Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics.

Journal: Bioinformatics (Oxford, England)
PMID: 34390568

Abstract

MOTIVATION: 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).

Authors

  • Mirae Kim
    Department of Computer Science, Rice University, Houston, TX 77005, USA.
  • Soonwoo Hong
    Department of Bio-convergence Engineering, Korea University, Seoul 02841, Republic of Korea.
  • Thomas E Yankeelov
    Department of Biomedical Engineering, The University of Texas at Austin, TX 78712, USA.
  • Hsin-Chih Yeh
    Department of Biomedical Engineering, The University of Texas at Austin, TX 78712, USA.
  • Yen-Liang Liu
    Master Program for Biomedical Engineering, China Medical University, Taichung 40678, Taiwan.

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