20
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      data-paper

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references40

          • Record: found
          • Abstract: found
          • Article: not found

          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/.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              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.
                Bookmark

                Author and article information

                Contributors
                Role: Data CurationRole: Formal AnalysisRole: VisualizationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: MethodologyRole: ValidationRole: Writing – Original Draft Preparation
                Role: Data CurationRole: MethodologyRole: ResourcesRole: Software
                Role: Data CurationRole: InvestigationRole: Software
                Role: ConceptualizationRole: Investigation
                Role: ConceptualizationRole: Software
                Role: MethodologyRole: SoftwareRole: ValidationRole: Visualization
                Role: MethodologyRole: SoftwareRole: ValidationRole: Visualization
                Role: MethodologyRole: SoftwareRole: ValidationRole: Visualization
                Role: MethodologyRole: SoftwareRole: ValidationRole: Visualization
                Role: MethodologyRole: SoftwareRole: SupervisionRole: Validation
                Role: SupervisionRole: Validation
                Role: Data CurationRole: InvestigationRole: ResourcesRole: Software
                Role: SupervisionRole: Validation
                Role: SupervisionRole: Validation
                Role: SupervisionRole: Validation
                Role: Funding AcquisitionRole: SupervisionRole: Validation
                Role: ConceptualizationRole: MethodologyRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – Original Draft Preparation
                Role: ConceptualizationRole: Funding AcquisitionRole: Supervision
                Role: ConceptualizationRole: Data CurationRole: InvestigationRole: MethodologyRole: Writing – Original Draft Preparation
                Role: ConceptualizationRole: Formal AnalysisRole: SupervisionRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: ConceptualizationRole: Data CurationRole: Formal AnalysisRole: InvestigationRole: MethodologyRole: Project AdministrationRole: SoftwareRole: SupervisionRole: VisualizationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Journal
                F1000Res
                F1000Res
                F1000Research
                F1000Research
                F1000 Research Limited (London, UK )
                2046-1402
                9 February 2018
                2017
                : 6
                : 296
                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
                [1 ]Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
                [1 ]Breast Medical Oncology, Yale School of Medicine, New Haven, CT, USA
                [1 ]Breast Medical Oncology, Yale School of Medicine, New Haven, CT, USA
                Sidra Medical and Research Center, Qatar
                [1 ]Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
                Sidra Medical and Research Center, Qatar
                Author notes

                *Contributed equally

                JR: curated, uploaded and annotated datasets, interpreted the data and drafted the manuscript and accepted final version. JD: critically reviewed the datasets annotation, drafted the manuscript and accepted final version. SBo: installed the software, uploaded datasets, programmed portions of the web application, tested the software, and assisted in drafting the manuscript. DR: critically reviewed gene expression data, sample annotation, drafted the manuscript and accepted final version. CM: reviewed manuscript and accepted final version. MC: technical support annotating datasets and accepted final version of manuscript. MB: assembled and curated datasets and accepted final version of manuscript. CP: assembled and curated datasets and accepted final version of manuscript. JC: assembled and curated datasets and accepted final version of manuscript. SP: participated in the design of the software, programmed portions of the original web application, installed the software, tested the software, assisted in drafting the manuscript and accepted final version. CQ: participated in design and programmed portions of the original web application, tested the software, assisted in drafting the manuscript and accepted final version. PJ: critically reviewed sample annotation and gene expression data and accepted final version of manuscript. NS: critically reviewed quality of gene expression data and accepted final version of manuscript. SBa: reviewed manuscript and accepted final version. SBe: reviewed manuscript and accepted final version. DC: participated in software and study design, tested the software, assisted in drafting the manuscript and accepted final version. EW, FM: critically reviewed the manuscript. PK: Interpreted the data, critically reviewed manuscript and accepted final version. LM: assembled and curated datasets, provided immunological attributes, interpreted the data, drafted the manuscript and accepted final version. DB: designed the study, provided immunological attributes, supervised the project, interpreted the data, drafted the manuscript and accepted final version. WH: coordinated the uploading and annotation of datasets, contributed to the study design, provided immunological attributes, interpreted the data, drafted the manuscript and accepted final version.

                No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: authors reply, no competing intrest

                Competing interests: No competing interests were disclosed.

                Competing interests: none

                Author information
                https://orcid.org/0000-0003-3631-2041
                https://orcid.org/0000-0002-4409-100X
                https://orcid.org/0000-0003-2734-3356
                https://orcid.org/0000-0002-7649-5092
                Article
                10.12688/f1000research.10960.2
                5820610
                29527288
                9898a354-e851-4c39-bcec-a91354551a4a
                Copyright: © 2018 Roelands J et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 25 January 2018
                Funding
                Funded by: Qatar National Research Fund
                Award ID: JSREP07-010-3-005
                Funded by: Qatar Foundation
                JD, SB, DR, DC, DB, WH received support from the Qatar Foundation. JR received support from Qatar National Research Fund (grant number: JSREP07-010-3-005).
                The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Data Note
                Articles
                Bioinformatics
                Breast Diseases: Benign & Malignant
                Data Sharing
                Genomics

                breast cancer,immune subtypes,cancer immune phenotype,gene expression browser,immunologic constant of rejection

                Comments

                Comment on this article