Assesment of patient response to targeted cancer therapy using multi-frequency impedance cytometry and supervised machine learning

We present a novel method to rapidly assess patient response to targeted cancer therapy, where anti-neoplastic agents are conjugated to antibodies targeting surface markers on tumor cells. We have fabricated and characterized a device capable of rapidly assessing tumor cell viability in response to the drug using multi-frequency impedance spectroscopy in combination with supervised machine learning for enhanced classification accuracy. Currently commercially available devices for the analysis of cell viability are based on staining with Trypan blue. Staining fundamentally limits the subsequent characterization of these cells as well as further molecular analysis, and requires 0.5-1.0 milliliter of volume. Our approach only requires 50 microliters of volume and avoids staining allowing for further molecular analysis. To the best of our knowledge, this work presents the first comprehensive attempt in using phase change obtained from impedance cytometry data to assess viability of cells. Use of impedance cytometry to quantify cancer cells from blood cells was also explored.M.S.Includes bibliographical referencesby Karan Shashi Ahuj