• Login
    View Item 
    •   Home
    • Theses and Dissertations
    • Theses and Dissertations
    • View Item
    •   Home
    • Theses and Dissertations
    • Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Scholarly CommonsCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsThis CollectionTitleAuthorsIssue DateSubmit DateSubjects

    My Account

    LoginRegister

    About

    AboutCreative CommonsAugusta University LibrariesUSG Copyright Policy

    Statistics

    Display statistics

    Genomic Predictions in Uterine Cancers

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Tran_gru_1907E_10160.pdf
    Size:
    2.098Mb
    Format:
    PDF
    Download
    Authors
    Tran, Lynn Kim Hoang
    Issue Date
    2020-05
    URI
    http://hdl.handle.net/10675.2/623269
    
    Metadata
    Show full item record
    Abstract
    Introduction: Current uterine cancer classification provides suboptimal treatment stratification and often groups together patients with significant differences in survival outcome and/or response. We used transcriptomic information to devise genomic scores for improved prediction of uterine cancer patient outcomes and validated these scores in our institutional cohorts. Project 1: In an early iteration of our gene signature discovery pipeline, we developed USC73, a genomic score for uterine serous carcinoma patients, which grouped patients into a low score (lower 66.7 percentile), good prognosis group and a high score (upper 33.3 percentile), poor prognosis group (5-year overall survival: 83.3% and 13.3%, respectively). USC73 predicts survival independently of stage, and can be combined with stage for further resolution of patient survival. Poor survivors have faster-growing tumors and lower rates of complete response to primary therapy. Project 2: We applied our pipeline to uterine endometrioid carcinoma, the most common histotype of uterine cancer, and developed UEC_IGS, an immune gene score that separates early stage patients into a high lymphocytic infiltration, good prognosis group (IGS 1) and a low lymphocytic infiltration, poor prognosis group (IGS 2). UEC_IGS predicts overall survival independent of grade and treatment. IGS 1 patients have higher levels of CD8+ tumor infiltrating lymphocytes (TILs), more CD45RO+/CD3+ memory T cells, and lower levels of FOXP3+ Tregs compared to IGS 2. Conclusion: Using transcriptomic data, we can reliably stratify uterine cancer patients into good and poor survival groups. This information can be used to facilitate recruitment of only poor prognosis patients into clinical trials, mitigating some heterogeneity in patient response and allowing clinicians to better identify treatments for patients who will not survive on the current therapy. Additionally, biological functions (e.g. cellular proliferation or immune infiltration) are associated with each genomic score, and these can serve as potential pathways to target for improving the outcome of poor survival groups.
    Affiliation
    Center for Biotechnology and Genomic Medicine
    Description
    Record is embargoed until 04/28/2022.
    Collections
    Center for Biotechnology and Genomic Medicine Theses and Dissertations
    Theses and Dissertations

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.