Metastasis is the cause of death in most patients of breast cancer and other solid malignancies. Identification of cancer cells with highly migratory capability to metastasize relies on markers for epithelial-to-mesenchymal transition (EMT), a process increasing cell migration and metastasis. Marker-based approaches are limited by inconsistences among patients, types of cancer, and partial EMT states. Alternatively, we analyzed cancer cell migration behavior using computer vision. Using a microfluidic single-cell migration chip and high-content imaging, we extracted morphological features and recorded migratory direction and speed of breast cancer cells. By applying a Random Decision Forest (RDF) and an Artificial Neural Network (ANN), we achieved over 99% accuracy for cell movement direction prediction and 91% for speed prediction. Unprecedentedly, we identified highly motile cells and non-motile cells based on microscope images and a machine learning model, and pinpointed and validated morphological features determining cell migration, including not only known features related to cell polarization but also novel ones that can drive future mechanistic studies. Predicting cell movement by computer vision and machine learning establishes a ground-breaking approach to analyze cell migration and metastasis.
Bibliographical noteFunding Information:
This work was supported in part by the Department of Defense (W81XWH-12-1-0325) and in part by the National Institutes of Health (1R21CA17585701, 1R21CA19501601A1, U01CA210152, and R01CA196018). Y.-C. Chen acknowledges the support from Forbes Institute for Cancer Discovery. The Lurie Nanofabrication Facility of the University of Michigan (Ann Arbor, MI) are greatly appreciated for device fabrication.
© The Royal Society of Chemistry.
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