Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells
- Vanderbilt University, United States
- Vanderbilt University Medical Center, United States
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
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Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapseGithub Mass cytometry data for DDPR project.
Article and author information
Author details
Akshitkumar M Mistry
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.
Reviewing Editor
- C Daniela Robles-Espinoza, International Laboratory for Human Genome Research, Mexico
Version history
- Received: March 12, 2020
- Accepted: June 4, 2020
- Accepted Manuscript published: June 23, 2020 (version 1)
- Version of Record published: July 7, 2020 (version 2)
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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Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells
eLife 9:e56879.
https://doi.org/10.7554/eLife.56879
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