1. Cancer Biology

Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer

  1. Kevin M Elias
  2. Wojciech Fendler
  3. Konrad Stawiski
  4. Stephen J Fiascone
  5. Allison F Vitonis
  6. Ross S Berkowitz
  7. Gyorgy Frendl
  8. Panagiotis Konstantinopoulos
  9. Christopher P Crum
  10. Magdalena Kedzierska
  11. Daniel W Cramer
  12. Dipanjan Chowdhury  Is a corresponding author
  1. Brigham and Women's Hospital, United States
  2. Harvard Medical School, United States
  3. Medical University of Lodz, Poland
https://doi.org/10.7554/eLife.28932
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Cite this article
  1. Kevin M Elias
  2. Wojciech Fendler
  3. Konrad Stawiski
  4. Stephen J Fiascone
  5. Allison F Vitonis
  6. Ross S Berkowitz
  7. Gyorgy Frendl
  8. Panagiotis Konstantinopoulos
  9. Christopher P Crum
  10. Magdalena Kedzierska
  11. Daniel W Cramer
  12. Dipanjan Chowdhury
(2017)
Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer
eLife 6:e28932.
https://doi.org/10.7554/eLife.28932
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  3. Download .RIS

Abstract

Recent studies posit a role for non-coding RNAs in epithelial ovarian cancer (EOC). Combining small RNA sequencing from 179 human serum samples with a neural network analysis produced a miRNA algorithm for diagnosis of EOC (AUC 0.90; 95% CI: 0.81-0.99). The model significantly outperformed CA125 and functioned well regardless of patient age, histology, or stage. Among 454 patients with various diagnoses, the miRNA neural network had 100% specificity for ovarian cancer. After using 325 samples to adapt the neural network to qPCR measurements, the model was validated using 51 independent clinical samples, with a positive predictive value of 91.3% (95% CI: 73.3% - 97.6%) and negative predictive value of 78.6% (95% CI: 64.2% - 88.2%). Finally, biologic relevance was tested using in situ hybridization on 30 pre-metastatic lesions, showing intratumoral concentration of relevant miRNAs. These data suggest circulating miRNAs have potential to develop a non-invasive diagnostic test for ovarian cancer.

Data availability

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Kevin M Elias

    Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1502-5553

  2. Wojciech Fendler

    Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5083-9168

  3. Konrad Stawiski

    Department of Biostatistics and Translational Medicine, Medical University of Lodz, Lodz, Poland
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6550-3384

  4. Stephen J Fiascone

    Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. Allison F Vitonis

    Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  6. Ross S Berkowitz

    Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  7. Gyorgy Frendl

    Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  8. Panagiotis Konstantinopoulos

    Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  9. Christopher P Crum

    Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  10. Magdalena Kedzierska

    Department of Clinical Oncology, Medical University of Lodz, Lodz, Poland
    Competing interests
    The authors declare that no competing interests exist.

  11. Daniel W Cramer

    Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  12. Dipanjan Chowdhury

    Harvard Medical School, Boston, United States
    For correspondence
    dipanjan_chowdhury@dfci.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5645-3752

Funding

Robert and Deborah First Family Fund

  • Kevin M Elias
  • Ross S Berkowitz
  • Dipanjan Chowdhury

U.S. Department of Defense (OC093426)

  • Panagiotis Konstantinopoulos

Honorable Tina Brozman Foundation

  • Kevin M Elias
  • Dipanjan Chowdhury

U.S. Department of Defense (OC140632)

  • Panagiotis Konstantinopoulos

National Institutes of Health (K12HD13015)

  • Kevin M Elias

Ruth N White Research Fellowship in Gynecologic Oncology

  • Kevin M Elias
  • Stephen J Fiascone

Saltonstall Research Fund

  • Kevin M Elias
  • Stephen J Fiascone
  • Ross S Berkowitz

Potter Research Fund

  • Kevin M Elias
  • Stephen J Fiascone
  • Ross S Berkowitz

Sperling Family Fund Fellowship

  • Kevin M Elias
  • Stephen J Fiascone
  • Ross S Berkowitz

Bach Underwood Fund

  • Kevin M Elias
  • Stephen J Fiascone
  • Ross S Berkowitz

First TEAM grant of the foundation for Polish Science and the Smart Growth Operational Programme of the European Union

  • Wojciech Fendler

National Institutes of Health (P50CA105009)

  • Allison F Vitonis
  • Daniel W Cramer

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Charles L Sawyers, Memorial Sloan-Kettering Cancer Center, United States

Ethics

Human subjects: All samples were collected after signed informed consent according to locally-approved Institutional Review Board protocols. These studies were approved by the Dana-Farber Cancer Institute Institutional Review Board Protocol 05-060 (NECC study), Brigham and Women's Hospital Institutional Review Board Protocol 2000-P-001678 (Pelvic Mass Protocol), and Dana-Farber/Harvard Cancer Center Institutional Review Board Protocol 12-532 (ERASMOS).

Version history

  1. Received: May 23, 2017
  2. Accepted: October 11, 2017
  3. Accepted Manuscript published: October 31, 2017 (version 1)
  4. Version of Record published: November 3, 2017 (version 2)

Copyright

© 2017, Elias et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Kevin M Elias
  2. Wojciech Fendler
  3. Konrad Stawiski
  4. Stephen J Fiascone
  5. Allison F Vitonis
  6. Ross S Berkowitz
  7. Gyorgy Frendl
  8. Panagiotis Konstantinopoulos
  9. Christopher P Crum
  10. Magdalena Kedzierska
  11. Daniel W Cramer
  12. Dipanjan Chowdhury
(2017)
Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer
eLife 6:e28932.
https://doi.org/10.7554/eLife.28932

Share this article

https://doi.org/10.7554/eLife.28932

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