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      A collection of annotated and harmonized human breast cancer transcriptome datasets, including immunologic classification.

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          Abstract

          The increased application of high-throughput approaches in translational research has expanded the number of publicly available data repositories. Gathering additional valuable information contained in the datasets represents a crucial opportunity in the biomedical field. To facilitate and stimulate utilization of these datasets, we have recently developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB). In this note, we describe a curated compendium of 13 public datasets on human breast cancer, representing a total of 2142 transcriptome profiles. We classified the samples according to different immune based classification systems and integrated this information into the datasets. Annotated and harmonized datasets were uploaded to GXB. Study samples were categorized in different groups based on their immunologic tumor response profiles, intrinsic molecular subtypes and multiple clinical parameters. Ranked gene lists were generated based on relevant group comparisons. In this data note, we demonstrate the utility of GXB to evaluate the expression of a gene of interest, find differential gene expression between groups and investigate potential associations between variables with a specific focus on immunologic classification in breast cancer. This interactive resource is publicly available online at: http://breastcancer.gxbsidra.org/dm3/geneBrowser/list.

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          Most cited references40

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          Adjusting batch effects in microarray expression data using empirical Bayes methods.

          Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
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            The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM.

            The American Joint Committee on Cancer and the International Union for Cancer Control update the tumor-node-metastasis (TNM) cancer staging system periodically. The most recent revision is the 7th edition, effective for cancers diagnosed on or after January 1, 2010. This editorial summarizes the background of the current revision and outlines the major issues revised. Most notable are the marked increase in the use of international datasets for more highly evidenced-based changes in staging, and the enhanced use of nonanatomic prognostic factors in defining the stage grouping. The future of cancer staging lies in the use of enhanced registry data standards to support personalization of cancer care through cancer outcome prediction models and nomograms.
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              Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients.

              The development of human cancer is a multistep process characterized by the accumulation of genetic and epigenetic alterations that drive or reflect tumour progression. These changes distinguish cancer cells from their normal counterparts, allowing tumours to be recognized as foreign by the immune system. However, tumours are rarely rejected spontaneously, reflecting their ability to maintain an immunosuppressive microenvironment. Programmed death-ligand 1 (PD-L1; also called B7-H1 or CD274), which is expressed on many cancer and immune cells, plays an important part in blocking the 'cancer immunity cycle' by binding programmed death-1 (PD-1) and B7.1 (CD80), both of which are negative regulators of T-lymphocyte activation. Binding of PD-L1 to its receptors suppresses T-cell migration, proliferation and secretion of cytotoxic mediators, and restricts tumour cell killing. The PD-L1-PD-1 axis protects the host from overactive T-effector cells not only in cancer but also during microbial infections. Blocking PD-L1 should therefore enhance anticancer immunity, but little is known about predictive factors of efficacy. This study was designed to evaluate the safety, activity and biomarkers of PD-L1 inhibition using the engineered humanized antibody MPDL3280A. Here we show that across multiple cancer types, responses (as evaluated by Response Evaluation Criteria in Solid Tumours, version 1.1) were observed in patients with tumours expressing high levels of PD-L1, especially when PD-L1 was expressed by tumour-infiltrating immune cells. Furthermore, responses were associated with T-helper type 1 (TH1) gene expression, CTLA4 expression and the absence of fractalkine (CX3CL1) in baseline tumour specimens. Together, these data suggest that MPDL3280A is most effective in patients in which pre-existing immunity is suppressed by PD-L1, and is re-invigorated on antibody treatment.
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                Author and article information

                Journal
                F1000Res
                F1000Research
                F1000 Research Ltd
                2046-1402
                2046-1402
                2017
                : 6
                Affiliations
                [1 ] Tumor Biology, Immunology and Therapy section, Sidra Medical and Research Center, Doha, Qatar.
                [2 ] Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
                [3 ] Systems Biology Department, Sidra Medical and Research Center, Doha, Qatar.
                [4 ] Qatar Computing Research Institute, Doha, Qatar.
                [5 ] Department of Biochemistry, Otago School of Medical Sciences, University of Otago, Dunedin, 9054, New Zealand.
                [6 ] Department of Molecular Medicine and Pathology and Maurice Wilkins Institute, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, 1142, New Zealand.
                [7 ] Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA.
                [8 ] Benaroya Research Institute at Virginia Mason, Seattle, WA, 98101, USA.
                [9 ] Translational Bioinformatics, Division of Biomedical Informatics Research, Sidra Medical and Research Center, Doha, Qatar.
                [10 ] Technical Bioinformatics team, Biomedical Informatics Division, Sidra Medical and Research Center, Doha, Qatar.
                [11 ] National Center for Cancer Care and Research (NCCCR), Hamad General Hospital, Doha, Qatar.
                [12 ] Weill Cornell Medicine - Qatar, Doha, Qatar.
                [13 ] Division of Translational Medicine, Research Branch, Sidra Medical and Research Center, Doha, Qatar.
                [14 ] Office of the Chief Research Officer (CRO), Research Branch, Sidra Medical and Research Center, Doha, Qatar.
                [15 ] Department of Surgery, Leiden University Medical Center, Leiden, 2333 ZA, Netherlands.
                Article
                10.12688/f1000research.10960.2
                5820610
                29527288
                9898a354-e851-4c39-bcec-a91354551a4a
                History

                Breast Cancer,Immune Subtypes,Gene Expression Browser,Immunologic Constant of Rejection,Cancer Immune Phenotype

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