Predicting nonsentinel node status in breast cancer preoperatively by molecular characterization of core needle biopsies with multiparametric protein analyses

Abstract

21068 Background: Axillary lymph node dissection (ALND) still remains the standard treatment for breast cancer patients with sentinel lymph node (SLN) metastases. However, since only 40% to 60% of patients show additional lymph involvement, complete ALND offers no additional benefit for almost 50% of patients but carries a significant risk of axillary morbidity. In an attempt to achieve a more precise prediction for the individual patient a multivariable logistic-regression analysis of a large data set of eight histopathological variables has been published by the Memorial Sloan-Kettering Cancer Center (MSKCC) that predicts the likelihood of metastases in Non-SLNs with a ROC of 0,71. To achieve a more precise estimation of additional lymph node involvement preoperatively we analysed molecular markers of potential predictive value. Methods: Beside histopathological variables, fifty proteins of potential prognostic and predictive value were preoperatively quantified in lysates from 120 core needle biopsies with multiplex sandwich immunoassays. Biopsies, taken at the Breast Cancer Center of the University of Ulm Medical School, were classified as invasive mammary carcinomas. Variables which could be used to improve the accuracy of prediction of non-sentinel lymph node (NSLN) involvement were defined. Results: We demonstrated appropriate sensitivity, reproducibility, and robustness for this protein microarray technology to characterize proteins in core needle biopsies and generate reliable data sets. In an initial univariate data analysis (Mann Whitney test) six of the analysed parameters (TIMP2, p= 0,027; FasR, p = 0,0025; MIF, p= 0,030; FGF-2, p= 0,0020, PDGF AA/BB p= 0,027; RANTES p= 0,024) correlated with NSLN involvement beside known histopathological variables (i.e. tumor size, p= 0,006; grading, p = 0,0009). Conclusions: We defined molecular markers of diagnostic value to predict NSLN involvement. This might be an acceptable substitute to the known histopathological variables by the MSKCC nomogram to predict potential NSLN involvement preoperatively. No significant financial relationships to disclose.