c-erbB2 and topoisomerase IIα protein expression independently predict poor survival in primary human breast cancer: a retrospective study

INTRODUCTION: c-erbB2 (also known as HER-2/neu) and topoisomerase IIα are frequently overexpressed in breast cancer. The aim of the study was to analyze retrospectively whether the expression of c-erbB2 and topoisomerase IIα protein influences the long-term outcome of patients with primary breast cancer. METHODS: In this study c-erbB2 and topoisomerase IIα protein were evaluated by immunohistochemistry in formalin-fixed paraffin-embedded tissue from 225 samples of primary breast cancer, obtained between 1986 and 1998. The prognostic value of these markers was analyzed. RESULTS: Of 225 primary breast tumor samples, 78 (34.7%) showed overexpression of either c-erbB2 (9.8%) or topoisomerase IIα protein (24.9%), whereas in 21 tumors (9.3%) both proteins were found to be overexpressed. Patients lacking both c-erbB2 and topoisomerase IIα overexpression had the best long-term survival. Overexpression of either c-erbB2 or topoisomerase IIα was associated with shortened survival, whereas patients overexpressing both c-erbB2 and topoisomerase IIα showed the worst disease outcome (P < 0.0001). Treatment with anthracyclines was not capable of reversing the negative prognostic impact of topoisomerase IIα or c-erbB2 overexpression. CONCLUSION: The results of this exploratory study suggest that protein expression of c-erbB2 and topoisomerase IIα in primary breast cancer tissues are independent prognostic factors and are not exclusively predictive factors for anthracycline response in patients with primary breast cancer

Short term culture of breast cancer tissues to study the activity of the anticancer drug taxol in an intact tumor environment

BACKGROUND: Sensitivity of breast tumors to anticancer drugs depends upon dynamic interactions between epithelial tumor cells and their microenvironment including stromal cells and extracellular matrix. To study drug-sensitivity within different compartments of an individual tumor ex vivo, culture models directly established from fresh tumor tissues are absolutely essential. METHODS: We prepared 0.2 mm thick tissue slices from freshly excised tumor samples and cultivated them individually in the presence or absence of taxol for 4 days. To visualize viability, cell death, and expression of surface molecules in different compartments of non-fixed primary breast cancer tissues we established a method based on confocal imaging using mitochondria- and DNA-selective dyes and fluorescent-conjugated antibodies. Proliferation and apoptosis was assessed by immunohistochemistry in sections from paraffin-embedded slices. Overall viability was also analyzed in homogenized tissue slices by a combined ATP/DNA quantification assay. RESULTS: We obtained a mean of 49 tissue slices from 22 breast cancer specimens allowing a wide range of experiments in each individual tumor. In our culture system, cells remained viable and proliferated for at least 4 days within their tissue environment. Viability of tissue slices decreased significantly in the presence of taxol in a dose-dependent manner. A three-color fluorescence viability assay enabled a rapid and authentic estimation of cell viability in the different tumor compartments within non-fixed tissue slices. CONCLUSION: We describe a tissue culture method combined with a novel read out system for both tissue cultivation and rapid assessment of drug efficacy together with the simultaneous identification of different cell types within non-fixed breast cancer tissues. This method has potential significance for studying tumor responses to anticancer drugs in the complex environment of a primary cancer tissue

DEFINING KPI SETS FOR INDUSTRIAL RESEARCH ORGANIZATIONS — A PERFORMANCE MEASUREMENT APPROACH

In today's challenging economic climate it is more important than ever for companies to acquire comparable competitive advantage in their market. While previous research has investigated the performance measurement of R&D as a whole organization, very little research has been done into the performance management for just the research function itself. This, however, is of particular interest to larger organizations. This paper (1) introduces a framework for performance measurement for industrial research, and (2) presents a set of clusters, representing the content dimension for measuring research organizations. Based on the clusters, we were able to evaluate the extent to which performance measurement in practice in different companies can be compared. We discovered that the clusters follow a particular consistent distribution across organizations when the clusters are ranked by importance. For this empirical analysis, data was collected through in-depth case studies including more than 60 interviews and thorough document analyses.Innovation, research and development (R&D), industrial research, performance measurement, research goals, key performance indicators (KPI), expert interviews, case studies