Deep Learning-Based classification of Breast Cancer Cells Using Transmembrane Receptor Dynamics.

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Abstract

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).The model successfully classified the trajectories within individual cell lines with 83% accuracy and predicted receptor status with 85% accuracy. To test the capability of the trained neural network, epithelial-mesenchymal transition (EMT) was induced in benign MCF10A cells, non-invasive MCF7 cancer cells and highly invasive MDA-MB-231 cancer cells, and EGFR trajectories from these cells were tested. As expected, after EMT induction, both MCF10A and MCF7 cells showed higher rates of classification as TN cells but not the MDA-MB-231 cells. Whereas deep learning-based cancer cell classifications are primarily based on the optical transmission images of cell morphology or the fluorescence images of cell organelle or cytoskeleton structures, here we demonstrated an alternative way to classify cancer cells using a dynamic, biophysical feature that is readily accessible.A python implementation of deep learning-based classification can be found at https://github.com/soonwoohong/Deep-learning-for-EGFR-trajectory-classification.Supplementary data are available at Bioinformatics online.© The Author(s) 2021. Published by Oxford University Press.