Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA.
Nat Biomed Eng. 2021 Jan;5(1):26-40. doi: 10.1038/s41551-020-00621-9. Epub 2020 Sep 28.
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.
临床评分、分子标志物和细胞表型已被用于预测胶质母细胞瘤患者的临床结局。然而，由于患者合并症等混杂因素、分子和细胞标志物的肿瘤异质性，以及高通量单细胞分析的复杂性和成本，它们的临床应用受到了限制。在这里，我们展示了一种用于定量细胞迁移和增殖的微流控分析方法，可以根据无进展生存期对胶质母细胞瘤患者进行分类。我们通过复合评分量化了原代胶质母细胞瘤细胞增殖（通过蛋白生物标志物 Ki-67）和通过微流控通道挤压的能力，模拟了脑实质中血管周围紧密通道和白质束的某些方面。该检测方法通过回顾性分析，以 86%的准确率根据无进展生存期（短期或长期）对 28 名患者进行了分类，预测了复发时间，并根据生存情况前瞻性地正确分类了另外 5 名患者。高迁移性细胞的 RNA 测序揭示了与预后不良相关的差异表达基因。我们的研究结果表明，细胞迁移和增殖水平可以预测患者的特定临床结局。
