Toward point-of-care assessment of patient response: a portable tool for rapidly assessing cancer drug efficacy using multifrequency impedance cytometry and supervised machine learning
Toward point-of-care assessment of patient response: a portable tool for rapidly assessing cancer drug efficacy using multifrequency impedance cytometry and supervised machine learning
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DOI：
10.1038/s41378-019-0073-2
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发表时间：
2019-07-15
影响因子：
7.9
通讯作者：
Javanmard, Mehdi
中科院分区：
文献类型：
--
作者：
Ahuja, Karan;Rather, Gulam M.;Javanmard, Mehdi
我们提出了一种新的方法来快速评估靶向癌症治疗中的药物疗效，其中抗肿瘤药物与靶向肿瘤细胞表面标记的抗体偶联。我们已经制造并表征了一种能够使用多频阻抗谱结合有监督的机器学习来快速评估肿瘤细胞对药物的敏感性的设备，以提高分类精度。目前商业上可用的细胞活性自动分析设备是基于染色的，这从根本上限制了后续对这些细胞的表征以及下游的分子分析。我们的方法只需要20亩L的体积，并且避免了染色，允许进一步的下游分子分析。据我们所知，这份手稿首次全面尝试使用高维数据和有监督的机器学习，特别是从多频阻抗细胞仪获得的相变频谱作为支持向量机分类器的特征，以评估细胞的活力，而不进行染色或标记。
We present a novel method to rapidly assess drug efficacy in targeted cancer therapy, where antineoplastic agents are conjugated to antibodies targeting surface markers on tumor cells. We have fabricated and characterized a device capable of rapidly assessing tumor cell sensitivity to drugs using multifrequency impedance spectroscopy in combination with supervised machine learning for enhanced classification accuracy. Currently commercially available devices for the automated analysis of cell viability are based on staining, which fundamentally limits the subsequent characterization of these cells as well as downstream molecular analysis. Our approach requires as little as 20 mu L of volume and avoids staining allowing for further downstream molecular analysis. To the best of our knowledge, this manuscript presents the first comprehensive attempt to using high-dimensional data and supervised machine learning, particularly phase change spectra obtained from multi-frequency impedance cytometry as features for the support vector machine classifier, to assess viability of cells without staining or labelling.