Rxivist logo

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

By 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

Posted 01 Jan 2020
bioRxiv DOI: 10.1101/2019.12.31.892091

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.

Download data

  • Downloaded 722 times
  • Download rankings, all-time:
    • Site-wide: 21,562 out of 100,699
    • In bioengineering: 438 out of 2,341
  • Year to date:
    • Site-wide: 3,912 out of 100,699
  • Since beginning of last month:
    • Site-wide: 26,753 out of 100,699

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News

  • 20 Oct 2020: Support for sorting preprints using Twitter activity has been removed, at least temporarily, until a new source of social media activity data becomes available.
  • 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
  • 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
  • 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
  • 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
  • 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
  • 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
  • 22 Jan 2019: Nature just published an article about Rxivist and our data.
  • 13 Jan 2019: The Rxivist preprint is live!