A combined experimental-computational approach uncovers a role for the Golgi matrix protein Giantin in breast cancer progression

PLoS Comput Biol. 2023 Apr 17;19(4):e1010995. doi: 10.1371/journal.pcbi.1010995. eCollection 2023 Apr.

Abstract

Our understanding of how speed and persistence of cell migration affects the growth rate and size of tumors remains incomplete. To address this, we developed a mathematical model wherein cells migrate in two-dimensional space, divide, die or intravasate into the vasculature. Exploring a wide range of speed and persistence combinations, we find that tumor growth positively correlates with increasing speed and higher persistence. As a biologically relevant example, we focused on Golgi fragmentation, a phenomenon often linked to alterations of cell migration. Golgi fragmentation was induced by depletion of Giantin, a Golgi matrix protein, the downregulation of which correlates with poor patient survival. Applying the experimentally obtained migration and invasion traits of Giantin depleted breast cancer cells to our mathematical model, we predict that loss of Giantin increases the number of intravasating cells. This prediction was validated, by showing that circulating tumor cells express significantly less Giantin than primary tumor cells. Altogether, our computational model identifies cell migration traits that regulate tumor progression and uncovers a role of Giantin in breast cancer progression.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Breast Neoplasms* / metabolism
  • Female
  • Golgi Apparatus / metabolism
  • Golgi Apparatus / pathology
  • Golgi Matrix Proteins / metabolism
  • Humans
  • Membrane Proteins* / metabolism

Substances

  • Membrane Proteins
  • macrogolgin
  • Golgi Matrix Proteins

Grants and funding

HF is supported by funding from the Norwegian Cancer Society (grants 182815 & 208015), by the Norwegian Research Council (grant 302452), by the Anders Jahre Foundation, and by the Rakel og Otto Kristian Bruun’s Legat and by a grant from the Austrian Science Foundation FWF (P 358320). A.K.L is supported by the center for research-based-innovation BigInsight funded by the Research Council of Norway (grant number 237718), by the Research Council of Norway (grant 311188) and by the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 847912. A.K.L and S.G were supported by the UiO:Life Science convergent environment PerCaThe. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.