Miranda, J. A. | Search bioRxiv

bioRxiv2026

Neural correlates of glioma progression using implanted neural interfaces

High-grade glioma is an incurable brain cancer with a median survival of approximately 14 months. Over the last 50 years, small improvements in patient outcomes have been overshadowed by significant progress in most other cancers. Yet, emerging research has revealed that neural circuits play an active and central role in driving glioma growth and proliferation, highlighting the nervous system as a promising avenue for both disease monitoring and therapeutic intervention. Here, we present a platform for chronically monitoring tumor progression using neural recordings in freely behaving mice with gliomas. Using this platform across multiple mouse strains and glioma models, we show that neural recordings can accurately track tumor progression in vivo. Cancer progression was consistently associated with elevated gamma-band neural activity in the tumor microenvironment across both adult glioblastoma (GBM) and pediatric diffuse intrinsic pontine glioma (DIPG) cancer models. Interestingly, lower frequency neural activity exhibited distinct, cell-line specific changes over time: GBM models exhibited decreases in low frequency neural activity whereas DIPG models exhibited increases over time. Finally, using machine learning models applied to chronic neural recordings from tumor-bearing mice treated with or without standard-of-care chemotherapy (temozolomide for GBM), we accurately predicted tumor burden as inferred through in vivo bioluminescence imaging. By fitting low-dimensional mathematical models to gamma-band neural trajectories, we could further predict individual tumor growth rates over a 5-week period with high accuracy. These results establish that pathological neural-tumor interactions can be harnessed to monitor glioma progression in vivo. Coupling this monitoring capability with therapeutic electrical stimulation in the same device could open up a new class of implantable, closed-loop neurotechnologies with the potential to transform glioma treatment.

neuroscience↗

bioRxiv2025

When neighbours play a role: a systems-level analysis of protein interactions conditioning cancer driver mutation effects

BackgroundCancer is driven by the accumulation of somatic mutations, including driver mutations that confer a selective advantage to cancer cells. Driver proteins operate within complex interaction networks, and their activity is conditioned by neighbour proteins. Understanding the interplay between driver mutations and the expression of their neighbour proteins can provide insights into cancer biology and potential therapeutic targets. MethodsWe assessed associations between expression of neighbour proteins and driver mutation status, comparing both between and within cancer types. We further evaluated if neighbours were enriched in significant associations with multiple drivers and characterised the impact of neighbour expression on overall survival for all cancer types. ResultsWe found a significant correlation between the number of driver associations a neighbour gene has and the number of sign-coherent survival associations, particularly for neighbours enriched in positive associations, where high neighbour expression correlated with increased driver mutations and poorer survival. We identified 119 neighbours enriched in positive driver associations with at least two unfavourable survival associations and 25 neighbours enriched in negative driver associations with at least two favourable survival associations. ConclusionsOur study systematically identified neighbours associated with driver mutation status. Complementary evidence from survival analysis and the literature suggests that neighbours enriched in driver associations can be further explored as drug target candidates. Significance StatementCancers are caused by mutations in driver genes. The impact of those mutations in the cell can be influenced by other proteins in the cell that physically interact with the mutated protein. In this work, we analysed cancer patient data to uncover such neighbour proteins that may influence the outcome of driver mutations. We discovered a subset of neighbour proteins that are associated with mutations in multiple cancer drivers and, simultaneously, are associated with changes in survival times of patients for multiple cancer types. These neighbour proteins may help explain why some driver mutations are more common in certain cancer types. We also propose that these neighbour proteins should be explored as candidate drug targets for cancer therapy.

cancer biology↗