Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell membrane mostly relies on the mean-squared displacement plots, much information is lost when producing these plots from the trajectories. Here we employ deep learning to classify breast cancer cell types based on the trajectories of epidermal growth factor receptor (EGFR). Our model is an artificial neural network trained on the EGFR motions acquired from six breast cancer cell lines of varying invasiveness and receptor status: MCF7 (hormone receptor-positive), BT474 (HER2-positive), SKBR3 (HER2-positive), MDA-MB-468 (triple-negative, TN), MDA-MB-231 (TN), and BT549 (TN).

Please cite our paper:
Mirae Kim, Soonwoo Hong, Thomas E. Yankeelov, Hsin-Chih Yeh, Yen-Liang Liu
Deep Learning-Based classification of Breast Cancer Cells Using Transmembrane Receptor Dynamics
Bioinformatics, 2022