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      Ki-67 is a prognostic parameter in breast cancer patients: results of a large population-based cohort of a cancer registry

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          The proliferation marker Ki-67 is one of the most controversially discussed parameters for treatment decisions in breast cancer patients. The purpose of this study was to evaluate the routine use and value of Ki-67 as a prognostic marker, and to analyze the associations between Ki-67 and common histopathological parameters in the routine clinical setting. Data from the clinical cancer registry Regensburg (Bavaria, Germany) were analyzed. Within the total data pool of 4,692 female patients, who had been diagnosed between 2005 and 2011, in 3,658 cases Ki-67 was routinely determined. Thus, a total of 3,658 patients with invasive breast cancer were included in the present study and used for statistical analysis. Ki-67 expression was associated with the common histopathological parameters. The strongest correlation was found between grading and Ki-67 ( P < 0.001). In terms of survival analyses, Ki-67 was categorized into five categories (reference category Ki-67 ≤15 %) due to a nonlinear relationship to overall survival (OS). In multivariable analysis, Ki-67 was an independent prognostic parameter both for disease-free survival (DFS) (Ki-67 > 45 %, HR = 1.96, P = 0.001) as well as for OS (Ki-67: 26–35 %, HR = 1.71, P = 0.017; Ki-67: 36–45 %, HR = 2.05, P = 0.011; Ki-67 > 45 %, HR = 2.06, P = 0.002) independent of common clinical and histopathological factors. The 5-year DFS (OS) rate was 86.7 % (89.3 %) in patients with a Ki-67 value ≤15 % compared to 75.8 % (82.8 %) in patients with a Ki-67 value >45 %. Based on the data from a large cohort of a clinical cancer registry, it was demonstrated that Ki-67 is frequently determined in routine clinical work. Ki-67 expression is associated with common histopathological parameters, but is an additional independent prognostic parameter for DFS and OS in breast cancer patients. Future work should focus on standardization of Ki-67 assessment and specification of its role in treatment decisions.

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            Human breast tumours are diverse in their natural history and in their responsiveness to treatments. Variation in transcriptional programs accounts for much of the biological diversity of human cells and tumours. In each cell, signal transduction and regulatory systems transduce information from the cell's identity to its environmental status, thereby controlling the level of expression of every gene in the genome. Here we have characterized variation in gene expression patterns in a set of 65 surgical specimens of human breast tumours from 42 different individuals, using complementary DNA microarrays representing 8,102 human genes. These patterns provided a distinctive molecular portrait of each tumour. Twenty of the tumours were sampled twice, before and after a 16-week course of doxorubicin chemotherapy, and two tumours were paired with a lymph node metastasis from the same patient. Gene expression patterns in two tumour samples from the same individual were almost always more similar to each other than either was to any other sample. Sets of co-expressed genes were identified for which variation in messenger RNA levels could be related to specific features of physiological variation. The tumours could be classified into subtypes distinguished by pervasive differences in their gene expression patterns.
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              A gene-expression signature as a predictor of survival in breast cancer.

              A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy. Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses. Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (+/-SE) overall 10-year survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6+/-4.5 percent in the group with a poor-prognosis signature and 85.2+/-4.3 percent in the group with a good-prognosis signature. The estimated hazard ratio for distant metastases in the group with a poor-prognosis signature, as compared with the group with the good-prognosis signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001). This ratio remained significant when the groups were analyzed according to lymph-node status. Multivariable Cox regression analysis showed that the prognosis profile was a strong independent factor in predicting disease outcome. The gene-expression profile we studied is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria. Copyright 2002 Massachusetts Medical Society

                Author and article information

                [ ]Department of Gynecology and Obstetrics, University of Regensburg, Caritas Krankenhaus St. Josef Regensburg, Landshuter Straße 65, 93053 Regensburg, Germany
                [ ]Tumor Center Regensburg e.V., University of Regensburg, Regensburg, Germany
                [ ]Institute of Pathology, University of Regensburg, Regensburg, Germany
                [ ]Center for Clinical Studies, University Hospital Regensburg, Regensburg, Germany
                +49-173-64-79-187 ,
                Breast Cancer Res Treat
                Breast Cancer Res. Treat
                Breast Cancer Research and Treatment
                Springer US (Boston )
                16 May 2013
                16 May 2013
                June 2013
                : 139
                : 2
                : 539-552
                © The Author(s) 2013

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

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                © Springer Science+Business Media New York 2013


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