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      Up-regulation of cell cycle arrest protein BTG2 correlates with increased overall survival in breast cancer, as detected by immunohistochemistry using tissue microarray

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          Abstract

          Background

          Previous studies have shown that the ADIPOR1, ADORA1, BTG2 and CD46 genes differ significantly between long-term survivors of breast cancer and deceased patients, both in levels of gene expression and DNA copy numbers. The aim of this study was to characterize the expression of the corresponding proteins in breast carcinoma and to determine their correlation with clinical outcome.

          Methods

          Protein expression was evaluated using immunohistochemistry in an independent breast cancer cohort of 144 samples represented on tissue microarrays. Fisher's exact test was used to analyze the differences in protein expression between dead and alive patients. We used Cox-regression multivariate analysis to assess whether the new markers predict the survival status of the patients better than the currently used markers.

          Results

          BTG2 expression was demonstrated in a significantly lower proportion of samples from dead patients compared to alive patients, both in overall expression ( P = 0.026) and cell membrane specific expression ( P = 0.013), whereas neither ADIPOR1, ADORA1 nor CD46 showed differential expression in the two survival groups. Furthermore, a multivariate analysis showed that a model containing BTG2 expression in combination with HER2 and Ki67 expression along with patient age performed better than a model containing the currently used prognostic markers (tumour size, nodal status, HER2 expression, hormone receptor status, histological grade, and patient age). Interestingly, BTG2 has previously been described as a tumour suppressor gene involved in cell cycle arrest and p53 signalling.

          Conclusions

          We conclude that high-level BTG2 protein expression correlates with prolonged survival in patients with breast carcinoma.

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          Most cited references28

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          Survival model predictive accuracy and ROC curves.

          The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R2, commonly used for continuous response models, or using extensions of sensitivity and specificity, which are commonly used for binary response models. In this article we propose new time-dependent accuracy summaries based on time-specific versions of sensitivity and specificity calculated over risk sets. We connect the accuracy summaries to a previously proposed global concordance measure, which is a variant of Kendall's tau. In addition, we show how standard Cox regression output can be used to obtain estimates of time-dependent sensitivity and specificity, and time-dependent receiver operating characteristic (ROC) curves. Semiparametric estimation methods appropriate for both proportional and nonproportional hazards data are introduced, evaluated in simulations, and illustrated using two familiar survival data sets.
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            Progress and promise: highlights of the international expert consensus on the primary therapy of early breast cancer 2007.

            The 10th St Gallen (Switzerland) expert consensus meeting in March 2007 refined and extended a target-oriented approach to adjuvant systemic therapy of early breast cancer. Target definition is inextricably intertwined with the availability of target-specific therapeutic agents. Since 2005, the presence of HER2 on the cell surface has been used as an effective target for trastuzumab much as steroid hormone receptors are targets for endocrine therapies. An expert Panel reaffirmed the primary importance of determining endocrine responsiveness of the cancer as a first approach to selecting systemic therapy. Three categories were acknowledged: highly endocrine responsive, incompletely endocrine responsive and endocrine non-responsive. The Panel accepted HER2-positivity to assign trastuzumab, and noted that adjuvant trastuzumab has only been assessed together with chemotherapy. They largely endorsed previous definitions of risk categories. While recognizing the existence of several molecularly-based tools for risk stratification, the Panel preferred to recommend the use of high-quality standard histopathological assessment for both risk allocation and target identification. Chemotherapy, although largely lacking specific target information, is the only option in cases which are both endocrine receptor-negative and HER2-negative. Chemotherapy is conventionally given with or preceding trastuzumab for patients with HER2-positive disease, and may be used for patients with endocrine responsive disease in cases where the sufficiency of endocrine therapy alone is uncertain. Recommendations are provided not as specific therapy guidelines but rather as a general guidance emphasizing main principles for tailoring therapeutic choice.
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              Variable selection under multiple imputation using the bootstrap in a prognostic study

              Background Missing data is a challenging problem in many prognostic studies. Multiple imputation (MI) accounts for imputation uncertainty that allows for adequate statistical testing. We developed and tested a methodology combining MI with bootstrapping techniques for studying prognostic variable selection. Method In our prospective cohort study we merged data from three different randomized controlled trials (RCTs) to assess prognostic variables for chronicity of low back pain. Among the outcome and prognostic variables data were missing in the range of 0 and 48.1%. We used four methods to investigate the influence of respectively sampling and imputation variation: MI only, bootstrap only, and two methods that combine MI and bootstrapping. Variables were selected based on the inclusion frequency of each prognostic variable, i.e. the proportion of times that the variable appeared in the model. The discriminative and calibrative abilities of prognostic models developed by the four methods were assessed at different inclusion levels. Results We found that the effect of imputation variation on the inclusion frequency was larger than the effect of sampling variation. When MI and bootstrapping were combined at the range of 0% (full model) to 90% of variable selection, bootstrap corrected c-index values of 0.70 to 0.71 and slope values of 0.64 to 0.86 were found. Conclusion We recommend to account for both imputation and sampling variation in sets of missing data. The new procedure of combining MI with bootstrapping for variable selection, results in multivariable prognostic models with good performance and is therefore attractive to apply on data sets with missing values.
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                Author and article information

                Journal
                BMC Cancer
                BMC Cancer
                BioMed Central
                1471-2407
                2010
                16 June 2010
                : 10
                : 296
                Affiliations
                [1 ]Department of Oncology, Institute of Clinical Sciences, Blå stråket 2, University of Gothenburg, SE-413 45 Göteborg, Sweden
                [2 ]Pathology section, Department of Pathology, Sahlgrenska University Hospital, Blå stråket 2, University of Gothenburg, SE-413 45 Göteborg, Sweden
                [3 ]Department of Oncology, Barngatan 2:2, Lund University Hospital, University of Lund, SE-221 85 Lund, Sweden
                [4 ]Division of Clinical Cancer Epidemiology, Department of Oncology, Sahlgrenska Academy, University of Gothenburg, Sweden
                [5 ]UCD School of Biomolecular and Biomedical Science, UCD Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland
                [6 ]Department of Laboratory Medicine, Center for Molecular Pathology, Lund University, University Hospital MAS, Malmö, Sweden
                [7 ]Department of Oncology, Sahlgrenska University Hospital, Blå stråket 2, University of Gothenburg, SE-413 45 Göteborg, Sweden
                Article
                1471-2407-10-296
                10.1186/1471-2407-10-296
                2902444
                20553615
                2534a7f1-ad29-4412-8552-3a7d62b656c0
                Copyright ©2010 Möllerström et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 29 September 2009
                : 16 June 2010
                Categories
                Research Article

                Oncology & Radiotherapy
                Oncology & Radiotherapy

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