Hui T H, Shao X, Au D W, Cho W C, Lin Y
Department of Electrical and Electronic Engineering, The University of Hong Kong Hong Kong SAR China.
Department of Mechanical Engineering, The University of Hong Kong Hong Kong SAR China
RSC Adv. 2020 Aug 14;10(50):29999-30006. doi: 10.1039/d0ra06255c. eCollection 2020 Aug 10.
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.
在肿瘤进展过程中，癌细胞会以动态方式从上皮样状态转变为间充质样状态。例如，众所周知，间充质向上皮转化（MET）对于癌细胞重新获得在次级/三级区域定植然后侵袭的能力至关重要。然而，目前尚无快速且可靠的方法来检测这种转变。在此，我们表明，侵袭性癌细胞的膜波动可作为一种用于检测MET的新型标志物，无论是在侵袭性模型细胞系中，还是在非小细胞肺癌（NSCLC）患者的再填充循环肿瘤细胞（rCTC）中。具体而言，使用原子力显微镜（AFM）发现，不同癌细胞在经历MET后，其表面振荡光谱在0.001至0.007 Hz范围内均呈现出两个明显的峰值，而在MET之前的光谱中则不存在这些峰值。此外，通过采用基于长短期记忆（LSTM）的递归神经网络学习算法，我们表明，记录的膜波动峰的位置可用于高精度预测侵袭性NSCLC细胞中MET的发生（与传统生物标志物波形蛋白相比，模型细胞系的准确率>90%，rCTC的准确率>80%）。这些发现证明了我们的方法在以大大减少的细胞样本量实现快速MET检测以及量化肿瘤细胞间充质水平变化方面的潜力。
