Author
Katherine A Hoadley
Bio: Katherine A Hoadley is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Induced pluripotent stem cell & Epigenome. The author has an hindex of 5, co-authored 5 publications receiving 2750 citations.
Papers
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Nationwide Children's Hospital1, University of North Carolina at Chapel Hill2, Henry Ford Health System3, University of Texas MD Anderson Cancer Center4, Broad Institute5, Walter Reed National Military Medical Center6, Buck Institute for Research on Aging7, New York University8, University of Pittsburgh9, Sage Bionetworks10, University of California, San Francisco11, Institute for Systems Biology12
TL;DR: These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.
1,928 citations
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TL;DR: Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, Pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which may inform strategies for future therapeutic development.
1,535 citations
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Henry Ford Health System1, Harvard University2, Stanford University3, University of Hasselt4, University of Texas MD Anderson Cancer Center5, Nencki Institute of Experimental Biology6, École Polytechnique Fédérale de Lausanne7, Sage Bionetworks8, Université libre de Bruxelles9, Poznan University of Medical Sciences10, George Washington University11, Cold Spring Harbor Laboratory12, University of Kansas13, University of California, Santa Cruz14, University of North Carolina at Chapel Hill15, Van Andel Institute16
TL;DR: Novel stemness indices for assessing the degree of oncogenic dedifferentiation are provided and it is found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors.
1,099 citations
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Washington University in St. Louis1, Discovery Institute2, Institute for Systems Biology3, Université libre de Bruxelles4, Genome Institute of Singapore5, Johns Hopkins University6, University of Cambridge7, Baylor College of Medicine8, Broad Institute9, Harvard University10, University of Texas MD Anderson Cancer Center11, University of California, Santa Cruz12, University of North Carolina at Chapel Hill13, National Institutes of Health14
TL;DR: Results from the TCGA PanCancer Atlas project will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing.
256 citations
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Joshua D. Campbell1, Joshua D. Campbell2, Joshua D. Campbell3, Christina Yau4 +766 more•Institutions (23)
TL;DR: This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas from five sites associated with smoking and/or human papillomavirus.
234 citations
Cited by
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01 Feb 2015
TL;DR: In this article, the authors describe the integrative analysis of 111 reference human epigenomes generated as part of the NIH Roadmap Epigenomics Consortium, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression.
Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.
4,409 citations
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Institute for Systems Biology1, BC Cancer Agency2, University of California, San Francisco3, University of North Carolina at Chapel Hill4, Columbia University5, Discovery Institute6, Massachusetts Institute of Technology7, Arizona State University8, Sage Bionetworks9, Harvard University10, Johns Hopkins University11, Stanford University12, University of Calgary13, Université libre de Bruxelles14, University of Texas MD Anderson Cancer Center15, Medical College of Wisconsin16, Qatar Airways17, Cold Spring Harbor Laboratory18, University of São Paulo19, Henry Ford Hospital20, University of Alabama at Birmingham21, Van Andel Institute22, Stony Brook University23
TL;DR: An extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA identifies six immune subtypes that encompass multiple cancer types and are hypothesized to define immune response patterns impacting prognosis.
3,246 citations
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Memorial Sloan Kettering Cancer Center1, Swiss Institute of Bioinformatics2, Harvard University3, Princeton University4, University of Texas at Dallas5, Washington University in St. Louis6, Institute for Systems Biology7, Bilkent University8, Van Andel Institute9, University of Pennsylvania10, University of Texas MD Anderson Cancer Center11, Mayo Clinic12, Columbia University Medical Center13, Fred Hutchinson Cancer Research Center14, University of California, San Francisco15, University of Michigan16, Peter MacCallum Cancer Centre17, Baylor College of Medicine18
TL;DR: This work charted the detailed landscape of pathway alterations in 33 cancer types, stratified into 64 subtypes, and identified patterns of co-occurrence and mutual exclusivity.
1,841 citations
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TL;DR: The hallmarks of cancer conceptualization is a heuristic tool for distilling the vast complexity of cancer phenotypes and genotypes into a provisional set of underlying principles as mentioned in this paper , which are used to understand mechanisms of cancer development and malignant progression, and apply that knowledge to cancer medicine.
Abstract: The hallmarks of cancer conceptualization is a heuristic tool for distilling the vast complexity of cancer phenotypes and genotypes into a provisional set of underlying principles. As knowledge of cancer mechanisms has progressed, other facets of the disease have emerged as potential refinements. Herein, the prospect is raised that phenotypic plasticity and disrupted differentiation is a discrete hallmark capability, and that nonmutational epigenetic reprogramming and polymorphic microbiomes both constitute distinctive enabling characteristics that facilitate the acquisition of hallmark capabilities. Additionally, senescent cells, of varying origins, may be added to the roster of functionally important cell types in the tumor microenvironment. SIGNIFICANCE: Cancer is daunting in the breadth and scope of its diversity, spanning genetics, cell and tissue biology, pathology, and response to therapy. Ever more powerful experimental and computational tools and technologies are providing an avalanche of "big data" about the myriad manifestations of the diseases that cancer encompasses. The integrative concept embodied in the hallmarks of cancer is helping to distill this complexity into an increasingly logical science, and the provisional new dimensions presented in this perspective may add value to that endeavor, to more fully understand mechanisms of cancer development and malignant progression, and apply that knowledge to cancer medicine.
1,838 citations
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TL;DR: Xena’s Visual Spreadsheet visualization integrates gene-centric and genomic-coordinate-centric views across multiple data modalities, providing a deep, comprehensive view of genomic events within a cohort of tumors.
Abstract: To the Editor — There is a great need for easy-to-use cancer genomics visualization tools for both large public data resources such as TCGA (The Cancer Genome Atlas)1 and the GDC (Genomic Data Commons)2, as well as smaller-scale datasets generated by individual labs. Commonly used interactive visualization tools are either web-based portals or desktop applications. Data portals have a dedicated back end and are a powerful means of viewing centrally hosted resource datasets (for example, Xena’s predecessor, the University of California, Santa Cruz (UCSC) Cancer Browser (currently retired3), cBioPortal4, ICGC (International Cancer Genomics Consortium) Data Portal5, GDC Data Portal2). However, researchers wishing to use a data portal to explore their own data have to either redeploy the entire platform, a difficult task even for bioinformaticians, or upload private data to a server outside the user’s control, a non-starter for protected patient data, such as germline variants (for example, MAGI (Mutation Annotation and Genome Interpretation6), WebMeV7 or Ordino8). Desktop tools can view a user’s own data securely (for example, Integrated Genomics Viewer (IGV)9, Gitools10), but lack well-maintained, prebuilt files for the ever-evolving and expanding public data resources. This dichotomy between data portals and desktop tools highlights the challenge of using a single platform for both large public data and smaller-scale datasets generated by individual labs. Complicating this dichotomy is the expanding amount, and complexity, of cancer genomics data resulting from numerous technological advances, including lower-cost high-throughput sequencing and single-cell-based technologies. Cancer genomics datasets are now being generated using new assays, such as whole-genome sequencing11, DNA methylation whole-genome bisulfite sequencing12 and ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing13). Visualizing and exploring these diverse data modalities is important but challenging, especially as many tools have traditionally specialized in only one or perhaps a few data types. And although these complex datasets generate insights individually, integration with other omics datasets is crucial to help researchers discover and validate findings. UCSC Xena was developed as a high-performance visualization and analysis tool for both large public repositories and private datasets. It was built to scale with the current and future data growth and complexity. Xena’s privacy-aware architecture enables cancer researchers of all computational backgrounds to explore large, diverse datasets. Researchers use the same system to securely explore their own data, together or separately from the public data, all the while keeping private data secure. The system easily supports many tens of thousands of samples and has been tested with up to a million cells. The simple and flexible architecture supports a variety of common and uncommon data types. Xena’s Visual Spreadsheet visualization integrates gene-centric and genomic-coordinate-centric views across multiple data modalities, providing a deep, comprehensive view of genomic events within a cohort of tumors. UCSC Xena (http://xena.ucsc.edu) has two components: the front end Xena Browser and the back end Xena Hubs (Fig. 1). The web-based Xena Browser empowers biologists to explore data across multiple Xena Hubs with a variety of visualizations and analyses. The back end Xena Hubs host genomics data from laptops, public servers, behind a firewall, or in the cloud, and can be public or private (Supplementary Fig. 1). The Xena Browser receives data simultaneously from multiple Xena Hubs and integrates them into a single coherent visualization within the browser. A private Xena Hub is a hub installed on a user’s own computer (Supplementary Fig. 2). It is configured to only respond to requests from the computer’s localhost network interface (that is, http://127.0.0.1). This ensures that the hub only communicates with the computer on which the hub is installed. A public hub is configured to respond to requests from external computers. There are two types of public Xena Hubs (Supplementary Fig. 2). The first type is an open-public hub, which is a public hub accessible by everyone. While we host several open-public hubs (Supplementary Table 1), users can also set up their own as a way to share data. An example of one is the Treehouse Hub set up by the Childhood Cancer Initiative to share pediatric cancer RNA-seq gene expression data (Supplementary Note). The second type W eb s er ve r
1,644 citations