摘要
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 20μL 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.
基金
This work was funded by the PhRMA foundation,the National Science Foundation IDBR award grant no.1556253
the National Science Foundation CAREER award grant no.1846740 awarded to MJ and the Breast Cancer Research Foundation grant awarded to JRB.