The role of surgery on locoregional treatment of patients with breast cancer newly diagnosed with ipsilateral supraclavicular lymph node metastasis.

Background: Radiotherapy is a practical locoregional treatment approach for women with breast cancer who show ipsilateral supraclavicular lymph node metastasis (ISLNM) on diagnosis. However, there is controversy around the role of supraclavicular lymph node dissection. Therefore, we aimed to study the significance of supraclavicular surgery based on radiotherapy. Patients and Methods: We retrospectively reviewed the data of 142 patients with breast cancer who presented with isolated ISLNM and received radiotherapy between the years 2000 and 2016. We also defined the effect of surgery on locoregional treatment of these patients by analyzing the prognostic factors for recurrence-free survival (RFS), distant metastasis-free survival (DMFS), and overall survival (OS). Results: We observed that, of the 142 patients, 104 who received radiotherapy underwent supraclavicular lymph node dissection. Also, among the study group, the progesterone receptor (PR) status (P = 0.044) and the number of axillary lymph nodes (ALNs) involved (P = 0.002) were significant independent predictors of RFS. Also, tumor size (P = 0.007), PR (P < 0.001), and number of ALNs (P < 0.001) were independent predictors of DMFS and were statistically significant. Also, PR was an independent prognostic factor of OS (P = 0.033), whereas the supraclavicular surgery was not an independent prognostic factor for RFS, DMFS, and OS. Furthermore, our study focused on 92 patients with negative estrogen receptors (ERs). The result showed that supraclavicular surgery was statistically significant for RFS (P = 0.023); no significant differences in DMFS and OS were found between patients who received supraclavicular surgery and those who did not. Conclusion: Radiotherapy may be the primary locoregional treatment approach for patients with breast cancer who present with newly diagnosed ISLNM. Additionally, supraclavicular surgery may be more appropriate for patients with negative ER who received radiotherapy.

A mathematical prediction model incorporating molecular subtype for risk of non-sentinel lymph node metastasis in sentinel lymph node-positive breast cancer patients: a retrospective analysis and nomogram development.

BACKGROUND: Molecular subtype of breast cancer is associated with sentinel lymph node status. We sought to establish a mathematical prediction model that included breast cancer molecular subtype for risk of positive non-sentinel lymph nodes in breast cancer patients with sentinel lymph node metastasis and further validate the model in a separate validation cohort. METHODS: We reviewed the clinicopathologic data of breast cancer patients with sentinel lymph node metastasis who underwent axillary lymph node dissection between June 16, 2014 and November 16, 2017 at our hospital. Sentinel lymph node biopsy was performed and patients with pathologically proven sentinel lymph node metastasis underwent axillary lymph node dissection. Independent risks for non-sentinel lymph node metastasis were assessed in a training cohort by multivariate analysis and incorporated into a mathematical prediction model. The model was further validated in a separate validation cohort, and a nomogram was developed and evaluated for diagnostic performance in predicting the risk of non-sentinel lymph node metastasis. Moreover, we assessed the performance of five different models in predicting non-sentinel lymph node metastasis in training cohort. RESULTS: Totally, 495 cases were eligible for the study, including 291 patients in the training cohort and 204 in the validation cohort. Non-sentinel lymph node metastasis was observed in 33.3% (97/291) patients in the training cohort. The AUC of MSKCC, Tenon, MDA, Ljubljana, and Louisville models in training cohort were 0.7613, 0.7142, 0.7076, 0.7483, and 0.671, respectively. Multivariate regression analysis indicated that tumor size (OR = 1.439; 95% CI 1.025-2.021; P = 0.036), sentinel lymph node macro-metastasis versus micro-metastasis (OR = 5.063; 95% CI 1.111-23.074; P = 0.036), the number of positive sentinel lymph nodes (OR = 2.583, 95% CI 1.714-3.892; P < 0.001), and the number of negative sentinel lymph nodes (OR = 0.686, 95% CI 0.575-0.817; P < 0.001) were independent statistically significant predictors of non-sentinel lymph node metastasis. Furthermore, luminal B (OR = 3.311, 95% CI 1.593-6.884; P = 0.001) and HER2 overexpression (OR = 4.308, 95% CI 1.097-16.912; P = 0.036) were independent and statistically significant predictor of non-sentinel lymph node metastasis versus luminal A. A regression model based on the results of multivariate analysis was established to predict the risk of non-sentinel lymph node metastasis, which had an AUC of 0.8188. The model was validated in the validation cohort and showed excellent diagnostic performance. CONCLUSIONS: The mathematical prediction model that incorporates five variables including breast cancer molecular subtype demonstrates excellent diagnostic performance in assessing the risk of non-sentinel lymph node metastasis in sentinel lymph node-positive patients. The prediction model could be of help surgeons in evaluating the risk of non-sentinel lymph node involvement for breast cancer patients; however, the model requires further validation in prospective studies.

Predicting protein structural class based on multi-features fusion.

Structural class characterizes the overall folding type of a protein or its domain and the prediction of protein structural class has become both an important and a challenging topic in protein science. Moreover, the prediction itself can stimulate the development of novel predictors that may be straightforwardly applied to many other relational areas. In this paper, 10 frequently used sequence-derived structural and physicochemical features, which can be easily computed by the PROFEAT (Protein Features) web server, were taken as inputs of support vector machines to develop statistical learning models for predicting the protein structural class. More importantly, a strategy of merging different features, called best-first search, was developed. It was shown through the rigorous jackknife cross-validation test that the success rates by our method were significantly improved. We anticipate that the present method may also have important impacts on boosting the predictive accuracies for a series of other protein attributes, such as subcellular localization, membrane types, enzyme family and subfamily classes, among many others.