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A microfluidic cell-migration assay for the prediction of progression-free survival and recurrence time of patients with glioblastoma

Nature Biomedical Engineering volume 5, pages 26–40 (2021)Cite this article

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Abstract

Clinical scores, molecular markers and cellular phenotypes have been used to predict the clinical outcomes of patients with glioblastoma. However, their clinical use has been hampered by confounders such as patient co-morbidities, by the tumoral heterogeneity of molecular and cellular markers, and by the complexity and cost of high-throughput single-cell analysis. Here, we show that a microfluidic assay for the quantification of cell migration and proliferation can categorize patients with glioblastoma according to progression-free survival. We quantified with a composite score the ability of primary glioblastoma cells to proliferate (via the protein biomarker Ki-67) and to squeeze through microfluidic channels, mimicking aspects of the tight perivascular conduits and white-matter tracts in brain parenchyma. The assay retrospectively categorized 28 patients according to progression-free survival (short-term or long-term) with an accuracy of 86%, predicted time to recurrence and correctly categorized five additional patients on the basis of survival prospectively. RNA sequencing of the highly motile cells revealed differentially expressed genes that correlated with poor prognosis. Our findings suggest that cell-migration and proliferation levels can predict patient-specific clinical outcomes.

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Fig. 1: MAqCI distinguishes patient-derived primary GBM cells based on their migratory and proliferative potentials.
Fig. 2: GBM migratory and proliferative potentials correlate with patient survival.
Fig. 3: Combining migratory and proliferative indices into a single composite score maximizes the prognostic performance of MAqCI.
Fig. 4: MAqCI predicts recurrence time in a retrospective cohort and correctly categorizes patients with GBM based on progression-free survival prospectively.
Fig. 5: Transcriptome differences between highly motile and unsorted bulk GBM cell subpopulations.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are too large to be publicly shared, but they are available for research purposes from the corresponding authors on reasonable request. RNA-seq data are available at the National Center for Biotechnology Information Gene Expression Omnibus under accession no. GSE144610.

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Acknowledgements

This line of research was supported by the National Cancer Institute through grant nos. R01-CA216855 (to A.Q.-H. and K.K.), R01-CA183804 (to K.K.) and R01-CA124704 (to S.S.M.) and Department of Defense grant no. CA160997 (to V.K.B.). A.Q.-H. was also supported by the Mayo Clinic Clinician Investigator Award and the William J. and Charles H. Mayo Professorship.

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Author notes

  1. These authors contributed equally: Bin Sheng Wong, Sagar R. Shah, Christopher L. Yankaskas.

Authors and Affiliations

  1. Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA

    Bin Sheng Wong, Christopher L. Yankaskas, Pei-Hsun Wu, Deborah Chin, Brent Ifemembi & Konstantinos Konstantopoulos

  2. Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA

    Bin Sheng Wong, Christopher L. Yankaskas, Pei-Hsun Wu, Brent Ifemembi & Konstantinos Konstantopoulos

  3. Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Sagar R. Shah & Konstantinos Konstantopoulos

  4. Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, USA

    Sagar R. Shah, Karim ReFaey, Paula Schiapparelli & Alfredo Quiñones-Hinojosa

  5. Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA

    Vivek K. Bajpai

  6. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Vivek K. Bajpai

  7. Department of Embryology, Carnegie Institution for Science, Baltimore, MD, USA

    Xiaobin Zheng & Chen-Ming Fan

  8. Marlene and Stewart Greenebaum National Cancer Institute Comprehensive Cancer Center, Department of Physiology, University of Maryland School of Medicine, Baltimore, MD, USA

    Stuart S. Martin

  9. Department of Oncology, Johns Hopkins University, Baltimore, MD, USA

    Konstantinos Konstantopoulos

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Contributions

B.S.W., S.R.S., A.Q.-H. and K.K. designed the study. B.S.W. performed most experiments, interpreted the data and wrote the manuscript. S.R.S. performed select experiments, conducted patient clinical analysis and assisted in writing the manuscript. C.L.Y. performed experiments using MAqCI, the transwell assay and isolation of cells for RNA-seq, and contributed to data analysis. D.C. performed select experiments. P.-H.W. and B.I. performed survival analyses based on RNA-seq data. K.R. and P.S. contributed to the design of the study and collected patient data. S.S.M. designed and provided support for the transwell-migration assay, while V.K.B., C.-M.F. and X.Z. performed the RNA-seq experiments and analysis. All authors interpreted data, provided critical insights and edited the manuscript. K.K. supervised the study and wrote the manuscript.

Corresponding authors

Correspondence to Alfredo Quiñones-Hinojosa or Konstantinos Konstantopoulos.

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Competing interests

MAqCI is the subject of US utility patent applications 15/780,768 and 14/906,055. Intellectual property related to MAqCI is owned by the Johns Hopkins University and licenced to RecurX Bio, Inc., of which K.K. is a co-founder, consultant and board member. K.K. has a financial interest in RecurX Bio, Inc., which is subject to certain restrictions under university policy. The terms of this arrangement are being managed by the Johns Hopkins University in accordance with its conflict of interest policies.

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Supplementary information

Supplementary Information

Supplementary figures, tables and video captions.

Reporting Summary

Supplementary Dataset 1

Measures of performance, patient characteristics and list of differentially expressed genes from two patients.

Supplementary Video 1

Migration of patient-derived primary GBM cells (GBM714) in MAqCI.

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Wong, B.S., Shah, S.R., Yankaskas, C.L. et al. A microfluidic cell-migration assay for the prediction of progression-free survival and recurrence time of patients with glioblastoma. Nat Biomed Eng 5, 26–40 (2021). https://doi.org/10.1038/s41551-020-00621-9

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