Molecular Classification and Prognostication of Breast Cancer:

Identification of the lymph node metastasis-related automated breast volume scanning features for predicting axillary lymph node tumor burden of invasive breast cancer via a clinical prediction model

Breast cancer has become the malignant tumor with the highest incidence in women. Axillary lymph node dissection (ALND) is an effective method of maintaining regional control; however, it is associated with a significant risk of complications. Meanwhile, whether the patients need ALND or not is according to sentinel lymph node biopsy (SLNB). However, the false-negative results of SLNB had been reported. Automated breast volume scanning (ABVS) is a routine examination in breast cancer. A real-world cohort consisting of 245 breast cancer patients who underwent ABVS examination were enrolled, including 251 tumor lesions. The ABVS manifestations were analyzed with the SLNB results, and the ALND results for selecting the lymph node metastasis were related to ABVS features. Finally, a nomogram was used to construct a breast cancer axillary lymph node tumor burden prediction model. Breast cancer patients with a molecular subtype of luminal B type, a maximum lesion diameter of ≥5 cm, tumor invasion of the Cooper's ligament, and tumor invasion of the nipple had heavy lymph node tumor burden. Molecular classification, tumor size, and Cooper's ligament status were used to construct a clinical prediction model of axillary lymph node tumor burden. The consistency indexes (or AUC) of the training cohort and the validation cohort were 0.743 and 0.711, respectively, which was close to SLNB (0.768). The best cutoff value of the ABVS nomogram was 81.146 points. After combination with ABVS features and SLNB, the AUC of the prediction model was 0.889, and the best cutoff value was 178.965 points. The calibration curve showed that the constructed nomogram clinical prediction model and the real results were highly consistent. The clinical prediction model constructed using molecular classification, tumor size, and Cooper's ligament status can effectively predict the probability of heavy axillary lymph node tumor burden, which can be the significant supplement to the SLNB. Therefore, this model may be used for individual decision-making in the diagnosis and treatments of breast cancer.

Pathological features and prognosis of different molecular subtypes of breast cancer

To examine the pathological features and prognosis of different molecular subtypes of breast cancer, the clinical data of 892 breast cancer patients were retrospectively analyzed and divided into four subtypes according to hormone receptor expression in breast cancer tissue: Her-2 overexpression, luminal A, luminal B and basal-like subtypes. The pathological data and prognosis of these subtypes were compared. Of the 892 breast cancer patients, there were 46 cases (5.2%) with Her-2 overexpression-type, 698 cases (78.3%) with luminal A-type, 38 cases (4.3%) with luminal B-type and 110 patients (12.2%) with basal-like-type. Immunohistochemistry was used to identify the progesterone and estrogen receptors in the tumor tissues. The χ2 test was used to verify the measurement data. The Cox proportional hazard regression model was used for the univariate and multivariate analyses. Results showed there was no statistical difference for lymphatic metastasis among the various molecular subtypes of breast cancer (P>0.05). The distant metastatic rate of patients with Her-2-type breast cancer was significantly higher compared to patients with the other three subtypes (P<0.05). The difference in local recurrence among molecular subtypes was not significantly significant (P>0.05). Lymph node metastasis, age and different molecular subtypes were found to have an impact on patient overall survival (OS) and disease-free survival (DFS). Her-2 overexpression-type breast cancer patients had the lowest 9-year DFS and 7-year OS compared to the other subtypes (P<0.05). Thus, Her-2-type was associated with the worst prognosis. In conlusion, the molecular typing of breast cancer has important clinical value in prognosis estimation and is expected to affect breast cancer treatment approaches.