1. Computational and Systems Biology

Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells

  1. Nalin Leelatian
  2. Justine Sinnaeve
  3. Akshitkumar M Mistry
  4. Sierra M Barone
  5. Asa A Brockman
  6. Kirsten E Diggins
  7. Allison R Greenplate
  8. Kyle D Weaver
  9. Reid C Thompson
  10. Lola B Chambless
  11. Bret C Mobley
  12. Rebecca A Ihrie  Is a corresponding author
  13. Jonathan M Irish  Is a corresponding author
  1. Vanderbilt University, United States
  2. Vanderbilt University Medical Center, United States
https://doi.org/10.7554/eLife.56879
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Cite this article
  1. Nalin Leelatian
  2. Justine Sinnaeve
  3. Akshitkumar M Mistry
  4. Sierra M Barone
  5. Asa A Brockman
  6. Kirsten E Diggins
  7. Allison R Greenplate
  8. Kyle D Weaver
  9. Reid C Thompson
  10. Lola B Chambless
  11. Bret C Mobley
  12. Rebecca A Ihrie
  13. Jonathan M Irish
(2020)
Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells
eLife 9:e56879.
https://doi.org/10.7554/eLife.56879
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Abstract

A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses. We introduce a machine learning algorithm, Risk Assessment Population IDentification (RAPID), that is unsupervised and automated, identifies phenotypically distinct cell populations, and determines whether these populations stratify patient survival. With a pilot mass cytometry dataset of 2 million cells from 28 glioblastomas, RAPID identified tumor cells whose abundance independently and continuously stratified patient survival. Statistical validation within the workflow included repeated runs of stochastic steps and cell subsampling. Biological validation used an orthogonal platform, immunohistochemistry, and a larger cohort of 73 glioblastoma patients to confirm the findings from the pilot cohort. RAPID was also validated to find known risk-stratifying cells and features using published data from blood cancer. Thus, RAPID provides an automated, unsupervised approach for finding statistically and biologically significant cells using cytometry data from patient samples.

Data availability

Annotated flow data files are available at the following link: https://flowrepository.org/id/FR-FCM-Z24K. Patient specific views of population abundance and channel mass signals for all analyzed patients in this study are currently available in Supplementary File 6. RAPID code is currently available on Github, together with example analysis data: https://github.com/cytolab/RAPID

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

Article and author information

Author details

  1. Nalin Leelatian

    Department of Cell and Developmental Biology, Vanderbilt University, Nashville, United States
    Competing interests
    No competing interests declared.

  2. Justine Sinnaeve

    Department of Cell and Developmental Biology, Vanderbilt University, Nashville, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9303-7969

  3. Akshitkumar M Mistry

    Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7918-5153

  4. Sierra M Barone

    Department of Cell and Developmental Biology, Vanderbilt University, Nashville, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5944-750X

  5. Asa A Brockman

    Department of Cell and Developmental Biology, Vanderbilt University, Nashville, United States
    Competing interests
    No competing interests declared.

  6. Kirsten E Diggins

    Department of Cell and Developmental Biology, Vanderbilt University, Nashville, United States
    Competing interests
    No competing interests declared.

  7. Allison R Greenplate

    Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, United States
    Competing interests
    No competing interests declared.

  8. Kyle D Weaver

    Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, United States
    Competing interests
    No competing interests declared.

  9. Reid C Thompson

    Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, United States
    Competing interests
    No competing interests declared.

  10. Lola B Chambless

    Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, United States
    Competing interests
    No competing interests declared.

  11. Bret C Mobley

    Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, United States
    Competing interests
    No competing interests declared.

  12. Rebecca A Ihrie

    Department of Cell and Developmental Biology, Vanderbilt University, Nashville, United States
    For correspondence
    rebecca.ihrie@vanderbilt.edu
    Competing interests
    No competing interests declared.

  13. Jonathan M Irish

    Department of Cell and Developmental Biology, Vanderbilt University, Nashville, United States
    For correspondence
    jonathan.irish@vanderbilt.edu
    Competing interests
    Jonathan M Irish, was a co-founder and a board member of Cytobank Inc. and received research support from Incyte Corp, Janssen, and Pharmacyclics.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9428-8866

Funding

National Institutes of Health (R00 CA143231)

  • Jonathan M Irish

National Institutes of Health (F31 CA199993)

  • Allison R Greenplate

National Institutes of Health (R25 CA136440-04)

  • Kirsten E Diggins

Vanderbilt Ingram Cancer Center (Provocative Question)

  • Jonathan M Irish

National Institutes of Health (R01 CA226833)

  • Jonathan M Irish

National Institutes of Health (U54 CA217450)

  • Jonathan M Irish

National Institutes of Health (U01 AI125056)

  • Sierra M Barone
  • Jonathan M Irish

National Institutes of Health (R01 NS096238)

  • Rebecca A Ihrie

U.S. Department of Defense (W81XWH-16-1-0171)

  • Rebecca A Ihrie

Michael David Greene Brain Cancer Fund

  • Rebecca A Ihrie
  • Jonathan M Irish

Vanderbilt Institute for Clinical and Translational Research (VR51342)

  • Bret C Mobley
  • Rebecca A Ihrie

Vanderbilt Ingram Cancer Center (P30 CA68485)

  • Jonathan M Irish

Vanderbilt Ingram Cancer Center (Ambassadors Award)

  • Rebecca A Ihrie
  • Jonathan M Irish

Southeastern Brain Tumor Foundation

  • Rebecca A Ihrie
  • Jonathan M Irish

Vanderbilt University (International Scholars Program)

  • Nalin Leelatian

Vanderbilt University (Discovery Grant)

  • Nalin Leelatian
  • Jonathan M Irish

Alpha Omega Alpha Honor Medical Society (Postgraduate Award)

  • Akshitkumar M Mistry

Society of Neurological Surgeons (RUNN Award)

  • Akshitkumar M Mistry

National Institutes of Health (F32 CA224962-01)

  • Akshitkumar M Mistry

Burroughs Wellcome Fund (1018894)

  • Akshitkumar M Mistry

National Institutes of Health (T32 HD007502)

  • Justine Sinnaeve

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

Copyright

© 2020, Leelatian 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. Nalin Leelatian
  2. Justine Sinnaeve
  3. Akshitkumar M Mistry
  4. Sierra M Barone
  5. Asa A Brockman
  6. Kirsten E Diggins
  7. Allison R Greenplate
  8. Kyle D Weaver
  9. Reid C Thompson
  10. Lola B Chambless
  11. Bret C Mobley
  12. Rebecca A Ihrie
  13. Jonathan M Irish
(2020)
Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells
eLife 9:e56879.
https://doi.org/10.7554/eLife.56879

Share this article

https://doi.org/10.7554/eLife.56879

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