Identification of phenotype-specific networks from paired gene expression-cell shape imaging data

Charlie George Barker, Eirini Petsalaki, Girolamo Giudice, Julia Sero, Emmanuel Nsa Ekpenyong, Chris Bakal, Evangelia Petsalaki

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The morphology of breast cancer cells is often used as an indicator of tumor severity and prognosis. Additionally, morphology can be used to identify more fine-grained, molecular developments within a cancer cell, such as transcriptomic changes and signaling pathway activity. Delineating the interface between morphology and signaling is important to understand the mechanical cues that a cell processes in order to undergo epithelial-to-mesenchymal transition and consequently metastasize. However, the exact regulatory systems that define these changes remain poorly characterized. In this study, we used a network-systems approach to integrate imaging data and RNA-seq expression data. Our workflow allowed the discovery of unbiased and context-specific gene expression signatures and cell signaling subnetworks relevant to the regulation of cell shape, rather than focusing on the identification of previously known, but not always representative, pathways. By constructing a cell-shape signaling network from shape-correlated gene expression modules and their upstream regulators, we found central roles for developmental pathways such as WNT and Notch, as well as evidence for the fine control of NF-kB signaling by numerous kinase and transcriptional regulators. Further analysis of our network implicates a gene expression module enriched in the RAP1 signaling pathway as a mediator between the sensing of mechanical stimuli and regulation of NF-kB activity, with specific relevance to cell shape in breast cancer.

Original languageEnglish
Pages (from-to)750-765
Number of pages16
JournalGenome Research
Issue number4
Early online date23 Feb 2022
Publication statusPublished - 1 Apr 2022

Bibliographical note

Funding Information:
We thank the European Molecular Biology Laboratory (EMBL) and EMBL-EBI for funding the project. C.G.B. was funded by the EMBL International PhD Program. We also thank Bishoy Wadie and Vivian Robin for critical reading of the manuscript.

The complete R scripts and data used for this methodology are available as Supplemental Code and at GitLab (https://gitlab.ebi

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)


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