Disease-free survival and prognostic significance of metastatic lymph node ratio in T1-T2 N positive breast cancer patients. A population registry-based study in a European country.
ABSTRACT The ratio of positive lymph nodes between the total number of harvested lymph nodes (metastatic lymph node ratio, MLNR) has been proposed as an alternative to the total number of lymph nodes alone in predicting outcomes for patients with breast cancer. Because there can be differences between European and non-European populations, the authors present the first study analyzing MLNR influence over disease-free survival (DFS) by using a population-based cancer registry in a European country.
Data from 441 patients with T1-2 N1-3 breast cancer included in the Castellon Cancer Registry (Comunidad Valenciana, Spain) were used. Cumulative DFS was determined using the Kaplan-Meier method, with univariate comparisons between groups through the log-rank test. The Cox proportional hazards model was used for multivariate analysis.
At univariate analysis, factors influencing the 10-year DFS rate were tumor size, conservative or nonconservative surgery, histologic grade, histologic type, radiotherapy, tamoxifen, estrogen and progesterone receptor status, p53 status, total number of positive lymph nodes, and MLNR. At multivariate analysis, tumor size, MLNR, and progesterone receptor status were revealed to be independent prognostic factors; the metastatic lymph node ratio was the most notably independent factor (hazard ratio 1.02, 5.21, and 0.61, respectively).
MLNR is a stronger prognostic factor for recurrence than the total number of positive lymph nodes in T1-T2 N1-3 breast cancer.
Article: Prediction of lymph node involvement in breast cancer from primary tumor tissue using gene expression profiling and miRNAs.[show abstract] [hide abstract]
ABSTRACT: The aim of this study was to investigate whether lymph node involvement in breast cancer is influenced by gene or miRNA expression of the primary tumor. For this purpose, we selected a very homogeneous patient population to minimize heterogeneity in other tumor and patient characteristics. First, we compared gene expression profiles of primary tumor tissue from a group of 96 breast cancer patients balanced for lymph node involvement using Affymetrix Human U133 Plus 2.0 microarray chip. A model was built by weighted Least-Squares Support Vector Machines and validated on an internal and external dataset. Next, miRNA profiling was performed on a subset of 82 tumors using Human MiRNA-microarray chips (Illumina). Finally, for each miRNA the number of significant inverse correlated targets was determined and compared with 1000 sets of randomly chosen targets. A model based on 241 genes was built (AUC 0.66). The AUC for the internal dataset was 0.646 and 0. 651 for the external datasets. The model includes multiple kinases, apoptosis-related, and zinc ion-binding genes. Integration of the microarray and miRNA data reveals ten miRNAs suppressing lymph node invasion and one miRNA promoting lymph node invasion. Our results provide evidence that measurable differences in gene and miRNA expression exist between node negative and node positive patients and thus that lymph node involvement is not a genetically random process. Moreover, our data suggest a general deregulation of the miRNA machinery that is potentially responsible for lymph node invasion.Breast Cancer Research and Treatment 11/2010; 129(3):767-76. · 4.43 Impact Factor