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      T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers

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          Abstract

          Introduction

          Lymphocyte infiltration (LI) is often seen in breast cancer but its importance remains controversial. A positive correlation of human epidermal growth factor receptor 2 (HER2) amplification and LI has been described, which was associated with a more favorable outcome. However, specific lymphocytes might also promote tumor progression by shifting the cytokine milieu in the tumor.

          Methods

          Affymetrix HG-U133A microarray data of 1,781 primary breast cancer samples from 12 datasets were included. The correlation of immune system-related metagenes with different immune cells, clinical parameters, and survival was analyzed.

          Results

          A large cluster of nearly 600 genes with functions in immune cells was consistently obtained in all datasets. Seven robust metagenes from this cluster can act as surrogate markers for the amount of different immune cell types in the breast cancer sample. An IgG metagene as a marker for B cells had no significant prognostic value. In contrast, a strong positive prognostic value for the T-cell surrogate marker (lymphocyte-specific kinase (LCK) metagene) was observed among all estrogen receptor (ER)-negative tumors and those ER-positive tumors with a HER2 overexpression. Moreover ER-negative tumors with high expression of both IgG and LCK metagenes seem to respond better to neoadjuvant chemotherapy.

          Conclusions

          Precise definitions of the specific subtypes of immune cells in the tumor can be accomplished from microarray data. These surrogate markers define subgroups of tumors with different prognosis. Importantly, all known prognostic gene signatures uniformly assign poor prognosis to all ER-negative tumors. In contrast, the LCK metagene actually separates the ER-negative group into better or worse prognosis.

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          Most cited references42

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          Stromal gene expression predicts clinical outcome in breast cancer.

          Although it is increasingly evident that cancer is influenced by signals emanating from tumor stroma, little is known regarding how changes in stromal gene expression affect epithelial tumor progression. We used laser capture microdissection to compare gene expression profiles of tumor stroma from 53 primary breast tumors and derived signatures strongly associated with clinical outcome. We present a new stroma-derived prognostic predictor (SDPP) that stratifies disease outcome independently of standard clinical prognostic factors and published expression-based predictors. The SDPP predicts outcome in several published whole tumor-derived expression data sets, identifies poor-outcome individuals from multiple clinical subtypes, including lymph node-negative tumors, and shows increased accuracy with respect to previously published predictors, especially for HER2-positive tumors. Prognostic power increases substantially when the predictor is combined with existing outcome predictors. Genes represented in the SDPP reveal the strong prognostic capacity of differential immune responses as well as angiogenic and hypoxic responses, highlighting the importance of stromal biology in tumor progression.
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            Strong Time Dependence of the 76-Gene Prognostic Signature for Node-Negative Breast Cancer Patients in the TRANSBIG Multicenter Independent Validation Series

            Recently, a 76-gene prognostic signature able to predict distant metastases in lymph node-negative (N(-)) breast cancer patients was reported. The aims of this study conducted by TRANSBIG were to independently validate these results and to compare the outcome with clinical risk assessment. Gene expression profiling of frozen samples from 198 N(-) systemically untreated patients was done at the Bordet Institute, blinded to clinical data and independent of Veridex. Genomic risk was defined by Veridex, blinded to clinical data. Survival analyses, done by an independent statistician, were done with the genomic risk and adjusted for the clinical risk, defined by Adjuvant! Online. The actual 5- and 10-year time to distant metastasis were 98% (88-100%) and 94% (83-98%), respectively, for the good profile group and 76% (68-82%) and 73% (65-79%), respectively, for the poor profile group. The actual 5- and 10-year overall survival were 98% (88-100%) and 87% (73-94%), respectively, for the good profile group and 84% (77-89%) and 72% (63-78%), respectively, for the poor profile group. We observed a strong time dependence of this signature, leading to an adjusted hazard ratio of 13.58 (1.85-99.63) and 8.20 (1.10-60.90) at 5 years and 5.11 (1.57-16.67) and 2.55 (1.07-6.10) at 10 years for time to distant metastasis and overall survival, respectively. This independent validation confirmed the performance of the 76-gene signature and adds to the growing evidence that gene expression signatures are of clinical relevance, especially for identifying patients at high risk of early distant metastases.
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              Concordance among gene-expression-based predictors for breast cancer.

              Gene-expression-profiling studies of primary breast tumors performed by different laboratories have resulted in the identification of a number of distinct prognostic profiles, or gene sets, with little overlap in terms of gene identity. To compare the predictions derived from these gene sets for individual samples, we obtained a single data set of 295 samples and applied five gene-expression-based models: intrinsic subtypes, 70-gene profile, wound response, recurrence score, and the two-gene ratio (for patients who had been treated with tamoxifen). We found that most models had high rates of concordance in their outcome predictions for the individual samples. In particular, almost all tumors identified as having an intrinsic subtype of basal-like, HER2-positive and estrogen-receptor-negative, or luminal B (associated with a poor prognosis) were also classified as having a poor 70-gene profile, activated wound response, and high recurrence score. The 70-gene and recurrence-score models, which are beginning to be used in the clinical setting, showed 77 to 81 percent agreement in outcome classification. Even though different gene sets were used for prognostication in patients with breast cancer, four of the five tested showed significant agreement in the outcome predictions for individual patients and are probably tracking a common set of biologic phenotypes. Copyright 2006 Massachusetts Medical Society.
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                Author and article information

                Journal
                Breast Cancer Res
                Breast Cancer Research : BCR
                BioMed Central
                1465-5411
                1465-542X
                2009
                9 March 2009
                : 11
                : 2
                : R15
                Affiliations
                [1 ]Department of Obstetrics and Gynecology, J.W. Goethe-University, Theodor-Stern-Kai 7, Frankfurt 60590, Germany
                [2 ]Department of Breast Medical Oncology, The University of Texas M.D. Anderson Cancer Center, PO Box 301439, Houston, TX 77230-1439, USA
                [3 ]Department of Obstetrics and Gynecology, University of Muenster, Albert-Schweitzer-Straße 33, Muenster 48149, Germany
                [4 ]LMU BioCenter, Ludwig Maximilians University Munich, Grosshaderner Straße 2, Planegg-Martinsried 82152, Germany
                [5 ]Department of Pathology, J.W. Goethe-University, Theodor-Stern-Kai 7, Frankfurt 60590, Germany
                Article
                bcr2234
                10.1186/bcr2234
                2688939
                19272155
                5a02248b-35e5-430c-9a55-05cd17205fb2
                Copyright © 2009 Rody 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
                : 27 November 2008
                : 10 February 2009
                : 20 February 2009
                : 9 March 2009
                Categories
                Research Article

                Oncology & Radiotherapy
                Oncology & Radiotherapy

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