Invasive properties of patient-derived glioblastoma cells after reversible electroporation in vitro

Electroporation-based therapies are being explored in glioblastoma (GB) treatment, as means of enhancing drug delivery or achieving nonthermal ablation. Yet, little is known about how sublethal exposure affects the invasive behaviour of GB tumour cells.

Five patient-derived GB cell lines were initially screened for intrinsic invasive potential, and two most invasive (NIB140 CORE and NIB216 CORE) were selected for further experiments with electroporation treatment. Cells in suspension were exposed to bursts of high-frequency biphasic electric pulses resulting in electric field strength of 1 kV/cm, which corresponded to conditions of reversible electroporation. Changes in cell invasion and gene regulation were assessed 24 hours after electroporation using transwell assay and RNA transcriptome analysis, respectively.

Reversible electroporation at 1.0 kV/cm enhanced invasion in a cell line-dependent manner. NIB140 CORE showed a consistent and pronounced increase, with a median of 3.74-fold (274%) higher number of invading cells compared to sham control. In contrast, NIB216 CORE exhibited only a modest increase in invasion (1.30-fold; 30%). Transcriptomic profiling identified modulation of genes linked to extracellular matrix organization and ion channel activity in NIB140 CORE, and cytoskeletal remodelling in NIB216 CORE, indicating the activation of invasion-related pathways.

These findings highlight a potential risk of pro-invasive responses in GB cells. In tumour ablation with irreversible electroporation, this concern relates to cells in the peripheral zone that may experience only sublethal electric fields, while in electrochemotherapy, a similar risk may arise if permeabilized cells are not effectively eliminated due to insufficient local drug delivery. Nevertheless, the two tested cell lines responded differently, underscoring patient-specific heterogeneity and the need for validation in more physiologically relevant models.