Cancer Bioelectricity & AI
BACKGROUND
Over the past years, a new research direction has developed at the intersection of electrophysiology and oncology, reframing cancer not only as a genetic and biochemical disorder but also as a disease of disrupted bioelectric signalling. At the centre of this shift is the transmembrane potential (Vm), now recognised as an active regulator of tumour behaviour.
Cancer cells typically display persistent membrane depolarization that promotes cell-cycle entry, proliferation and metastatic activity, while experimentally induced hyperpolarization halts growth, encourages differentiation in cancer stem–like cells and diminishes malignancy. Thus, Vm appears to function as a mechanistic driver of cancer progression.
Realising this potential will require a structured translational roadmap: standardised Vm measurement protocols, validation of Vm modulation as a pharmacodynamic endpoint and integration of bioelectric therapies alongside current chemotherapy and immunotherapy. With such a framework, Vm-based strategies could evolve into a new pillar of precision oncology, enabling earlier detection and more targeted, effective treatment.
Cancer Biophysics
From this perspective, Vm can be postulated as both a clinically actionable biomarker and a therapeutic target. Because alterations in Vm occur prior to structural changes visible under histopathology, Vm measurement may enable earlier tumour detection and more accurate prediction of disease development. Moreover, deliberate modulation of Vm through ion-channel agents, optogenetic tools or tumour-treating fields demonstrates tumour-selective efficacy with minimal off-target toxicity, suggesting strong potential as a complementary mode of intervention.
Computational Oncology
Computational modeling and simulation are playing an increasingly important role in oncology, bridging biological research, data science, and clinical practice to better understand cancer complexity and inform therapeutic development. This research deals with recent advances in multiscale modeling, AI-driven systems, digital twins, and in silico trials, illustrating the evolving potential of computational tools to support innovation from bench to bedside. These contributions outline a future in which precision medicine, adaptive therapies, and personalized diagnostics are guided by integrative and predictive modeling.
Publications
Desoyer C, Ruf M, Baumgartner C. Towards a digital cancer cell twin: external pharmacological validation of a mechanistic A549 electrophysiology model. Clin Transl Discov. 2026, e70112.
https://doi.org/10.1002/ctd2.70112
Desoyer C, Loibner D, Brislinger D, Baumgartner C. Computational Frame-works for Modeling Cancer Across Scales. Clin Transl Discov. 2025, 5, e70093.
https://doi.org/10.1002/ctd2.70093
Baumgartner C. Computational Modeling and Simulation in Oncology. Clin Transl Med. 2025, 15(7), e70456.
https://doi.org/10.1002/ctm2.70456
Langthaler S, Zumpf C, Rienmüller R, Fuchs J, Zhou R, Shrestha N, Pelzmann B, Zorn-Pauly K, Fröhlich E, Weinberg S, Baumgartner C. The bioelectric mechanisms of local calcium dynamics in cancer cell proliferation: An extension of the A549 in-silico cell model. Front Mol Biosci. 2024, 11, 1394398