Rapid Isolation and Detection of Breast Cancer Circulating Tumor Cells Using Microfluidic Sequential Trapping Array

Amin Hassanzadeh-Barforoushi, Simon Chang-Hao Tsao, Audrey Nadalini, David W. Inglis, Yuling Wang
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Abstract

Circulating tumor cells (CTCs) have garnered special attention as promising cancer biomarkers. Phenotypic changes of CTCs reveal invaluable information for oncologists in disease prognosis and adjusting their treatment options. Microfluidic technology has emerged as a promising tool for CTC isolation; however, two major hurdles remain to be solved in employing them in CTC analysis. First, a rapid CTC isolation scheme is needed to allow immediate use of patient samples for point-of-care treatment monitoring. Second, multiplexed and streamlined CTC imaging is needed to facilitate CTC detection. Here, a microfluidic CTC sequential trapping array (STA) is proposed which addresses these hurdles enabling pipette-based CTC isolation and simultaneous profiling of multiple CTC protein expressions. The STA device isolates CTCs based on their size difference from blood cells and increases sample processing throughput through its parallel design configuration. It successfully isolates CTC from a depleted peripheral blood mononuclear cells sample of breast cancer patients with a high recovery rate of 80% and discriminates the number and types of CTCs in breast cancer based on their disease stage. These findings will open a new avenue in clinical translation of CTC profiling technologies. It will be an example for future translational developments in CTC-based cancer management.

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利用微流体序贯捕获阵列快速分离和检测乳腺癌循环肿瘤细胞
循环肿瘤细胞(CTCs)作为一种有前途的癌症生物标记物受到了特别关注。CTC 的表型变化为肿瘤学家预后和调整治疗方案提供了宝贵的信息。微流控技术已成为一种很有前途的 CTC 分离工具,然而,将其用于 CTC 分析仍有两大障碍有待解决。首先,需要一种快速的 CTC 分离方案,以便立即将患者样本用于床旁治疗监测。其次,需要多路复用和简化的 CTC 成像来促进 CTC 检测。本文提出了一种微流体 CTC 连续捕获阵列(STA),它能解决这些障碍,实现基于移液管的 CTC 分离,并同时分析多种 CTC 蛋白表达。STA 设备根据 CTC 与血细胞的大小差异来分离 CTC,并通过并行设计配置来提高样本处理量。它成功地从乳腺癌患者耗竭的外周血单核细胞样本中分离出了 CTC,回收率高达 80%,并能根据乳腺癌的疾病分期来区分 CTC 的数量和类型。这些发现将为 CTC 分析技术的临床转化开辟一条新途径。它将成为未来基于 CTC 的癌症管理转化发展的范例。
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