Defining the biological basis of radiomic phenotypes in lung cancer

  1. Patrick Grossmann
  2. Olya Stringfield
  3. Nehme El-Hachem
  4. Marilyn M Bui
  5. Emmanuel Rios Velazquez
  6. Chintan Parmar
  7. Ralph TH Leijenaar
  8. Benjamin Haibe-Kains
  9. Philippe Lambin
  10. Robert Gillies
  11. Hugo JWL Aerts  Is a corresponding author
  1. Dana-Farber Cancer Institute, United States
  2. H. Lee Moffitt Cancer Center and Research Institute, United States
  3. Institut de recherches cliniques de Montreal, Canada
  4. Research Institute GROW, Maastricht University, Netherlands
  5. University Health Network, Canada
  6. H. Lee Moffitt Cancer Center and Research Institute, United Kingdom

Abstract

Medical imaging can visualize characteristics of human cancer noninvasively. Radiomics is an emerging field that translates these medical images into quantitative data to enable phenotypic profiling of tumors. While radiomics has been associated with several clinical endpoints, the complex relationships of radiomics, clinical factors, and tumor biology are largely unknown. To this end, we analyzed two independent cohorts of respectively 262 North American and 89 European patients with lung cancer, and consistently identified previously undescribed associations between radiomic imaging features, molecular pathways, and clinical factors. In particular, we found a relationship between imaging features, immune response, inflammation, and survival, which was further validated by immunohistochemical staining. Moreover, a number of imaging features showed predictive value for specific pathways; for example, intra-tumor heterogeneity features predicted activity of RNA polymerase transcription (AUC=0.62, p=0.03) and intensity dispersion was predictive of the autodegration pathway of a ubiquitin ligase (AUC = 0.69, p < 10-4). Finally, we observed that prognostic biomarkers performed highest when combining radiomic, genetic, and clinical information (CI=0.73, p<10-9) indicating complementary value of these data. In conclusion, we demonstrate that radiomic approaches permit noninvasive assessment of both molecular and clinical characteristics of tumors, and therefore have the potential to advance clinical decision-making by systematically analyzing standard of care medical images.

Data availability

The following previously published data sets were used

Article and author information

Author details

  1. Patrick Grossmann

    Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4918-6902
  2. Olya Stringfield

    Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, United States
    Competing interests
    No competing interests declared.
  3. Nehme El-Hachem

    Integrative systems biology, Institut de recherches cliniques de Montreal, Montreal, Canada
    Competing interests
    No competing interests declared.
  4. Marilyn M Bui

    Department of Anatomic Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, United States
    Competing interests
    No competing interests declared.
  5. Emmanuel Rios Velazquez

    Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, United States
    Competing interests
    No competing interests declared.
  6. Chintan Parmar

    Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, United States
    Competing interests
    No competing interests declared.
  7. Ralph TH Leijenaar

    Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, Maastricht, Netherlands
    Competing interests
    No competing interests declared.
  8. Benjamin Haibe-Kains

    Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
    Competing interests
    No competing interests declared.
  9. Philippe Lambin

    Department of Radiation Oncology (MAASTRO), Research Institute GROW, Maastricht University, Maastricht, Netherlands
    Competing interests
    No competing interests declared.
  10. Robert Gillies

    Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, United Kingdom
    Competing interests
    Robert Gillies, declares a collaboration with HealthMyne..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8888-7747
  11. Hugo JWL Aerts

    Department of Radiation Oncology, Dana-Farber Cancer Institute, Boston, United States
    For correspondence
    hugo_aerts@dfci.harvard.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2122-2003

Funding

National Institutes of Health (NIH-USA U24CA194354)

  • Hugo JWL Aerts

National Institutes of Health (NIH-USA U01CA190234)

  • Hugo JWL Aerts

National Institutes of Health (NIH/NCI U01CA143062)

  • Robert Gillies

National Institutes of Health (NIH/NCI P50CA119997)

  • Robert Gillies

QuIC-ConCePT (IMI JU Grant No. 115151)

  • Philippe Lambin

Technologiestichting STW (10696 DuCA)

  • Philippe Lambin

Dutch Cancer Society (KWF UM 2009-4454)

  • Philippe Lambin

Dutch Cancer Society (KWF MAC 2013-6425)

  • Philippe Lambin

Gattuso Slaight Personalized Cancer Medicine Fund

  • Benjamin Haibe-Kains

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

Ethics

Human subjects: The University of South Florida institutional review board approved and waived the informed consent requirement (IRB # 16069) retrospective study of Dataset1; data were collected and handled in accordance with the Health Insurance Portability and Accountability Act. Informed consent for gene expression collection was written and oral. For acquisition of imaging and clinical data USF IRB approved protocol (108426) provided a waiver of informed consent for this retrospective study. Data collection and analysis of Dataset2 was carried out in accordance with Dutch law; the corresponding institutional review board approved the study. All patient data were anonymized and de-identified prior to the analyses.

Reviewing Editor

  1. Trever Bivona, University of California, San Francisco, United States

Publication history

  1. Received: November 20, 2016
  2. Accepted: July 17, 2017
  3. Accepted Manuscript published: July 21, 2017 (version 1)
  4. Version of Record published: September 8, 2017 (version 2)
  5. Version of Record updated: February 13, 2018 (version 3)

Copyright

© 2017, Grossmann 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. Patrick Grossmann
  2. Olya Stringfield
  3. Nehme El-Hachem
  4. Marilyn M Bui
  5. Emmanuel Rios Velazquez
  6. Chintan Parmar
  7. Ralph TH Leijenaar
  8. Benjamin Haibe-Kains
  9. Philippe Lambin
  10. Robert Gillies
  11. Hugo JWL Aerts
(2017)
Defining the biological basis of radiomic phenotypes in lung cancer
eLife 6:e23421.
https://doi.org/10.7554/eLife.23421

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