Zalcman, G. | bioRxiv Science

bioRxiv2025

Tumor-on-Chip as a Personalised Platform for Rapid Drug-Testing in Breast Cancer

Breast cancer (BC) remains one of the most common malignancies worldwide and continues to pose major therapeutic challenges, emphasizing the need for functional models that can inform fast treatment selection. Currently, patient-derived xenografts (PDXs) and organoids (PDOs) are valuable biological models for functional precision oncology; however, variable success rates of establishment and prolonged timelines limit their clinical application for real-time drug testing. To overcome this, we developed a Tumor-on-Chip (ToC) platform that enables functional drug sensitivity profiling within a clinically actionable 4-day timeframe. We compared ToC models with PDX results, and demonstrated high reproducibility and strong concordance with in vivo PDX responses. Drug sensitivity was correctly identified ex vivo in 88% of PDX-responsive cases, while resistance was detected in 91%, with no false positives at clinically relevant drug concentrations. To facilitate clinical translation, we engineered a custom microfluidic chip optimized for minimal breast cancer biopsy material, yielding results similar to those obtained from resection samples. We ultimately demonstrated the proof-of-concept for applying this platform to patient samples as a further tool for guiding clinical decision-making and discriminated between resistance and sensitive profiles among patients. These findings demonstrate the feasibility and translational potential of ToC models for personalized drug profiling in breast cancer, laying the groundwork for their integration into real-time clinical decision-making workflows.

bioengineering↗

bioRxiv2024

Somatostatin interneurons select dorsomedial striatal representations of the initial learning phase

The dorsomedial striatum (DMS) is an associative node involved in the adaptation of ongoing actions to the environmental context and in the initial formation of motor sequences. In early associative or motor learning phases, DMS activity shows a global decrease in neuron firing, eventually giving rise to a select group of active cells, whose number is correlated with animal performance. Unveiling how those representation emerge from DMS circuits is crucial for understanding plasticity mechanisms of early adjustments to learning a task. Here, we hypothesized that inhibitory microcircuits formed by local interneurons are responsible for the genesis of early DMS representation and associated task performance. Despite the low density of somatostatin (SOM)-positive cells, we observed that selective manipulation of SOM cells disrupted reorganization of DMS activity and modulated initial phases of learning in two behavioral contexts. This effect was cell-specific as manipulation of parvalbumin-positive interneurons had no significant effect. Finally, we identified the high plasticity of SOM innervation in the DMS as a key modulator of the SPN excitability and firing activity. Hence, SOM interneurons set the pace of early learning by actively controlling the remapping of DMS network activity.

neuroscience↗