Detection of the mesenchymal-to-epithelial transition of invasive non-small cell lung cancer cells by their membrane undulation spectra.

A cancer cell changes its state from being epithelial- to mesenchymal-like in a dynamic manner during tumor progression. For example, it is well known that mesenchymal-to-epithelial transition (MET) is essential for cancer cells to regain the capability of seeding on and then invading secondary/tertiary regions. However, there is no fast yet reliable method for detecting this transition. Here, we showed that membrane undulation of invasive cancer cells could be used as a novel marker for MET detection, both in invasive model cell lines and repopulated circulating tumor cells (rCTCs) from non-small cell lung cancer (NSCLC) patients. Specifically, using atomic force microscopy (AFM), it was found that the surface oscillation spectra of different cancer cells, after undergoing MET, all exhibited two distinct peaks from 0.001 to 0.007 Hz that are absent in the spectra before MET. In addition, by adopting the long short-term memory (LSTM) based recurrent neural network learning algorithm, we showed that the positions of recorded membrane undulation peaks can be used to predict the occurrence of MET in invasive NSCLC cells with high accuracy (>90% for model cell lines and >80% for rCTCs when benchmarking against the conventional bio-marker vimentin). These findings demonstrate the potential of our approach in achieving rapid MET detection with a much reduced cell sample size as well as quantifying changes in the mesenchymal level of tumor cells.