Breast tumor progression is a complex process involving intricate crosstalk between the primary tumor and its microenvironment. In the context of breast tumor‐lymphatic interactions, it is unclear how breast cancer cells alter the gene expression of lymphatic endothelial cells and how these transcriptional changes potentiate lymphatic dysfunction. Thus, there is a need for in vitro lymphatic vessel models to study these interactions. In this work, a tumor‐lymphatic microfluidic model is developed to study the differential conditioning of lymphatic vessels by estrogen receptor‐positive (i.e., MCF7) and triple‐negative (i.e., MDA‐MB‐231) breast cancer cells. The model consists of a lymphatic endothelial vessel cultured adjacently to either MCF7 or MDA‐MB‐231 cells. Quantitative transcriptional analysis reveals expression changes in genes related to vessel growth, permeability, metabolism, hypoxia, and apoptosis in lymphatic endothelial cells cocultured with breast cancer cells. Interestingly, these changes are different in the MCF7‐lymphatic cocultures as compared to the 231‐lymphatic cocultures. Importantly, these changes in gene expression correlate to functional responses, such as endothelial barrier dysfunction. These results collectively demonstrate the utility of this model for studying breast tumor‐lymphatic crosstalk for multiple breast cancer subtypes.
Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics
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). Results The model successfully classified the trajectories within individual cell lines with 83% accuracy and predicted receptor status with 85% accuracy. To further validate the method, epithelial–mesenchymal transition (EMT) was induced in benign MCF10A cells, noninvasive 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 and the fluorescence images of cell organelles or cytoskeletal structures, here we demonstrated an alternative way to classify cancer cells using a dynamic, biophysical feature that is readily accessible. Availability and implementation A python implementation of deep learning-based classification can be found at https://github.com/soonwoohong/Deep-learning-for-EGFR-trajectory-classification. Supplementary information Supplementary data are available at Bioinformatics online.
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- Award ID(s):
- 2029266
- NSF-PAR ID:
- 10309984
- Editor(s):
- Xu, Jinbo
- Date Published:
- Journal Name:
- Bioinformatics
- ISSN:
- 1367-4803
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/bioinformatics/btab581
