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Expression and prognostic value of HER-2/neu in primary breast cancer with sentinel lymph node metastasis.

ABSTRACT: The present study explores the correlation of human epidermal growth factor receptor-2 (HER-2) protein expression with sentinel lymph node (SLN) metastasis and prognosis of breast cancer. The breast cancer tissues and adjacent tissues were obtained from patients with primary breast cancer. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect the mRNA level of HER-2. Spearman correlation analysis was used to analyze the correlation of HER-2 expression with SLN metastasis. The disease-free survival (DFS) and overall survival (OS) of breast cancer patients were investigated. Univariate and multivariate analyses were performed to explore factors influencing SLN metastasis and prognosis of breast cancer. Compared with adjacent tissues, HER-2 expression was significantly up-regulated in breast cancer tissues. HER-2 expression was correlated with the pathological type, tumor node metastasis (TNM) staging, histological grade, blood vessel invasion, SLN metastasis, estrogen receptor (ER), and progesterone receptor (PR). The expression level of HER-2 was positively related to the SLN metastasis (r=0.548). Median DFS and OS were longer in patients with negative HER-2 expression than in patients with positive HER-2 expression. TNM staging, SLN metastasis, and expression levels of HER-2 and ER were independent factors for DFS of breast cancer patients, while TNM staging, blood vessel invasion, histological grade, SLN metastasis, and expression levels of HER-2 and PR were independent factors for OS of breast cancer patients. Our study suggests that high expression of HER-2 promoted SLN metastasis. HER-2 expression and SLN metastasis were the independent factors for the prognosis of breast cancer.

SUBMITTER: Tong ZJ

PROVIDER: S-EPMC5539487 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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Project description:Background The rate of breast-conserving surgery is very low in China, compared with that in developed countries; most breast cancer patients receive mastectomy. It is great important to explore the possibility of omitting axillary lymph node dissection (ALND) in early-stage breast cancer patients with 1 or 2 positive sentinel lymph nodes (SLNs) in China. The aim of this study was to develop a nomogram based on elastography for the prediction of the risk of non-SLN (NSLN) metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. Methods A total of 601 breast cancer patients were initially recruited. According to the inclusion and exclusion criteria, 118 early-stage breast cancer patients with 1 or 2 positive SLNs were finally enrolled and were assigned to the training cohort (n=82) and the validation cohort (n=36), respectively. In the training cohort, the independent predictors were screened by logistic regression analysis and then were used to conducted the nomogram for the prediction of NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The calibration curves, concordance index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and Decision curve analysis (DCA) were used to verified the performance of the nomogram. Results The multivariable analysis showed that the enrolled patients with positive HER2 expression (OR=6.179, P=0.013), Ki67≥14% (OR=8.976, P=0.015), larger lesion size (OR=1.038, P=0.045), and higher Emean (OR=2.237, P=0.006) were observed to be the independent factors of NSLN metastasis. Based on the above four independent predictors, a nomogram was conducted to predict the risk of the NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The nomogram showed good discrimination in the prediction of NSLN metastasis, with bias-corrected C-index of 0.855 (95% CI, 0.754-0.956) and 0.853 (95% CI, 0.724-0.983) in the training and validation cohorts, respectively. Furthermore, the AUC was 0.877 (95%CI: 0.776- 0.978) and 0.861 (95%CI: 0.732-0.991), respectively, indicating a good performance of the nomogram. The calibration curve suggested a satisfactory agreement between the predictive and actual risk in both the training (χ2 = 11.484, P=0.176, HL test) and validation (χ2 = 6.247, p = 0.620, HL test) cohorts, and the obvious clinical nets were revealed by DCA. Conclusions We conducted a satisfactory nomogram model to evaluate the risk of NSLN metastasis in early-stage breast cancer patients with 1 or 2 SLN metastases. This model could be considered as an ancillary tool to help such patients to be selectively exempted from ALND.

Project description:PurposeTo explore the value of machine learning model based on CE-MRI radiomic features in preoperative prediction of sentinel lymph node (SLN) metastasis of breast cancer.MethodsThe clinical, pathological and MRI data of 177 patients with pathologically confirmed breast cancer (81 with SLN positive and 96 with SLN negative) and underwent conventional DCE-MRI before surgery in the First Affiliated Hospital of Soochow University from January 2015 to May 2021 were analyzed retrospectively. The samples were randomly divided into the training set (n=123) and validation set (n= 54) according to the ratio of 7:3. The radiomic features were derived from DCE-MRI phase 2 images, and 1,316 original eigenvectors are normalized by maximum and minimum normalization. The optimal feature filter and selection operator (LASSO) algorithm were used to obtain the optimal features. Five machine learning models of Support Vector Machine, Random Forest, Logistic Regression, Gradient Boosting Decision Tree, and Decision Tree were constructed based on the selected features. Radiomics signature and independent risk factors were incorporated to build a combined model. The receiver operating characteristic curve and area under the curve were used to evaluate the performance of the above models, and the accuracy, sensitivity, and specificity were calculated.ResultsThere is no significant difference between all clinical and histopathological variables in breast cancer patients with and without SLN metastasis (P >0.05), except tumor size and BI-RADS classification (P< 0.01). Thirteen features were obtained as optimal features for machine learning model construction. In the validation set, the AUC (0.86) of SVM was the highest among the five machine learning models. Meanwhile, the combined model showed better performance in sentinel lymph node metastasis (SLNM) prediction and achieved a higher AUC (0.88) in the validation set.ConclusionsWe revealed the clinical value of machine learning models established based on CE-MRI radiomic features, providing a highly accurate, non-invasive, and convenient method for preoperative prediction of SLNM in breast cancer patients.

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Expression and prognostic value of HER-2/neu in primary breast cancer with sentinel lymph node metastasis.

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