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      Molecular Subtype Classification Is a Determinant of Non-Sentinel Lymph Node Metastasis in Breast Cancer Patients with Positive Sentinel Lymph Nodes

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          Abstract

          Background

          Previous studies suggested that the molecular subtypes were strongly associated with sentinel lymph node (SLN) status. The purpose of this study was to determine whether molecular subtype classification was associated with non-sentinel lymph nodes (NSLN) metastasis in patients with a positive SLN.

          Methodology and Principal Findings

          Between January 2001 and March 2011, a total of 130 patients with a positive SLN were recruited. All these patients underwent a complete axillary lymph node dissection. The univariate and multivariate analyses of NSLN metastasis were performed. In univariate and multivariate analyses, large tumor size, macrometastasis and high tumor grade were all significant risk factors of NSLN metastasis in patients with a positive SLN. In univariate analysis, luminal B subgroup showed higher rate of NSLN metastasis than other subgroup ( P = 0.027). When other variables were adjusted in multivariate analysis, the molecular subtype classification was a determinant of NSLN metastasis. Relative to triple negative subgroup, both luminal A ( P = 0.047) and luminal B ( P = 0.010) subgroups showed a higher risk of NSLN metastasis. Otherwise, HER2 over-expression subgroup did not have a higher risk than triple negative subgroup ( P = 0.183). The area under the curve (AUC) value was 0.8095 for the Cambridge model. When molecular subtype classification was added to the Cambridge model, the AUC value was 0.8475.

          Conclusions

          Except for other factors, molecular subtype classification was a determinant of NSLN metastasis in patients with a positive SLN. The predictive accuracy of mathematical models including molecular subtype should be determined in the future.

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          Most cited references26

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          A randomized comparison of sentinel-node biopsy with routine axillary dissection in breast cancer.

          Although numerous studies have shown that the status of the sentinel node is an accurate predictor of the status of the axillary nodes in breast cancer, the efficacy and safety of sentinel-node biopsy require validation. From March 1998 to December 1999, we randomly assigned 516 patients with primary breast cancer in whom the tumor was less than or equal to 2 cm in diameter either to sentinel-node biopsy and total axillary dissection (the axillary-dissection group) or to sentinel-node biopsy followed by axillary dissection only if the sentinel node contained metastases (the sentinel-node group). The number of sentinel nodes found was the same in the two groups. A sentinel node was positive in 83 of the 257 patients in the axillary-dissection group (32.3 percent), and in 92 of the 259 patients in the sentinel-node group (35.5 percent). In the axillary-dissection group, the overall accuracy of the sentinel-node status was 96.9 percent, the sensitivity 91.2 percent, and the specificity 100 percent. There was less pain and better arm mobility in the patients who underwent sentinel-node biopsy only than in those who also underwent axillary dissection. There were 15 events associated with breast cancer in the axillary-dissection group and 10 such events in the sentinel-node group. Among the 167 patients who did not undergo axillary dissection, there were no cases of overt axillary metastasis during follow-up. Sentinel-node biopsy is a safe and accurate method of screening the axillary nodes for metastasis in women with a small breast cancer. Copyright 2003 Massachusetts Medical Society
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            A nomogram for predicting the likelihood of additional nodal metastases in breast cancer patients with a positive sentinel node biopsy.

            The standard of care for breast cancer patients with sentinel lymph node (SLN) metastases includes complete axillary lymph node dissection (ALND). However, many question the need for complete ALND in every patient with detectable SLN metastases, particularly those perceived to have a low risk of non-SLN metastases. Accurate estimates of the likelihood of additional disease in the axilla could assist greatly in decision-making regarding further treatment. Pathological features of the primary tumor and SLN metastases of 702 patients who underwent complete ALND were assessed with multivariable logistic regression to predict the presence of additional disease in the non-SLNs of these patients. A nomogram was created using pathological size, tumor type and nuclear grade, lymphovascular invasion, multifocality, and estrogen-receptor status of the primary tumor; method of detection of SLN metastases; number of positive SLNs; and number of negative SLNs. The model was subsequently applied prospectively to 373 patients. The nomogram for the retrospective population was accurate and discriminating, with an area under the receiver operating characteristic (ROC) curve of 0.76. When applied to the prospective group, the model accurately predicted likelihood of non-SLN disease (ROC, 0.77). We have developed a user-friendly nomogram that uses information commonly available to the surgeon to easily and accurately calculate the likelihood of having additional, non-SLN metastases for an individual patient.
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              Breast cancer molecular profiling with single sample predictors: a retrospective analysis.

              Microarray expression profiling classifies breast cancer into five molecular subtypes: luminal A, luminal B, basal-like, HER2, and normal breast-like. Three microarray-based single sample predictors (SSPs) have been used to define molecular classification of individual samples. We aimed to establish agreement between these SSPs for identification of breast cancer molecular subtypes. Previously described microarray-based SSPs were applied to one in-house (n=53) and three publicly available (n=779) breast cancer datasets. Agreement was analysed between SSPs for the whole classification system and for the five molecular subtypes individually in each cohort. Fair-to-substantial agreement between every pair of SSPs in each cohort was recorded (kappa=0.238-0.740). Of the five molecular subtypes, only basal-like cancers consistently showed almost-perfect agreement (kappa>0.812). The proportion of cases classified as basal-like in each cohort was consistent irrespective of the SSP used; however, the proportion of each remaining molecular subtype varied substantially. Assignment of individual cases to luminal A, luminal B, HER2, and normal breast-like subtypes was dependent on the SSP used. The significance of associations with outcome of each molecular subtype, other than basal-like and luminal A, varied depending on SSP used. However, different SSPs produced broadly similar survival curves. Although every SSP identifies molecular subtypes with similar survival, they do not reliably assign the same patients to the same molecular subtypes. For molecular subtype classification to be incorporated into routine clinical practice and treatment decision making, stringent standardisation of methodologies and definitions for identification of breast cancer molecular subtypes is needed. Breakthrough Breast Cancer, Cancer Research UK. 2010 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                26 April 2012
                : 7
                : 4
                : e35881
                Affiliations
                [1]Department of Breast Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
                Health Canada, Canada
                Author notes

                Conceived and designed the experiments: XL SW. Performed the experiments: WZ ZH JX MW. Analyzed the data: WZ XL. Contributed reagents/materials/analysis tools: XZ LL LC. Wrote the paper: WZ ZH.

                Article
                PONE-D-12-04305
                10.1371/journal.pone.0035881
                3338552
                22563412
                873075ef-47ac-4239-b0eb-ee8c3908dbaf
                Zhou et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 9 February 2012
                : 23 March 2012
                Page count
                Pages: 5
                Categories
                Research Article
                Medicine
                Clinical Research Design
                Retrospective Studies
                Diagnostic Medicine
                Pathology
                General Pathology
                Biomarkers
                Obstetrics and Gynecology
                Breast Cancer
                Oncology
                Basic Cancer Research
                Metastasis
                Cancer Detection and Diagnosis
                Lymphatic Mapping
                Cancers and Neoplasms
                Breast Tumors
                Surgery
                Surgical Oncology

                Uncategorized
                Uncategorized

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