The recurrence of cancer following chemotherapy treatment is a major cause of death across solid and hematologic cancers. In B-cell acute lymphoblastic leukemia (B-ALL), relapse after initial chemotherapy treatment leads to poor patient outcomes. Here we test the hypothesis that chemotherapy-treated versus control B-ALL cells can be characterized based on cellular physical phenotypes. To quantify physical phenotypes of chemotherapy-treated leukemia cells, we use cells derived from B-ALL patients that are treated for 7 days with a standard multidrug chemotherapy regimen of vincristine, dexamethasone, and L-asparaginase (VDL). We conduct physical phenotyping of VDL-treated versus control cells by tracking the sequential deformations of single cells as they flow through a series of micron-scale constrictions in a microfluidic device; we call this method Quantitative Cyclical Deformability Cytometry. Using automated image analysis, we extract time-dependent features of deforming cells including cell size and transit time (TT) with single-cell resolution. Our findings show that VDL-treated B-ALL cells have faster TTs and transit velocity than control cells, indicating that VDL-treated cells are more deformable. We then test how effectively physical phenotypes can predict the presence of VDL-treated cells in mixed populations of VDL-treated and control cells using machine learning approaches. We find that TT measurements across a series of sequential constrictions can enhance the classification accuracy of VDL-treated cells in mixed populations using a variety of classifiers. Our findings suggest the predictive power of cell physical phenotyping as a complementary prognostic tool to detect the presence of cells that survive chemotherapy treatment. Ultimately such complementary physical phenotyping approaches could guide treatment strategies and therapeutic interventions. Insight box Cancer cells that survive chemotherapy treatment are major contributors to patient relapse, but the ability to predict recurrence remains a challenge. Here we investigate the physical properties of leukemia cells that survive treatment with chemotherapy drugs by deforming individual cells through a series of micron-scale constrictions in a microfluidic channel. Our findings reveal that leukemia cells that survive chemotherapy treatment are more deformable than control cells. We further show that machine learning algorithms applied to physical phenotyping data can predict the presence of cells that survive chemotherapy treatment in a mixed population. Such an integrated approach using physical phenotyping and machine learning could be valuable to guide patient treatments.

中文翻译：

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化疗后存活的白血病细胞物理表型发生改变。

化疗后癌症的复发是实体癌和血液癌死亡的主要原因。在 B 细胞急性淋巴细胞白血病 (B-ALL) 中，初始化疗治疗后的复发会导致患者预后不佳。在这里，我们检验了化疗治疗与对照 B-ALL 细胞可以根据细胞物理表型进行表征的假设。为了量化化疗治疗的白血病细胞的物理表型，我们使用来自 B-ALL 患者的细胞，这些患者接受长春新碱、地塞米松和 L-天冬酰胺酶 (VDL) 的标准多药化疗方案治疗 7 天。我们通过跟踪单个细胞在微流体装置中流经一系列微米级收缩时的顺序变形，对 VDL 处理的细胞与对照细胞进行物理表型分析；我们称这种方法为定量循环变形性细胞计数法。使用自动图像分析，我们提取变形细胞的时间相关特征，包括具有单细胞分辨率的细胞大小和转运时间 (TT)。我们的研究结果表明，VDL 处理的 B-ALL 细胞比对照细胞具有更快的 TT 和传输速度，表明 VDL 处理的细胞更易变形。然后，我们使用机器学习方法测试物理表型如何有效地预测 VDL 处理细胞和对照细胞混合群体中 VDL 处理细胞的存在。我们发现，跨一系列连续收缩的 TT 测量可以使用各种分类器提高混合群体中 VDL 处理细胞的分类准确性。我们的研究结果表明，细胞物理表型的预测能力可作为一种补充性预后工具，用于检测在化疗后存活的细胞的存在。最终，这种互补的物理表型分析方法可以指导治疗策略和治疗干预。Insight box 化疗后存活下来的癌细胞是患者复发的主要原因，但预测复发的能力仍然是一个挑战。在这里，我们通过微流体通道中的一系列微米级收缩使单个细胞变形，从而研究在化疗药物治疗后存活下来的白血病细胞的物理特性。我们的研究结果表明，在化疗后存活下来的白血病细胞比对照细胞更易变形。我们进一步表明，应用于物理表型数据的机器学习算法可以预测混合人群中化疗治疗后存活的细胞的存在。这种使用物理表型和机器学习的综合方法对于指导患者治疗可能很有价值。