases (n=11). For all genes analyzed, only the PIK3R5 gene exhibited differential expression in samples of primary tumors with positive lymph node involvement compared with primary tumors with negative lymph node involvement (P=0.0347). These results demonstrate that the PIK3R5 gene may be considered predictive of lymph node involvement in breast carcinoma. Although the other genes evaluated in the present study have been previously characterized to be involved with the development of distant metastases, they did not have predictive potential.

Specific upregulation of RHOA and RAC1 in cancer-associated fibroblasts found at primary tumor and lymph node metastatic sites in breast cancer

The importance of tumor-stromal cell interactions in breast tumor progression and invasion is well established. Here, an evaluation of differential genomic profiles of carcinoma-associated fibroblasts (CAFs) compared to fibroblasts derived from tissues adjacent to fibroadenomas (NAFs) revealed altered focal adhesion pathways. These data were validated through confocal assays. To verify the possible role of fibroblasts in lymph node invasion, we constructed a tissue microarray consisting of primary breast cancer samples and corresponding lymph node metastasis and compared the expression of adhesion markers RhoA and Rac1 in fibroblasts located at these different locations. Two distinct tissue microarrays were constructed from the stromal component of 43 primary tumors and matched lymph node samples, respectively. Fibroblasts were characterized for their expression of α-smooth muscle actin (α-SMA) and vimentin. Moreover, we verified the level of these proteins in the stromal compartment from normal adjacent tissue and in non-compromised lymph nodes. Our immunohistochemistry revealed that 59 % of fibroblasts associated with primary tumors and 41 % of the respective metastatic lymph nodes (p = 0.271) displayed positive staining for RhoA. In line with this, 57.1 % of fibroblasts associated with primary tumors presented Rac1-positive staining, and the frequency of co-positivity within the lymph nodes was 42.9 % (p = 0.16). Expression of RhoA and Rac1 was absent in fibroblasts of adjacent normal tissue and in compromised lymph nodes. Based on our findings that no significant changes were observed between primary and metastatic lymph nodes, we suggest that fibroblasts are active participants in the invasion of cancer cells to lymph nodes and support the hypothesis that metastatic tumor cells continue to depend on their microenvironment.

Carcinoma en Cuirasse from Recurrent Breast Cancer seen on FDG-PET/CT

Our patient was a 36-year-old female diagnosed with Grade II ER+/PR-/Her-2 - ductal carcinoma in situ (DCIS) in the left breast. She underwent left lumpectomy and received treatment with tamoxifen and radiotherapy. Three years later, she presented with multiple diffused skin nodules on the chest and upper left arm. 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) exam showed widespread metastasis in the chest, upper left arm, left axillary lymph nodes, and left suprascapular muscle. FDG-PET/CT imaging of breast carcinoma en cuirasse is very rare. FDG-PET/CT is useful in detecting recurrent breast cancer.

Clinicopathological study of centrally necrotizing carcinoma of the breast

Background

Centrally necrotizing carcinoma of the breast (CNC) represents a newly-identified subset of breast cancer. The clinical and pathological characteristics of this breast cancer subtype are not yet completely understood.

Methods

We assessed the clinicopathological characteristics of 73 cases of CNC and 30 control cases of high-grade infiltrating ductal carcinoma (IDC) with focal necrosis based on light microscopy and immunohistochemical staining for estrogen receptor, progesterone receptor, Cerb-B2/HER2, Ki-67, epidermal growth factor receptor, cytokeratin 5/6, smooth muscle actin, S-100 protein, p63 and CD10.

Results

All the tumors showed extensive central necrotic or acellular zones with different degrees of fibrotic or hyaline material surrounded by ring-like or ribbon-like residual tumour tissue which were usually high-grade IDCs. The central necrotic zone accounted for at least 30% of the cross-sectional area of the tumor. Thirty-six cases (49.3%) showed a component of ductal carcinoma in situ. The tumorous stroma around the central necrotic zone was accompanied by myxoid matrix formation in 28 cases (40%). Lymphocytic infiltration was present in 53 cases (72.6%). Granulomatous reactions were detected at the periphery of the tumors in 49 cases (67.1%). Immunohistochemistry showed greater expression of basal-like markers (72.2%, 52 cases) than myoepithelial markers (60.6%, 43 cases), both of which were significantly higher than in controls (26.7%, 8 cases) (P < 0.001). According to molecular typing, most CNCs were basal-like subtype (37 cases, 50.7%). Follow-up data were available for 28 patients. Disease progression occurred in 11 patients. The combined rate of recurrence, distant metastasis or death was significantly higher in CNC patients compared with controls (P < 0.05).

Conclusions

CNC was associated with distinctive clinicopathologic features mostly characterized as basal-like type. Its high proliferative activity, highly-aggressive biological behavior, and high rates of recurrence and metastasis, suggest that CNC should be classified as a new type of breast carcinoma.

Predictive role of tumor size in breast cancer with axillary lymph node involvement - can size of primary tumor be used to omit an unnecessary axillary lymph node dissection?

BACKGROUND

Breast cancer is the most common cancer among women worldwide. The aim of this study was to investigate the relationship between tumor size and axillary lymph node involvement (ALNI) in patients with invasive lesions, to find the best candidates for a full axillary dissection. Additionally, we evaluated the association between tumor size and invasive behavior. The study was based on data from 789 patients with histopathologically proven invasive breast cancer diagnosed in Shohada University hospital in Tehran, Iran (1993-2009). Cinical and histopathological characteristics of tumors were collected. Patients were divided into 6 groups according to primary tumor size: group I (0.1-≤1cm), II (1.1-≤2cm), III (2.1-≤3cm), IV (3.1-≤4cm), V (4.1-≤5cm) and VI (>5cm). The mean(±SD) size of primary tumor at the time of diagnosis was 3.59±2.69 cm that gradually declined during the course of study. There was a significant correlation between tumor size and ALNI (p<0.001). A significant positive correlation between primary tumor size and involvement of surrounding tissue was also found (p<0.001). The mean number of LNI in group VI was significantly higher than other groups (p<0.05).We observed more involvement of lymph nodes, blood vessels, skin and areola-nipple tissue with increase in tumor size.We found 15.3% overall incidence of ALNI in tumors ≤2 cm, indicating the need for more investigation to omit full axillary lymph node dissection with an acceptable risk for tumors below this diameter. While in patients with tumors ≥2 cm, 84.3% of them had nodal metastases, so the best management for this group would be a full ALND. Tumor size is a significant predictor of ALNM and involvement of surrounding tissue, so that an exact estimation of the size of primary tumor is necessary prior to surgery to make the best decision for management of patients with invasive breast cancer.

A prediction model for the presence of axillary lymph node involvement in women with invasive breast cancer: a focus on older women

Axillary lymph node (ALN) status at diagnosis is the most powerful prognostic indicator for patients with breast cancer. Our aim is to examine the contribution of variables that lead to ALN metastases in a large dataset with a high proportion of patients greater than 70 years old. Using the data from two multicenter prospective studies, a retrospective review was performed on 2,812 patients diagnosed with clinically node-negative invasive breast cancer from 1996 to 2005 and who underwent ALN sampling. Univariate and multivariate logistic regression were used to identify variables that were strongly associated with axillary metastases, and an equation was developed to estimate risk of ALN metastases. Of the 2,812 patients with invasive breast cancer, 18% had ALN metastases at diagnosis. Based on univariate analysis, tumor size, lymphovascular invasion (LVI), tumor grade, age at diagnosis, menopausal status, race, tumor location, tumor type, and estrogen and progesterone receptor status were statistically significant. The relationship between age and involvement of axillary metastases was nonlinear. In multivariate analysis, LVI, tumor size and menopausal status were the most significant factors associated with ALN metastases. Age, however, was not a significant contributing factor for axillary metastases. Tumor size, LVI, and menopausal status are strongly associated with ALN metastases. We believe that age may have been a strong factor in previous analyses because there was not an adequate representation of women in older age groups and because of the violation of the assumption of linearity in their multivariate analyses.

Differing deregulation of HER2 in primary gastric cancer and synchronous related metastatic lymph nodes

BACKGROUND

The aim of this study was to investigate how differences in expression of HER2 between primary gastric cancers (PGCs) and their corresponding metastatic lymph nodes (LMNs) might affect its potential as a prognostic indicator in treatments including anti-HER2 agents.

METHODS

The analysis was conducted in 102 patients who underwent surgical resection for primary gastric cancers (PGCs; adenocarcinoma, intestinal type) with synchronous LNMs. HER2 gene status and protein expression were investigated by immunohistochemistry (IHC) in all patients; fluorescence in situ hybridization (FISH) was performed in 22 patients. The correlation between HER2 gene status in PGCs and their LNMs was evaluated.

RESULTS

Positive HER2 expression as detected by IHC + FISH was observed in 27/102 PGC samples (26.5%) and 29/102 LNM samples (28.4%). HER2 amplification status in 102 paired PGC and LNM samples as evaluated by FISH + IHC was concordant in 92 patients (90.2%), 69 (67.6%) were unamplified and 23/102 (22.5%) were amplified at both sites, and discordant in 10 patients (9.8%), 4 (3.9%) were positive for PGC and negative for LNM, while 6 (5.9%) were positive for LNM and negative for PGC. The results of FISH + IHC showed very strong concordance in HER2 status between the PGC and LNM groups (k = 0.754).

CONCLUSION

The high concordance between HER2 results for PGCs and their LNMs indicates that assessment of HER2 status in the primary cancer alone is a reliable basis for deciding treatment with anti-HER2 agents in patients with LNMs from gastric adenocarcinoma.

VIRTUAL SLIDES

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/9365749431029643.

An analysis of cyclin D1, cytokeratin 5/6 and cytokeratin 8/18 expression in breast papillomas and papillary carcinomas

Background

To evaluate the expression levels of Cyclin D1 in breast papillomas and papillary carcinomas, and to analyze the types of cells that co-express Cyclin D1 with Cytokeratin 5/6 (CK 5/6) or with Cytokeratin 8/18(CK 8/18).

Methods

Fifty-nine cases of papillary lesions including 36 papillomas and 23 intracystic papillary carcinomas were examined. Cyclin D1, CK 5/6 and CK 8/18 expression levels were evaluated by double immunostaining.

Results

Cyclin D1 is highly expressed in papillary carcinomas (27.54% ± 15.43%) compared with papillomas (8.81% ± 8.41%, p < 0.01). Cyclin D1 is predominantly expressed in Cytokeratin 8/18- expressing cells, rather than in Cytokeratin 5/6-expressing cells, regardless of the type of lesion. In Papillomas, Cyclin D1 exhibited a mean 11.42% (11.42% ± 10.17%) co-expression rate with Cytokeratin 8/18 compared with a mean 2.50% (2.50% ± 3.24%) co-expression rate with Cytokeratin 5/6 (p < 0.01). In papillary carcinomas, Cyclin D1 exhibited a mean 34.74% (34.74% ± 16.32%) co-expression rate with Cytokeratin 8/18 compared with a co-expression rate of 0.70% (0.70% ± 0.93%) with Cytokeratin 5/6 (p < 0.01).

Conclusions

The increase in Cyclin D1 suggests an association of Cyclin D1 staining with papillary carcinomas. Although Cyclin D1 is an effective marker for the differential diagnosis of other papillary lesions, it cannot be used to distinguish between papilloma and papillary carcinoma lesions because its expression occurs in both lesions. Our results show that Cyclin D1 and CK 5/6 staining could be used in concert to distinguish between the diagnosis of papilloma (Cyclin D1 < 4.20%, CK 5/6 positive) or papillary carcinoma (Cyclin D1 > 37.00%, CK 5/6 negative). In addition, our data suggest that Cyclin D1 is expressed only in the cancer stem or progenitor cells that co-immunostained with CK 8/18 in papillary carcinomas, and predominantly with CK 8/18 in the papillomas.

Virtual slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/7299340558756848