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Cell shape, and not 2D migration, predicts ECM-driven 3D cell invasion in breast cancer

Janani P. Baskaran, Anna Weldy, Justinne Guarin, Gabrielle Munoz, Michael Kotlik, Nandita Subbiah, Andrew Wishart, Yifan Peng, View ORCID ProfileMiles A. Miller, View ORCID ProfileLenore Cowen, View ORCID ProfileMadeleine J. Oudin
doi: https://doi.org/10.1101/2019.12.31.892091
Janani P. Baskaran
1Department of Biomedical Engineering, Tufts University
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Anna Weldy
1Department of Biomedical Engineering, Tufts University
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Justinne Guarin
1Department of Biomedical Engineering, Tufts University
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Gabrielle Munoz
1Department of Biomedical Engineering, Tufts University
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Michael Kotlik
2Department of Computer Science, Tufts University
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Nandita Subbiah
1Department of Biomedical Engineering, Tufts University
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Andrew Wishart
1Department of Biomedical Engineering, Tufts University
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Yifan Peng
1Department of Biomedical Engineering, Tufts University
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Miles A. Miller
3Center for Systems Biology, Massachusetts General Hospital Research Institute
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Lenore Cowen
2Department of Computer Science, Tufts University
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Madeleine J. Oudin
1Department of Biomedical Engineering, Tufts University
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  • ORCID record for Madeleine J. Oudin
  • For correspondence: madeleine.oudin@tufts.edu
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Abstract

Metastasis, the leading cause of death in cancer patients, requires the invasion of tumor cells through the stroma in response to migratory cues, such as those provided by the extracellular matrix (ECM). Recent advances in proteomics have led to the identification of hundreds of ECM proteins which are more abundant in tumors relative to healthy tissue. Our goal was to develop a pipeline to easily predict which of these ECM proteins is more likely to have an effect on cancer invasion and metastasis. We evaluated the effect of 4 ECM proteins upregulated in breast tumor tissue in multiple human breast cancer cell lines in 3 assays. We found there was no linear relationship between the 11 cell shape parameters we quantified when cells adhere to ECM proteins and 2D cell migration speed, persistence or 3D invasion. We then used classifiers and partial-least squares regression analysis to identify which metrics best predicted ECM-driven 2D migration and 3D invasion responses. ECM-driven 2D cell migration speed or persistence did not correlate with or predict 3D invasion in response to that same cue. However, cell adhesion, and in particular cell elongation and irregularity accurately predicted the magnitude of ECM-driven 2D migration and 3D invasion in all cell lines. Testing predictions revealed that our models are good at predicting the effect of novel ECM proteins within a given cell line, but that ECM responses are cell-line specific. Overall, our studies identify the cell morphological features that determine 3D invasion responses to individual ECM proteins. This platform will help provide insight into the functional role of ECM proteins abundant tumor tissue and help prioritize strategies for targeting tumor-ECM interactions to treat metastasis.

Funding This work was supported by the National Institutes of Health [R00-CA207866-04 to M.J.O.]; Tufts University [Start-up funds from the School of Engineering to M.J.O.] and funds from NSF REU to A.W.

Conflict-of-interest: None.

Insight Box Metastasis, the dissemination of tumor cells, is driven by the interaction of invading tumor cells with their local environment, in particular with the ECM, which provides structure and support to our tissues. This study presents an integrated approach to predict the effect of individual ECM proteins on 3D invasion and metastasis based on simple adhesion assays which quantify cell shape. Machine learning classification and partial-least squares regression models reveal that ECM-driven 2D cell migration metrics are not predictive of 3D invasion, and that cell shape of cells adhered to ECM can predict that protein’s effect on 3D invasion. These data provide a pipeline for predicting the effect of ECM proteins on breast cancer cell invasion and metastasis.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 01, 2020.
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Cell shape, and not 2D migration, predicts ECM-driven 3D cell invasion in breast cancer
Janani P. Baskaran, Anna Weldy, Justinne Guarin, Gabrielle Munoz, Michael Kotlik, Nandita Subbiah, Andrew Wishart, Yifan Peng, Miles A. Miller, Lenore Cowen, Madeleine J. Oudin
bioRxiv 2019.12.31.892091; doi: https://doi.org/10.1101/2019.12.31.892091
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Cell shape, and not 2D migration, predicts ECM-driven 3D cell invasion in breast cancer
Janani P. Baskaran, Anna Weldy, Justinne Guarin, Gabrielle Munoz, Michael Kotlik, Nandita Subbiah, Andrew Wishart, Yifan Peng, Miles A. Miller, Lenore Cowen, Madeleine J. Oudin
bioRxiv 2019.12.31.892091; doi: https://doi.org/10.1101/2019.12.31.892091

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