Prediction of Nodal Involvement in Breast Cancer Based on Multiparametric Protein Analyses from Preoperative Core Needle Biopsies of the Primary Lesion.

摘要

PURPOSE: Identification of molecular characteristics that are useful to define subgroups of patients fitting into differential treatment schemes is considered a most promising approach in cancer research. In this first study of such type, we therefore investigated the potential of multiplexed sandwich immunoassays to define protein expression profiles indicative of clinically relevant properties of malignant tumors. EXPERIMENTAL DESIGN: Lysates prepared from large core needle biopsies of 113 invasive breast carcinomas were analyzed with bead-based miniaturized sandwich immunoassays specific for 54 preselected proteins. RESULTS: Five protein concentrations [fibroblast growth factor-2 (FGF-2), Fas, Fas ligand, tissue inhibitor of metalloproteinase-1, and RANTES] were significantly different in the groups of patients with or without axillary lymph node metastasis. All 15 protein parameters that resulted in P values <0.2 and other diagnostic information [estrogen receptor (ER) status, tumor size, and histologic grading] were analyzed together by multivariate logistic regression. This yielded sets of five (FGF-2, Fas, Fas ligand, IP10, and PDGF-AB/BB) or six (ER staining intensity, FGF-2, Fas ligand, matrix metalloproteinase-13, PDGF-AB/BB, and IP10) parameters for which receiver-operator characteristic analyses revealed high sensitivities and specificities [area under curve (AUC) = 0.75 and AUC = 0.83] to predict the nodal status. A similar analysis including all identified parameters of potential value (15 proteins, ER staining intensity, T) without selection resulted in a receiver-operator characteristic curve with an AUC of 0.87. CONCLUSION: We clearly showed that this approach can be used to quantify numerous proteins from breast biopsies accurately in parallel and define sets of proteins whose combined analyses allow the prediction of nodal involvement with high specificity and sensitivity.