IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0051002.html
   My bibliography  Save this article

Application of a High Throughput Method of Biomarker Discovery to Improvement of the EarlyCDT®-Lung Test

Author

Listed:
  • Isabel K Macdonald
  • Andrea Murray
  • Graham F Healey
  • Celine B Parsy-Kowalska
  • Jared Allen
  • Jane McElveen
  • Chris Robertson
  • Herbert F Sewell
  • Caroline J Chapman
  • John F R Robertson

Abstract

Background: The National Lung Screening Trial showed that CT screening for lung cancer led to a 20% reduction in mortality. However, CT screening has a number of disadvantages including low specificity. A validated autoantibody assay is available commercially (EarlyCDT®-Lung) to aid in the early detection of lung cancer and risk stratification in patients with pulmonary nodules detected by CT. Methods and Findings: Serum from two matched independent cohorts of lung cancer patients were used (n = 100 and n = 165). Sixty nine proteins were initially screened on an abridged HTP version of the autoantibody ELISA using protein prepared on small scale by a HTP expression and purification screen. Promising leads were produced in shake flask culture and tested on the full assay. These results were analyzed in combination with those from the EarlyCDT-Lung panel in order to provide a set of re-optimized cut-offs. Five proteins that still displayed cancer/normal differentiation were tested for reproducibility and validation on a second batch of protein and a separate patient cohort. Addition of these proteins resulted in an improvement in the sensitivity and specificity of the test from 38% and 86% to 49% and 93% respectively (PPV improvement from 1 in 16 to 1 in 7). Conclusion: This is a practical example of the value of investing resources to develop a HTP technology. Such technology may lead to improvement in the clinical utility of the EarlyCDT­-Lung test, and so further aid the early detection of lung cancer.

Suggested Citation

  • Isabel K Macdonald & Andrea Murray & Graham F Healey & Celine B Parsy-Kowalska & Jared Allen & Jane McElveen & Chris Robertson & Herbert F Sewell & Caroline J Chapman & John F R Robertson, 2012. "Application of a High Throughput Method of Biomarker Discovery to Improvement of the EarlyCDT®-Lung Test," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-9, December.
  • Handle: RePEc:plo:pone00:0051002
    DOI: 10.1371/journal.pone.0051002
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0051002
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0051002&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0051002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Isabel K Macdonald & Jared Allen & Andrea Murray & Celine B Parsy-Kowalska & Graham F Healey & Caroline J Chapman & Herbert F Sewell & John F R Robertson, 2012. "Development and Validation of a High Throughput System for Discovery of Antigens for Autoantibody Detection," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-10, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhen-Ming Tang & Zhou-Gui Ling & Chun-Mei Wang & Yan-Bin Wu & Jin-Liang Kong, 2017. "Serum tumor-associated autoantibodies as diagnostic biomarkers for lung cancer: A systematic review and meta-analysis," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-21, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Statistics

      Access and download statistics

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0051002. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.