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

Bin Sheng Wong, Sagar R. Shah, Christopher L. Yankaskas, Vivek K. Bajpai, Pei Hsun Wu, Deborah Chin, Brent Ifemembi, Karim ReFaey, Paula Schiapparelli, Xiaobin Zheng, Stuart S. Martin, Chen Ming Fan, Alfredo Quiñones-Hinojosa, Konstantinos Konstantopoulos

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)26-40
Number of pages15
JournalNature Biomedical Engineering
Volume5
Issue number1
DOIs
StatePublished - Jan 2021

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Medicine (miscellaneous)
  • Biomedical Engineering
  • Computer Science Applications

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