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

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.

Initial refinement of data from video‐based single‐cell tracking

“…This information holds significant prognostic value by providing indications of cellular response and potential outcomes associated with specific interventions. For instance, it can contribute to the identification of distinct migration and persistence values that may correlate with the rate of intravasation [ 46 ].…”

“…Such data can also have direct interest in special studies. [ 46 ] for example, argue for the importance of acquiring such data for better understanding tumor growth rate and size.…”

“…Such data can also have direct interest in special studies. [46] for example, argue for the importance of acquiring such data for better understanding tumor growth rate and size. This section explores potential methods for extracting features from such data, aligning with the principles discussed in Sections 3.2, 3.3, and 3.4.…”